\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " dist population net_density(hab/ha) num_immi \\\n",
+ "0 Ciutat Vella 101387 809.25 3152.75 \n",
+ "1 Eixample 266416 800.33 3174.50 \n",
+ "2 Gràcia 121347 622.20 1450.80 \n",
+ "3 Horta-Guinardó 168751 559.18 709.00 \n",
+ "4 Les Corts 82033 528.67 1458.33 \n",
+ "5 Nou Barris 166579 669.46 636.46 \n",
+ "6 Sant Andreu 147594 762.86 905.00 \n",
+ "7 Sant Martí 235513 877.20 1272.00 \n",
+ "8 Sants-Montjuïc 181910 757.50 1460.38 \n",
+ "9 Sarrià-Sant Gervasi 149279 345.17 1204.50 \n",
+ "\n",
+ " avg_occupation mort_rate rent_price num_crimes facilities_rate \\\n",
+ "0 2.45 1002.95 4.81 44241.0 0.37 \n",
+ "1 2.38 794.48 4.80 46210.0 0.36 \n",
+ "2 2.34 805.10 3.98 7731.0 0.25 \n",
+ "3 2.43 871.53 2.92 7871.0 0.21 \n",
+ "4 2.60 677.30 5.17 7444.0 0.52 \n",
+ "5 2.59 885.38 2.29 8627.0 0.12 \n",
+ "6 2.59 819.19 2.83 10657.0 0.20 \n",
+ "7 2.54 777.40 3.85 22417.0 0.23 \n",
+ "8 2.48 827.70 3.41 20219.0 0.19 \n",
+ "9 2.77 821.88 5.27 9252.0 0.31 \n",
+ "\n",
+ " crime_rate %immi \n",
+ "0 272.956283 3.11 \n",
+ "1 285.104537 1.19 \n",
+ "2 47.698402 1.20 \n",
+ "3 48.562169 0.42 \n",
+ "4 45.927682 1.78 \n",
+ "5 53.226506 0.38 \n",
+ "6 65.751116 0.61 \n",
+ "7 138.307475 0.54 \n",
+ "8 124.746346 0.80 \n",
+ "9 57.082605 0.81 "
+ ]
+ },
+ "execution_count": 466,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "Dist3"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 376,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | dist | \n", + "population | \n", + "net_density(hab/ha) | \n", + "num_immi | \n", + "avg_occupation | \n", + "mort_rate | \n", + "rent_price | \n", + "num_crimes | \n", + "facilities_rate | \n", + "crime_rate | \n", + "%immi | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "Ciutat Vella | \n", + "101387 | \n", + "809.25 | \n", + "3152.75 | \n", + "2.45 | \n", + "1002.95 | \n", + "4.81 | \n", + "44241.0 | \n", + "0.37 | \n", + "272.956283 | \n", + "3.11 | \n", + "
| 1 | \n", + "Eixample | \n", + "266416 | \n", + "800.33 | \n", + "3174.50 | \n", + "2.38 | \n", + "794.48 | \n", + "4.80 | \n", + "46210.0 | \n", + "0.36 | \n", + "285.104537 | \n", + "1.19 | \n", + "
| 2 | \n", + "Gràcia | \n", + "121347 | \n", + "622.20 | \n", + "1450.80 | \n", + "2.34 | \n", + "805.10 | \n", + "3.98 | \n", + "7731.0 | \n", + "0.25 | \n", + "47.698402 | \n", + "1.20 | \n", + "
| 3 | \n", + "Horta-Guinardó | \n", + "168751 | \n", + "559.18 | \n", + "709.00 | \n", + "2.43 | \n", + "871.53 | \n", + "2.92 | \n", + "7871.0 | \n", + "0.21 | \n", + "48.562169 | \n", + "0.42 | \n", + "
| 4 | \n", + "Les Corts | \n", + "82033 | \n", + "528.67 | \n", + "1458.33 | \n", + "2.60 | \n", + "677.30 | \n", + "5.17 | \n", + "7444.0 | \n", + "0.52 | \n", + "45.927682 | \n", + "1.78 | \n", + "
| 5 | \n", + "Nou Barris | \n", + "166579 | \n", + "669.46 | \n", + "636.46 | \n", + "2.59 | \n", + "885.38 | \n", + "2.29 | \n", + "8627.0 | \n", + "0.12 | \n", + "53.226506 | \n", + "0.38 | \n", + "
| 6 | \n", + "Sant Andreu | \n", + "147594 | \n", + "762.86 | \n", + "905.00 | \n", + "2.59 | \n", + "819.19 | \n", + "2.83 | \n", + "10657.0 | \n", + "0.20 | \n", + "65.751116 | \n", + "0.61 | \n", + "
| 7 | \n", + "Sant Martí | \n", + "235513 | \n", + "877.20 | \n", + "1272.00 | \n", + "2.54 | \n", + "777.40 | \n", + "3.85 | \n", + "22417.0 | \n", + "0.23 | \n", + "138.307475 | \n", + "0.54 | \n", + "
| 8 | \n", + "Sants-Montjuïc | \n", + "181910 | \n", + "757.50 | \n", + "1460.38 | \n", + "2.48 | \n", + "827.70 | \n", + "3.41 | \n", + "20219.0 | \n", + "0.19 | \n", + "124.746346 | \n", + "0.80 | \n", + "
| 9 | \n", + "Sarrià-Sant Gervasi | \n", + "149279 | \n", + "345.17 | \n", + "1204.50 | \n", + "2.77 | \n", + "821.88 | \n", + "5.27 | \n", + "9252.0 | \n", + "0.31 | \n", + "57.082605 | \n", + "0.81 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " dist crime_rate facilities_rate\n",
+ "1 Eixample 285.10 0.36\n",
+ "0 Ciutat Vella 272.96 0.37\n",
+ "7 Sant Martí 138.31 0.23\n",
+ "8 Sants-Montjuïc 124.75 0.19\n",
+ "6 Sant Andreu 65.75 0.20\n",
+ "9 Sarrià-Sant Gervasi 57.08 0.31\n",
+ "5 Nou Barris 53.23 0.12\n",
+ "3 Horta-Guinardó 48.56 0.21\n",
+ "2 Gràcia 47.70 0.25\n",
+ "4 Les Corts 45.93 0.52"
+ ]
+ },
+ "execution_count": 376,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#USING CRIME_RATE (crime cases/total pop*10000) INSTEAD OF CRIME CASES\n",
+ "\n",
+ "Dist4=Dist3[[\"dist\",\"crime_rate\",\"facilities_rate\"]]\n",
+ "Dist4.sort_values(\"crime_rate\",ascending=False).round(decimals=2)\n",
+ "#most secure: >50\n",
+ "#neutral: 50-100\n",
+ "#high:>100"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 468,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | dist | \n", + "crime_rate | \n", + "facilities_rate | \n", + "
|---|---|---|---|
| 1 | \n", + "Eixample | \n", + "285.10 | \n", + "0.36 | \n", + "
| 0 | \n", + "Ciutat Vella | \n", + "272.96 | \n", + "0.37 | \n", + "
| 7 | \n", + "Sant Martí | \n", + "138.31 | \n", + "0.23 | \n", + "
| 8 | \n", + "Sants-Montjuïc | \n", + "124.75 | \n", + "0.19 | \n", + "
| 6 | \n", + "Sant Andreu | \n", + "65.75 | \n", + "0.20 | \n", + "
| 9 | \n", + "Sarrià-Sant Gervasi | \n", + "57.08 | \n", + "0.31 | \n", + "
| 5 | \n", + "Nou Barris | \n", + "53.23 | \n", + "0.12 | \n", + "
| 3 | \n", + "Horta-Guinardó | \n", + "48.56 | \n", + "0.21 | \n", + "
| 2 | \n", + "Gràcia | \n", + "47.70 | \n", + "0.25 | \n", + "
| 4 | \n", + "Les Corts | \n", + "45.93 | \n", + "0.52 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " population net_density(hab/ha) num_immi \\\n",
+ "population 1.000000 0.453958 0.141743 \n",
+ "net_density(hab/ha) 0.453958 1.000000 0.404369 \n",
+ "num_immi 0.141743 0.404369 1.000000 \n",
+ "avg_occupation -0.204465 -0.511504 -0.434911 \n",
+ "mort_rate -0.095976 0.231659 0.256742 \n",
+ "rent_price -0.177120 -0.278312 0.622590 \n",
+ "num_crimes 0.402391 0.624093 0.922054 \n",
+ "facilities_rate -0.354558 -0.218737 0.552726 \n",
+ "crime_rate 0.402391 0.624093 0.922054 \n",
+ "%immi -0.507518 0.143191 0.755280 \n",
+ "\n",
+ " avg_occupation mort_rate rent_price num_crimes \\\n",
+ "population -0.204465 -0.095976 -0.177120 0.402391 \n",
+ "net_density(hab/ha) -0.511504 0.231659 -0.278312 0.624093 \n",
+ "num_immi -0.434911 0.256742 0.622590 0.922054 \n",
+ "avg_occupation 1.000000 -0.193699 0.132333 -0.418129 \n",
+ "mort_rate -0.193699 1.000000 -0.245994 0.386923 \n",
+ "rent_price 0.132333 -0.245994 1.000000 0.379735 \n",
+ "num_crimes -0.418129 0.386923 0.379735 1.000000 \n",
+ "facilities_rate 0.049523 -0.361552 0.866505 0.287412 \n",
+ "crime_rate -0.418129 0.386923 0.379735 1.000000 \n",
+ "%immi -0.223254 0.331780 0.620850 0.565336 \n",
+ "\n",
+ " facilities_rate crime_rate %immi \n",
+ "population -0.354558 0.402391 -0.507518 \n",
+ "net_density(hab/ha) -0.218737 0.624093 0.143191 \n",
+ "num_immi 0.552726 0.922054 0.755280 \n",
+ "avg_occupation 0.049523 -0.418129 -0.223254 \n",
+ "mort_rate -0.361552 0.386923 0.331780 \n",
+ "rent_price 0.866505 0.379735 0.620850 \n",
+ "num_crimes 0.287412 1.000000 0.565336 \n",
+ "facilities_rate 1.000000 0.287412 0.685910 \n",
+ "crime_rate 0.287412 1.000000 0.565336 \n",
+ "%immi 0.685910 0.565336 1.000000 "
+ ]
+ },
+ "execution_count": 468,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "Dist3.corr()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 423,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "Dist3.to_csv(\"../datasets/Data_filtered/data_extraFiltered.csv\",index=False)\n",
+ "Population=pd.read_csv(\"../datasets/Data_filtered/data_extraFiltered.csv\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 424,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "Population=Population.round(decimals=2)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 416,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#crime ranking(high,neutral,low)\n",
+ "\n",
+ "Population.loc[df.crime_rate>100, \"crime rate\"] = \"high\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 387,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df.loc[df.Weight == \"155\", \"Name\"] = \"John\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 388,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#Population['crime_rate'] = np.where(Population['crime_rate'] > 100, \"high\", df['crime_rate'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 417,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#most secure: >50\n",
+ "#neutral: 50-100\n",
+ "#high:>100\n",
+ "\n",
+ "\n",
+ "def crime_ranker(num):\n",
+ " if num >= 100:\n",
+ " return 'High'\n",
+ " elif (num < 100) & (num > 50):\n",
+ " return 'Neutral'\n",
+ " else:\n",
+ " return 'Low'\n",
+ " \n",
+ "Population.crime_rate = Population.crime_rate.apply(crime_ranker)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 428,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#eliminate num_crimes since we have now the crime rate\n",
+ "\n",
+ "Population=Population.drop(['num_crimes'],axis=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 430,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "Population.rename(columns={\"facilities_rate\":\"facilities_sum\"},inplace=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 438,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "choices = ['High', 'Neutral']\n",
+ "\n",
+ "#crime_rate\n",
+ "#conditions = [(Population.crime_rate >= 100), ((Population.crime_rate < 100) & (Population.crime_rate > 50))]\n",
+ "\n",
+ "\n",
+ "#facilities_sum\n",
+ "conditions2 = [(Population.facilities_sum >=0.5 ), ((Population.facilities_sum < 0.5) & (Population.facilities_sum >0.2))]\n",
+ "\n",
+ "#mort_rate\n",
+ "conditions3 = [(Population.mort_rate >= 900), ((Population.mort_rate < 900) & (Population.mort_rate > 700))]\n",
+ "\n",
+ "#net_density(hab/ha)\n",
+ "conditions4 = [(Population[\"net_density(hab/ha)\"] >= 800), ((Population[\"net_density(hab/ha)\"] < 800) & (Population[\"net_density(hab/ha)\"] > 600))]\n",
+ "\n",
+ "\n",
+ "#Population.crime_rate = np.select(conditions, choices, default='Low')\n",
+ "\n",
+ "Population.facilities_sum = np.select(conditions2, choices, default='Low')\n",
+ "\n",
+ "Population.mort_rate = np.select(conditions3, choices, default='Low')\n",
+ "\n",
+ "Population[\"net_density(hab/ha)\"] = np.select(conditions4, choices, default='Low')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 440,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#rent_price\n",
+ "\n",
+ "choices = ['High', 'Neutral']\n",
+ "conditions5 = [(Population.rent_price >= 5), ((Population.rent_price < 5) & (Population.rent_price > 3))]\n",
+ "Population.rent_price = np.select(conditions5, choices, default='Low')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#choices = ['High', 'Neutral']\n",
+ "#conditions5 = [(Population.rent_price >= 5), ((Population.rent_price < 5) & (Population.rent_price > 3))]\n",
+ "#Population.%immi_total_dist = np.select(conditions5, choices, default='Low')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 442,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#eliminate avg_occupation since it is not needed\n",
+ "\n",
+ "Population=Population.drop(['avg_occupation'],axis=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 447,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "Population=Population[['dist', 'population','num_immi','net_density(hab/ha)', 'mort_rate',\n",
+ " 'rent_price', 'facilities_sum', 'crime_rate']]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 452,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#sort by crime_rate\n",
+ "\n",
+ "Population=Population.sort_values(\"crime_rate\",ascending=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 457,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#reset index\n",
+ "\n",
+ "Population.reset_index(inplace=True,drop=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 460,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | population | \n", + "net_density(hab/ha) | \n", + "num_immi | \n", + "avg_occupation | \n", + "mort_rate | \n", + "rent_price | \n", + "num_crimes | \n", + "facilities_rate | \n", + "crime_rate | \n", + "%immi | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|
| population | \n", + "1.000000 | \n", + "0.453958 | \n", + "0.141743 | \n", + "-0.204465 | \n", + "-0.095976 | \n", + "-0.177120 | \n", + "0.402391 | \n", + "-0.354558 | \n", + "0.402391 | \n", + "-0.507518 | \n", + "
| net_density(hab/ha) | \n", + "0.453958 | \n", + "1.000000 | \n", + "0.404369 | \n", + "-0.511504 | \n", + "0.231659 | \n", + "-0.278312 | \n", + "0.624093 | \n", + "-0.218737 | \n", + "0.624093 | \n", + "0.143191 | \n", + "
| num_immi | \n", + "0.141743 | \n", + "0.404369 | \n", + "1.000000 | \n", + "-0.434911 | \n", + "0.256742 | \n", + "0.622590 | \n", + "0.922054 | \n", + "0.552726 | \n", + "0.922054 | \n", + "0.755280 | \n", + "
| avg_occupation | \n", + "-0.204465 | \n", + "-0.511504 | \n", + "-0.434911 | \n", + "1.000000 | \n", + "-0.193699 | \n", + "0.132333 | \n", + "-0.418129 | \n", + "0.049523 | \n", + "-0.418129 | \n", + "-0.223254 | \n", + "
| mort_rate | \n", + "-0.095976 | \n", + "0.231659 | \n", + "0.256742 | \n", + "-0.193699 | \n", + "1.000000 | \n", + "-0.245994 | \n", + "0.386923 | \n", + "-0.361552 | \n", + "0.386923 | \n", + "0.331780 | \n", + "
| rent_price | \n", + "-0.177120 | \n", + "-0.278312 | \n", + "0.622590 | \n", + "0.132333 | \n", + "-0.245994 | \n", + "1.000000 | \n", + "0.379735 | \n", + "0.866505 | \n", + "0.379735 | \n", + "0.620850 | \n", + "
| num_crimes | \n", + "0.402391 | \n", + "0.624093 | \n", + "0.922054 | \n", + "-0.418129 | \n", + "0.386923 | \n", + "0.379735 | \n", + "1.000000 | \n", + "0.287412 | \n", + "1.000000 | \n", + "0.565336 | \n", + "
| facilities_rate | \n", + "-0.354558 | \n", + "-0.218737 | \n", + "0.552726 | \n", + "0.049523 | \n", + "-0.361552 | \n", + "0.866505 | \n", + "0.287412 | \n", + "1.000000 | \n", + "0.287412 | \n", + "0.685910 | \n", + "
| crime_rate | \n", + "0.402391 | \n", + "0.624093 | \n", + "0.922054 | \n", + "-0.418129 | \n", + "0.386923 | \n", + "0.379735 | \n", + "1.000000 | \n", + "0.287412 | \n", + "1.000000 | \n", + "0.565336 | \n", + "
| %immi | \n", + "-0.507518 | \n", + "0.143191 | \n", + "0.755280 | \n", + "-0.223254 | \n", + "0.331780 | \n", + "0.620850 | \n", + "0.565336 | \n", + "0.685910 | \n", + "0.565336 | \n", + "1.000000 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " dist population num_immi net_density(hab/ha) mort_rate \\\n",
+ "0 Ciutat Vella 101387 3152.75 High High \n",
+ "1 Eixample 266416 3174.50 High Neutral \n",
+ "2 Sant Martí 235513 1272.00 High Neutral \n",
+ "3 Sants-Montjuïc 181910 1460.38 Neutral Neutral \n",
+ "4 Gràcia 121347 1450.80 Neutral Neutral \n",
+ "5 Horta-Guinardó 168751 709.00 Low Neutral \n",
+ "6 Les Corts 82033 1458.33 Low Low \n",
+ "7 Nou Barris 166579 636.46 Neutral Neutral \n",
+ "8 Sant Andreu 147594 905.00 Neutral Neutral \n",
+ "9 Sarrià-Sant Gervasi 149279 1204.50 Low Neutral \n",
+ "\n",
+ " rent_price facilities_sum crime_rate \n",
+ "0 Neutral Neutral High \n",
+ "1 Neutral Neutral High \n",
+ "2 Neutral Neutral High \n",
+ "3 Neutral Low High \n",
+ "4 Neutral Neutral Low \n",
+ "5 Low Neutral Low \n",
+ "6 High High Low \n",
+ "7 Low Low Neutral \n",
+ "8 Low Low Neutral \n",
+ "9 High Neutral Neutral "
+ ]
+ },
+ "execution_count": 460,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "Population"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 461,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "Population.to_csv(\"../datasets/Data_filtered/Population_ranked.csv\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Conclusions"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Question 1\n",
+ "\n",
+ "We wanted to define with the data available the quality of a neighbourhood, nonetheless, there are a lot more factors that determine the quality and it is hard to conclude anything unless a certain specific variable such as crime is used.\n",
+ "If we use crime as an indicator, the more secure Neighbourhoods would be (crime seems to be related with number of immigrants in neighbourhood):\n",
+ "- Gràcia\n",
+ "- Les Corts\n",
+ "- Horta-Guinardó\n",
+ "- Sants\n",
+ "| \n", + " | dist | \n", + "population | \n", + "num_immi | \n", + "net_density(hab/ha) | \n", + "mort_rate | \n", + "rent_price | \n", + "facilities_sum | \n", + "crime_rate | \n", + "
|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "Ciutat Vella | \n", + "101387 | \n", + "3152.75 | \n", + "High | \n", + "High | \n", + "Neutral | \n", + "Neutral | \n", + "High | \n", + "
| 1 | \n", + "Eixample | \n", + "266416 | \n", + "3174.50 | \n", + "High | \n", + "Neutral | \n", + "Neutral | \n", + "Neutral | \n", + "High | \n", + "
| 2 | \n", + "Sant Martí | \n", + "235513 | \n", + "1272.00 | \n", + "High | \n", + "Neutral | \n", + "Neutral | \n", + "Neutral | \n", + "High | \n", + "
| 3 | \n", + "Sants-Montjuïc | \n", + "181910 | \n", + "1460.38 | \n", + "Neutral | \n", + "Neutral | \n", + "Neutral | \n", + "Low | \n", + "High | \n", + "
| 4 | \n", + "Gràcia | \n", + "121347 | \n", + "1450.80 | \n", + "Neutral | \n", + "Neutral | \n", + "Neutral | \n", + "Neutral | \n", + "Low | \n", + "
| 5 | \n", + "Horta-Guinardó | \n", + "168751 | \n", + "709.00 | \n", + "Low | \n", + "Neutral | \n", + "Low | \n", + "Neutral | \n", + "Low | \n", + "
| 6 | \n", + "Les Corts | \n", + "82033 | \n", + "1458.33 | \n", + "Low | \n", + "Low | \n", + "High | \n", + "High | \n", + "Low | \n", + "
| 7 | \n", + "Nou Barris | \n", + "166579 | \n", + "636.46 | \n", + "Neutral | \n", + "Neutral | \n", + "Low | \n", + "Low | \n", + "Neutral | \n", + "
| 8 | \n", + "Sant Andreu | \n", + "147594 | \n", + "905.00 | \n", + "Neutral | \n", + "Neutral | \n", + "Low | \n", + "Low | \n", + "Neutral | \n", + "
| 9 | \n", + "Sarrià-Sant Gervasi | \n", + "149279 | \n", + "1204.50 | \n", + "Low | \n", + "Neutral | \n", + "High | \n", + "Neutral | \n", + "Neutral | \n", + "
```(<8000 cases)```
\n", + "And the less secure:
\n", + "- Eixample\n", + "- Ciutat vella\n", + "- Sant Martí\n", + "
```(>20000 cases)```\n", + "
\n", + "\n", + "If we use the number of facilities (schools, hospitals, parks,etc), the most equiped are:\n", + "- Les Corts\n", + "- Ciutat Vella\n", + "- Eixample\n", + "(closely followed by Sarrià)
\n", + "And the worst equiped:
\n", + "- Sant Andreu\n", + "- Sants-Monjuïc\n", + "- Nou Barris\n", + "
\n", + "\n", + "Finally, if we have a quick look at the rental prices we obtain that the most expensive are:\n", + "- Sarrià-Sant Gervasi\n", + "- Les Corts\n", + "- Ciutat Vella\n", + "- Eixample\n", + "
```(>4€/m2)```
\n", + "And the least expensive:
\n", + "- Horta-Guinardó\n", + "- Sant Andreu\n", + "- Nou Barris\n", + "
```(<3€/m2)```
\n", + "\n", + "So, what conclusions can we take? First, that we would need a deeper analysis in order to stablish the quality of a Neighbourhood. Second, that according to our data and analysis the only Neighbourhood that would be catalogued as High quality due to having a lower crime category and a higher facilities rate is **Les Corts** which has also one of the highest rental prices, that could confirm our hypothesis that is a valued neighbourhood." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Question 2\n", + "\n", + "Does the quality (bad,normal,good,etc) of the neighbourhood relate with its death rate?
\n", + "The overall quality of the neighbourhoods could not be stablished but data showed that **Les Corts** was a good neighbourhood due to low crime and high quantity of facilities. Interestingly, it is the district with the lowest mortality rate.
\n", + "Nonetheless, there does not seem to be a correlation (0.39) between mortality and crime since two districts with high crime levels such as Eixample and Sant Martí have two of the lowest mortality rates." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Question 3\n", + "Do the neighbourhoods with higher population numbers have more facilities?
\n", + "The districts with highest net density are: \n", + "- Sant Martí\n", + "- Ciutat Vella\n", + "- Eixample\n", + "
```(>800hab/ha)```
\n", + "And the better equiped are:
\n", + "- Les Corts\n", + "- Ciutat Vella\n", + "- Eixample\n", + "
\n", + "It seems that there could be a correlation since Ciutat Vella and Eixample are well equiped and have high net density, however, once again there is no correlation" + ] + }, + { + "cell_type": "code", + "execution_count": 486, + "metadata": {}, + "outputs": [], + "source": [ + "immigrants=pd.read_csv(\"../datasets/Data_filtered/comparison_table.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": 478, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " dist %immi_total_immi %immi_total_dist mort_rate \\\n",
+ "0 Ciutat Vella 16.25 9.93 1002.95 \n",
+ "1 Eixample 20.15 4.69 794.48 \n",
+ "2 Sant Martí 12.90 3.39 777.40 \n",
+ "3 Sants-Montjuïc 12.04 4.10 827.70 \n",
+ "4 Gràcia 6.95 3.55 805.10 \n",
+ "\n",
+ " facilities_rate crime_rate \n",
+ "0 0.37 272.956283 \n",
+ "1 0.36 285.104537 \n",
+ "2 0.23 138.307475 \n",
+ "3 0.19 124.746346 \n",
+ "4 0.25 47.698402 "
+ ]
+ },
+ "execution_count": 478,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "immigrants.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 487,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | dist | \n", + "%immi_total_immi | \n", + "%immi_total_dist | \n", + "mort_rate | \n", + "facilities_rate | \n", + "crime_rate | \n", + "
|---|---|---|---|---|---|---|
| 0 | \n", + "Ciutat Vella | \n", + "16.25 | \n", + "9.93 | \n", + "1002.95 | \n", + "0.37 | \n", + "272.956283 | \n", + "
| 1 | \n", + "Eixample | \n", + "20.15 | \n", + "4.69 | \n", + "794.48 | \n", + "0.36 | \n", + "285.104537 | \n", + "
| 2 | \n", + "Sant Martí | \n", + "12.90 | \n", + "3.39 | \n", + "777.40 | \n", + "0.23 | \n", + "138.307475 | \n", + "
| 3 | \n", + "Sants-Montjuïc | \n", + "12.04 | \n", + "4.10 | \n", + "827.70 | \n", + "0.19 | \n", + "124.746346 | \n", + "
| 4 | \n", + "Gràcia | \n", + "6.95 | \n", + "3.55 | \n", + "805.10 | \n", + "0.25 | \n", + "47.698402 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Unnamed: 0 dist %immi_total_immi %immi_total_dist \\\n",
+ "1 1 Eixample 20.15 4.69 \n",
+ "0 0 Ciutat Vella 16.25 9.93 \n",
+ "2 2 Sant Martí 12.90 3.39 \n",
+ "3 3 Sants-Montjuïc 12.04 4.10 \n",
+ "7 7 Nou Barris 8.49 3.16 \n",
+ "5 5 Horta-Guinardó 7.33 2.69 \n",
+ "4 4 Gràcia 6.95 3.55 \n",
+ "9 9 Sarrià-Sant Gervasi 6.13 2.55 \n",
+ "8 8 Sant Andreu 5.84 2.45 \n",
+ "6 6 Les Corts 3.90 2.95 \n",
+ "\n",
+ " mort_rate facilities_rate crime_rate \n",
+ "1 794.48 0.36 285.104537 \n",
+ "0 1002.95 0.37 272.956283 \n",
+ "2 777.40 0.23 138.307475 \n",
+ "3 827.70 0.19 124.746346 \n",
+ "7 885.38 0.12 53.226506 \n",
+ "5 871.53 0.21 48.562169 \n",
+ "4 805.10 0.25 47.698402 \n",
+ "9 821.88 0.31 57.082605 \n",
+ "8 819.19 0.20 65.751116 \n",
+ "6 677.30 0.52 45.927682 "
+ ]
+ },
+ "execution_count": 487,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "immigrants.sort_values(\"%immi_total_immi\",ascending=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 488,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "immigrants=immigrants.drop([\"Unnamed: 0\"],axis=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 489,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "choices = ['High', 'Neutral']\n",
+ "conditions6 = [(immigrants[\"%immi_total_dist\"] >= 5), ((immigrants[\"%immi_total_dist\"] < 5) & (immigrants[\"%immi_total_dist\"] > 3))]\n",
+ "immigrants[\"%immi_total_dist\"] = np.select(conditions6, choices, default='Low')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 490,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "choices = ['High', 'Neutral']\n",
+ "conditions7 = [(immigrants[\"%immi_total_immi\"] >= 15), ((immigrants[\"%immi_total_immi\"] < 15) & (immigrants[\"%immi_total_immi\"] > 5))]\n",
+ "immigrants[\"%immi_total_immi\"] = np.select(conditions7, choices, default='Low')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 497,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "immigrants.rename(columns={\"facilities_rate\":\"facilities_sum\"},inplace=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 498,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | Unnamed: 0 | \n", + "dist | \n", + "%immi_total_immi | \n", + "%immi_total_dist | \n", + "mort_rate | \n", + "facilities_rate | \n", + "crime_rate | \n", + "
|---|---|---|---|---|---|---|---|
| 1 | \n", + "1 | \n", + "Eixample | \n", + "20.15 | \n", + "4.69 | \n", + "794.48 | \n", + "0.36 | \n", + "285.104537 | \n", + "
| 0 | \n", + "0 | \n", + "Ciutat Vella | \n", + "16.25 | \n", + "9.93 | \n", + "1002.95 | \n", + "0.37 | \n", + "272.956283 | \n", + "
| 2 | \n", + "2 | \n", + "Sant Martí | \n", + "12.90 | \n", + "3.39 | \n", + "777.40 | \n", + "0.23 | \n", + "138.307475 | \n", + "
| 3 | \n", + "3 | \n", + "Sants-Montjuïc | \n", + "12.04 | \n", + "4.10 | \n", + "827.70 | \n", + "0.19 | \n", + "124.746346 | \n", + "
| 7 | \n", + "7 | \n", + "Nou Barris | \n", + "8.49 | \n", + "3.16 | \n", + "885.38 | \n", + "0.12 | \n", + "53.226506 | \n", + "
| 5 | \n", + "5 | \n", + "Horta-Guinardó | \n", + "7.33 | \n", + "2.69 | \n", + "871.53 | \n", + "0.21 | \n", + "48.562169 | \n", + "
| 4 | \n", + "4 | \n", + "Gràcia | \n", + "6.95 | \n", + "3.55 | \n", + "805.10 | \n", + "0.25 | \n", + "47.698402 | \n", + "
| 9 | \n", + "9 | \n", + "Sarrià-Sant Gervasi | \n", + "6.13 | \n", + "2.55 | \n", + "821.88 | \n", + "0.31 | \n", + "57.082605 | \n", + "
| 8 | \n", + "8 | \n", + "Sant Andreu | \n", + "5.84 | \n", + "2.45 | \n", + "819.19 | \n", + "0.20 | \n", + "65.751116 | \n", + "
| 6 | \n", + "6 | \n", + "Les Corts | \n", + "3.90 | \n", + "2.95 | \n", + "677.30 | \n", + "0.52 | \n", + "45.927682 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " dist %immi_total_immi %immi_total_dist mort_rate \\\n",
+ "0 Ciutat Vella High High 1002.95 \n",
+ "1 Eixample High Neutral 794.48 \n",
+ "2 Sant Martí Neutral Neutral 777.40 \n",
+ "3 Sants-Montjuïc Neutral Neutral 827.70 \n",
+ "4 Gràcia Neutral Neutral 805.10 \n",
+ "5 Horta-Guinardó Neutral Low 871.53 \n",
+ "6 Les Corts Low Low 677.30 \n",
+ "7 Nou Barris Neutral Neutral 885.38 \n",
+ "8 Sant Andreu Neutral Low 819.19 \n",
+ "9 Sarrià-Sant Gervasi Neutral Low 821.88 \n",
+ "\n",
+ " facilities_sum crime_rate \n",
+ "0 0.37 272.956283 \n",
+ "1 0.36 285.104537 \n",
+ "2 0.23 138.307475 \n",
+ "3 0.19 124.746346 \n",
+ "4 0.25 47.698402 \n",
+ "5 0.21 48.562169 \n",
+ "6 0.52 45.927682 \n",
+ "7 0.12 53.226506 \n",
+ "8 0.20 65.751116 \n",
+ "9 0.31 57.082605 "
+ ]
+ },
+ "execution_count": 498,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "immigrants"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 499,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "choices = ['High', 'Neutral']\n",
+ "\n",
+ "#crime_rate\n",
+ "conditions = [(immigrants.crime_rate >= 100), ((immigrants.crime_rate < 100) & (immigrants.crime_rate > 50))]\n",
+ "\n",
+ "\n",
+ "#facilities_sum\n",
+ "conditions2 = [(immigrants.facilities_sum >=0.5 ), ((immigrants.facilities_sum < 0.5) & (immigrants.facilities_sum >0.2))]\n",
+ "\n",
+ "#mort_rate\n",
+ "conditions3 = [(immigrants.mort_rate >= 900), ((immigrants.mort_rate < 900) & (immigrants.mort_rate > 700))]\n",
+ "\n",
+ "\n",
+ "\n",
+ "immigrants.crime_rate = np.select(conditions, choices, default='Low')\n",
+ "\n",
+ "immigrants.facilities_sum = np.select(conditions2, choices, default='Low')\n",
+ "\n",
+ "immigrants.mort_rate = np.select(conditions3, choices, default='Low')\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 512,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | dist | \n", + "%immi_total_immi | \n", + "%immi_total_dist | \n", + "mort_rate | \n", + "facilities_sum | \n", + "crime_rate | \n", + "
|---|---|---|---|---|---|---|
| 0 | \n", + "Ciutat Vella | \n", + "High | \n", + "High | \n", + "1002.95 | \n", + "0.37 | \n", + "272.956283 | \n", + "
| 1 | \n", + "Eixample | \n", + "High | \n", + "Neutral | \n", + "794.48 | \n", + "0.36 | \n", + "285.104537 | \n", + "
| 2 | \n", + "Sant Martí | \n", + "Neutral | \n", + "Neutral | \n", + "777.40 | \n", + "0.23 | \n", + "138.307475 | \n", + "
| 3 | \n", + "Sants-Montjuïc | \n", + "Neutral | \n", + "Neutral | \n", + "827.70 | \n", + "0.19 | \n", + "124.746346 | \n", + "
| 4 | \n", + "Gràcia | \n", + "Neutral | \n", + "Neutral | \n", + "805.10 | \n", + "0.25 | \n", + "47.698402 | \n", + "
| 5 | \n", + "Horta-Guinardó | \n", + "Neutral | \n", + "Low | \n", + "871.53 | \n", + "0.21 | \n", + "48.562169 | \n", + "
| 6 | \n", + "Les Corts | \n", + "Low | \n", + "Low | \n", + "677.30 | \n", + "0.52 | \n", + "45.927682 | \n", + "
| 7 | \n", + "Nou Barris | \n", + "Neutral | \n", + "Neutral | \n", + "885.38 | \n", + "0.12 | \n", + "53.226506 | \n", + "
| 8 | \n", + "Sant Andreu | \n", + "Neutral | \n", + "Low | \n", + "819.19 | \n", + "0.20 | \n", + "65.751116 | \n", + "
| 9 | \n", + "Sarrià-Sant Gervasi | \n", + "Neutral | \n", + "Low | \n", + "821.88 | \n", + "0.31 | \n", + "57.082605 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " dist %immi_total_immi %immi_total_dist mort_rate \\\n",
+ "0 Ciutat Vella High High High \n",
+ "5 Horta-Guinardó Neutral Low Neutral \n",
+ "6 Les Corts Low Low Low \n",
+ "8 Sant Andreu Neutral Low Neutral \n",
+ "9 Sarrià-Sant Gervasi Neutral Low Neutral \n",
+ "1 Eixample High Neutral Neutral \n",
+ "2 Sant Martí Neutral Neutral Neutral \n",
+ "3 Sants-Montjuïc Neutral Neutral Neutral \n",
+ "4 Gràcia Neutral Neutral Neutral \n",
+ "7 Nou Barris Neutral Neutral Neutral \n",
+ "\n",
+ " facilities_sum crime_rate \n",
+ "0 Neutral High \n",
+ "5 Neutral Low \n",
+ "6 High Low \n",
+ "8 Low Neutral \n",
+ "9 Neutral Neutral \n",
+ "1 Neutral High \n",
+ "2 Neutral High \n",
+ "3 Low High \n",
+ "4 Neutral Low \n",
+ "7 Low Neutral "
+ ]
+ },
+ "execution_count": 512,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "immigrants.sort_values(\"%immi_total_dist\",ascending=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 501,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "immipop = pd.merge(Population,immigrants, how=\"right\", on=\"dist\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 502,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | dist | \n", + "%immi_total_immi | \n", + "%immi_total_dist | \n", + "mort_rate | \n", + "facilities_sum | \n", + "crime_rate | \n", + "
|---|---|---|---|---|---|---|
| 0 | \n", + "Ciutat Vella | \n", + "High | \n", + "High | \n", + "High | \n", + "Neutral | \n", + "High | \n", + "
| 5 | \n", + "Horta-Guinardó | \n", + "Neutral | \n", + "Low | \n", + "Neutral | \n", + "Neutral | \n", + "Low | \n", + "
| 6 | \n", + "Les Corts | \n", + "Low | \n", + "Low | \n", + "Low | \n", + "High | \n", + "Low | \n", + "
| 8 | \n", + "Sant Andreu | \n", + "Neutral | \n", + "Low | \n", + "Neutral | \n", + "Low | \n", + "Neutral | \n", + "
| 9 | \n", + "Sarrià-Sant Gervasi | \n", + "Neutral | \n", + "Low | \n", + "Neutral | \n", + "Neutral | \n", + "Neutral | \n", + "
| 1 | \n", + "Eixample | \n", + "High | \n", + "Neutral | \n", + "Neutral | \n", + "Neutral | \n", + "High | \n", + "
| 2 | \n", + "Sant Martí | \n", + "Neutral | \n", + "Neutral | \n", + "Neutral | \n", + "Neutral | \n", + "High | \n", + "
| 3 | \n", + "Sants-Montjuïc | \n", + "Neutral | \n", + "Neutral | \n", + "Neutral | \n", + "Low | \n", + "High | \n", + "
| 4 | \n", + "Gràcia | \n", + "Neutral | \n", + "Neutral | \n", + "Neutral | \n", + "Neutral | \n", + "Low | \n", + "
| 7 | \n", + "Nou Barris | \n", + "Neutral | \n", + "Neutral | \n", + "Neutral | \n", + "Low | \n", + "Neutral | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " dist population num_immi net_density(hab/ha) mort_rate_x \\\n",
+ "0 Ciutat Vella 101387 3152.75 High High \n",
+ "1 Eixample 266416 3174.50 High Neutral \n",
+ "2 Sant Martí 235513 1272.00 High Neutral \n",
+ "3 Sants-Montjuïc 181910 1460.38 Neutral Neutral \n",
+ "4 Gràcia 121347 1450.80 Neutral Neutral \n",
+ "\n",
+ " rent_price facilities_sum_x crime_rate_x %immi_total_immi %immi_total_dist \\\n",
+ "0 Neutral Neutral High High High \n",
+ "1 Neutral Neutral High High Neutral \n",
+ "2 Neutral Neutral High Neutral Neutral \n",
+ "3 Neutral Low High Neutral Neutral \n",
+ "4 Neutral Neutral Low Neutral Neutral \n",
+ "\n",
+ " mort_rate_y facilities_sum_y crime_rate_y \n",
+ "0 High Neutral High \n",
+ "1 Neutral Neutral High \n",
+ "2 Neutral Neutral High \n",
+ "3 Neutral Low High \n",
+ "4 Neutral Neutral Low "
+ ]
+ },
+ "execution_count": 502,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "immipop.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 504,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Index(['dist', 'population', 'num_immi', 'net_density(hab/ha)', 'mort_rate_x',\n",
+ " 'rent_price', 'facilities_sum_x', 'crime_rate_x', '%immi_total_immi',\n",
+ " '%immi_total_dist', 'mort_rate_y', 'facilities_sum_y', 'crime_rate_y'],\n",
+ " dtype='object')"
+ ]
+ },
+ "execution_count": 504,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "immipop.columns"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 509,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "immipop=immipop.drop(['num_immi'],axis=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 508,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "immipop.columns=['dist', 'population', 'num_immi', 'net_density(hab/ha)', 'mort_rate',\n",
+ " 'rent_price', 'facilities_sum', 'crime_rate', '%immi_total_immi',\n",
+ " '%immi_total_dist']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 510,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | dist | \n", + "population | \n", + "num_immi | \n", + "net_density(hab/ha) | \n", + "mort_rate_x | \n", + "rent_price | \n", + "facilities_sum_x | \n", + "crime_rate_x | \n", + "%immi_total_immi | \n", + "%immi_total_dist | \n", + "mort_rate_y | \n", + "facilities_sum_y | \n", + "crime_rate_y | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "Ciutat Vella | \n", + "101387 | \n", + "3152.75 | \n", + "High | \n", + "High | \n", + "Neutral | \n", + "Neutral | \n", + "High | \n", + "High | \n", + "High | \n", + "High | \n", + "Neutral | \n", + "High | \n", + "
| 1 | \n", + "Eixample | \n", + "266416 | \n", + "3174.50 | \n", + "High | \n", + "Neutral | \n", + "Neutral | \n", + "Neutral | \n", + "High | \n", + "High | \n", + "Neutral | \n", + "Neutral | \n", + "Neutral | \n", + "High | \n", + "
| 2 | \n", + "Sant Martí | \n", + "235513 | \n", + "1272.00 | \n", + "High | \n", + "Neutral | \n", + "Neutral | \n", + "Neutral | \n", + "High | \n", + "Neutral | \n", + "Neutral | \n", + "Neutral | \n", + "Neutral | \n", + "High | \n", + "
| 3 | \n", + "Sants-Montjuïc | \n", + "181910 | \n", + "1460.38 | \n", + "Neutral | \n", + "Neutral | \n", + "Neutral | \n", + "Low | \n", + "High | \n", + "Neutral | \n", + "Neutral | \n", + "Neutral | \n", + "Low | \n", + "High | \n", + "
| 4 | \n", + "Gràcia | \n", + "121347 | \n", + "1450.80 | \n", + "Neutral | \n", + "Neutral | \n", + "Neutral | \n", + "Neutral | \n", + "Low | \n", + "Neutral | \n", + "Neutral | \n", + "Neutral | \n", + "Neutral | \n", + "Low | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " dist population net_density(hab/ha) mort_rate rent_price \\\n",
+ "0 Ciutat Vella 101387 High High Neutral \n",
+ "1 Eixample 266416 High Neutral Neutral \n",
+ "2 Sant Martí 235513 High Neutral Neutral \n",
+ "3 Sants-Montjuïc 181910 Neutral Neutral Neutral \n",
+ "4 Gràcia 121347 Neutral Neutral Neutral \n",
+ "5 Horta-Guinardó 168751 Low Neutral Low \n",
+ "6 Les Corts 82033 Low Low High \n",
+ "7 Nou Barris 166579 Neutral Neutral Low \n",
+ "8 Sant Andreu 147594 Neutral Neutral Low \n",
+ "9 Sarrià-Sant Gervasi 149279 Low Neutral High \n",
+ "\n",
+ " facilities_sum crime_rate %immi_total_immi %immi_total_dist \n",
+ "0 Neutral High High High \n",
+ "1 Neutral High High Neutral \n",
+ "2 Neutral High Neutral Neutral \n",
+ "3 Low High Neutral Neutral \n",
+ "4 Neutral Low Neutral Neutral \n",
+ "5 Neutral Low Neutral Low \n",
+ "6 High Low Low Low \n",
+ "7 Low Neutral Neutral Neutral \n",
+ "8 Low Neutral Neutral Low \n",
+ "9 Neutral Neutral Neutral Low "
+ ]
+ },
+ "execution_count": 510,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "immipop"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.3"
+ },
+ "toc": {
+ "base_numbering": 1,
+ "nav_menu": {},
+ "number_sections": true,
+ "sideBar": true,
+ "skip_h1_title": false,
+ "title_cell": "Table of Contents",
+ "title_sidebar": "Contents",
+ "toc_cell": false,
+ "toc_position": {},
+ "toc_section_display": true,
+ "toc_window_display": false
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/your-project/Facilities.ipynb b/your-project/Facilities.ipynb
new file mode 100644
index 0000000..4122dea
--- /dev/null
+++ b/your-project/Facilities.ipynb
@@ -0,0 +1,649 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df = pd.read_csv(\"../datasets/Data_filtered/complete_dataset.csv\")\n",
+ "df[\"perc_immi\"]=round(df[\"num_immi\"]/df[\"population\"]*100,2)\n",
+ "df_original = df.copy()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Index(['Unnamed: 0', 'nbh', 'bars', 'children_places', 'cinemas_theatres',\n",
+ " 'schools', 'pre-schools', 'hospitals', 'libraries_theatres',\n",
+ " 'park_gardens', 'sport_centers', 'population', 'net_density(hab/ha)',\n",
+ " 'avg_occupation', 'dist', 'num_immi', 'mort_rate', 'rent_price',\n",
+ " 'num_crimes', 'children_places_pop', 'cinemas_theatres_pop',\n",
+ " 'schools_pop', 'pre-schools_pop', 'hospitals_pop',\n",
+ " 'libraries_theatres_pop', 'park_gardens_pop', 'sport_centers_pop',\n",
+ " 'facilities_pop', 'perc_immi'],\n",
+ " dtype='object')"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.columns"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Index(['nbh', 'dist', 'children_places_pop', 'cinemas_theatres_pop',\n",
+ " 'schools_pop', 'pre-schools_pop', 'hospitals_pop',\n",
+ " 'libraries_theatres_pop', 'park_gardens_pop', 'sport_centers_pop',\n",
+ " 'facilities_pop'],\n",
+ " dtype='object')"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_facilities = df[[\"nbh\",\"dist\",'children_places_pop', 'cinemas_theatres_pop',\n",
+ " 'schools_pop', 'pre-schools_pop', 'hospitals_pop',\n",
+ " 'libraries_theatres_pop', 'park_gardens_pop', 'sport_centers_pop',\n",
+ " 'facilities_pop']]\n",
+ "df_facilities.columns"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | dist | \n", + "population | \n", + "net_density(hab/ha) | \n", + "mort_rate | \n", + "rent_price | \n", + "facilities_sum | \n", + "crime_rate | \n", + "%immi_total_immi | \n", + "%immi_total_dist | \n", + "
|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "Ciutat Vella | \n", + "101387 | \n", + "High | \n", + "High | \n", + "Neutral | \n", + "Neutral | \n", + "High | \n", + "High | \n", + "High | \n", + "
| 1 | \n", + "Eixample | \n", + "266416 | \n", + "High | \n", + "Neutral | \n", + "Neutral | \n", + "Neutral | \n", + "High | \n", + "High | \n", + "Neutral | \n", + "
| 2 | \n", + "Sant Martí | \n", + "235513 | \n", + "High | \n", + "Neutral | \n", + "Neutral | \n", + "Neutral | \n", + "High | \n", + "Neutral | \n", + "Neutral | \n", + "
| 3 | \n", + "Sants-Montjuïc | \n", + "181910 | \n", + "Neutral | \n", + "Neutral | \n", + "Neutral | \n", + "Low | \n", + "High | \n", + "Neutral | \n", + "Neutral | \n", + "
| 4 | \n", + "Gràcia | \n", + "121347 | \n", + "Neutral | \n", + "Neutral | \n", + "Neutral | \n", + "Neutral | \n", + "Low | \n", + "Neutral | \n", + "Neutral | \n", + "
| 5 | \n", + "Horta-Guinardó | \n", + "168751 | \n", + "Low | \n", + "Neutral | \n", + "Low | \n", + "Neutral | \n", + "Low | \n", + "Neutral | \n", + "Low | \n", + "
| 6 | \n", + "Les Corts | \n", + "82033 | \n", + "Low | \n", + "Low | \n", + "High | \n", + "High | \n", + "Low | \n", + "Low | \n", + "Low | \n", + "
| 7 | \n", + "Nou Barris | \n", + "166579 | \n", + "Neutral | \n", + "Neutral | \n", + "Low | \n", + "Low | \n", + "Neutral | \n", + "Neutral | \n", + "Neutral | \n", + "
| 8 | \n", + "Sant Andreu | \n", + "147594 | \n", + "Neutral | \n", + "Neutral | \n", + "Low | \n", + "Low | \n", + "Neutral | \n", + "Neutral | \n", + "Low | \n", + "
| 9 | \n", + "Sarrià-Sant Gervasi | \n", + "149279 | \n", + "Low | \n", + "Neutral | \n", + "High | \n", + "Neutral | \n", + "Neutral | \n", + "Neutral | \n", + "Low | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " children_places_pop cinemas_theatres_pop schools_pop \\\n",
+ "dist \n",
+ "Ciutat Vella 0.841614 1.261204 1.271879 \n",
+ "Eixample 1.051116 0.389449 1.443506 \n",
+ "Sants-Montjuïc 3.850100 0.480488 4.042112 \n",
+ "Sant Martí 4.204484 0.897170 3.780758 \n",
+ "Gràcia 1.434094 0.384426 1.874243 \n",
+ "Les Corts 1.132781 0.230521 1.432938 \n",
+ "Nou Barris 6.890549 0.274415 4.831139 \n",
+ "Sant Andreu 3.906377 0.192663 3.679676 \n",
+ "Horta-Guinardó 5.894020 0.112296 5.341223 \n",
+ "Sarrià-Sant Gervasi 2.512037 0.666398 3.199712 \n",
+ "\n",
+ " pre-schools_pop hospitals_pop libraries_theatres_pop \\\n",
+ "dist \n",
+ "Ciutat Vella 2.859510 0.369339 3.220423 \n",
+ "Eixample 1.646192 0.273516 0.763860 \n",
+ "Sants-Montjuïc 4.143657 0.356400 2.029622 \n",
+ "Sant Martí 4.595351 0.507897 0.955476 \n",
+ "Gràcia 1.739069 0.870079 0.406422 \n",
+ "Les Corts 6.099632 0.212211 1.434424 \n",
+ "Nou Barris 4.631401 0.840238 0.795183 \n",
+ "Sant Andreu 2.772317 0.335993 0.534160 \n",
+ "Horta-Guinardó 6.201795 1.053489 1.847809 \n",
+ "Sarrià-Sant Gervasi 3.814591 1.397647 1.065950 \n",
+ "\n",
+ " park_gardens_pop sport_centers_pop facilities_pop \n",
+ "dist \n",
+ "Ciutat Vella 0.513950 0.410593 0.986125 \n",
+ "Eixample 1.032618 0.111092 0.473510 \n",
+ "Sants-Montjuïc 1.610135 0.271946 0.345214 \n",
+ "Sant Martí 1.714455 0.537414 0.839048 \n",
+ "Gràcia 0.603838 0.552885 0.246473 \n",
+ "Les Corts 1.465427 0.211506 0.402687 \n",
+ "Nou Barris 0.771735 0.922278 0.000000 \n",
+ "Sant Andreu 1.165218 0.266634 0.243051 \n",
+ "Horta-Guinardó 6.644612 1.096897 0.168445 \n",
+ "Sarrià-Sant Gervasi 1.959767 0.406711 1.119175 "
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Calculating facilities by 10,000 people, for each district (order by percentage of immigrants)\n",
+ "df_facilities = df_facilities.groupby(\"dist\")[['children_places_pop', 'cinemas_theatres_pop',\n",
+ " 'schools_pop', 'pre-schools_pop', 'hospitals_pop',\n",
+ " 'libraries_theatres_pop', 'park_gardens_pop', 'sport_centers_pop',\n",
+ " 'facilities_pop']].sum()\n",
+ "df_facilities\n",
+ "\n",
+ "df_facilities.reindex([\"Ciutat Vella\", \"Eixample\", \"Sants-Montjuïc\", \"Sant Martí\", \"Gràcia\", \"Les Corts\", \"Nou Barris\", \"Sant Andreu\", \"Horta-Guinardó\", \"Sarrià-Sant Gervasi\" ])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | children_places_pop | \n", + "cinemas_theatres_pop | \n", + "schools_pop | \n", + "pre-schools_pop | \n", + "hospitals_pop | \n", + "libraries_theatres_pop | \n", + "park_gardens_pop | \n", + "sport_centers_pop | \n", + "facilities_pop | \n", + "
|---|---|---|---|---|---|---|---|---|---|
| dist | \n", + "\n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " |
| Ciutat Vella | \n", + "0.841614 | \n", + "1.261204 | \n", + "1.271879 | \n", + "2.859510 | \n", + "0.369339 | \n", + "3.220423 | \n", + "0.513950 | \n", + "0.410593 | \n", + "0.986125 | \n", + "
| Eixample | \n", + "1.051116 | \n", + "0.389449 | \n", + "1.443506 | \n", + "1.646192 | \n", + "0.273516 | \n", + "0.763860 | \n", + "1.032618 | \n", + "0.111092 | \n", + "0.473510 | \n", + "
| Sants-Montjuïc | \n", + "3.850100 | \n", + "0.480488 | \n", + "4.042112 | \n", + "4.143657 | \n", + "0.356400 | \n", + "2.029622 | \n", + "1.610135 | \n", + "0.271946 | \n", + "0.345214 | \n", + "
| Sant Martí | \n", + "4.204484 | \n", + "0.897170 | \n", + "3.780758 | \n", + "4.595351 | \n", + "0.507897 | \n", + "0.955476 | \n", + "1.714455 | \n", + "0.537414 | \n", + "0.839048 | \n", + "
| Gràcia | \n", + "1.434094 | \n", + "0.384426 | \n", + "1.874243 | \n", + "1.739069 | \n", + "0.870079 | \n", + "0.406422 | \n", + "0.603838 | \n", + "0.552885 | \n", + "0.246473 | \n", + "
| Les Corts | \n", + "1.132781 | \n", + "0.230521 | \n", + "1.432938 | \n", + "6.099632 | \n", + "0.212211 | \n", + "1.434424 | \n", + "1.465427 | \n", + "0.211506 | \n", + "0.402687 | \n", + "
| Nou Barris | \n", + "6.890549 | \n", + "0.274415 | \n", + "4.831139 | \n", + "4.631401 | \n", + "0.840238 | \n", + "0.795183 | \n", + "0.771735 | \n", + "0.922278 | \n", + "0.000000 | \n", + "
| Sant Andreu | \n", + "3.906377 | \n", + "0.192663 | \n", + "3.679676 | \n", + "2.772317 | \n", + "0.335993 | \n", + "0.534160 | \n", + "1.165218 | \n", + "0.266634 | \n", + "0.243051 | \n", + "
| Horta-Guinardó | \n", + "5.894020 | \n", + "0.112296 | \n", + "5.341223 | \n", + "6.201795 | \n", + "1.053489 | \n", + "1.847809 | \n", + "6.644612 | \n", + "1.096897 | \n", + "0.168445 | \n", + "
| Sarrià-Sant Gervasi | \n", + "2.512037 | \n", + "0.666398 | \n", + "3.199712 | \n", + "3.814591 | \n", + "1.397647 | \n", + "1.065950 | \n", + "1.959767 | \n", + "0.406711 | \n", + "1.119175 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " children_places_pop cinemas_theatres_pop schools_pop \\\n",
+ "count 10.000000 10.000000 10.000000 \n",
+ "mean 3.171717 0.488903 3.089719 \n",
+ "std 2.130344 0.358681 1.493817 \n",
+ "min 0.841614 0.112296 1.271879 \n",
+ "25% 1.208109 0.241494 1.551190 \n",
+ "50% 3.181068 0.386937 3.439694 \n",
+ "75% 4.129957 0.619920 3.976773 \n",
+ "max 6.890549 1.261204 5.341223 \n",
+ "\n",
+ " pre-schools_pop hospitals_pop libraries_theatres_pop \\\n",
+ "count 10.000000 10.000000 10.000000 \n",
+ "mean 3.850351 0.621681 1.305333 \n",
+ "std 1.609026 0.396590 0.858876 \n",
+ "min 1.646192 0.212211 0.406422 \n",
+ "25% 2.794115 0.341095 0.771691 \n",
+ "50% 3.979124 0.438618 1.010713 \n",
+ "75% 4.622388 0.862619 1.744462 \n",
+ "max 6.201795 1.397647 3.220423 \n",
+ "\n",
+ " park_gardens_pop sport_centers_pop facilities_pop \n",
+ "count 10.000000 10.000000 10.000000 \n",
+ "mean 1.748175 0.478796 0.482373 \n",
+ "std 1.787553 0.314810 0.373556 \n",
+ "min 0.513950 0.111092 0.000000 \n",
+ "25% 0.836955 0.267962 0.243907 \n",
+ "50% 1.315322 0.408652 0.373950 \n",
+ "75% 1.688375 0.549017 0.747663 \n",
+ "max 6.644612 1.096897 1.119175 "
+ ]
+ },
+ "execution_count": 32,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_facilities.describe()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "children_places_pop 4.538365\n",
+ "cinemas_theatres_pop 0.128652\n",
+ "schools_pop 2.231490\n",
+ "pre-schools_pop 2.588964\n",
+ "hospitals_pop 0.157283\n",
+ "libraries_theatres_pop 0.737667\n",
+ "park_gardens_pop 3.195347\n",
+ "sport_centers_pop 0.099105\n",
+ "facilities_pop 0.139544\n",
+ "dtype: float64"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_facilities.var()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | children_places_pop | \n", + "cinemas_theatres_pop | \n", + "schools_pop | \n", + "pre-schools_pop | \n", + "hospitals_pop | \n", + "libraries_theatres_pop | \n", + "park_gardens_pop | \n", + "sport_centers_pop | \n", + "facilities_pop | \n", + "
|---|---|---|---|---|---|---|---|---|---|
| count | \n", + "10.000000 | \n", + "10.000000 | \n", + "10.000000 | \n", + "10.000000 | \n", + "10.000000 | \n", + "10.000000 | \n", + "10.000000 | \n", + "10.000000 | \n", + "10.000000 | \n", + "
| mean | \n", + "3.171717 | \n", + "0.488903 | \n", + "3.089719 | \n", + "3.850351 | \n", + "0.621681 | \n", + "1.305333 | \n", + "1.748175 | \n", + "0.478796 | \n", + "0.482373 | \n", + "
| std | \n", + "2.130344 | \n", + "0.358681 | \n", + "1.493817 | \n", + "1.609026 | \n", + "0.396590 | \n", + "0.858876 | \n", + "1.787553 | \n", + "0.314810 | \n", + "0.373556 | \n", + "
| min | \n", + "0.841614 | \n", + "0.112296 | \n", + "1.271879 | \n", + "1.646192 | \n", + "0.212211 | \n", + "0.406422 | \n", + "0.513950 | \n", + "0.111092 | \n", + "0.000000 | \n", + "
| 25% | \n", + "1.208109 | \n", + "0.241494 | \n", + "1.551190 | \n", + "2.794115 | \n", + "0.341095 | \n", + "0.771691 | \n", + "0.836955 | \n", + "0.267962 | \n", + "0.243907 | \n", + "
| 50% | \n", + "3.181068 | \n", + "0.386937 | \n", + "3.439694 | \n", + "3.979124 | \n", + "0.438618 | \n", + "1.010713 | \n", + "1.315322 | \n", + "0.408652 | \n", + "0.373950 | \n", + "
| 75% | \n", + "4.129957 | \n", + "0.619920 | \n", + "3.976773 | \n", + "4.622388 | \n", + "0.862619 | \n", + "1.744462 | \n", + "1.688375 | \n", + "0.549017 | \n", + "0.747663 | \n", + "
| max | \n", + "6.890549 | \n", + "1.261204 | \n", + "5.341223 | \n", + "6.201795 | \n", + "1.397647 | \n", + "3.220423 | \n", + "6.644612 | \n", + "1.096897 | \n", + "1.119175 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " children_places_pop cinemas_theatres_pop schools_pop pre-schools_pop \\\n",
+ "min 0.841614 0.112296 1.271879 1.646192 \n",
+ "max 6.890549 1.261204 5.341223 6.201795 \n",
+ "\n",
+ " hospitals_pop libraries_theatres_pop park_gardens_pop \\\n",
+ "min 0.212211 0.406422 0.513950 \n",
+ "max 1.397647 3.220423 6.644612 \n",
+ "\n",
+ " sport_centers_pop facilities_pop \n",
+ "min 0.111092 0.000000 \n",
+ "max 1.096897 1.119175 "
+ ]
+ },
+ "execution_count": 21,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_facilities.agg([min,max])\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.3"
+ },
+ "toc": {
+ "base_numbering": 1,
+ "nav_menu": {},
+ "number_sections": true,
+ "sideBar": true,
+ "skip_h1_title": false,
+ "title_cell": "Table of Contents",
+ "title_sidebar": "Contents",
+ "toc_cell": false,
+ "toc_position": {},
+ "toc_section_display": true,
+ "toc_window_display": false
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/your-project/Notes b/your-project/Notes
new file mode 100644
index 0000000..64248cd
--- /dev/null
+++ b/your-project/Notes
@@ -0,0 +1,44 @@
+Notes
+
+Immigration
+
+Is there a link between the country (country characteristics) they come from and the neighbourhood the immigrants end up living?
+
+Do the immigrants from poorer /less developed countries end up in the “worst” neighbourhoods?
+
+Characteristics worst neighbourhoods:
+- Cost habitatge/m2
+- Crime rate (robatoris…)
+
+Country characteristics:
+- Nivell de vida: developed/underdeveloped
+- Cost the vida?
+- Base salary
+- Sanitary system?
+
+
+Do the deaths/births in a certain neighbourhood relate to the “type” of immigrant that lives in that neighbourhood?
+
+How many percentage of immigrants live in certain neighbourhoods?
+Necessary to
+
+In the neighbourhood with more immigrant females are the births higher or lower?
+
+Have the neighbourhoods with higher births and population more schools?
+Resources that we need:
+- Immigrant files
+
+
+- Cost habitatge/m2
+- Crime rate (robatoris…)
+- Base salary
+
+Average Wages
+
+https://data.oecd.org/earnwage/average-wages.htm
+
+Average wages are obtained by dividing the national-accounts-based total wage bill by the average number of employees in the total economy, which is then multiplied by the ratio of the average usual weekly hours per full-time employee to the average usually weekly hours for all employees. This indicator is measured in USD constant prices using 2016 base year and Purchasing Power Parities (PPPs) for private consumption of the same year.
+
+
+- Escoles Barcelona
+- Gent per barri
diff --git a/your-project/README.md b/your-project/README.md
index 0103b93..e16c7f7 100644
--- a/your-project/README.md
+++ b/your-project/README.md
@@ -1,9 +1,9 @@
| \n", + " | children_places_pop | \n", + "cinemas_theatres_pop | \n", + "schools_pop | \n", + "pre-schools_pop | \n", + "hospitals_pop | \n", + "libraries_theatres_pop | \n", + "park_gardens_pop | \n", + "sport_centers_pop | \n", + "facilities_pop | \n", + "
|---|---|---|---|---|---|---|---|---|---|
| min | \n", + "0.841614 | \n", + "0.112296 | \n", + "1.271879 | \n", + "1.646192 | \n", + "0.212211 | \n", + "0.406422 | \n", + "0.513950 | \n", + "0.111092 | \n", + "0.000000 | \n", + "
| max | \n", + "6.890549 | \n", + "1.261204 | \n", + "5.341223 | \n", + "6.201795 | \n", + "1.397647 | \n", + "3.220423 | \n", + "6.644612 | \n", + "1.096897 | \n", + "1.119175 | \n", + "
From the datasets mention above, data was processed and standarized into new datasets for data comparison. ## Database -What is the structure of your database? Have you created more than one table and if yes, how are they related to each other? Include a drawing or computer-generated image of the ERD (Entity Relationship Diagram) of your database. +Our database is comprised by multiple datasets that are connected to each other by the Barcelona's district and Barcelona's neighbourhood, being the complete_dataset the link (the center point) between them all. + +Different tables were created but again all are related by the district as a common value and derive from the complete_dataset. + ## Workflow -Outline the workflow you used in your project. What are the steps you went through? +The steps followed in our workflow were: +- Step 1: decide the questions and the hypothesis +- Step 2: decide the data needed to do so +- Step 3: have a quick look to the datasets available to see if all the data needed was there +- Step 4: finding new datasets to complement the data available +- Step 5: filter the data and create a dataset with all the data needed merged +- Step 6: analyse the data +- Step 7: obtaining results +- Step 8: conclusions ## Organization -How did you organize your work? Did you use any tools like a kanban board? -What does your repository look like? Explain your folder and file structure. +For this project a kanban board was not necesary. The work was organised with meetings in which the steps to follow were decided. + +The repository is organised in two main folders: datasets and your-project. +Inside the datasets folder there are the datasets already provided in individual folders and then a folder called Data-filtered with the datasets filtered and ready to merge as well as the merged version and then an unfiltered folder with all the raw datasets that we used. +Inside the your-project folder there are the .ipynb files in which the data was filtered (to_filter),merged(merging) and analysed (the rest). + ## Links Include links to your repository, slides and kanban board. Feel free to include any other links associated with your project. -[Repository](https://github.com/) -[Slides](https://slides.com/) -[Trello](https://trello.com/en) +[Repository](https://github.com/albabalcells/Project-Week-2-Barcelona) +[Slides](https://docs.google.com/presentation/d/1nE8OsijBlGWmCbeEfTgpk23ado72Slf0hYDsBz9YJ0Q/edit?usp=sharing) + diff --git a/your-project/To_filter_data-Alba.ipynb b/your-project/To_filter_data-Alba.ipynb new file mode 100644 index 0000000..449dcda --- /dev/null +++ b/your-project/To_filter_data-Alba.ipynb @@ -0,0 +1,1786 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Data filtering" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "#import the datasets from which we need to obtain the info and summarize\n", + "\n", + "children=pd.read_csv(\"../datasets/unfiltered/Children_spaces_bcn.csv\")\n", + "\n", + "cinema=pd.read_csv(\"../datasets/unfiltered/Cinemas_and_theatres_bcn.csv\")\n", + "\n", + "education=pd.read_csv(\"../datasets/unfiltered/Education_centers_children_bcn.csv\")\n", + "\n", + "educationkids=pd.read_csv(\"../datasets/unfiltered/Educative_centers_bcn.csv\")\n", + "\n", + "hospital=pd.read_csv(\"../datasets/unfiltered/Hospitals_bcn.csv\")\n", + "\n", + "library=pd.read_csv(\"../datasets/unfiltered/Library_and_museums_bcn.csv\")\n", + "\n", + "bars=pd.read_csv(\"../datasets/unfiltered/Music_and_drinks_venues_bcn.csv\")\n", + "\n", + "park=pd.read_csv(\"../datasets/unfiltered/Parks_and_gardens_bcn.csv\")\n", + "\n", + "sport=pd.read_csv(\"../datasets/unfiltered/Sport_facilities_centers.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " CODI_EQUIPAMENT EQUIPAMENT \\\n",
+ "394 99400593384 Hospital Plató \n",
+ "395 99400593385 Hospital Universitari del Sagrat Cor \n",
+ "396 99400607886 Clínica Podològica Integral \n",
+ "397 99400632531 Centre d'Atenció Primària Roger \n",
+ "398 99400632531 Centre d'Atenció Primària Roger \n",
+ "\n",
+ " SECCIO TIPUS_VIA NOM_CARRER \\\n",
+ "394 Centre d'Urgències d'Atenció Primària C Plató \n",
+ "395 Centre d'Urgències d'Atenció Primària C Viladomat \n",
+ "396 # Pg Zona Franca \n",
+ "397 # C Roger \n",
+ "398 # C Roger \n",
+ "\n",
+ " NUM_CARRER_1 NUM_CARRER_2 CODI_BARRI NUM_BARRI \\\n",
+ "394 21 21.0 26 Sant Gervasi - Galvany \n",
+ "395 288 288.0 9 la Nova Esquerra de l'Eixample \n",
+ "396 242 242.0 13 la Marina de Port \n",
+ "397 48 64.0 17 Sants - Badal \n",
+ "398 48 64.0 17 Sants - Badal \n",
+ "\n",
+ " CODI_DISTRICTE ... TELEFON_NUM TELEFON_TIPUS TELEFON_INFO_COM \\\n",
+ "394 5 ... 933069900 Centraleta NaN \n",
+ "395 2 ... 933221111 Centraleta NaN \n",
+ "396 3 ... 606142924 Tel. NaN \n",
+ "397 3 ... 933268901 Tel. Programació de visites \n",
+ "398 3 ... 934310039 Centraleta NaN \n",
+ "\n",
+ " HORARI_PERIODE_INICI HORARI_PERIODE_FI HORARI_DIES HORARI_HORES_INICI \\\n",
+ "394 01/01 31/12 0.0 00:00:00 \n",
+ "395 01/01 31/12 0.0 00:00:00 \n",
+ "396 NaN NaN NaN NaN \n",
+ "397 01/01 31/12 11.0 08:00:00 \n",
+ "398 01/01 31/12 11.0 08:00:00 \n",
+ "\n",
+ " HORARI_HORES_FI HORARI_OBSERVACIONS \\\n",
+ "394 23:59:00 \n",
+ "395 23:59:00 \n",
+ "396 NaN NaN \n",
+ "397 20:00:00 Fora d'aquest horari truqueu al 061| \n", + " | CODI_EQUIPAMENT | \n", + "EQUIPAMENT | \n", + "SECCIO | \n", + "TIPUS_VIA | \n", + "NOM_CARRER | \n", + "NUM_CARRER_1 | \n", + "NUM_CARRER_2 | \n", + "CODI_BARRI | \n", + "NUM_BARRI | \n", + "CODI_DISTRICTE | \n", + "... | \n", + "TELEFON_NUM | \n", + "TELEFON_TIPUS | \n", + "TELEFON_INFO_COM | \n", + "HORARI_PERIODE_INICI | \n", + "HORARI_PERIODE_FI | \n", + "HORARI_DIES | \n", + "HORARI_HORES_INICI | \n", + "HORARI_HORES_FI | \n", + "HORARI_OBSERVACIONS | \n", + "Unnamed: 29 | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 394 | \n", + "99400593384 | \n", + "Hospital Plató | \n", + "Centre d'Urgències d'Atenció Primària | \n", + "C | \n", + "Plató | \n", + "21 | \n", + "21.0 | \n", + "26 | \n", + "Sant Gervasi - Galvany | \n", + "5 | \n", + "... | \n", + "933069900 | \n", + "Centraleta | \n", + "NaN | \n", + "01/01 | \n", + "31/12 | \n", + "0.0 | \n", + "00:00:00 | \n", + "23:59:00 | \n", + "\n", + " | NaN | \n", + "
| 395 | \n", + "99400593385 | \n", + "Hospital Universitari del Sagrat Cor | \n", + "Centre d'Urgències d'Atenció Primària | \n", + "C | \n", + "Viladomat | \n", + "288 | \n", + "288.0 | \n", + "9 | \n", + "la Nova Esquerra de l'Eixample | \n", + "2 | \n", + "... | \n", + "933221111 | \n", + "Centraleta | \n", + "NaN | \n", + "01/01 | \n", + "31/12 | \n", + "0.0 | \n", + "00:00:00 | \n", + "23:59:00 | \n", + "\n", + " | NaN | \n", + "
| 396 | \n", + "99400607886 | \n", + "Clínica Podològica Integral | \n", + "# | \n", + "Pg | \n", + "Zona Franca | \n", + "242 | \n", + "242.0 | \n", + "13 | \n", + "la Marina de Port | \n", + "3 | \n", + "... | \n", + "606142924 | \n", + "Tel. | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "
| 397 | \n", + "99400632531 | \n", + "Centre d'Atenció Primària Roger | \n", + "# | \n", + "C | \n", + "Roger | \n", + "48 | \n", + "64.0 | \n", + "17 | \n", + "Sants - Badal | \n", + "3 | \n", + "... | \n", + "933268901 | \n", + "Tel. | \n", + "Programació de visites | \n", + "01/01 | \n", + "31/12 | \n", + "11.0 | \n", + "08:00:00 | \n", + "20:00:00 | \n", + "Fora d'aquest horari truqueu al 061<br>o CUAP ... | \n", + "NaN | \n", + "
| 398 | \n", + "99400632531 | \n", + "Centre d'Atenció Primària Roger | \n", + "# | \n", + "C | \n", + "Roger | \n", + "48 | \n", + "64.0 | \n", + "17 | \n", + "Sants - Badal | \n", + "3 | \n", + "... | \n", + "934310039 | \n", + "Centraleta | \n", + "NaN | \n", + "01/01 | \n", + "31/12 | \n", + "11.0 | \n", + "08:00:00 | \n", + "20:00:00 | \n", + "Fora d'aquest horari truqueu al 061<br>o CUAP ... | \n", + "NaN | \n", + "
5 rows × 30 columns
\n", + "o CUAP ... \n", + "398 20:00:00 Fora d'aquest horari truqueu al 061
o CUAP ... \n", + "\n", + " Unnamed: 29 \n", + "394 NaN \n", + "395 NaN \n", + "396 NaN \n", + "397 NaN \n", + "398 NaN \n", + "\n", + "[5 rows x 30 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Quick look it they uploaded ok\n", + "\n", + "children.head()\n", + "hospital.tail()\n", + "\n", + "#all good and with no index" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#necessary to obtain the times that each neighbourhood appears (variable \"NUM_BARRI\")\n", + "#and save it" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "#to get a frequency table\n", + "\n", + "COUNT=hospital.groupby(\"NUM_BARRI\").size()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "NUM_BARRI\n", + "Diagonal Mar i el Front Marítim del Poblenou 3\n", + "Horta 6\n", + "Montbau 13\n", + "Provençals del Poblenou 1\n", + "Sant Andreu 13\n", + "Sant Antoni 6\n", + "Sant Gervasi - Galvany 22\n", + "Sant Gervasi - la Bonanova 12\n", + "Sant Martí de Provençals 4\n", + "Sant Pere, Santa Caterina i la Ribera 6\n", + "Sants 14\n", + "Sants - Badal 2\n", + "Sarrià 15\n", + "Torre Baró 6\n", + "Vallcarca i els Penitents 14\n", + "Vallvidrera, el Tibidabo i les Planes 2\n", + "Verdun 2\n", + "Vilapicina i la Torre Llobeta 5\n", + "el Baix Guinardó 4\n", + "el Barri Gòtic 3\n", + "el Besòs i el Maresme 8\n", + "el Bon Pastor 3\n", + "el Camp d'en Grassot i Gràcia Nova 5\n", + "el Camp de l'Arpa del Clot 9\n", + "el Carmel 10\n", + "el Clot 5\n", + "el Fort Pienc 7\n", + "el Guinardó 11\n", + "el Poble-sec 4\n", + "el Poblenou 6\n", + "el Putxet i el Farró 4\n", + "el Raval 12\n", + "el Turó de la Peira 4\n", + "l'Antiga Esquerra de l'Eixample 12\n", + "la Barceloneta 13\n", + "la Bordeta 3\n", + "la Dreta de Eixample 7\n", + "la Guineueta 6\n", + "la Marina de Port 13\n", + "la Maternitat i Sant Ramon 10\n", + "la Nova Esquerra de l'Eixample 7\n", + "la Prosperitat 6\n", + "la Sagrada Família 9\n", + "la Sagrera 4\n", + "la Salut 19\n", + "la Trinitat Nova 4\n", + "la Trinitat Vella 4\n", + "la Verneda i la Pau 3\n", + "la Vila Olímpica del Poblenou 4\n", + "la Vila de Gràcia 6\n", + "les Corts 9\n", + "les Roquetes 6\n", + "les Tres Torres 13\n", + "dtype: int64" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "COUNT" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": {}, + "outputs": [], + "source": [ + "COUNT.to_csv(\"Count.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 101, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " NUM_BARRI 0\n",
+ "0 Baró de Viver 8\n",
+ "1 Can Baró 6\n",
+ "2 Can Peguera 4\n",
+ "3 Canyelles 4\n",
+ "4 Ciutat Meridiana 14\n",
+ ".. ... ..\n",
+ "67 la Vila Olímpica del Poblenou 8\n",
+ "68 la Vila de Gràcia 20\n",
+ "69 les Corts 24\n",
+ "70 les Roquetes 12\n",
+ "71 les Tres Torres 10\n",
+ "\n",
+ "[72 rows x 2 columns]"
+ ]
+ },
+ "execution_count": 101,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "count=pd.read_csv(\"Count.csv\")\n",
+ "count"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 107,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "count2=count.rename(columns={\"NUM_BARRI\":\"Nbh\",\"0\":\"Children_places\"})"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 109,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Index(['Nbh', 'Children_places'], dtype='object')"
+ ]
+ },
+ "execution_count": 109,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "count2.columns"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 110,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "count2.to_csv(\"../datasets/Data_filtered/Children_places.csv\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Now we do the same with all the data sets"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#we already imported them before\n",
+ "cinema\n",
+ "education\n",
+ "educationkids\n",
+ "hospital\n",
+ "library #there are empty values here \n",
+ "bars\n",
+ "park #empty values here too\n",
+ "sport"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 119,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#checking if the value exists for all of them, it does\n",
+ "#sport[\"NUM_BARRI\"]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#step2: frequency table\n",
+ "\n",
+ "cinema=cinema.groupby(\"NUM_BARRI\").size()\n",
+ "\n",
+ "education=education.groupby(\"NUM_BARRI\").size()\n",
+ "\n",
+ "educationkids=educationkids.groupby(\"NUM_BARRI\").size()\n",
+ "\n",
+ "hospital=hospital.groupby(\"NUM_BARRI\").size()\n",
+ "\n",
+ "library=library.groupby(\"NUM_BARRI\").size()\n",
+ "\n",
+ "bars=bars.groupby(\"NUM_BARRI\").size()\n",
+ "\n",
+ "park=park.groupby(\"NUM_BARRI\").size()\n",
+ "\n",
+ "sport=sport.groupby(\"NUM_BARRI\").size()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "NUM_BARRI\n",
+ "Baró de Viver 5\n",
+ "Can Baró 4\n",
+ "Canyelles 3\n",
+ "Ciutat Meridiana 7\n",
+ "Diagonal Mar i el Front Marítim del Poblenou 7\n",
+ " ..\n",
+ "la Vila Olímpica del Poblenou 8\n",
+ "la Vila de Gràcia 35\n",
+ "les Corts 35\n",
+ "les Roquetes 7\n",
+ "les Tres Torres 3\n",
+ "Length: 71, dtype: int64"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "education"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 122,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#step3: saving the files as .csv\n",
+ "\n",
+ "cinema.to_csv(\"../datasets/Data_filtered/cinema.csv\")\n",
+ "\n",
+ "education.to_csv(\"../datasets/Data_filtered/education.csv\")\n",
+ "\n",
+ "educationkids.to_csv(\"../datasets/Data_filtered/educationkids.csv\")\n",
+ "\n",
+ "hospital.to_csv(\"../datasets/Data_filtered/hospital.csv\")\n",
+ "\n",
+ "library.to_csv(\"../datasets/Data_filtered/library.csv\")\n",
+ "\n",
+ "bars.to_csv(\"../datasets/Data_filtered/bars.csv\")\n",
+ "\n",
+ "park.to_csv(\"../datasets/Data_filtered/park.csv\")\n",
+ "\n",
+ "sport.to_csv(\"../datasets/Data_filtered/sport.csv\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 123,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#importing them again\n",
+ "\n",
+ "cinema=pd.read_csv(\"../datasets/Data_filtered/cinema.csv\")\n",
+ "\n",
+ "education=pd.read_csv(\"../datasets/Data_filtered/education.csv\")\n",
+ "\n",
+ "educationkids=pd.read_csv(\"../datasets/Data_filtered/educationkids.csv\")\n",
+ "\n",
+ "hospital=pd.read_csv(\"../datasets/Data_filtered/hospital.csv\")\n",
+ "\n",
+ "library=pd.read_csv(\"../datasets/Data_filtered/library.csv\")\n",
+ "\n",
+ "bars=pd.read_csv(\"../datasets/Data_filtered/bars.csv\")\n",
+ "\n",
+ "park=pd.read_csv(\"../datasets/Data_filtered/park.csv\")\n",
+ "\n",
+ "sport=pd.read_csv(\"../datasets/Data_filtered/sport.csv\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 124,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#renaming the columns\n",
+ "\n",
+ "cinema=cinema.rename(columns={\"NUM_BARRI\":\"Nbh\",\"0\":\"Cinemas_Theatres\"})\n",
+ "\n",
+ "education=count.rename(columns={\"NUM_BARRI\":\"Nbh\",\"0\":\"Schools\"})\n",
+ "\n",
+ "educationkids=count.rename(columns={\"NUM_BARRI\":\"Nbh\",\"0\":\"Pre-Schools\"})\n",
+ "\n",
+ "hospital=count.rename(columns={\"NUM_BARRI\":\"Nbh\",\"0\":\"Hospitals\"})\n",
+ "\n",
+ "library=count.rename(columns={\"NUM_BARRI\":\"Nbh\",\"0\":\"Libraries_Theatres\"})\n",
+ "\n",
+ "bars=count.rename(columns={\"NUM_BARRI\":\"Nbh\",\"0\":\"Bars\"})\n",
+ "\n",
+ "park=count.rename(columns={\"NUM_BARRI\":\"Nbh\",\"0\":\"Park_Gardens\"})\n",
+ "\n",
+ "sport=count.rename(columns={\"NUM_BARRI\":\"Nbh\",\"0\":\"Sport_Centers\"})"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 125,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#saving the files again\n",
+ "\n",
+ "cinema.to_csv(\"../datasets/Data_filtered/cinema.csv\")\n",
+ "\n",
+ "education.to_csv(\"../datasets/Data_filtered/education.csv\")\n",
+ "\n",
+ "educationkids.to_csv(\"../datasets/Data_filtered/educationkids.csv\")\n",
+ "\n",
+ "hospital.to_csv(\"../datasets/Data_filtered/hospital.csv\")\n",
+ "\n",
+ "library.to_csv(\"../datasets/Data_filtered/library.csv\")\n",
+ "\n",
+ "bars.to_csv(\"../datasets/Data_filtered/bars.csv\")\n",
+ "\n",
+ "park.to_csv(\"../datasets/Data_filtered/park.csv\")\n",
+ "\n",
+ "sport.to_csv(\"../datasets/Data_filtered/sport.csv\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 126,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#Let's filter what's left\n",
+ "\n",
+ "population=pd.read_csv(\"../datasets/unfiltered/2017_densitat.csv\")\n",
+ "\n",
+ "rental=pd.read_csv(\"../datasets/unfiltered/2017_lloguer_preu_trim.csv\")\n",
+ "\n",
+ "tenants=pd.read_csv(\"../datasets/unfiltered/2017_ocupacio_mitjana_domicilis.csv\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 127,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | NUM_BARRI | \n", + "0 | \n", + "
|---|---|---|
| 0 | \n", + "Baró de Viver | \n", + "8 | \n", + "
| 1 | \n", + "Can Baró | \n", + "6 | \n", + "
| 2 | \n", + "Can Peguera | \n", + "4 | \n", + "
| 3 | \n", + "Canyelles | \n", + "4 | \n", + "
| 4 | \n", + "Ciutat Meridiana | \n", + "14 | \n", + "
| ... | \n", + "... | \n", + "... | \n", + "
| 67 | \n", + "la Vila Olímpica del Poblenou | \n", + "8 | \n", + "
| 68 | \n", + "la Vila de Gràcia | \n", + "20 | \n", + "
| 69 | \n", + "les Corts | \n", + "24 | \n", + "
| 70 | \n", + "les Roquetes | \n", + "12 | \n", + "
| 71 | \n", + "les Tres Torres | \n", + "10 | \n", + "
72 rows × 2 columns
\n", + "\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Any Codi_Districte Nom_Districte Codi_Barri \\\n",
+ "0 2017 1 Ciutat Vella 1 \n",
+ "1 2017 1 Ciutat Vella 2 \n",
+ "2 2017 1 Ciutat Vella 3 \n",
+ "3 2017 1 Ciutat Vella 4 \n",
+ "4 2017 2 Eixample 5 \n",
+ "\n",
+ " Nom_Barri Població Superfície (ha) \\\n",
+ "0 el Raval 47608 109.8 \n",
+ "1 el Barri Gòtic 16062 84.2 \n",
+ "2 la Barceloneta 14996 131.4 \n",
+ "3 Sant Pere, Santa Caterina i la Ribera 22721 111.4 \n",
+ "4 el Fort Pienc 32016 92.9 \n",
+ "\n",
+ " Superfície Residencial (ha) Densitat (hab/ha) Densitat neta (hab/ha) \n",
+ "0 49.7 433 959 \n",
+ "1 34.1 191 471 \n",
+ "2 13.5 114 1107 \n",
+ "3 32.5 204 700 \n",
+ "4 33.4 345 959 "
+ ]
+ },
+ "execution_count": 127,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#we want to keep:\n",
+ "#population (Població) and Densitat neta which is the number of habitans per habitable hectares(ha) residental surface\n",
+ "population.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 135,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | Any | \n", + "Codi_Districte | \n", + "Nom_Districte | \n", + "Codi_Barri | \n", + "Nom_Barri | \n", + "Població | \n", + "Superfície (ha) | \n", + "Superfície Residencial (ha) | \n", + "Densitat (hab/ha) | \n", + "Densitat neta (hab/ha) | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "2017 | \n", + "1 | \n", + "Ciutat Vella | \n", + "1 | \n", + "el Raval | \n", + "47608 | \n", + "109.8 | \n", + "49.7 | \n", + "433 | \n", + "959 | \n", + "
| 1 | \n", + "2017 | \n", + "1 | \n", + "Ciutat Vella | \n", + "2 | \n", + "el Barri Gòtic | \n", + "16062 | \n", + "84.2 | \n", + "34.1 | \n", + "191 | \n", + "471 | \n", + "
| 2 | \n", + "2017 | \n", + "1 | \n", + "Ciutat Vella | \n", + "3 | \n", + "la Barceloneta | \n", + "14996 | \n", + "131.4 | \n", + "13.5 | \n", + "114 | \n", + "1107 | \n", + "
| 3 | \n", + "2017 | \n", + "1 | \n", + "Ciutat Vella | \n", + "4 | \n", + "Sant Pere, Santa Caterina i la Ribera | \n", + "22721 | \n", + "111.4 | \n", + "32.5 | \n", + "204 | \n", + "700 | \n", + "
| 4 | \n", + "2017 | \n", + "2 | \n", + "Eixample | \n", + "5 | \n", + "el Fort Pienc | \n", + "32016 | \n", + "92.9 | \n", + "33.4 | \n", + "345 | \n", + "959 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Nom_Barri Població \\\n",
+ "0 el Raval 47608 \n",
+ "1 el Barri Gòtic 16062 \n",
+ "2 la Barceloneta 14996 \n",
+ "3 Sant Pere, Santa Caterina i la Ribera 22721 \n",
+ "4 el Fort Pienc 32016 \n",
+ ".. ... ... \n",
+ "68 Diagonal Mar i el Front Marítim del Poblenou 13629 \n",
+ "69 el Besòs i el Maresme 23009 \n",
+ "70 Provençals del Poblenou 20487 \n",
+ "71 Sant Martí de Provençals 26146 \n",
+ "72 la Verneda i la Pau 28691 \n",
+ "\n",
+ " Densitat neta (hab/ha) \n",
+ "0 959 \n",
+ "1 471 \n",
+ "2 1107 \n",
+ "3 700 \n",
+ "4 959 \n",
+ ".. ... \n",
+ "68 592 \n",
+ "69 885 \n",
+ "70 1490 \n",
+ "71 1116 \n",
+ "72 716 \n",
+ "\n",
+ "[73 rows x 3 columns]"
+ ]
+ },
+ "execution_count": 135,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#select what I want\n",
+ "\n",
+ "population= population[[\"Nom_Barri\",\"Població\",\"Densitat neta (hab/ha)\"]]\n",
+ "\n",
+ "population"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 159,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | Nom_Barri | \n", + "Població | \n", + "Densitat neta (hab/ha) | \n", + "
|---|---|---|---|
| 0 | \n", + "el Raval | \n", + "47608 | \n", + "959 | \n", + "
| 1 | \n", + "el Barri Gòtic | \n", + "16062 | \n", + "471 | \n", + "
| 2 | \n", + "la Barceloneta | \n", + "14996 | \n", + "1107 | \n", + "
| 3 | \n", + "Sant Pere, Santa Caterina i la Ribera | \n", + "22721 | \n", + "700 | \n", + "
| 4 | \n", + "el Fort Pienc | \n", + "32016 | \n", + "959 | \n", + "
| ... | \n", + "... | \n", + "... | \n", + "... | \n", + "
| 68 | \n", + "Diagonal Mar i el Front Marítim del Poblenou | \n", + "13629 | \n", + "592 | \n", + "
| 69 | \n", + "el Besòs i el Maresme | \n", + "23009 | \n", + "885 | \n", + "
| 70 | \n", + "Provençals del Poblenou | \n", + "20487 | \n", + "1490 | \n", + "
| 71 | \n", + "Sant Martí de Provençals | \n", + "26146 | \n", + "1116 | \n", + "
| 72 | \n", + "la Verneda i la Pau | \n", + "28691 | \n", + "716 | \n", + "
73 rows × 3 columns
\n", + "\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Nbh Population \\\n",
+ "0 el Raval 47608 \n",
+ "1 el Barri Gòtic 16062 \n",
+ "2 la Barceloneta 14996 \n",
+ "3 Sant Pere, Santa Caterina i la Ribera 22721 \n",
+ "4 el Fort Pienc 32016 \n",
+ ".. ... ... \n",
+ "68 Diagonal Mar i el Front Marítim del Poblenou 13629 \n",
+ "69 el Besòs i el Maresme 23009 \n",
+ "70 Provençals del Poblenou 20487 \n",
+ "71 Sant Martí de Provençals 26146 \n",
+ "72 la Verneda i la Pau 28691 \n",
+ "\n",
+ " Net_density(hab/ha) \n",
+ "0 959 \n",
+ "1 471 \n",
+ "2 1107 \n",
+ "3 700 \n",
+ "4 959 \n",
+ ".. ... \n",
+ "68 592 \n",
+ "69 885 \n",
+ "70 1490 \n",
+ "71 1116 \n",
+ "72 716 \n",
+ "\n",
+ "[73 rows x 3 columns]"
+ ]
+ },
+ "execution_count": 159,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#rename\n",
+ "population.rename(columns={\"Nom_Barri\":\"Nbh\",\"Població\":\"Population\",\"Densitat neta (hab/ha)\":\"Net_density(hab/ha)\"})"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 160,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#safe the file\n",
+ "population.to_csv(\"../datasets/Data_filtered/population.csv\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 136,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | Nbh | \n", + "Population | \n", + "Net_density(hab/ha) | \n", + "
|---|---|---|---|
| 0 | \n", + "el Raval | \n", + "47608 | \n", + "959 | \n", + "
| 1 | \n", + "el Barri Gòtic | \n", + "16062 | \n", + "471 | \n", + "
| 2 | \n", + "la Barceloneta | \n", + "14996 | \n", + "1107 | \n", + "
| 3 | \n", + "Sant Pere, Santa Caterina i la Ribera | \n", + "22721 | \n", + "700 | \n", + "
| 4 | \n", + "el Fort Pienc | \n", + "32016 | \n", + "959 | \n", + "
| ... | \n", + "... | \n", + "... | \n", + "... | \n", + "
| 68 | \n", + "Diagonal Mar i el Front Marítim del Poblenou | \n", + "13629 | \n", + "592 | \n", + "
| 69 | \n", + "el Besòs i el Maresme | \n", + "23009 | \n", + "885 | \n", + "
| 70 | \n", + "Provençals del Poblenou | \n", + "20487 | \n", + "1490 | \n", + "
| 71 | \n", + "Sant Martí de Provençals | \n", + "26146 | \n", + "1116 | \n", + "
| 72 | \n", + "la Verneda i la Pau | \n", + "28691 | \n", + "716 | \n", + "
73 rows × 3 columns
\n", + "\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Any Trimestre Codi_Districte Nom_Districte Codi_Barri \\\n",
+ "0 2017 1 1 Ciutat Vella 1 \n",
+ "1 2017 1 1 Ciutat Vella 2 \n",
+ "2 2017 1 1 Ciutat Vella 3 \n",
+ "3 2017 1 1 Ciutat Vella 4 \n",
+ "4 2017 1 2 Eixample 5 \n",
+ "\n",
+ " Nom_Barri Lloguer_mitja \\\n",
+ "0 el Raval Lloguer mitjà mensual (Euros/mes) \n",
+ "1 el Barri Gòtic Lloguer mitjà mensual (Euros/mes) \n",
+ "2 la Barceloneta Lloguer mitjà mensual (Euros/mes) \n",
+ "3 Sant Pere, Santa Caterina i la Ribera Lloguer mitjà mensual (Euros/mes) \n",
+ "4 el Fort Pienc Lloguer mitjà mensual (Euros/mes) \n",
+ "\n",
+ " Preu \n",
+ "0 734.99 \n",
+ "1 905.26 \n",
+ "2 722.78 \n",
+ "3 895.28 \n",
+ "4 871.08 "
+ ]
+ },
+ "execution_count": 136,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "rental.head() #Nom_Barri,Preu"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 137,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "rental= rental[[\"Nom_Barri\",\"Preu\"]]\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 142,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | Any | \n", + "Trimestre | \n", + "Codi_Districte | \n", + "Nom_Districte | \n", + "Codi_Barri | \n", + "Nom_Barri | \n", + "Lloguer_mitja | \n", + "Preu | \n", + "
|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "2017 | \n", + "1 | \n", + "1 | \n", + "Ciutat Vella | \n", + "1 | \n", + "el Raval | \n", + "Lloguer mitjà mensual (Euros/mes) | \n", + "734.99 | \n", + "
| 1 | \n", + "2017 | \n", + "1 | \n", + "1 | \n", + "Ciutat Vella | \n", + "2 | \n", + "el Barri Gòtic | \n", + "Lloguer mitjà mensual (Euros/mes) | \n", + "905.26 | \n", + "
| 2 | \n", + "2017 | \n", + "1 | \n", + "1 | \n", + "Ciutat Vella | \n", + "3 | \n", + "la Barceloneta | \n", + "Lloguer mitjà mensual (Euros/mes) | \n", + "722.78 | \n", + "
| 3 | \n", + "2017 | \n", + "1 | \n", + "1 | \n", + "Ciutat Vella | \n", + "4 | \n", + "Sant Pere, Santa Caterina i la Ribera | \n", + "Lloguer mitjà mensual (Euros/mes) | \n", + "895.28 | \n", + "
| 4 | \n", + "2017 | \n", + "1 | \n", + "2 | \n", + "Eixample | \n", + "5 | \n", + "el Fort Pienc | \n", + "Lloguer mitjà mensual (Euros/mes) | \n", + "871.08 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Nbh Rental_price(€)/month\n",
+ "0 el Raval 734.99\n",
+ "1 el Barri Gòtic 905.26\n",
+ "2 la Barceloneta 722.78\n",
+ "3 Sant Pere, Santa Caterina i la Ribera 895.28\n",
+ "4 el Fort Pienc 871.08\n",
+ ".. ... ...\n",
+ "579 Diagonal Mar i el Front Marítim del Poblenou 16.51\n",
+ "580 el Besòs i el Maresme 10.40\n",
+ "581 Provençals del Poblenou 12.66\n",
+ "582 Sant Martí de Provençals 10.83\n",
+ "583 la Verneda i la Pau 10.38\n",
+ "\n",
+ "[584 rows x 2 columns]"
+ ]
+ },
+ "execution_count": 142,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "rental.rename(columns={\"Nom_Barri\":\"Nbh\",\"Preu\":\"Rental_price(€)/month\"})"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 148,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#safe the file\n",
+ "rental.to_csv(\"../datasets/Data_filtered/rental.csv\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 149,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | Nbh | \n", + "Rental_price(€)/month | \n", + "
|---|---|---|
| 0 | \n", + "el Raval | \n", + "734.99 | \n", + "
| 1 | \n", + "el Barri Gòtic | \n", + "905.26 | \n", + "
| 2 | \n", + "la Barceloneta | \n", + "722.78 | \n", + "
| 3 | \n", + "Sant Pere, Santa Caterina i la Ribera | \n", + "895.28 | \n", + "
| 4 | \n", + "el Fort Pienc | \n", + "871.08 | \n", + "
| ... | \n", + "... | \n", + "... | \n", + "
| 579 | \n", + "Diagonal Mar i el Front Marítim del Poblenou | \n", + "16.51 | \n", + "
| 580 | \n", + "el Besòs i el Maresme | \n", + "10.40 | \n", + "
| 581 | \n", + "Provençals del Poblenou | \n", + "12.66 | \n", + "
| 582 | \n", + "Sant Martí de Provençals | \n", + "10.83 | \n", + "
| 583 | \n", + "la Verneda i la Pau | \n", + "10.38 | \n", + "
584 rows × 2 columns
\n", + "\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Any Codi_Districte Nom_Districte Codi_Barri \\\n",
+ "0 2017 1 Ciutat Vella 1 \n",
+ "1 2017 1 Ciutat Vella 2 \n",
+ "2 2017 1 Ciutat Vella 3 \n",
+ "3 2017 1 Ciutat Vella 4 \n",
+ "4 2017 2 Eixample 5 \n",
+ "\n",
+ " Nom_Barri Poblacio Nombre_domicilis \\\n",
+ "0 el Raval 47986 17090 \n",
+ "1 el Barri Gòtic 16240 6537 \n",
+ "2 la Barceloneta 15101 6911 \n",
+ "3 Sant Pere, Santa Caterina i la Ribera 22923 10154 \n",
+ "4 el Fort Pienc 32048 12838 \n",
+ "\n",
+ " Ocupacio_mitjana \n",
+ "0 2.8 \n",
+ "1 2.5 \n",
+ "2 2.2 \n",
+ "3 2.3 \n",
+ "4 2.5 "
+ ]
+ },
+ "execution_count": 149,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "tenants.head() #Nom_Barri,Ocupació_mitjana"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 151,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "tenants=tenants[[\"Nom_Barri\",\"Ocupacio_mitjana\"]]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 155,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | Any | \n", + "Codi_Districte | \n", + "Nom_Districte | \n", + "Codi_Barri | \n", + "Nom_Barri | \n", + "Poblacio | \n", + "Nombre_domicilis | \n", + "Ocupacio_mitjana | \n", + "
|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "2017 | \n", + "1 | \n", + "Ciutat Vella | \n", + "1 | \n", + "el Raval | \n", + "47986 | \n", + "17090 | \n", + "2.8 | \n", + "
| 1 | \n", + "2017 | \n", + "1 | \n", + "Ciutat Vella | \n", + "2 | \n", + "el Barri Gòtic | \n", + "16240 | \n", + "6537 | \n", + "2.5 | \n", + "
| 2 | \n", + "2017 | \n", + "1 | \n", + "Ciutat Vella | \n", + "3 | \n", + "la Barceloneta | \n", + "15101 | \n", + "6911 | \n", + "2.2 | \n", + "
| 3 | \n", + "2017 | \n", + "1 | \n", + "Ciutat Vella | \n", + "4 | \n", + "Sant Pere, Santa Caterina i la Ribera | \n", + "22923 | \n", + "10154 | \n", + "2.3 | \n", + "
| 4 | \n", + "2017 | \n", + "2 | \n", + "Eixample | \n", + "5 | \n", + "el Fort Pienc | \n", + "32048 | \n", + "12838 | \n", + "2.5 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Nbh Avg_hab_per_house\n",
+ "0 el Raval 2.8\n",
+ "1 el Barri Gòtic 2.5\n",
+ "2 la Barceloneta 2.2\n",
+ "3 Sant Pere, Santa Caterina i la Ribera 2.3\n",
+ "4 el Fort Pienc 2.5\n",
+ ".. ... ...\n",
+ "68 Diagonal Mar i el Front Marítim del Poblenou 2.5\n",
+ "69 el Besòs i el Maresme 2.8\n",
+ "70 Provençals del Poblenou 2.5\n",
+ "71 Sant Martí de Provençals 2.5\n",
+ "72 la Verneda i la Pau 2.5\n",
+ "\n",
+ "[73 rows x 2 columns]"
+ ]
+ },
+ "execution_count": 155,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "tenants.rename(columns={\"Nom_Barri\":\"Nbh\",\"Ocupacio_mitjana\":\"Avg_hab_per_house\"})"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 156,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "tenants.to_csv(\"../datasets/Data_filtered/tenants.csv\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.2"
+ },
+ "toc": {
+ "base_numbering": 1,
+ "nav_menu": {},
+ "number_sections": true,
+ "sideBar": true,
+ "skip_h1_title": false,
+ "title_cell": "Table of Contents",
+ "title_sidebar": "Contents",
+ "toc_cell": false,
+ "toc_position": {},
+ "toc_section_display": true,
+ "toc_window_display": false
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/your-project/To_filter_data.ipynb b/your-project/To_filter_data.ipynb
new file mode 100644
index 0000000..f60a9e0
--- /dev/null
+++ b/your-project/To_filter_data.ipynb
@@ -0,0 +1,609 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Data filtering"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Try outs to figure out what to do"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "import numpy as np"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 70,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#import the datasets from which we need to obtain the info and summarize\n",
+ "\n",
+ "children=pd.read_csv(\"../datasets/unfiltered/Children_spaces_bcn.csv\")\n",
+ "\n",
+ "cinema=pd.read_csv(\"../datasets/unfiltered/Cinemas_and_theatres_bcn.csv\")\n",
+ "\n",
+ "education=pd.read_csv(\"../datasets/unfiltered/Education_centers_children_bcn.csv\")\n",
+ "\n",
+ "educationkids=pd.read_csv(\"../datasets/unfiltered/Educative_centers_bcn.csv\")\n",
+ "\n",
+ "hospital=pd.read_csv(\"../datasets/unfiltered/Hospitals_bcn.csv\")\n",
+ "\n",
+ "library=pd.read_csv(\"../datasets/unfiltered/Library_and_museums_bcn.csv\")\n",
+ "\n",
+ "bars=pd.read_csv(\"../datasets/unfiltered/Music_and_drinks_venues_bcn.csv\")\n",
+ "\n",
+ "park=pd.read_csv(\"../datasets/unfiltered/Parks_and_gardens_bcn.csv\")\n",
+ "\n",
+ "sport=pd.read_csv(\"../datasets/unfiltered/Sport_facilities_centers.csv\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 133,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#Quick look it they uploaded ok\n",
+ "\n",
+ "#children.head()\n",
+ "#hospital.tail(10)\n",
+ "\n",
+ "#all good and with no index"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#necessary to obtain the times that each neighbourhood appears (variable \"NUM_BARRI\")\n",
+ "#and save it"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#to get a frequency table\n",
+ "\n",
+ "#COUNT=children.groupby(\"NUM_BARRI\").size()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 49,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#test1=children.groupby(\"NUM_BARRI\")[\"EQUIPAMENT\"].size()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 71,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#test2=children.sort_values(\"EQUIPAMENT\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 134,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#test2[\"EQUIPAMENT\"].value_counts()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 80,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#test3=children.drop_duplicates(subset =\"EQUIPAMENT\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 86,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#test4=test3.reset_index(drop=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 92,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#test5=test4.groupby(\"NUM_BARRI\")[\"EQUIPAMENT\"].size()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 94,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#test5.to_csv(\"/Users/mireiaguinovart/Desktop/test5.csv\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 107,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#test6=test5.rename(columns={\"NUM_BARRI\":\"Nbh\",\"0\":\"Children_places\"})"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 135,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#test6.columns"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 110,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#test6.to_csv(\"../datasets/Data_filtered/Children_places.csv\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Data filtering Barcelona Facilites datasets"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 125,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Step 1 import the datasets from which we need to obtain the info and summarize\n",
+ "\n",
+ "children=pd.read_csv(\"../datasets/unfiltered/Children_spaces_bcn.csv\")\n",
+ "\n",
+ "cinema=pd.read_csv(\"../datasets/unfiltered/Cinemas_and_theatres_bcn.csv\")\n",
+ "\n",
+ "education=pd.read_csv(\"../datasets/unfiltered/Education_centers_children_bcn.csv\")\n",
+ "\n",
+ "educationkids=pd.read_csv(\"../datasets/unfiltered/Educative_centers_bcn.csv\")\n",
+ "\n",
+ "hospital=pd.read_csv(\"../datasets/unfiltered/Hospitals_bcn.csv\")\n",
+ "\n",
+ "library=pd.read_csv(\"../datasets/unfiltered/Library_and_museums_bcn.csv\")\n",
+ "\n",
+ "bars=pd.read_csv(\"../datasets/unfiltered/Music_and_drinks_venues_bcn.csv\")\n",
+ "\n",
+ "park=pd.read_csv(\"../datasets/unfiltered/Parks_and_gardens_bcn.csv\")\n",
+ "\n",
+ "sport=pd.read_csv(\"../datasets/unfiltered/Sport_facilities_centers.csv\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 126,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#sort by EQUIPAMENT-----children.sort_values(\"EQUIPAMENT\",inplace=True)\n",
+ "#drop duplicates--------children.drop_duplicates(subset =\"EQUIPAMENT\",inplace=True)\n",
+ "#reset index------------children.reset_index(drop=True,inplace=True)\n",
+ "#groupby----------------children.groupby(\"NUM_BARRI\")[\"EQUIPAMENT\"].size()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 127,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#step 1: removing duplicates\n",
+ "def sorting (DF):\n",
+ " DF.sort_values(\"EQUIPAMENT\",inplace=True)\n",
+ " DF.drop_duplicates(subset =\"EQUIPAMENT\",inplace=True)\n",
+ " DF.reset_index(drop=True,inplace=True)\n",
+ " \n",
+ "sorting(children)\n",
+ "sorting(cinema)\n",
+ "sorting(education)\n",
+ "sorting(educationkids)\n",
+ "sorting(hospital)\n",
+ "sorting(library)\n",
+ "sorting(bars)\n",
+ "sorting(park)\n",
+ "sorting(sport)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 128,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#step2: frequency table\n",
+ "\n",
+ "children=children.groupby(\"NUM_BARRI\").size()\n",
+ "\n",
+ "cinema=cinema.groupby(\"NUM_BARRI\").size()\n",
+ "\n",
+ "education=education.groupby(\"NUM_BARRI\").size()\n",
+ "\n",
+ "educationkids=educationkids.groupby(\"NUM_BARRI\").size()\n",
+ "\n",
+ "hospital=hospital.groupby(\"NUM_BARRI\").size()\n",
+ "\n",
+ "library=library.groupby(\"NUM_BARRI\").size()\n",
+ "\n",
+ "bars=bars.groupby(\"NUM_BARRI\").size()\n",
+ "\n",
+ "park=park.groupby(\"NUM_BARRI\").size()\n",
+ "\n",
+ "sport=sport.groupby(\"NUM_BARRI\").size()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 129,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#step3: saving the files as .csv\n",
+ "\n",
+ "children.to_csv(\"../datasets/Data_filtered/children.csv\")\n",
+ "\n",
+ "cinema.to_csv(\"../datasets/Data_filtered/cinema.csv\")\n",
+ "\n",
+ "education.to_csv(\"../datasets/Data_filtered/education.csv\")\n",
+ "\n",
+ "educationkids.to_csv(\"../datasets/Data_filtered/educationkids.csv\")\n",
+ "\n",
+ "hospital.to_csv(\"../datasets/Data_filtered/hospital.csv\")\n",
+ "\n",
+ "library.to_csv(\"../datasets/Data_filtered/library.csv\")\n",
+ "\n",
+ "bars.to_csv(\"../datasets/Data_filtered/bars.csv\")\n",
+ "\n",
+ "park.to_csv(\"../datasets/Data_filtered/park.csv\")\n",
+ "\n",
+ "sport.to_csv(\"../datasets/Data_filtered/sport.csv\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 130,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#step4: importing them again\n",
+ "\n",
+ "children=pd.read_csv(\"../datasets/Data_filtered/children.csv\")\n",
+ "\n",
+ "cinema=pd.read_csv(\"../datasets/Data_filtered/cinema.csv\")\n",
+ "\n",
+ "education=pd.read_csv(\"../datasets/Data_filtered/education.csv\")\n",
+ "\n",
+ "educationkids=pd.read_csv(\"../datasets/Data_filtered/educationkids.csv\")\n",
+ "\n",
+ "hospital=pd.read_csv(\"../datasets/Data_filtered/hospital.csv\")\n",
+ "\n",
+ "library=pd.read_csv(\"../datasets/Data_filtered/library.csv\")\n",
+ "\n",
+ "bars=pd.read_csv(\"../datasets/Data_filtered/bars.csv\")\n",
+ "\n",
+ "park=pd.read_csv(\"../datasets/Data_filtered/park.csv\")\n",
+ "\n",
+ "sport=pd.read_csv(\"../datasets/Data_filtered/sport.csv\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 131,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#step5:renaming the columns\n",
+ "\n",
+ "children=children.rename(columns={\"NUM_BARRI\":\"Nbh\",\"0\":\"Children_places\"})\n",
+ "\n",
+ "cinema=cinema.rename(columns={\"NUM_BARRI\":\"Nbh\",\"0\":\"Cinemas_Theatres\"})\n",
+ "\n",
+ "education=education.rename(columns={\"NUM_BARRI\":\"Nbh\",\"0\":\"Schools\"})\n",
+ "\n",
+ "educationkids=educationkids.rename(columns={\"NUM_BARRI\":\"Nbh\",\"0\":\"Pre-Schools\"})\n",
+ "\n",
+ "hospital=hospital.rename(columns={\"NUM_BARRI\":\"Nbh\",\"0\":\"Hospitals\"})\n",
+ "\n",
+ "library=library.rename(columns={\"NUM_BARRI\":\"Nbh\",\"0\":\"Libraries_Theatres\"})\n",
+ "\n",
+ "bars=bars.rename(columns={\"NUM_BARRI\":\"Nbh\",\"0\":\"Bars\"})\n",
+ "\n",
+ "park=park.rename(columns={\"NUM_BARRI\":\"Nbh\",\"0\":\"Park_Gardens\"})\n",
+ "\n",
+ "sport=sport.rename(columns={\"NUM_BARRI\":\"Nbh\",\"0\":\"Sport_Centers\"})"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 132,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#step6:saving the files again\n",
+ "\n",
+ "children.to_csv(\"../datasets/Data_filtered/children.csv\")\n",
+ "\n",
+ "cinema.to_csv(\"../datasets/Data_filtered/cinema.csv\")\n",
+ "\n",
+ "education.to_csv(\"../datasets/Data_filtered/education.csv\")\n",
+ "\n",
+ "educationkids.to_csv(\"../datasets/Data_filtered/educationkids.csv\")\n",
+ "\n",
+ "hospital.to_csv(\"../datasets/Data_filtered/hospital.csv\")\n",
+ "\n",
+ "library.to_csv(\"../datasets/Data_filtered/library.csv\")\n",
+ "\n",
+ "bars.to_csv(\"../datasets/Data_filtered/bars.csv\")\n",
+ "\n",
+ "park.to_csv(\"../datasets/Data_filtered/park.csv\")\n",
+ "\n",
+ "sport.to_csv(\"../datasets/Data_filtered/sport.csv\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Data filtering Barcelona's population, rental prices and tenants/house"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 139,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#Step 1: import\n",
+ "\n",
+ "population=pd.read_csv(\"../datasets/unfiltered/2017_densitat.csv\")\n",
+ "\n",
+ "rental=pd.read_csv(\"../datasets/unfiltered/2017_lloguer_preu_trim.csv\")\n",
+ "\n",
+ "tenants=pd.read_csv(\"../datasets/unfiltered/2017_ocupacio_mitjana_domicilis.csv\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 140,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#Step 2: select the columns needed\n",
+ "\n",
+ "population= population[[\"Nom_Barri\",\"Població\",\"Densitat neta (hab/ha)\"]]\n",
+ "\n",
+ "rental= rental[[\"Nom_Barri\",\"Preu\"]]\n",
+ "\n",
+ "tenants=tenants[[\"Nom_Barri\",\"Ocupacio_mitjana\"]]\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 141,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#Step 3: rename\n",
+ "\n",
+ "population=population.rename(columns={\"Nom_Barri\":\"Nbh\",\"Població\":\"Population\",\"Densitat neta (hab/ha)\":\"Net_density(hab/ha)\"})\n",
+ "\n",
+ "rental=rental.rename(columns={\"Nom_Barri\":\"Nbh\",\"Preu\":\"Rental_price(€)/month\"})\n",
+ "\n",
+ "tenants=tenants.rename(columns={\"Nom_Barri\":\"Nbh\",\"Ocupacio_mitjana\":\"Avg_hab_per_house\"})\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 142,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#Step 4: safe the file\n",
+ "\n",
+ "population.to_csv(\"../datasets/Data_filtered/population.csv\")\n",
+ "\n",
+ "rental.to_csv(\"../datasets/Data_filtered/rental.csv\")\n",
+ "\n",
+ "tenants.to_csv(\"../datasets/Data_filtered/tenants.csv\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## More tryouts"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 136,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#we want to keep:\n",
+ "#population (Població) and Densitat neta which is the number of habitans per habitable hectares(ha) residental surface\n",
+ "#population.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 138,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#select what I want\n",
+ "\n",
+ "#population= population[[\"Nom_Barri\",\"Població\",\"Densitat neta (hab/ha)\"]]\n",
+ "\n",
+ "#population"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 162,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#rename\n",
+ "#population=population.rename(columns={\"Nom_Barri\":\"Nbh\",\"Població\":\"Population\",\"Densitat neta (hab/ha)\":\"Net_density(hab/ha)\"})"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 164,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#safe the file\n",
+ "#population.to_csv(\"../datasets/Data_filtered/population.csv\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 143,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#rental.head() #Nom_Barri,Preu"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 137,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#rental= rental[[\"Nom_Barri\",\"Preu\"]]\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 144,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#rental=rental.rename(columns={\"Nom_Barri\":\"Nbh\",\"Preu\":\"Rental_price(€)/month\"})"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 148,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#safe the file\n",
+ "#rental.to_csv(\"../datasets/Data_filtered/rental.csv\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 145,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#tenants.head() #Nom_Barri,Ocupació_mitjana"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 151,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#tenants=tenants[[\"Nom_Barri\",\"Ocupacio_mitjana\"]]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 146,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#tenants=tenants.rename(columns={\"Nom_Barri\":\"Nbh\",\"Ocupacio_mitjana\":\"Avg_hab_per_house\"})"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 156,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#tenants.to_csv(\"../datasets/Data_filtered/tenants.csv\")"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.3"
+ },
+ "toc": {
+ "base_numbering": 1,
+ "nav_menu": {},
+ "number_sections": true,
+ "sideBar": true,
+ "skip_h1_title": false,
+ "title_cell": "Table of Contents",
+ "title_sidebar": "Contents",
+ "toc_cell": false,
+ "toc_position": {},
+ "toc_section_display": true,
+ "toc_window_display": true
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/your-project/immigration.ipynb b/your-project/immigration.ipynb
new file mode 100644
index 0000000..a22740a
--- /dev/null
+++ b/your-project/immigration.ipynb
@@ -0,0 +1,6143 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Immigrants, Nationalities, Districts and Neighbourhoods\n",
+ "- What is the percentage of immigrants living in certain neighbourhoods? And percentage by nationality?\n",
+ "- Is there a link between the country of origin of immigrants and the Barcelona’s neighbourhood chosen to live?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### What is the percentage of immigrants living in certain neighbourhoods? And percentage by nationality?\n",
+ "\n",
+ "- The vast majority of immigrants come from Spain (36.30%). The next biggest group is Italy (6.44%), followed by China (3.40%), Colombia (3.32%) and Venezuela (3.10%).\n",
+ "- The most populated neighbourhood by immigrants is *el Barri Gòtic* (16.73% of total neighbourhood population).\n",
+ "- The most populated district by immigrants is *Ciutat Vella* (12.99% of total neighbourhood population) and the least populated is *Sant Andreu* (4.64% of total neighbourhood population).\n",
+ "- *Ciutat Vella* hold the biggest number of foreign vs national immigrants, at over 75% in all its neighbourhoods. *El Gòtic* is the top neighbourhood at 82.06%.\n",
+ "- Immigrants from one country are dispersed among differnet dist ????"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "import numpy as np"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df = pd.read_csv(\"../datasets/Data_filtered/complete_dataset.csv\", index_col=0)\n",
+ "df_original = df.copy()\n",
+ "\n",
+ "immi = pd.read_csv(\"../datasets/Data_filtered/immi.csv\", index_col=0)\n",
+ "immi.columns = ['dist', 'nbh', 'nationality', 'num_immi']\n",
+ "immi_original = immi.copy()\n",
+ "\n",
+ "immi_nbh = pd.read_csv(\"../datasets/Data_filtered/immi_nbh.csv\", index_col=0)\n",
+ "immi_nbh.columns = ['nbh', 'dist', 'num_immi']\n",
+ "immi_nbh_original = immi_nbh.copy()\n",
+ "\n",
+ "immi_foreign = pd.read_csv(\"../datasets/Data_filtered/immi_foreign.csv\", index_col=0)\n",
+ "immi_foreign.columns = ['dist', 'nbh', 'nationality', 'num_immi']\n",
+ "immi_foreign_original = immi_foreign.copy()\n",
+ "\n",
+ "immi_foreign_nbh = pd.read_csv(\"../datasets/Data_filtered/immi_foreign_nbh.csv\", index_col=0)\n",
+ "immi_foreign_nbh.columns = ['nbh', 'dist', 'num_immi']\n",
+ "immi_foreign_nbh_original = immi_foreign_nbh.copy()\n",
+ "\n",
+ "df = pd.read_csv(\"../datasets/Data_filtered/complete_dataset.csv\")\n",
+ "df[\"perc_immi\"]=round(df[\"num_immi\"]/df[\"population\"]*100,2)\n",
+ "df_original = df.copy()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | Nbh | \n", + "Avg_hab_per_house | \n", + "
|---|---|---|
| 0 | \n", + "el Raval | \n", + "2.8 | \n", + "
| 1 | \n", + "el Barri Gòtic | \n", + "2.5 | \n", + "
| 2 | \n", + "la Barceloneta | \n", + "2.2 | \n", + "
| 3 | \n", + "Sant Pere, Santa Caterina i la Ribera | \n", + "2.3 | \n", + "
| 4 | \n", + "el Fort Pienc | \n", + "2.5 | \n", + "
| ... | \n", + "... | \n", + "... | \n", + "
| 68 | \n", + "Diagonal Mar i el Front Marítim del Poblenou | \n", + "2.5 | \n", + "
| 69 | \n", + "el Besòs i el Maresme | \n", + "2.8 | \n", + "
| 70 | \n", + "Provençals del Poblenou | \n", + "2.5 | \n", + "
| 71 | \n", + "Sant Martí de Provençals | \n", + "2.5 | \n", + "
| 72 | \n", + "la Verneda i la Pau | \n", + "2.5 | \n", + "
73 rows × 2 columns
\n", + "\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " num_immi\n",
+ "dist \n",
+ "Eixample 19047\n",
+ "Sant Martí 12720\n",
+ "Ciutat Vella 12611\n",
+ "Sants-Montjuïc 11683\n",
+ "Nou Barris 8274\n",
+ "Horta-Guinardó 7799\n",
+ "Gràcia 7254\n",
+ "Sarrià-Sant Gervasi 7227\n",
+ "Sant Andreu 6335\n",
+ "Les Corts 4375\n",
+ "No consta 2"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "immi.groupby(\"dist\").sum().sort_values(\"num_immi\", ascending=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | num_immi | \n", + "
|---|---|
| dist | \n", + "\n", + " |
| Eixample | \n", + "19047 | \n", + "
| Sant Martí | \n", + "12720 | \n", + "
| Ciutat Vella | \n", + "12611 | \n", + "
| Sants-Montjuïc | \n", + "11683 | \n", + "
| Nou Barris | \n", + "8274 | \n", + "
| Horta-Guinardó | \n", + "7799 | \n", + "
| Gràcia | \n", + "7254 | \n", + "
| Sarrià-Sant Gervasi | \n", + "7227 | \n", + "
| Sant Andreu | \n", + "6335 | \n", + "
| Les Corts | \n", + "4375 | \n", + "
| No consta | \n", + "2 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " num_immi\n",
+ "dist \n",
+ "Eixample 12487\n",
+ "Ciutat Vella 10069\n",
+ "Sant Martí 7994\n",
+ "Sants-Montjuïc 7464\n",
+ "Nou Barris 5261\n",
+ "Horta-Guinardó 4544\n",
+ "Gràcia 4310\n",
+ "Sarrià-Sant Gervasi 3802\n",
+ "Sant Andreu 3622\n",
+ "Les Corts 2420\n",
+ "No consta 0"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Immigrant by district as percentage of total immigrants in Barcelona\n",
+ "immi_foreign_grouped = immi_foreign.groupby(\"dist\").sum().sort_values(\"num_immi\", ascending=False)\n",
+ "immi_foreign_grouped"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### First analysis: include \"Spain\" in the dataset"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Basic analysis *Immi*"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | num_immi | \n", + "
|---|---|
| dist | \n", + "\n", + " |
| Eixample | \n", + "12487 | \n", + "
| Ciutat Vella | \n", + "10069 | \n", + "
| Sant Martí | \n", + "7994 | \n", + "
| Sants-Montjuïc | \n", + "7464 | \n", + "
| Nou Barris | \n", + "5261 | \n", + "
| Horta-Guinardó | \n", + "4544 | \n", + "
| Gràcia | \n", + "4310 | \n", + "
| Sarrià-Sant Gervasi | \n", + "3802 | \n", + "
| Sant Andreu | \n", + "3622 | \n", + "
| Les Corts | \n", + "2420 | \n", + "
| No consta | \n", + "0 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " dist nbh nationality num_immi\n",
+ "0 Ciutat Vella el Raval Spain 1109\n",
+ "1 Ciutat Vella el Barri Gòtic Spain 482\n",
+ "2 Ciutat Vella la Barceloneta Spain 414\n",
+ "3 Ciutat Vella Sant Pere, Santa Caterina i la Ribera Spain 537\n",
+ "4 Eixample el Fort Pienc Spain 663"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "immi.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "| \n", + " | dist | \n", + "nbh | \n", + "nationality | \n", + "num_immi | \n", + "
|---|---|---|---|---|
| 0 | \n", + "Ciutat Vella | \n", + "el Raval | \n", + "Spain | \n", + "1109 | \n", + "
| 1 | \n", + "Ciutat Vella | \n", + "el Barri Gòtic | \n", + "Spain | \n", + "482 | \n", + "
| 2 | \n", + "Ciutat Vella | \n", + "la Barceloneta | \n", + "Spain | \n", + "414 | \n", + "
| 3 | \n", + "Ciutat Vella | \n", + "Sant Pere, Santa Caterina i la Ribera | \n", + "Spain | \n", + "537 | \n", + "
| 4 | \n", + "Eixample | \n", + "el Fort Pienc | \n", + "Spain | \n", + "663 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " num_immi\n",
+ "count 11766.000000\n",
+ "mean 8.271885\n",
+ "std 50.821491\n",
+ "min 0.000000\n",
+ "25% 0.000000\n",
+ "50% 0.000000\n",
+ "75% 2.000000\n",
+ "max 1593.000000"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "immi.describe()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | num_immi | \n", + "
|---|---|
| count | \n", + "11766.000000 | \n", + "
| mean | \n", + "8.271885 | \n", + "
| std | \n", + "50.821491 | \n", + "
| min | \n", + "0.000000 | \n", + "
| 25% | \n", + "0.000000 | \n", + "
| 50% | \n", + "0.000000 | \n", + "
| 75% | \n", + "2.000000 | \n", + "
| max | \n", + "1593.000000 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " num_immi population perc_immi\n",
+ "nbh \n",
+ "el Barri Gòtic 2687.0 16062.0 16.73\n",
+ "Sant Pere, Santa Caterina i la Ribera 2765.0 22721.0 12.17\n",
+ "la Barceloneta 1759.0 14996.0 11.73\n",
+ "el Raval 5400.0 47608.0 11.34\n",
+ "el Besòs i el Maresme 2016.0 23009.0 8.76\n",
+ "... ... ... ...\n",
+ "la Marina del Prat Vermell 32.0 1149.0 2.79\n",
+ "Can Peguera 57.0 2271.0 2.51\n",
+ "Canyelles 140.0 6856.0 2.04\n",
+ "el Poble-sec 0.0 0.0 NaN\n",
+ "la Dreta de Eixample 0.0 0.0 NaN\n",
+ "\n",
+ "[75 rows x 3 columns]"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# list of nbh, ordered by highest % of immigrants\n",
+ "df.groupby(\"nbh\").aggregate({\"num_immi\":\"sum\",\n",
+ " \"population\":\"sum\",\n",
+ " \"perc_immi\":\"mean\"}).sort_values(\"perc_immi\", ascending=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | num_immi | \n", + "population | \n", + "perc_immi | \n", + "
|---|---|---|---|
| nbh | \n", + "\n", + " | \n", + " | \n", + " |
| el Barri Gòtic | \n", + "2687.0 | \n", + "16062.0 | \n", + "16.73 | \n", + "
| Sant Pere, Santa Caterina i la Ribera | \n", + "2765.0 | \n", + "22721.0 | \n", + "12.17 | \n", + "
| la Barceloneta | \n", + "1759.0 | \n", + "14996.0 | \n", + "11.73 | \n", + "
| el Raval | \n", + "5400.0 | \n", + "47608.0 | \n", + "11.34 | \n", + "
| el Besòs i el Maresme | \n", + "2016.0 | \n", + "23009.0 | \n", + "8.76 | \n", + "
| ... | \n", + "... | \n", + "... | \n", + "... | \n", + "
| la Marina del Prat Vermell | \n", + "32.0 | \n", + "1149.0 | \n", + "2.79 | \n", + "
| Can Peguera | \n", + "57.0 | \n", + "2271.0 | \n", + "2.51 | \n", + "
| Canyelles | \n", + "140.0 | \n", + "6856.0 | \n", + "2.04 | \n", + "
| el Poble-sec | \n", + "0.0 | \n", + "0.0 | \n", + "NaN | \n", + "
| la Dreta de Eixample | \n", + "0.0 | \n", + "0.0 | \n", + "NaN | \n", + "
75 rows × 3 columns
\n", + "\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " num_immi population perc_immi\n",
+ "dist \n",
+ "Ciutat Vella 12611.0 101387.0 12.992500\n",
+ "Eixample 19047.0 266416.0 7.148333\n",
+ "Sants-Montjuïc 11683.0 181910.0 5.876250\n",
+ "Les Corts 4375.0 82033.0 5.616667\n",
+ "Gràcia 7254.0 121347.0 5.554000\n",
+ "Sant Martí 12720.0 235513.0 5.418000\n",
+ "Nou Barris 8274.0 166579.0 4.755385\n",
+ "Horta-Guinardó 7799.0 168751.0 4.700909\n",
+ "Sarrià-Sant Gervasi 7227.0 149279.0 4.645000\n",
+ "Sant Andreu 6335.0 147594.0 4.635714"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# list of dist, ordered by highest % of immigrants\n",
+ "df.groupby(\"dist\").aggregate({\"num_immi\":\"sum\",\n",
+ " \"population\":\"sum\",\n",
+ " \"perc_immi\":\"mean\"}).sort_values(\"perc_immi\", ascending=False)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "__*Droping rows where num_immi = 0*__"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | num_immi | \n", + "population | \n", + "perc_immi | \n", + "
|---|---|---|---|
| dist | \n", + "\n", + " | \n", + " | \n", + " |
| Ciutat Vella | \n", + "12611.0 | \n", + "101387.0 | \n", + "12.992500 | \n", + "
| Eixample | \n", + "19047.0 | \n", + "266416.0 | \n", + "7.148333 | \n", + "
| Sants-Montjuïc | \n", + "11683.0 | \n", + "181910.0 | \n", + "5.876250 | \n", + "
| Les Corts | \n", + "4375.0 | \n", + "82033.0 | \n", + "5.616667 | \n", + "
| Gràcia | \n", + "7254.0 | \n", + "121347.0 | \n", + "5.554000 | \n", + "
| Sant Martí | \n", + "12720.0 | \n", + "235513.0 | \n", + "5.418000 | \n", + "
| Nou Barris | \n", + "8274.0 | \n", + "166579.0 | \n", + "4.755385 | \n", + "
| Horta-Guinardó | \n", + "7799.0 | \n", + "168751.0 | \n", + "4.700909 | \n", + "
| Sarrià-Sant Gervasi | \n", + "7227.0 | \n", + "149279.0 | \n", + "4.645000 | \n", + "
| Sant Andreu | \n", + "6335.0 | \n", + "147594.0 | \n", + "4.635714 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " dist nbh nationality num_immi\n",
+ "0 Ciutat Vella el Raval Spain 1109\n",
+ "1 Ciutat Vella el Barri Gòtic Spain 482\n",
+ "2 Ciutat Vella la Barceloneta Spain 414\n",
+ "3 Ciutat Vella Sant Pere, Santa Caterina i la Ribera Spain 537\n",
+ "4 Eixample el Fort Pienc Spain 663"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "immi = immi[immi[\"num_immi\"]!=0]\n",
+ "immi.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "| \n", + " | dist | \n", + "nbh | \n", + "nationality | \n", + "num_immi | \n", + "
|---|---|---|---|---|
| 0 | \n", + "Ciutat Vella | \n", + "el Raval | \n", + "Spain | \n", + "1109 | \n", + "
| 1 | \n", + "Ciutat Vella | \n", + "el Barri Gòtic | \n", + "Spain | \n", + "482 | \n", + "
| 2 | \n", + "Ciutat Vella | \n", + "la Barceloneta | \n", + "Spain | \n", + "414 | \n", + "
| 3 | \n", + "Ciutat Vella | \n", + "Sant Pere, Santa Caterina i la Ribera | \n", + "Spain | \n", + "537 | \n", + "
| 4 | \n", + "Eixample | \n", + "el Fort Pienc | \n", + "Spain | \n", + "663 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " num_immi\n",
+ "count 4716.000000\n",
+ "mean 20.637617\n",
+ "std 78.673256\n",
+ "min 1.000000\n",
+ "25% 1.000000\n",
+ "50% 4.000000\n",
+ "75% 13.000000\n",
+ "max 1593.000000"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "immi.describe()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | num_immi | \n", + "
|---|---|
| count | \n", + "4716.000000 | \n", + "
| mean | \n", + "20.637617 | \n", + "
| std | \n", + "78.673256 | \n", + "
| min | \n", + "1.000000 | \n", + "
| 25% | \n", + "1.000000 | \n", + "
| 50% | \n", + "4.000000 | \n", + "
| 75% | \n", + "13.000000 | \n", + "
| max | \n", + "1593.000000 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " dist nbh num_immi \\\n",
+ "10 Ciutat Vella el Barri Gòtic 2687.0 \n",
+ "7 Ciutat Vella Sant Pere, Santa Caterina i la Ribera 2765.0 \n",
+ "21 Ciutat Vella la Barceloneta 1759.0 \n",
+ "19 Ciutat Vella el Raval 5400.0 \n",
+ "50 Sant Martí el Besòs i el Maresme 2016.0 \n",
+ ".. ... ... ... \n",
+ "62 Sants-Montjuïc la Marina del Prat Vermell 32.0 \n",
+ "32 Nou Barris Can Peguera 57.0 \n",
+ "33 Nou Barris Canyelles 140.0 \n",
+ "16 NaN el Poble-sec NaN \n",
+ "22 NaN la Dreta de Eixample NaN \n",
+ "\n",
+ " population perc_immi \n",
+ "10 16062.0 16.73 \n",
+ "7 22721.0 12.17 \n",
+ "21 14996.0 11.73 \n",
+ "19 47608.0 11.34 \n",
+ "50 23009.0 8.76 \n",
+ ".. ... ... \n",
+ "62 1149.0 2.79 \n",
+ "32 2271.0 2.51 \n",
+ "33 6856.0 2.04 \n",
+ "16 NaN NaN \n",
+ "22 NaN NaN \n",
+ "\n",
+ "[75 rows x 5 columns]"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Merging dataset to add percentage of immigrants by nbh\n",
+ "perc_immi = df[[\"dist\", \"nbh\", \"num_immi\", \"population\", \"perc_immi\"]]\n",
+ "perc_immi.sort_values(\"perc_immi\", ascending=False)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Second analysis: include \"Spain\" in the dataset"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | dist | \n", + "nbh | \n", + "num_immi | \n", + "population | \n", + "perc_immi | \n", + "
|---|---|---|---|---|---|
| 10 | \n", + "Ciutat Vella | \n", + "el Barri Gòtic | \n", + "2687.0 | \n", + "16062.0 | \n", + "16.73 | \n", + "
| 7 | \n", + "Ciutat Vella | \n", + "Sant Pere, Santa Caterina i la Ribera | \n", + "2765.0 | \n", + "22721.0 | \n", + "12.17 | \n", + "
| 21 | \n", + "Ciutat Vella | \n", + "la Barceloneta | \n", + "1759.0 | \n", + "14996.0 | \n", + "11.73 | \n", + "
| 19 | \n", + "Ciutat Vella | \n", + "el Raval | \n", + "5400.0 | \n", + "47608.0 | \n", + "11.34 | \n", + "
| 50 | \n", + "Sant Martí | \n", + "el Besòs i el Maresme | \n", + "2016.0 | \n", + "23009.0 | \n", + "8.76 | \n", + "
| ... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "
| 62 | \n", + "Sants-Montjuïc | \n", + "la Marina del Prat Vermell | \n", + "32.0 | \n", + "1149.0 | \n", + "2.79 | \n", + "
| 32 | \n", + "Nou Barris | \n", + "Can Peguera | \n", + "57.0 | \n", + "2271.0 | \n", + "2.51 | \n", + "
| 33 | \n", + "Nou Barris | \n", + "Canyelles | \n", + "140.0 | \n", + "6856.0 | \n", + "2.04 | \n", + "
| 16 | \n", + "NaN | \n", + "el Poble-sec | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "
| 22 | \n", + "NaN | \n", + "la Dreta de Eixample | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "
75 rows × 5 columns
\n", + "\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " dist nbh num_immi_total \\\n",
+ "10 Ciutat Vella el Barri Gòtic 2687.0 \n",
+ "7 Ciutat Vella Sant Pere, Santa Caterina i la Ribera 2765.0 \n",
+ "19 Ciutat Vella el Raval 5400.0 \n",
+ "21 Ciutat Vella la Barceloneta 1759.0 \n",
+ "67 Sant Andreu la Trinitat Vella 675.0 \n",
+ "50 Sant Martí el Besòs i el Maresme 2016.0 \n",
+ "34 Nou Barris Ciutat Meridiana 838.0 \n",
+ "74 Sants-Montjuïc el Poble Sec 3087.0 \n",
+ "13 Eixample el Fort Pienc 2237.0 \n",
+ "66 Nou Barris la Trinitat Nova 458.0 \n",
+ "\n",
+ " num_immi_foreign perc_foreign \n",
+ "10 2205.0 82.06 \n",
+ "7 2228.0 80.58 \n",
+ "19 4291.0 79.46 \n",
+ "21 1345.0 76.46 \n",
+ "67 501.0 74.22 \n",
+ "50 1478.0 73.31 \n",
+ "34 614.0 73.27 \n",
+ "74 2261.0 73.24 \n",
+ "13 1574.0 70.36 \n",
+ "66 320.0 69.87 "
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Nbh by % of foreigners immigrants vs total number of immigrants\n",
+ "\n",
+ "df_foreign = pd.merge(df, immi_foreign_nbh, how=\"left\", on=[\"nbh\", \"dist\"])\n",
+ "\n",
+ "df_foreign.columns = ['Unnamed: 0', 'nbh', 'bars', 'children_places', 'cinemas_theatres',\n",
+ " 'schools', 'pre-schools', 'hospitals', 'libraries_theatres',\n",
+ " 'park_gardens', 'sport_centers', 'population', 'net_density(hab/ha)',\n",
+ " 'avg_occupation', 'dist', 'num_immi_total', 'mort_rate', 'rent_price',\n",
+ " 'num_crimes', 'children_places_pop', 'cinemas_theatres_pop',\n",
+ " 'schools_pop', 'pre-schools_pop', 'hospitals_pop',\n",
+ " 'libraries_theatres_pop', 'park_gardens_pop', 'sport_centers_pop',\n",
+ " 'facilities_pop', 'perc_immi', 'num_immi_foreign']\n",
+ "\n",
+ "df_foreign[\"perc_foreign\"]=round(df_foreign[\"num_immi_foreign\"]/df_foreign[\"num_immi_total\"]*100,2)\n",
+ "\n",
+ "df_foreign[\"perc_immi_foreign\"]=round(df_foreign[\"num_immi_foreign\"]/df_foreign[\"population\"]*100, 2)\n",
+ "\n",
+ "df_foreign1 = df_foreign[[\"dist\", \"nbh\", \"num_immi_total\", \"num_immi_foreign\", \"perc_foreign\"]].sort_values(\"perc_foreign\", ascending=False)\n",
+ "df_foreign1.head(10)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | dist | \n", + "nbh | \n", + "num_immi_total | \n", + "num_immi_foreign | \n", + "perc_foreign | \n", + "
|---|---|---|---|---|---|
| 10 | \n", + "Ciutat Vella | \n", + "el Barri Gòtic | \n", + "2687.0 | \n", + "2205.0 | \n", + "82.06 | \n", + "
| 7 | \n", + "Ciutat Vella | \n", + "Sant Pere, Santa Caterina i la Ribera | \n", + "2765.0 | \n", + "2228.0 | \n", + "80.58 | \n", + "
| 19 | \n", + "Ciutat Vella | \n", + "el Raval | \n", + "5400.0 | \n", + "4291.0 | \n", + "79.46 | \n", + "
| 21 | \n", + "Ciutat Vella | \n", + "la Barceloneta | \n", + "1759.0 | \n", + "1345.0 | \n", + "76.46 | \n", + "
| 67 | \n", + "Sant Andreu | \n", + "la Trinitat Vella | \n", + "675.0 | \n", + "501.0 | \n", + "74.22 | \n", + "
| 50 | \n", + "Sant Martí | \n", + "el Besòs i el Maresme | \n", + "2016.0 | \n", + "1478.0 | \n", + "73.31 | \n", + "
| 34 | \n", + "Nou Barris | \n", + "Ciutat Meridiana | \n", + "838.0 | \n", + "614.0 | \n", + "73.27 | \n", + "
| 74 | \n", + "Sants-Montjuïc | \n", + "el Poble Sec | \n", + "3087.0 | \n", + "2261.0 | \n", + "73.24 | \n", + "
| 13 | \n", + "Eixample | \n", + "el Fort Pienc | \n", + "2237.0 | \n", + "1574.0 | \n", + "70.36 | \n", + "
| 66 | \n", + "Nou Barris | \n", + "la Trinitat Nova | \n", + "458.0 | \n", + "320.0 | \n", + "69.87 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " dist nbh num_immi_total \\\n",
+ "6 Sarrià-Sant Gervasi Sant Gervasi - la Bonanova 1181.0 \n",
+ "71 Sarrià-Sant Gervasi les Tres Torres 702.0 \n",
+ "30 Sant Andreu Baró de Viver 92.0 \n",
+ "44 Nou Barris Vallbona 69.0 \n",
+ "3 Sant Andreu Sant Andreu 1965.0 \n",
+ "60 Nou Barris la Guineueta 493.0 \n",
+ "58 Horta-Guinardó la Font d'en Fargues 283.0 \n",
+ "32 Nou Barris Can Peguera 57.0 \n",
+ "33 Nou Barris Canyelles 140.0 \n",
+ "62 Sants-Montjuïc la Marina del Prat Vermell 32.0 \n",
+ "16 NaN el Poble-sec NaN \n",
+ "22 NaN la Dreta de Eixample NaN \n",
+ "\n",
+ " num_immi_foreign perc_foreign \n",
+ "6 594.0 50.30 \n",
+ "71 339.0 48.29 \n",
+ "30 44.0 47.83 \n",
+ "44 33.0 47.83 \n",
+ "3 923.0 46.97 \n",
+ "60 228.0 46.25 \n",
+ "58 126.0 44.52 \n",
+ "32 24.0 42.11 \n",
+ "33 40.0 28.57 \n",
+ "62 9.0 28.12 \n",
+ "16 NaN NaN \n",
+ "22 NaN NaN "
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_foreign1.tail(12)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | dist | \n", + "nbh | \n", + "num_immi_total | \n", + "num_immi_foreign | \n", + "perc_foreign | \n", + "
|---|---|---|---|---|---|
| 6 | \n", + "Sarrià-Sant Gervasi | \n", + "Sant Gervasi - la Bonanova | \n", + "1181.0 | \n", + "594.0 | \n", + "50.30 | \n", + "
| 71 | \n", + "Sarrià-Sant Gervasi | \n", + "les Tres Torres | \n", + "702.0 | \n", + "339.0 | \n", + "48.29 | \n", + "
| 30 | \n", + "Sant Andreu | \n", + "Baró de Viver | \n", + "92.0 | \n", + "44.0 | \n", + "47.83 | \n", + "
| 44 | \n", + "Nou Barris | \n", + "Vallbona | \n", + "69.0 | \n", + "33.0 | \n", + "47.83 | \n", + "
| 3 | \n", + "Sant Andreu | \n", + "Sant Andreu | \n", + "1965.0 | \n", + "923.0 | \n", + "46.97 | \n", + "
| 60 | \n", + "Nou Barris | \n", + "la Guineueta | \n", + "493.0 | \n", + "228.0 | \n", + "46.25 | \n", + "
| 58 | \n", + "Horta-Guinardó | \n", + "la Font d'en Fargues | \n", + "283.0 | \n", + "126.0 | \n", + "44.52 | \n", + "
| 32 | \n", + "Nou Barris | \n", + "Can Peguera | \n", + "57.0 | \n", + "24.0 | \n", + "42.11 | \n", + "
| 33 | \n", + "Nou Barris | \n", + "Canyelles | \n", + "140.0 | \n", + "40.0 | \n", + "28.57 | \n", + "
| 62 | \n", + "Sants-Montjuïc | \n", + "la Marina del Prat Vermell | \n", + "32.0 | \n", + "9.0 | \n", + "28.12 | \n", + "
| 16 | \n", + "NaN | \n", + "el Poble-sec | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "
| 22 | \n", + "NaN | \n", + "la Dreta de Eixample | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " num_immi_foreign population \\\n",
+ "nbh \n",
+ "el Barri Gòtic 2205.0 16062.0 \n",
+ "Sant Pere, Santa Caterina i la Ribera 2228.0 22721.0 \n",
+ "el Raval 4291.0 47608.0 \n",
+ "la Barceloneta 1345.0 14996.0 \n",
+ "el Besòs i el Maresme 1478.0 23009.0 \n",
+ "... ... ... \n",
+ "Can Peguera 24.0 2271.0 \n",
+ "la Marina del Prat Vermell 9.0 1149.0 \n",
+ "Canyelles 40.0 6856.0 \n",
+ "el Poble-sec 0.0 0.0 \n",
+ "la Dreta de Eixample 0.0 0.0 \n",
+ "\n",
+ " perc_immi_foreign \n",
+ "nbh \n",
+ "el Barri Gòtic 13.73 \n",
+ "Sant Pere, Santa Caterina i la Ribera 9.81 \n",
+ "el Raval 9.01 \n",
+ "la Barceloneta 8.97 \n",
+ "el Besòs i el Maresme 6.42 \n",
+ "... ... \n",
+ "Can Peguera 1.06 \n",
+ "la Marina del Prat Vermell 0.78 \n",
+ "Canyelles 0.58 \n",
+ "el Poble-sec NaN \n",
+ "la Dreta de Eixample NaN \n",
+ "\n",
+ "[75 rows x 3 columns]"
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# list of nbh, ordered by highest % of foreign immigrants\n",
+ "df_foreign.groupby(\"nbh\").aggregate({\"num_immi_foreign\":\"sum\",\n",
+ " \"population\":\"sum\",\n",
+ " \"perc_immi_foreign\":\"mean\"}).sort_values(\"perc_immi_foreign\", ascending=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | num_immi_foreign | \n", + "population | \n", + "perc_immi_foreign | \n", + "
|---|---|---|---|
| nbh | \n", + "\n", + " | \n", + " | \n", + " |
| el Barri Gòtic | \n", + "2205.0 | \n", + "16062.0 | \n", + "13.73 | \n", + "
| Sant Pere, Santa Caterina i la Ribera | \n", + "2228.0 | \n", + "22721.0 | \n", + "9.81 | \n", + "
| el Raval | \n", + "4291.0 | \n", + "47608.0 | \n", + "9.01 | \n", + "
| la Barceloneta | \n", + "1345.0 | \n", + "14996.0 | \n", + "8.97 | \n", + "
| el Besòs i el Maresme | \n", + "1478.0 | \n", + "23009.0 | \n", + "6.42 | \n", + "
| ... | \n", + "... | \n", + "... | \n", + "... | \n", + "
| Can Peguera | \n", + "24.0 | \n", + "2271.0 | \n", + "1.06 | \n", + "
| la Marina del Prat Vermell | \n", + "9.0 | \n", + "1149.0 | \n", + "0.78 | \n", + "
| Canyelles | \n", + "40.0 | \n", + "6856.0 | \n", + "0.58 | \n", + "
| el Poble-sec | \n", + "0.0 | \n", + "0.0 | \n", + "NaN | \n", + "
| la Dreta de Eixample | \n", + "0.0 | \n", + "0.0 | \n", + "NaN | \n", + "
75 rows × 3 columns
\n", + "\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " num_immi_foreign population perc_immi_foreign\n",
+ "dist \n",
+ "Ciutat Vella 10069.0 101387.0 10.380000\n",
+ "Eixample 12487.0 266416.0 4.716667\n",
+ "Sants-Montjuïc 7464.0 181910.0 3.557500\n",
+ "Sant Martí 7994.0 235513.0 3.425000\n",
+ "Gràcia 4310.0 121347.0 3.242000\n",
+ "Les Corts 2420.0 82033.0 3.170000\n",
+ "Nou Barris 5261.0 166579.0 2.948462\n",
+ "Sant Andreu 3622.0 147594.0 2.802857\n",
+ "Horta-Guinardó 4544.0 168751.0 2.682727\n",
+ "Sarrià-Sant Gervasi 3802.0 149279.0 2.421667"
+ ]
+ },
+ "execution_count": 17,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# list of dist, ordered by highest % of foreign immigrants\n",
+ "df_foreign.groupby(\"dist\").aggregate({\"num_immi_foreign\":\"sum\",\n",
+ " \"population\":\"sum\",\n",
+ " \"perc_immi_foreign\":\"mean\"}).sort_values(\"perc_immi_foreign\", ascending=False)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Third analysis"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | num_immi_foreign | \n", + "population | \n", + "perc_immi_foreign | \n", + "
|---|---|---|---|
| dist | \n", + "\n", + " | \n", + " | \n", + " |
| Ciutat Vella | \n", + "10069.0 | \n", + "101387.0 | \n", + "10.380000 | \n", + "
| Eixample | \n", + "12487.0 | \n", + "266416.0 | \n", + "4.716667 | \n", + "
| Sants-Montjuïc | \n", + "7464.0 | \n", + "181910.0 | \n", + "3.557500 | \n", + "
| Sant Martí | \n", + "7994.0 | \n", + "235513.0 | \n", + "3.425000 | \n", + "
| Gràcia | \n", + "4310.0 | \n", + "121347.0 | \n", + "3.242000 | \n", + "
| Les Corts | \n", + "2420.0 | \n", + "82033.0 | \n", + "3.170000 | \n", + "
| Nou Barris | \n", + "5261.0 | \n", + "166579.0 | \n", + "2.948462 | \n", + "
| Sant Andreu | \n", + "3622.0 | \n", + "147594.0 | \n", + "2.802857 | \n", + "
| Horta-Guinardó | \n", + "4544.0 | \n", + "168751.0 | \n", + "2.682727 | \n", + "
| Sarrià-Sant Gervasi | \n", + "3802.0 | \n", + "149279.0 | \n", + "2.421667 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " dist nbh nationality num_immi\n",
+ "649 Sant Andreu Baró de Viver Peru 7\n",
+ "279 Sant Andreu Baró de Viver Colombia 6\n",
+ "797 Sant Andreu Baró de Viver Argentina 6\n",
+ "107 Horta-Guinardó Can Baró Italy 27\n",
+ "255 Horta-Guinardó Can Baró Colombia 18\n",
+ "... ... ... ... ...\n",
+ "197 Nou Barris les Roquetes China 37\n",
+ "1159 Nou Barris les Roquetes Ecuador 33\n",
+ "911 Sarrià-Sant Gervasi les Tres Torres United States 59\n",
+ "2613 Sarrià-Sant Gervasi les Tres Torres Japan 34\n",
+ "97 Sarrià-Sant Gervasi les Tres Torres Italy 26\n",
+ "\n",
+ "[222 rows x 4 columns]"
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# More frequent immigrant nationality by Nbh\n",
+ "immi_foreign.groupby('nbh', group_keys=False).apply(lambda x: x.nlargest(3, \"num_immi\"))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | dist | \n", + "nbh | \n", + "nationality | \n", + "num_immi | \n", + "
|---|---|---|---|---|
| 649 | \n", + "Sant Andreu | \n", + "Baró de Viver | \n", + "Peru | \n", + "7 | \n", + "
| 279 | \n", + "Sant Andreu | \n", + "Baró de Viver | \n", + "Colombia | \n", + "6 | \n", + "
| 797 | \n", + "Sant Andreu | \n", + "Baró de Viver | \n", + "Argentina | \n", + "6 | \n", + "
| 107 | \n", + "Horta-Guinardó | \n", + "Can Baró | \n", + "Italy | \n", + "27 | \n", + "
| 255 | \n", + "Horta-Guinardó | \n", + "Can Baró | \n", + "Colombia | \n", + "18 | \n", + "
| ... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "
| 197 | \n", + "Nou Barris | \n", + "les Roquetes | \n", + "China | \n", + "37 | \n", + "
| 1159 | \n", + "Nou Barris | \n", + "les Roquetes | \n", + "Ecuador | \n", + "33 | \n", + "
| 911 | \n", + "Sarrià-Sant Gervasi | \n", + "les Tres Torres | \n", + "United States | \n", + "59 | \n", + "
| 2613 | \n", + "Sarrià-Sant Gervasi | \n", + "les Tres Torres | \n", + "Japan | \n", + "34 | \n", + "
| 97 | \n", + "Sarrià-Sant Gervasi | \n", + "les Tres Torres | \n", + "Italy | \n", + "26 | \n", + "
222 rows × 4 columns
\n", + "\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " num_immi\n",
+ "dist nationality \n",
+ "Ciutat Vella Afghanistan 10\n",
+ " Albania 9\n",
+ " Algeria 75\n",
+ " Andorra 1\n",
+ " Angola 1\n",
+ "... ...\n",
+ "Sarrià-Sant Gervasi United States 263\n",
+ " Uruguay 13\n",
+ " Uzbekistan 1\n",
+ " Venezuela 215\n",
+ " Vietnam 4\n",
+ "\n",
+ "[1190 rows x 1 columns]"
+ ]
+ },
+ "execution_count": 19,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Ordering immi dataset by district, including Spain\n",
+ "immi_dist = immi.groupby([\"dist\",\"nationality\"])[\"num_immi\"].sum()\n",
+ "immi_dist = immi_dist.to_frame()\n",
+ "immi_dist"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | \n", + " | num_immi | \n", + "
|---|---|---|
| dist | \n", + "nationality | \n", + "\n", + " |
| Ciutat Vella | \n", + "Afghanistan | \n", + "10 | \n", + "
| Albania | \n", + "9 | \n", + "|
| Algeria | \n", + "75 | \n", + "|
| Andorra | \n", + "1 | \n", + "|
| Angola | \n", + "1 | \n", + "|
| ... | \n", + "... | \n", + "... | \n", + "
| Sarrià-Sant Gervasi | \n", + "United States | \n", + "263 | \n", + "
| Uruguay | \n", + "13 | \n", + "|
| Uzbekistan | \n", + "1 | \n", + "|
| Venezuela | \n", + "215 | \n", + "|
| Vietnam | \n", + "4 | \n", + "
1190 rows × 1 columns
\n", + "\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " num_immi\n",
+ "dist nationality \n",
+ "Ciutat Vella Spain 2542\n",
+ " Italy 1275\n",
+ " Pakistan 998\n",
+ " France 596\n",
+ " Bangladesh 566\n",
+ "Eixample Spain 6560\n",
+ " Italy 1568\n",
+ " China 918\n",
+ " Colombia 781\n",
+ " Venezuela 724\n",
+ "Gràcia Spain 2944\n",
+ " Italy 598\n",
+ " France 277\n",
+ " Colombia 200\n",
+ " Venezuela 189\n",
+ "Horta-Guinardó Spain 3255\n",
+ " Italy 413\n",
+ " Honduras 365\n",
+ " Venezuela 340\n",
+ " Colombia 320\n",
+ "Les Corts Spain 1955\n",
+ " Italy 205\n",
+ " Venezuela 178\n",
+ " China 137\n",
+ " Colombia 133\n",
+ "No consta Spain 2\n",
+ "Nou Barris Spain 3013\n",
+ " Honduras 1091\n",
+ " Colombia 348\n",
+ " Peru 298\n",
+ " China 266\n",
+ "Sant Andreu Spain 2713\n",
+ " Peru 280\n",
+ " Colombia 270\n",
+ " China 250\n",
+ " Honduras 234\n",
+ "Sant Martí Spain 4726\n",
+ " Italy 798\n",
+ " Pakistan 629\n",
+ " China 606\n",
+ " Colombia 375\n",
+ "Sants-Montjuïc Spain 4219\n",
+ " Italy 684\n",
+ " China 512\n",
+ " Colombia 431\n",
+ " Pakistan 422\n",
+ "Sarrià-Sant Gervasi Spain 3425\n",
+ " Italy 396\n",
+ " France 315\n",
+ " United States 263\n",
+ " Venezuela 215"
+ ]
+ },
+ "execution_count": 20,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Top 5 most frequent immigrant nationality by District, including Spain\n",
+ "immi_dist.groupby('dist', group_keys=False).apply(lambda x: x.nlargest(5, \"num_immi\"))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | \n", + " | num_immi | \n", + "
|---|---|---|
| dist | \n", + "nationality | \n", + "\n", + " |
| Ciutat Vella | \n", + "Spain | \n", + "2542 | \n", + "
| Italy | \n", + "1275 | \n", + "|
| Pakistan | \n", + "998 | \n", + "|
| France | \n", + "596 | \n", + "|
| Bangladesh | \n", + "566 | \n", + "|
| Eixample | \n", + "Spain | \n", + "6560 | \n", + "
| Italy | \n", + "1568 | \n", + "|
| China | \n", + "918 | \n", + "|
| Colombia | \n", + "781 | \n", + "|
| Venezuela | \n", + "724 | \n", + "|
| Gràcia | \n", + "Spain | \n", + "2944 | \n", + "
| Italy | \n", + "598 | \n", + "|
| France | \n", + "277 | \n", + "|
| Colombia | \n", + "200 | \n", + "|
| Venezuela | \n", + "189 | \n", + "|
| Horta-Guinardó | \n", + "Spain | \n", + "3255 | \n", + "
| Italy | \n", + "413 | \n", + "|
| Honduras | \n", + "365 | \n", + "|
| Venezuela | \n", + "340 | \n", + "|
| Colombia | \n", + "320 | \n", + "|
| Les Corts | \n", + "Spain | \n", + "1955 | \n", + "
| Italy | \n", + "205 | \n", + "|
| Venezuela | \n", + "178 | \n", + "|
| China | \n", + "137 | \n", + "|
| Colombia | \n", + "133 | \n", + "|
| No consta | \n", + "Spain | \n", + "2 | \n", + "
| Nou Barris | \n", + "Spain | \n", + "3013 | \n", + "
| Honduras | \n", + "1091 | \n", + "|
| Colombia | \n", + "348 | \n", + "|
| Peru | \n", + "298 | \n", + "|
| China | \n", + "266 | \n", + "|
| Sant Andreu | \n", + "Spain | \n", + "2713 | \n", + "
| Peru | \n", + "280 | \n", + "|
| Colombia | \n", + "270 | \n", + "|
| China | \n", + "250 | \n", + "|
| Honduras | \n", + "234 | \n", + "|
| Sant Martí | \n", + "Spain | \n", + "4726 | \n", + "
| Italy | \n", + "798 | \n", + "|
| Pakistan | \n", + "629 | \n", + "|
| China | \n", + "606 | \n", + "|
| Colombia | \n", + "375 | \n", + "|
| Sants-Montjuïc | \n", + "Spain | \n", + "4219 | \n", + "
| Italy | \n", + "684 | \n", + "|
| China | \n", + "512 | \n", + "|
| Colombia | \n", + "431 | \n", + "|
| Pakistan | \n", + "422 | \n", + "|
| Sarrià-Sant Gervasi | \n", + "Spain | \n", + "3425 | \n", + "
| Italy | \n", + "396 | \n", + "|
| France | \n", + "315 | \n", + "|
| United States | \n", + "263 | \n", + "|
| Venezuela | \n", + "215 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " num_immi\n",
+ "dist nationality \n",
+ "Ciutat Vella Afghanistan 10\n",
+ " Albania 9\n",
+ " Algeria 75\n",
+ " Andorra 1\n",
+ " Angola 1\n",
+ "... ...\n",
+ "Sarrià-Sant Gervasi Venezuela 215\n",
+ " Vietnam 4\n",
+ " Yemen 0\n",
+ " Zambia 0\n",
+ " Zimbabwe 0\n",
+ "\n",
+ "[1738 rows x 1 columns]"
+ ]
+ },
+ "execution_count": 21,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Ordering immi dataset by district, not including Spain\n",
+ "immi_dist_foreign = immi_foreign.groupby([\"dist\",\"nationality\"])[\"num_immi\"].sum()\n",
+ "immi_dist_foreign = immi_dist_foreign.to_frame()\n",
+ "immi_dist_foreign"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | \n", + " | num_immi | \n", + "
|---|---|---|
| dist | \n", + "nationality | \n", + "\n", + " |
| Ciutat Vella | \n", + "Afghanistan | \n", + "10 | \n", + "
| Albania | \n", + "9 | \n", + "|
| Algeria | \n", + "75 | \n", + "|
| Andorra | \n", + "1 | \n", + "|
| Angola | \n", + "1 | \n", + "|
| ... | \n", + "... | \n", + "... | \n", + "
| Sarrià-Sant Gervasi | \n", + "Venezuela | \n", + "215 | \n", + "
| Vietnam | \n", + "4 | \n", + "|
| Yemen | \n", + "0 | \n", + "|
| Zambia | \n", + "0 | \n", + "|
| Zimbabwe | \n", + "0 | \n", + "
1738 rows × 1 columns
\n", + "\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " num_immi\n",
+ "dist nationality \n",
+ "Ciutat Vella Italy 1275\n",
+ " Pakistan 998\n",
+ " France 596\n",
+ "Eixample Italy 1568\n",
+ " China 918\n",
+ " Colombia 781\n",
+ "Gràcia Italy 598\n",
+ " France 277\n",
+ " Colombia 200\n",
+ "Horta-Guinardó Italy 413\n",
+ " Honduras 365\n",
+ " Venezuela 340\n",
+ "Les Corts Italy 205\n",
+ " Venezuela 178\n",
+ " China 137\n",
+ "No consta Afghanistan 0\n",
+ " Albania 0\n",
+ " Algeria 0\n",
+ "Nou Barris Honduras 1091\n",
+ " Colombia 348\n",
+ " Peru 298\n",
+ "Sant Andreu Peru 280\n",
+ " Colombia 270\n",
+ " China 250\n",
+ "Sant Martí Italy 798\n",
+ " Pakistan 629\n",
+ " China 606\n",
+ "Sants-Montjuïc Italy 684\n",
+ " China 512\n",
+ " Colombia 431\n",
+ "Sarrià-Sant Gervasi Italy 396\n",
+ " France 315\n",
+ " United States 263"
+ ]
+ },
+ "execution_count": 22,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Top 5 most frequent immigrant nationality by District, including Spain\n",
+ "immi_dist_foreign.groupby('dist', group_keys=False).apply(lambda x: x.nlargest(3, \"num_immi\"))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | \n", + " | num_immi | \n", + "
|---|---|---|
| dist | \n", + "nationality | \n", + "\n", + " |
| Ciutat Vella | \n", + "Italy | \n", + "1275 | \n", + "
| Pakistan | \n", + "998 | \n", + "|
| France | \n", + "596 | \n", + "|
| Eixample | \n", + "Italy | \n", + "1568 | \n", + "
| China | \n", + "918 | \n", + "|
| Colombia | \n", + "781 | \n", + "|
| Gràcia | \n", + "Italy | \n", + "598 | \n", + "
| France | \n", + "277 | \n", + "|
| Colombia | \n", + "200 | \n", + "|
| Horta-Guinardó | \n", + "Italy | \n", + "413 | \n", + "
| Honduras | \n", + "365 | \n", + "|
| Venezuela | \n", + "340 | \n", + "|
| Les Corts | \n", + "Italy | \n", + "205 | \n", + "
| Venezuela | \n", + "178 | \n", + "|
| China | \n", + "137 | \n", + "|
| No consta | \n", + "Afghanistan | \n", + "0 | \n", + "
| Albania | \n", + "0 | \n", + "|
| Algeria | \n", + "0 | \n", + "|
| Nou Barris | \n", + "Honduras | \n", + "1091 | \n", + "
| Colombia | \n", + "348 | \n", + "|
| Peru | \n", + "298 | \n", + "|
| Sant Andreu | \n", + "Peru | \n", + "280 | \n", + "
| Colombia | \n", + "270 | \n", + "|
| China | \n", + "250 | \n", + "|
| Sant Martí | \n", + "Italy | \n", + "798 | \n", + "
| Pakistan | \n", + "629 | \n", + "|
| China | \n", + "606 | \n", + "|
| Sants-Montjuïc | \n", + "Italy | \n", + "684 | \n", + "
| China | \n", + "512 | \n", + "|
| Colombia | \n", + "431 | \n", + "|
| Sarrià-Sant Gervasi | \n", + "Italy | \n", + "396 | \n", + "
| France | \n", + "315 | \n", + "|
| United States | \n", + "263 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " dist nbh nationality \\\n",
+ "0 Ciutat Vella el Raval Spain \n",
+ "1 Ciutat Vella el Barri Gòtic Spain \n",
+ "2 Ciutat Vella la Barceloneta Spain \n",
+ "3 Ciutat Vella Sant Pere, Santa Caterina i la Ribera Spain \n",
+ "4 Eixample el Fort Pienc Spain \n",
+ "... ... ... ... \n",
+ "11724 Horta-Guinardó el Baix Guinardó No information \n",
+ "11726 Horta-Guinardó el Guinardó No information \n",
+ "11735 Nou Barris Vilapicina i la Torre Llobeta No information \n",
+ "11747 Nou Barris Vallbona No information \n",
+ "11756 Sant Martí el Clot No information \n",
+ "\n",
+ " num_immi perc_immi \n",
+ "0 1109 1.14 \n",
+ "1 482 0.50 \n",
+ "2 414 0.43 \n",
+ "3 537 0.55 \n",
+ "4 663 0.68 \n",
+ "... ... ... \n",
+ "11724 1 0.00 \n",
+ "11726 1 0.00 \n",
+ "11735 1 0.00 \n",
+ "11747 1 0.00 \n",
+ "11756 1 0.00 \n",
+ "\n",
+ "[4716 rows x 5 columns]"
+ ]
+ },
+ "execution_count": 23,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Computing percentage of immigrants from each nationality vs total number of immigrants\n",
+ "immi[\"perc_immi\"]=round((immi[\"num_immi\"]/immi[\"num_immi\"].sum())*100,2)\n",
+ "immi"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | dist | \n", + "nbh | \n", + "nationality | \n", + "num_immi | \n", + "perc_immi | \n", + "
|---|---|---|---|---|---|
| 0 | \n", + "Ciutat Vella | \n", + "el Raval | \n", + "Spain | \n", + "1109 | \n", + "1.14 | \n", + "
| 1 | \n", + "Ciutat Vella | \n", + "el Barri Gòtic | \n", + "Spain | \n", + "482 | \n", + "0.50 | \n", + "
| 2 | \n", + "Ciutat Vella | \n", + "la Barceloneta | \n", + "Spain | \n", + "414 | \n", + "0.43 | \n", + "
| 3 | \n", + "Ciutat Vella | \n", + "Sant Pere, Santa Caterina i la Ribera | \n", + "Spain | \n", + "537 | \n", + "0.55 | \n", + "
| 4 | \n", + "Eixample | \n", + "el Fort Pienc | \n", + "Spain | \n", + "663 | \n", + "0.68 | \n", + "
| ... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "
| 11724 | \n", + "Horta-Guinardó | \n", + "el Baix Guinardó | \n", + "No information | \n", + "1 | \n", + "0.00 | \n", + "
| 11726 | \n", + "Horta-Guinardó | \n", + "el Guinardó | \n", + "No information | \n", + "1 | \n", + "0.00 | \n", + "
| 11735 | \n", + "Nou Barris | \n", + "Vilapicina i la Torre Llobeta | \n", + "No information | \n", + "1 | \n", + "0.00 | \n", + "
| 11747 | \n", + "Nou Barris | \n", + "Vallbona | \n", + "No information | \n", + "1 | \n", + "0.00 | \n", + "
| 11756 | \n", + "Sant Martí | \n", + "el Clot | \n", + "No information | \n", + "1 | \n", + "0.00 | \n", + "
4716 rows × 5 columns
\n", + "\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " num_immi perc_immi\n",
+ "nationality \n",
+ "Spain 35354 36.30\n",
+ "Italy 6309 6.44\n",
+ "China 3299 3.40\n",
+ "Colombia 3255 3.32\n",
+ "Venezuela 3021 3.10\n",
+ "Pakistan 2967 3.01\n",
+ "Honduras 2767 2.85\n",
+ "France 2670 2.75\n",
+ "Peru 2473 2.54\n",
+ "Argentina 1885 1.99\n",
+ "Morocco 1931 1.96\n",
+ "Brasil 1757 1.76\n",
+ "United States 1507 1.56\n",
+ "India 1441 1.50\n",
+ "United Kingdom 1459 1.50\n",
+ "Ecuador 1432 1.44\n",
+ "Russia 1299 1.33\n",
+ "Germany 1240 1.27\n",
+ "Dominican Republic 1196 1.18\n",
+ "Mexico 1153 1.16"
+ ]
+ },
+ "execution_count": 24,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Listing countries by percentage of immigrants vs total number of immigrants, including Spain\n",
+ "nationality_immi= immi.groupby(\"nationality\").sum().sort_values(\"perc_immi\", ascending=False)\n",
+ "nationality_immi.head(20)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "immi_foreign[\"perc_immi\"]=round((immi_foreign[\"num_immi\"]/immi_foreign[\"num_immi\"].sum())*100, 2)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | num_immi | \n", + "perc_immi | \n", + "
|---|---|---|
| nationality | \n", + "\n", + " | \n", + " |
| Spain | \n", + "35354 | \n", + "36.30 | \n", + "
| Italy | \n", + "6309 | \n", + "6.44 | \n", + "
| China | \n", + "3299 | \n", + "3.40 | \n", + "
| Colombia | \n", + "3255 | \n", + "3.32 | \n", + "
| Venezuela | \n", + "3021 | \n", + "3.10 | \n", + "
| Pakistan | \n", + "2967 | \n", + "3.01 | \n", + "
| Honduras | \n", + "2767 | \n", + "2.85 | \n", + "
| France | \n", + "2670 | \n", + "2.75 | \n", + "
| Peru | \n", + "2473 | \n", + "2.54 | \n", + "
| Argentina | \n", + "1885 | \n", + "1.99 | \n", + "
| Morocco | \n", + "1931 | \n", + "1.96 | \n", + "
| Brasil | \n", + "1757 | \n", + "1.76 | \n", + "
| United States | \n", + "1507 | \n", + "1.56 | \n", + "
| India | \n", + "1441 | \n", + "1.50 | \n", + "
| United Kingdom | \n", + "1459 | \n", + "1.50 | \n", + "
| Ecuador | \n", + "1432 | \n", + "1.44 | \n", + "
| Russia | \n", + "1299 | \n", + "1.33 | \n", + "
| Germany | \n", + "1240 | \n", + "1.27 | \n", + "
| Dominican Republic | \n", + "1196 | \n", + "1.18 | \n", + "
| Mexico | \n", + "1153 | \n", + "1.16 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " nationality num_immi perc_immi\n",
+ "69 Italy 6309 10.18\n",
+ "29 China 3299 5.31\n",
+ "30 Colombia 3255 5.26\n",
+ "153 Venezuela 3021 4.91\n",
+ "110 Pakistan 2967 4.73\n",
+ "60 Honduras 2767 4.45\n",
+ "48 France 2670 4.26\n",
+ "113 Peru 2473 3.97\n",
+ "97 Morocco 1931 3.12\n",
+ "6 Argentina 1885 3.03"
+ ]
+ },
+ "execution_count": 26,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Listing countries by percentage of immigrants vs total number of immigrants, not including Spain\n",
+ "nationality_immi_foreign = immi_foreign.groupby(\"nationality\", as_index = False).sum().sort_values(\"perc_immi\", ascending=False)\n",
+ "nationality_immi_foreign.head(10)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "list_of_nationalities = list(nationality_immi_foreign.head(5)['nationality'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | nationality | \n", + "num_immi | \n", + "perc_immi | \n", + "
|---|---|---|---|
| 69 | \n", + "Italy | \n", + "6309 | \n", + "10.18 | \n", + "
| 29 | \n", + "China | \n", + "3299 | \n", + "5.31 | \n", + "
| 30 | \n", + "Colombia | \n", + "3255 | \n", + "5.26 | \n", + "
| 153 | \n", + "Venezuela | \n", + "3021 | \n", + "4.91 | \n", + "
| 110 | \n", + "Pakistan | \n", + "2967 | \n", + "4.73 | \n", + "
| 60 | \n", + "Honduras | \n", + "2767 | \n", + "4.45 | \n", + "
| 48 | \n", + "France | \n", + "2670 | \n", + "4.26 | \n", + "
| 113 | \n", + "Peru | \n", + "2473 | \n", + "3.97 | \n", + "
| 97 | \n", + "Morocco | \n", + "1931 | \n", + "3.12 | \n", + "
| 6 | \n", + "Argentina | \n", + "1885 | \n", + "3.03 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " num_immi\n",
+ "dist nationality \n",
+ "Ciutat Vella Afghanistan 10\n",
+ " Albania 9\n",
+ " Algeria 75\n",
+ " Andorra 1\n",
+ " Angola 1\n",
+ "... ...\n",
+ "Sarrià-Sant Gervasi United States 263\n",
+ " Uruguay 13\n",
+ " Uzbekistan 1\n",
+ " Venezuela 215\n",
+ " Vietnam 4\n",
+ "\n",
+ "[1190 rows x 1 columns]"
+ ]
+ },
+ "execution_count": 28,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "immi_dist = immi.groupby([\"dist\",\"nationality\"])[\"num_immi\"].sum()\n",
+ "immi_dist = immi_dist.to_frame()\n",
+ "immi_dist"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | \n", + " | num_immi | \n", + "
|---|---|---|
| dist | \n", + "nationality | \n", + "\n", + " |
| Ciutat Vella | \n", + "Afghanistan | \n", + "10 | \n", + "
| Albania | \n", + "9 | \n", + "|
| Algeria | \n", + "75 | \n", + "|
| Andorra | \n", + "1 | \n", + "|
| Angola | \n", + "1 | \n", + "|
| ... | \n", + "... | \n", + "... | \n", + "
| Sarrià-Sant Gervasi | \n", + "United States | \n", + "263 | \n", + "
| Uruguay | \n", + "13 | \n", + "|
| Uzbekistan | \n", + "1 | \n", + "|
| Venezuela | \n", + "215 | \n", + "|
| Vietnam | \n", + "4 | \n", + "
1190 rows × 1 columns
\n", + "\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " nationality dist num_immi\n",
+ "209 China Eixample 918\n",
+ "215 China Sant Martí 606\n",
+ "216 China Sants-Montjuïc 512\n",
+ "219 Colombia Eixample 781\n",
+ "226 Colombia Sants-Montjuïc 431\n",
+ "225 Colombia Sant Martí 375\n",
+ "553 Italy Eixample 1568\n",
+ "552 Italy Ciutat Vella 1275\n",
+ "559 Italy Sant Martí 798\n",
+ "831 Pakistan Ciutat Vella 998\n",
+ "838 Pakistan Sant Martí 629\n",
+ "839 Pakistan Sants-Montjuïc 422\n",
+ "1164 Venezuela Eixample 724\n",
+ "1170 Venezuela Sant Martí 355\n",
+ "1166 Venezuela Horta-Guinardó 340"
+ ]
+ },
+ "execution_count": 29,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Top 5 nationalities, num_immi by districy\n",
+ "immi_countries = immi.groupby([\"nationality\", \"dist\"], as_index=False)[\"num_immi\"].sum()\n",
+ "immi_countries = immi.groupby([\"nationality\", \"dist\"], as_index=False)[\"num_immi\"].sum()\n",
+ "immi_countries = immi_countries.loc[immi_countries.nationality.isin(list_of_nationalities)]\n",
+ "immi_countries = immi_countries.groupby(\"nationality\", group_keys=False).apply(lambda x: x.nlargest(3, \"num_immi\"))\n",
+ "immi_countries"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | nationality | \n", + "dist | \n", + "num_immi | \n", + "
|---|---|---|---|
| 209 | \n", + "China | \n", + "Eixample | \n", + "918 | \n", + "
| 215 | \n", + "China | \n", + "Sant Martí | \n", + "606 | \n", + "
| 216 | \n", + "China | \n", + "Sants-Montjuïc | \n", + "512 | \n", + "
| 219 | \n", + "Colombia | \n", + "Eixample | \n", + "781 | \n", + "
| 226 | \n", + "Colombia | \n", + "Sants-Montjuïc | \n", + "431 | \n", + "
| 225 | \n", + "Colombia | \n", + "Sant Martí | \n", + "375 | \n", + "
| 553 | \n", + "Italy | \n", + "Eixample | \n", + "1568 | \n", + "
| 552 | \n", + "Italy | \n", + "Ciutat Vella | \n", + "1275 | \n", + "
| 559 | \n", + "Italy | \n", + "Sant Martí | \n", + "798 | \n", + "
| 831 | \n", + "Pakistan | \n", + "Ciutat Vella | \n", + "998 | \n", + "
| 838 | \n", + "Pakistan | \n", + "Sant Martí | \n", + "629 | \n", + "
| 839 | \n", + "Pakistan | \n", + "Sants-Montjuïc | \n", + "422 | \n", + "
| 1164 | \n", + "Venezuela | \n", + "Eixample | \n", + "724 | \n", + "
| 1170 | \n", + "Venezuela | \n", + "Sant Martí | \n", + "355 | \n", + "
| 1166 | \n", + "Venezuela | \n", + "Horta-Guinardó | \n", + "340 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " nationality dist num_immi_dist num_immi_total\n",
+ "0 China Eixample 918 3299\n",
+ "1 China Sant Martí 606 3299\n",
+ "2 China Sants-Montjuïc 512 3299\n",
+ "3 Colombia Eixample 781 3255\n",
+ "4 Colombia Sants-Montjuïc 431 3255\n",
+ "5 Colombia Sant Martí 375 3255\n",
+ "6 Italy Eixample 1568 6309\n",
+ "7 Italy Ciutat Vella 1275 6309\n",
+ "8 Italy Sant Martí 798 6309\n",
+ "9 Pakistan Ciutat Vella 998 2967\n",
+ "10 Pakistan Sant Martí 629 2967\n",
+ "11 Pakistan Sants-Montjuïc 422 2967\n",
+ "12 Venezuela Eixample 724 3021\n",
+ "13 Venezuela Sant Martí 355 3021\n",
+ "14 Venezuela Horta-Guinardó 340 3021"
+ ]
+ },
+ "execution_count": 30,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Trying to get it in percentage\n",
+ " #num_immi_x = immigrants per district, by nationality\n",
+ " #num immi_y = total immigrants, by nationality\n",
+ "\n",
+ "immi_countries_perc = pd.merge(immi_countries, nationality_immi_foreign, on=\"nationality\")\n",
+ "immi_countries_perc = immi_countries_perc[[\"nationality\", \"dist\", \"num_immi_x\", \"num_immi_y\"]]\n",
+ "immi_countries_perc.columns = [\"nationality\", \"dist\", \"num_immi_dist\", \"num_immi_total\"]\n",
+ "immi_countries_perc"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | nationality | \n", + "dist | \n", + "num_immi_dist | \n", + "num_immi_total | \n", + "
|---|---|---|---|---|
| 0 | \n", + "China | \n", + "Eixample | \n", + "918 | \n", + "3299 | \n", + "
| 1 | \n", + "China | \n", + "Sant Martí | \n", + "606 | \n", + "3299 | \n", + "
| 2 | \n", + "China | \n", + "Sants-Montjuïc | \n", + "512 | \n", + "3299 | \n", + "
| 3 | \n", + "Colombia | \n", + "Eixample | \n", + "781 | \n", + "3255 | \n", + "
| 4 | \n", + "Colombia | \n", + "Sants-Montjuïc | \n", + "431 | \n", + "3255 | \n", + "
| 5 | \n", + "Colombia | \n", + "Sant Martí | \n", + "375 | \n", + "3255 | \n", + "
| 6 | \n", + "Italy | \n", + "Eixample | \n", + "1568 | \n", + "6309 | \n", + "
| 7 | \n", + "Italy | \n", + "Ciutat Vella | \n", + "1275 | \n", + "6309 | \n", + "
| 8 | \n", + "Italy | \n", + "Sant Martí | \n", + "798 | \n", + "6309 | \n", + "
| 9 | \n", + "Pakistan | \n", + "Ciutat Vella | \n", + "998 | \n", + "2967 | \n", + "
| 10 | \n", + "Pakistan | \n", + "Sant Martí | \n", + "629 | \n", + "2967 | \n", + "
| 11 | \n", + "Pakistan | \n", + "Sants-Montjuïc | \n", + "422 | \n", + "2967 | \n", + "
| 12 | \n", + "Venezuela | \n", + "Eixample | \n", + "724 | \n", + "3021 | \n", + "
| 13 | \n", + "Venezuela | \n", + "Sant Martí | \n", + "355 | \n", + "3021 | \n", + "
| 14 | \n", + "Venezuela | \n", + "Horta-Guinardó | \n", + "340 | \n", + "3021 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " nationality dist num_immi_dist num_immi_total num_immi_%\n",
+ "0 China Eixample 918 3299 27.83\n",
+ "1 China Sant Martí 606 3299 18.37\n",
+ "2 China Sants-Montjuïc 512 3299 15.52\n",
+ "3 Colombia Eixample 781 3255 23.99\n",
+ "4 Colombia Sants-Montjuïc 431 3255 13.24\n",
+ "5 Colombia Sant Martí 375 3255 11.52\n",
+ "6 Italy Eixample 1568 6309 24.85\n",
+ "7 Italy Ciutat Vella 1275 6309 20.21\n",
+ "8 Italy Sant Martí 798 6309 12.65\n",
+ "9 Pakistan Ciutat Vella 998 2967 33.64\n",
+ "10 Pakistan Sant Martí 629 2967 21.20\n",
+ "11 Pakistan Sants-Montjuïc 422 2967 14.22\n",
+ "12 Venezuela Eixample 724 3021 23.97\n",
+ "13 Venezuela Sant Martí 355 3021 11.75\n",
+ "14 Venezuela Horta-Guinardó 340 3021 11.25"
+ ]
+ },
+ "execution_count": 31,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Trying to get it in percentage\n",
+ "\n",
+ "immi_countries_perc[\"num_immi_%\"] = round(immi_countries_perc[\"num_immi_dist\"]/immi_countries_perc[\"num_immi_total\"]*100, 2)\n",
+ "immi_countries_perc"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | nationality | \n", + "dist | \n", + "num_immi_dist | \n", + "num_immi_total | \n", + "num_immi_% | \n", + "
|---|---|---|---|---|---|
| 0 | \n", + "China | \n", + "Eixample | \n", + "918 | \n", + "3299 | \n", + "27.83 | \n", + "
| 1 | \n", + "China | \n", + "Sant Martí | \n", + "606 | \n", + "3299 | \n", + "18.37 | \n", + "
| 2 | \n", + "China | \n", + "Sants-Montjuïc | \n", + "512 | \n", + "3299 | \n", + "15.52 | \n", + "
| 3 | \n", + "Colombia | \n", + "Eixample | \n", + "781 | \n", + "3255 | \n", + "23.99 | \n", + "
| 4 | \n", + "Colombia | \n", + "Sants-Montjuïc | \n", + "431 | \n", + "3255 | \n", + "13.24 | \n", + "
| 5 | \n", + "Colombia | \n", + "Sant Martí | \n", + "375 | \n", + "3255 | \n", + "11.52 | \n", + "
| 6 | \n", + "Italy | \n", + "Eixample | \n", + "1568 | \n", + "6309 | \n", + "24.85 | \n", + "
| 7 | \n", + "Italy | \n", + "Ciutat Vella | \n", + "1275 | \n", + "6309 | \n", + "20.21 | \n", + "
| 8 | \n", + "Italy | \n", + "Sant Martí | \n", + "798 | \n", + "6309 | \n", + "12.65 | \n", + "
| 9 | \n", + "Pakistan | \n", + "Ciutat Vella | \n", + "998 | \n", + "2967 | \n", + "33.64 | \n", + "
| 10 | \n", + "Pakistan | \n", + "Sant Martí | \n", + "629 | \n", + "2967 | \n", + "21.20 | \n", + "
| 11 | \n", + "Pakistan | \n", + "Sants-Montjuïc | \n", + "422 | \n", + "2967 | \n", + "14.22 | \n", + "
| 12 | \n", + "Venezuela | \n", + "Eixample | \n", + "724 | \n", + "3021 | \n", + "23.97 | \n", + "
| 13 | \n", + "Venezuela | \n", + "Sant Martí | \n", + "355 | \n", + "3021 | \n", + "11.75 | \n", + "
| 14 | \n", + "Venezuela | \n", + "Horta-Guinardó | \n", + "340 | \n", + "3021 | \n", + "11.25 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " nationality dist num_immi_%\n",
+ "0 China Eixample 27.83\n",
+ "1 China Sant Martí 18.37\n",
+ "2 China Sants-Montjuïc 15.52\n",
+ "3 Colombia Eixample 23.99\n",
+ "4 Colombia Sants-Montjuïc 13.24\n",
+ "5 Colombia Sant Martí 11.52\n",
+ "6 Italy Eixample 24.85\n",
+ "7 Italy Ciutat Vella 20.21\n",
+ "8 Italy Sant Martí 12.65\n",
+ "9 Pakistan Ciutat Vella 33.64\n",
+ "10 Pakistan Sant Martí 21.20\n",
+ "11 Pakistan Sants-Montjuïc 14.22\n",
+ "12 Venezuela Eixample 23.97\n",
+ "13 Venezuela Sant Martí 11.75\n",
+ "14 Venezuela Horta-Guinardó 11.25"
+ ]
+ },
+ "execution_count": 32,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Showing only % column\n",
+ "immi_countries_perc = immi_countries_perc[[\"nationality\", \"dist\", \"num_immi_%\"]]\n",
+ "immi_countries_perc"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Analyzing immigration and districts"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 117,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | nationality | \n", + "dist | \n", + "num_immi_% | \n", + "
|---|---|---|---|
| 0 | \n", + "China | \n", + "Eixample | \n", + "27.83 | \n", + "
| 1 | \n", + "China | \n", + "Sant Martí | \n", + "18.37 | \n", + "
| 2 | \n", + "China | \n", + "Sants-Montjuïc | \n", + "15.52 | \n", + "
| 3 | \n", + "Colombia | \n", + "Eixample | \n", + "23.99 | \n", + "
| 4 | \n", + "Colombia | \n", + "Sants-Montjuïc | \n", + "13.24 | \n", + "
| 5 | \n", + "Colombia | \n", + "Sant Martí | \n", + "11.52 | \n", + "
| 6 | \n", + "Italy | \n", + "Eixample | \n", + "24.85 | \n", + "
| 7 | \n", + "Italy | \n", + "Ciutat Vella | \n", + "20.21 | \n", + "
| 8 | \n", + "Italy | \n", + "Sant Martí | \n", + "12.65 | \n", + "
| 9 | \n", + "Pakistan | \n", + "Ciutat Vella | \n", + "33.64 | \n", + "
| 10 | \n", + "Pakistan | \n", + "Sant Martí | \n", + "21.20 | \n", + "
| 11 | \n", + "Pakistan | \n", + "Sants-Montjuïc | \n", + "14.22 | \n", + "
| 12 | \n", + "Venezuela | \n", + "Eixample | \n", + "23.97 | \n", + "
| 13 | \n", + "Venezuela | \n", + "Sant Martí | \n", + "11.75 | \n", + "
| 14 | \n", + "Venezuela | \n", + "Horta-Guinardó | \n", + "11.25 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " dist population net_density(hab/ha) mort_rate rent_price \\\n",
+ "0 Ciutat Vella 101387 High High Neutral \n",
+ "1 Eixample 266416 High Neutral Neutral \n",
+ "2 Sant Martí 235513 High Neutral Neutral \n",
+ "3 Sants-Montjuïc 181910 Neutral Neutral Neutral \n",
+ "4 Gràcia 121347 Neutral Neutral Neutral \n",
+ "5 Horta-Guinardó 168751 Low Neutral Low \n",
+ "6 Les Corts 82033 Low Low High \n",
+ "7 Nou Barris 166579 Neutral Neutral Low \n",
+ "8 Sant Andreu 147594 Neutral Neutral Low \n",
+ "9 Sarrià-Sant Gervasi 149279 Low Neutral High \n",
+ "\n",
+ " facilities_sum crime_rate \n",
+ "0 Neutral High \n",
+ "1 Neutral High \n",
+ "2 Neutral High \n",
+ "3 Low High \n",
+ "4 Neutral Low \n",
+ "5 Neutral Low \n",
+ "6 High Low \n",
+ "7 Low Neutral \n",
+ "8 Low Neutral \n",
+ "9 Neutral Neutral "
+ ]
+ },
+ "execution_count": 117,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "population_ranked = pd.read_csv(\"../datasets/Data_filtered/Population_ranked.csv\", index_col=0)\n",
+ "population_ranked = population_ranked[['dist', 'population', 'net_density(hab/ha)', 'mort_rate',\n",
+ " 'rent_price', 'facilities_sum', 'crime_rate']]\n",
+ "population_ranked"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 81,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 118,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_foreign_compare[\"%immi_total_dist\"]=round(df_foreign_compare[\"num_immi_foreign\"]/df_foreign_compare[\"population\"]*100, 2)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 119,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | dist | \n", + "population | \n", + "net_density(hab/ha) | \n", + "mort_rate | \n", + "rent_price | \n", + "facilities_sum | \n", + "crime_rate | \n", + "
|---|---|---|---|---|---|---|---|
| 0 | \n", + "Ciutat Vella | \n", + "101387 | \n", + "High | \n", + "High | \n", + "Neutral | \n", + "Neutral | \n", + "High | \n", + "
| 1 | \n", + "Eixample | \n", + "266416 | \n", + "High | \n", + "Neutral | \n", + "Neutral | \n", + "Neutral | \n", + "High | \n", + "
| 2 | \n", + "Sant Martí | \n", + "235513 | \n", + "High | \n", + "Neutral | \n", + "Neutral | \n", + "Neutral | \n", + "High | \n", + "
| 3 | \n", + "Sants-Montjuïc | \n", + "181910 | \n", + "Neutral | \n", + "Neutral | \n", + "Neutral | \n", + "Low | \n", + "High | \n", + "
| 4 | \n", + "Gràcia | \n", + "121347 | \n", + "Neutral | \n", + "Neutral | \n", + "Neutral | \n", + "Neutral | \n", + "Low | \n", + "
| 5 | \n", + "Horta-Guinardó | \n", + "168751 | \n", + "Low | \n", + "Neutral | \n", + "Low | \n", + "Neutral | \n", + "Low | \n", + "
| 6 | \n", + "Les Corts | \n", + "82033 | \n", + "Low | \n", + "Low | \n", + "High | \n", + "High | \n", + "Low | \n", + "
| 7 | \n", + "Nou Barris | \n", + "166579 | \n", + "Neutral | \n", + "Neutral | \n", + "Low | \n", + "Low | \n", + "Neutral | \n", + "
| 8 | \n", + "Sant Andreu | \n", + "147594 | \n", + "Neutral | \n", + "Neutral | \n", + "Low | \n", + "Low | \n", + "Neutral | \n", + "
| 9 | \n", + "Sarrià-Sant Gervasi | \n", + "149279 | \n", + "Low | \n", + "Neutral | \n", + "High | \n", + "Neutral | \n", + "Neutral | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " num_immi_foreign population perc_immi_foreign \\\n",
+ "dist \n",
+ "Ciutat Vella 10069.0 101387.0 10.380000 \n",
+ "Eixample 12487.0 266416.0 4.716667 \n",
+ "Sants-Montjuïc 7464.0 181910.0 3.557500 \n",
+ "Sant Martí 7994.0 235513.0 3.425000 \n",
+ "Gràcia 4310.0 121347.0 3.242000 \n",
+ "Les Corts 2420.0 82033.0 3.170000 \n",
+ "Nou Barris 5261.0 166579.0 2.948462 \n",
+ "Sant Andreu 3622.0 147594.0 2.802857 \n",
+ "Horta-Guinardó 4544.0 168751.0 2.682727 \n",
+ "Sarrià-Sant Gervasi 3802.0 149279.0 2.421667 \n",
+ "\n",
+ " %immi_total_dist \n",
+ "dist \n",
+ "Ciutat Vella 9.93 \n",
+ "Eixample 4.69 \n",
+ "Sants-Montjuïc 4.10 \n",
+ "Sant Martí 3.39 \n",
+ "Gràcia 3.55 \n",
+ "Les Corts 2.95 \n",
+ "Nou Barris 3.16 \n",
+ "Sant Andreu 2.45 \n",
+ "Horta-Guinardó 2.69 \n",
+ "Sarrià-Sant Gervasi 2.55 "
+ ]
+ },
+ "execution_count": 119,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_foreign_compare"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 120,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | num_immi_foreign | \n", + "population | \n", + "perc_immi_foreign | \n", + "%immi_total_dist | \n", + "
|---|---|---|---|---|
| dist | \n", + "\n", + " | \n", + " | \n", + " | \n", + " |
| Ciutat Vella | \n", + "10069.0 | \n", + "101387.0 | \n", + "10.380000 | \n", + "9.93 | \n", + "
| Eixample | \n", + "12487.0 | \n", + "266416.0 | \n", + "4.716667 | \n", + "4.69 | \n", + "
| Sants-Montjuïc | \n", + "7464.0 | \n", + "181910.0 | \n", + "3.557500 | \n", + "4.10 | \n", + "
| Sant Martí | \n", + "7994.0 | \n", + "235513.0 | \n", + "3.425000 | \n", + "3.39 | \n", + "
| Gràcia | \n", + "4310.0 | \n", + "121347.0 | \n", + "3.242000 | \n", + "3.55 | \n", + "
| Les Corts | \n", + "2420.0 | \n", + "82033.0 | \n", + "3.170000 | \n", + "2.95 | \n", + "
| Nou Barris | \n", + "5261.0 | \n", + "166579.0 | \n", + "2.948462 | \n", + "3.16 | \n", + "
| Sant Andreu | \n", + "3622.0 | \n", + "147594.0 | \n", + "2.802857 | \n", + "2.45 | \n", + "
| Horta-Guinardó | \n", + "4544.0 | \n", + "168751.0 | \n", + "2.682727 | \n", + "2.69 | \n", + "
| Sarrià-Sant Gervasi | \n", + "3802.0 | \n", + "149279.0 | \n", + "2.421667 | \n", + "2.55 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " dist population net_density(hab/ha) mort_rate rent_price \\\n",
+ "0 Ciutat Vella 101387 High High Neutral \n",
+ "1 Eixample 266416 High Neutral Neutral \n",
+ "2 Sant Martí 235513 High Neutral Neutral \n",
+ "3 Sants-Montjuïc 181910 Neutral Neutral Neutral \n",
+ "4 Gràcia 121347 Neutral Neutral Neutral \n",
+ "5 Horta-Guinardó 168751 Low Neutral Low \n",
+ "6 Les Corts 82033 Low Low High \n",
+ "7 Nou Barris 166579 Neutral Neutral Low \n",
+ "8 Sant Andreu 147594 Neutral Neutral Low \n",
+ "9 Sarrià-Sant Gervasi 149279 Low Neutral High \n",
+ "\n",
+ " facilities_sum crime_rate num_immi %immi_total_immi \n",
+ "0 Neutral High 10069 16.25 \n",
+ "1 Neutral High 12487 20.15 \n",
+ "2 Neutral High 7994 12.90 \n",
+ "3 Low High 7464 12.04 \n",
+ "4 Neutral Low 4310 6.95 \n",
+ "5 Neutral Low 4544 7.33 \n",
+ "6 High Low 2420 3.90 \n",
+ "7 Low Neutral 5261 8.49 \n",
+ "8 Low Neutral 3622 5.84 \n",
+ "9 Neutral Neutral 3802 6.13 "
+ ]
+ },
+ "execution_count": 120,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "new_df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 121,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | dist | \n", + "population | \n", + "net_density(hab/ha) | \n", + "mort_rate | \n", + "rent_price | \n", + "facilities_sum | \n", + "crime_rate | \n", + "num_immi | \n", + "%immi_total_immi | \n", + "
|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "Ciutat Vella | \n", + "101387 | \n", + "High | \n", + "High | \n", + "Neutral | \n", + "Neutral | \n", + "High | \n", + "10069 | \n", + "16.25 | \n", + "
| 1 | \n", + "Eixample | \n", + "266416 | \n", + "High | \n", + "Neutral | \n", + "Neutral | \n", + "Neutral | \n", + "High | \n", + "12487 | \n", + "20.15 | \n", + "
| 2 | \n", + "Sant Martí | \n", + "235513 | \n", + "High | \n", + "Neutral | \n", + "Neutral | \n", + "Neutral | \n", + "High | \n", + "7994 | \n", + "12.90 | \n", + "
| 3 | \n", + "Sants-Montjuïc | \n", + "181910 | \n", + "Neutral | \n", + "Neutral | \n", + "Neutral | \n", + "Low | \n", + "High | \n", + "7464 | \n", + "12.04 | \n", + "
| 4 | \n", + "Gràcia | \n", + "121347 | \n", + "Neutral | \n", + "Neutral | \n", + "Neutral | \n", + "Neutral | \n", + "Low | \n", + "4310 | \n", + "6.95 | \n", + "
| 5 | \n", + "Horta-Guinardó | \n", + "168751 | \n", + "Low | \n", + "Neutral | \n", + "Low | \n", + "Neutral | \n", + "Low | \n", + "4544 | \n", + "7.33 | \n", + "
| 6 | \n", + "Les Corts | \n", + "82033 | \n", + "Low | \n", + "Low | \n", + "High | \n", + "High | \n", + "Low | \n", + "2420 | \n", + "3.90 | \n", + "
| 7 | \n", + "Nou Barris | \n", + "166579 | \n", + "Neutral | \n", + "Neutral | \n", + "Low | \n", + "Low | \n", + "Neutral | \n", + "5261 | \n", + "8.49 | \n", + "
| 8 | \n", + "Sant Andreu | \n", + "147594 | \n", + "Neutral | \n", + "Neutral | \n", + "Low | \n", + "Low | \n", + "Neutral | \n", + "3622 | \n", + "5.84 | \n", + "
| 9 | \n", + "Sarrià-Sant Gervasi | \n", + "149279 | \n", + "Low | \n", + "Neutral | \n", + "High | \n", + "Neutral | \n", + "Neutral | \n", + "3802 | \n", + "6.13 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " num_immi %immi_total_immi\n",
+ "dist \n",
+ "Eixample 12487 20.15\n",
+ "Ciutat Vella 10069 16.25\n",
+ "Sant Martí 7994 12.90\n",
+ "Sants-Montjuïc 7464 12.04\n",
+ "Nou Barris 5261 8.49\n",
+ "Horta-Guinardó 4544 7.33\n",
+ "Gràcia 4310 6.95\n",
+ "Sarrià-Sant Gervasi 3802 6.13\n",
+ "Sant Andreu 3622 5.84\n",
+ "Les Corts 2420 3.90\n",
+ "No consta 0 0.00"
+ ]
+ },
+ "execution_count": 121,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "immi_foreign_grouped"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 122,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "num_immi 61973.00\n",
+ "%immi_total_immi 99.98\n",
+ "dtype: float64"
+ ]
+ },
+ "execution_count": 122,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "total_immi = immi_foreign_grouped.sum()\n",
+ "total_immi"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 123,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "immi_foreign_grouped[\"%immi_total_immi\"]=immi_foreign_grouped[\"num_immi\"]/61973*100"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 124,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "immi_foreign_grouped[\"%immi_total_immi\"]=round(immi_foreign_grouped[\"%immi_total_immi\"], 2)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 125,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | num_immi | \n", + "%immi_total_immi | \n", + "
|---|---|---|
| dist | \n", + "\n", + " | \n", + " |
| Eixample | \n", + "12487 | \n", + "20.15 | \n", + "
| Ciutat Vella | \n", + "10069 | \n", + "16.25 | \n", + "
| Sant Martí | \n", + "7994 | \n", + "12.90 | \n", + "
| Sants-Montjuïc | \n", + "7464 | \n", + "12.04 | \n", + "
| Nou Barris | \n", + "5261 | \n", + "8.49 | \n", + "
| Horta-Guinardó | \n", + "4544 | \n", + "7.33 | \n", + "
| Gràcia | \n", + "4310 | \n", + "6.95 | \n", + "
| Sarrià-Sant Gervasi | \n", + "3802 | \n", + "6.13 | \n", + "
| Sant Andreu | \n", + "3622 | \n", + "5.84 | \n", + "
| Les Corts | \n", + "2420 | \n", + "3.90 | \n", + "
| No consta | \n", + "0 | \n", + "0.00 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " num_immi %immi_total_immi\n",
+ "dist \n",
+ "Eixample 12487 20.15\n",
+ "Ciutat Vella 10069 16.25\n",
+ "Sant Martí 7994 12.90\n",
+ "Sants-Montjuïc 7464 12.04\n",
+ "Nou Barris 5261 8.49\n",
+ "Horta-Guinardó 4544 7.33\n",
+ "Gràcia 4310 6.95\n",
+ "Sarrià-Sant Gervasi 3802 6.13\n",
+ "Sant Andreu 3622 5.84\n",
+ "Les Corts 2420 3.90\n",
+ "No consta 0 0.00"
+ ]
+ },
+ "execution_count": 125,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "immi_foreign_grouped"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 126,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | num_immi | \n", + "%immi_total_immi | \n", + "
|---|---|---|
| dist | \n", + "\n", + " | \n", + " |
| Eixample | \n", + "12487 | \n", + "20.15 | \n", + "
| Ciutat Vella | \n", + "10069 | \n", + "16.25 | \n", + "
| Sant Martí | \n", + "7994 | \n", + "12.90 | \n", + "
| Sants-Montjuïc | \n", + "7464 | \n", + "12.04 | \n", + "
| Nou Barris | \n", + "5261 | \n", + "8.49 | \n", + "
| Horta-Guinardó | \n", + "4544 | \n", + "7.33 | \n", + "
| Gràcia | \n", + "4310 | \n", + "6.95 | \n", + "
| Sarrià-Sant Gervasi | \n", + "3802 | \n", + "6.13 | \n", + "
| Sant Andreu | \n", + "3622 | \n", + "5.84 | \n", + "
| Les Corts | \n", + "2420 | \n", + "3.90 | \n", + "
| No consta | \n", + "0 | \n", + "0.00 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " dist population net_density(hab/ha) mort_rate rent_price \\\n",
+ "0 Ciutat Vella 101387 High High Neutral \n",
+ "1 Eixample 266416 High Neutral Neutral \n",
+ "2 Sant Martí 235513 High Neutral Neutral \n",
+ "3 Sants-Montjuïc 181910 Neutral Neutral Neutral \n",
+ "4 Gràcia 121347 Neutral Neutral Neutral \n",
+ "5 Horta-Guinardó 168751 Low Neutral Low \n",
+ "6 Les Corts 82033 Low Low High \n",
+ "7 Nou Barris 166579 Neutral Neutral Low \n",
+ "8 Sant Andreu 147594 Neutral Neutral Low \n",
+ "9 Sarrià-Sant Gervasi 149279 Low Neutral High \n",
+ "\n",
+ " facilities_sum crime_rate num_immi %immi_total_immi \n",
+ "0 Neutral High 10069 16.25 \n",
+ "1 Neutral High 12487 20.15 \n",
+ "2 Neutral High 7994 12.90 \n",
+ "3 Low High 7464 12.04 \n",
+ "4 Neutral Low 4310 6.95 \n",
+ "5 Neutral Low 4544 7.33 \n",
+ "6 High Low 2420 3.90 \n",
+ "7 Low Neutral 5261 8.49 \n",
+ "8 Low Neutral 3622 5.84 \n",
+ "9 Neutral Neutral 3802 6.13 "
+ ]
+ },
+ "execution_count": 126,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "new_df = pd.merge(population_ranked, immi_foreign_grouped, on=\"dist\", how=\"left\")\n",
+ "new_df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 133,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | dist | \n", + "population | \n", + "net_density(hab/ha) | \n", + "mort_rate | \n", + "rent_price | \n", + "facilities_sum | \n", + "crime_rate | \n", + "num_immi | \n", + "%immi_total_immi | \n", + "
|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "Ciutat Vella | \n", + "101387 | \n", + "High | \n", + "High | \n", + "Neutral | \n", + "Neutral | \n", + "High | \n", + "10069 | \n", + "16.25 | \n", + "
| 1 | \n", + "Eixample | \n", + "266416 | \n", + "High | \n", + "Neutral | \n", + "Neutral | \n", + "Neutral | \n", + "High | \n", + "12487 | \n", + "20.15 | \n", + "
| 2 | \n", + "Sant Martí | \n", + "235513 | \n", + "High | \n", + "Neutral | \n", + "Neutral | \n", + "Neutral | \n", + "High | \n", + "7994 | \n", + "12.90 | \n", + "
| 3 | \n", + "Sants-Montjuïc | \n", + "181910 | \n", + "Neutral | \n", + "Neutral | \n", + "Neutral | \n", + "Low | \n", + "High | \n", + "7464 | \n", + "12.04 | \n", + "
| 4 | \n", + "Gràcia | \n", + "121347 | \n", + "Neutral | \n", + "Neutral | \n", + "Neutral | \n", + "Neutral | \n", + "Low | \n", + "4310 | \n", + "6.95 | \n", + "
| 5 | \n", + "Horta-Guinardó | \n", + "168751 | \n", + "Low | \n", + "Neutral | \n", + "Low | \n", + "Neutral | \n", + "Low | \n", + "4544 | \n", + "7.33 | \n", + "
| 6 | \n", + "Les Corts | \n", + "82033 | \n", + "Low | \n", + "Low | \n", + "High | \n", + "High | \n", + "Low | \n", + "2420 | \n", + "3.90 | \n", + "
| 7 | \n", + "Nou Barris | \n", + "166579 | \n", + "Neutral | \n", + "Neutral | \n", + "Low | \n", + "Low | \n", + "Neutral | \n", + "5261 | \n", + "8.49 | \n", + "
| 8 | \n", + "Sant Andreu | \n", + "147594 | \n", + "Neutral | \n", + "Neutral | \n", + "Low | \n", + "Low | \n", + "Neutral | \n", + "3622 | \n", + "5.84 | \n", + "
| 9 | \n", + "Sarrià-Sant Gervasi | \n", + "149279 | \n", + "Low | \n", + "Neutral | \n", + "High | \n", + "Neutral | \n", + "Neutral | \n", + "3802 | \n", + "6.13 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " dist population net_density(hab/ha) mort_rate rent_price \\\n",
+ "0 Ciutat Vella 101387 High High Neutral \n",
+ "1 Eixample 266416 High Neutral Neutral \n",
+ "2 Sant Martí 235513 High Neutral Neutral \n",
+ "3 Sants-Montjuïc 181910 Neutral Neutral Neutral \n",
+ "4 Gràcia 121347 Neutral Neutral Neutral \n",
+ "5 Horta-Guinardó 168751 Low Neutral Low \n",
+ "6 Les Corts 82033 Low Low High \n",
+ "7 Nou Barris 166579 Neutral Neutral Low \n",
+ "8 Sant Andreu 147594 Neutral Neutral Low \n",
+ "9 Sarrià-Sant Gervasi 149279 Low Neutral High \n",
+ "\n",
+ " facilities_sum crime_rate num_immi %immi_total_immi num_immi_foreign \\\n",
+ "0 Neutral High 10069 16.25 10069.0 \n",
+ "1 Neutral High 12487 20.15 12487.0 \n",
+ "2 Neutral High 7994 12.90 7994.0 \n",
+ "3 Low High 7464 12.04 7464.0 \n",
+ "4 Neutral Low 4310 6.95 4310.0 \n",
+ "5 Neutral Low 4544 7.33 4544.0 \n",
+ "6 High Low 2420 3.90 2420.0 \n",
+ "7 Low Neutral 5261 8.49 5261.0 \n",
+ "8 Low Neutral 3622 5.84 3622.0 \n",
+ "9 Neutral Neutral 3802 6.13 3802.0 \n",
+ "\n",
+ " perc_immi_foreign %immi_total_dist \n",
+ "0 10.380000 9.93 \n",
+ "1 4.716667 4.69 \n",
+ "2 3.425000 3.39 \n",
+ "3 3.557500 4.10 \n",
+ "4 3.242000 3.55 \n",
+ "5 2.682727 2.69 \n",
+ "6 3.170000 2.95 \n",
+ "7 2.948462 3.16 \n",
+ "8 2.802857 2.45 \n",
+ "9 2.421667 2.55 "
+ ]
+ },
+ "execution_count": 133,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "new_df1 = pd.merge(new_df, df_foreign_compare, on=[\"dist\", \"population\"], how=\"left\")\n",
+ "new_df1"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 143,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "new_df1_good = new_df1[['dist', 'population', 'net_density(hab/ha)', 'mort_rate', 'rent_price',\n",
+ " 'facilities_sum', 'crime_rate', 'num_immi', '%immi_total_immi', '%immi_total_dist']]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 145,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | dist | \n", + "population | \n", + "net_density(hab/ha) | \n", + "mort_rate | \n", + "rent_price | \n", + "facilities_sum | \n", + "crime_rate | \n", + "num_immi | \n", + "%immi_total_immi | \n", + "num_immi_foreign | \n", + "perc_immi_foreign | \n", + "%immi_total_dist | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "Ciutat Vella | \n", + "101387 | \n", + "High | \n", + "High | \n", + "Neutral | \n", + "Neutral | \n", + "High | \n", + "10069 | \n", + "16.25 | \n", + "10069.0 | \n", + "10.380000 | \n", + "9.93 | \n", + "
| 1 | \n", + "Eixample | \n", + "266416 | \n", + "High | \n", + "Neutral | \n", + "Neutral | \n", + "Neutral | \n", + "High | \n", + "12487 | \n", + "20.15 | \n", + "12487.0 | \n", + "4.716667 | \n", + "4.69 | \n", + "
| 2 | \n", + "Sant Martí | \n", + "235513 | \n", + "High | \n", + "Neutral | \n", + "Neutral | \n", + "Neutral | \n", + "High | \n", + "7994 | \n", + "12.90 | \n", + "7994.0 | \n", + "3.425000 | \n", + "3.39 | \n", + "
| 3 | \n", + "Sants-Montjuïc | \n", + "181910 | \n", + "Neutral | \n", + "Neutral | \n", + "Neutral | \n", + "Low | \n", + "High | \n", + "7464 | \n", + "12.04 | \n", + "7464.0 | \n", + "3.557500 | \n", + "4.10 | \n", + "
| 4 | \n", + "Gràcia | \n", + "121347 | \n", + "Neutral | \n", + "Neutral | \n", + "Neutral | \n", + "Neutral | \n", + "Low | \n", + "4310 | \n", + "6.95 | \n", + "4310.0 | \n", + "3.242000 | \n", + "3.55 | \n", + "
| 5 | \n", + "Horta-Guinardó | \n", + "168751 | \n", + "Low | \n", + "Neutral | \n", + "Low | \n", + "Neutral | \n", + "Low | \n", + "4544 | \n", + "7.33 | \n", + "4544.0 | \n", + "2.682727 | \n", + "2.69 | \n", + "
| 6 | \n", + "Les Corts | \n", + "82033 | \n", + "Low | \n", + "Low | \n", + "High | \n", + "High | \n", + "Low | \n", + "2420 | \n", + "3.90 | \n", + "2420.0 | \n", + "3.170000 | \n", + "2.95 | \n", + "
| 7 | \n", + "Nou Barris | \n", + "166579 | \n", + "Neutral | \n", + "Neutral | \n", + "Low | \n", + "Low | \n", + "Neutral | \n", + "5261 | \n", + "8.49 | \n", + "5261.0 | \n", + "2.948462 | \n", + "3.16 | \n", + "
| 8 | \n", + "Sant Andreu | \n", + "147594 | \n", + "Neutral | \n", + "Neutral | \n", + "Low | \n", + "Low | \n", + "Neutral | \n", + "3622 | \n", + "5.84 | \n", + "3622.0 | \n", + "2.802857 | \n", + "2.45 | \n", + "
| 9 | \n", + "Sarrià-Sant Gervasi | \n", + "149279 | \n", + "Low | \n", + "Neutral | \n", + "High | \n", + "Neutral | \n", + "Neutral | \n", + "3802 | \n", + "6.13 | \n", + "3802.0 | \n", + "2.421667 | \n", + "2.55 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " dist population net_density(hab/ha) mort_rate rent_price \\\n",
+ "0 Ciutat Vella 101387 High High Neutral \n",
+ "1 Eixample 266416 High Neutral Neutral \n",
+ "2 Sant Martí 235513 High Neutral Neutral \n",
+ "3 Sants-Montjuïc 181910 Neutral Neutral Neutral \n",
+ "4 Gràcia 121347 Neutral Neutral Neutral \n",
+ "5 Horta-Guinardó 168751 Low Neutral Low \n",
+ "6 Les Corts 82033 Low Low High \n",
+ "7 Nou Barris 166579 Neutral Neutral Low \n",
+ "8 Sant Andreu 147594 Neutral Neutral Low \n",
+ "9 Sarrià-Sant Gervasi 149279 Low Neutral High \n",
+ "\n",
+ " facilities_sum crime_rate num_immi %immi_total_immi %immi_total_dist \n",
+ "0 Neutral High 10069 16.25 9.93 \n",
+ "1 Neutral High 12487 20.15 4.69 \n",
+ "2 Neutral High 7994 12.90 3.39 \n",
+ "3 Low High 7464 12.04 4.10 \n",
+ "4 Neutral Low 4310 6.95 3.55 \n",
+ "5 Neutral Low 4544 7.33 2.69 \n",
+ "6 High Low 2420 3.90 2.95 \n",
+ "7 Low Neutral 5261 8.49 3.16 \n",
+ "8 Low Neutral 3622 5.84 2.45 \n",
+ "9 Neutral Neutral 3802 6.13 2.55 "
+ ]
+ },
+ "execution_count": 145,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "new_df1_good"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 147,
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "NameError",
+ "evalue": "name 'df_dist' is not defined",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m| \n", + " | dist | \n", + "population | \n", + "net_density(hab/ha) | \n", + "mort_rate | \n", + "rent_price | \n", + "facilities_sum | \n", + "crime_rate | \n", + "num_immi | \n", + "%immi_total_immi | \n", + "%immi_total_dist | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "Ciutat Vella | \n", + "101387 | \n", + "High | \n", + "High | \n", + "Neutral | \n", + "Neutral | \n", + "High | \n", + "10069 | \n", + "16.25 | \n", + "9.93 | \n", + "
| 1 | \n", + "Eixample | \n", + "266416 | \n", + "High | \n", + "Neutral | \n", + "Neutral | \n", + "Neutral | \n", + "High | \n", + "12487 | \n", + "20.15 | \n", + "4.69 | \n", + "
| 2 | \n", + "Sant Martí | \n", + "235513 | \n", + "High | \n", + "Neutral | \n", + "Neutral | \n", + "Neutral | \n", + "High | \n", + "7994 | \n", + "12.90 | \n", + "3.39 | \n", + "
| 3 | \n", + "Sants-Montjuïc | \n", + "181910 | \n", + "Neutral | \n", + "Neutral | \n", + "Neutral | \n", + "Low | \n", + "High | \n", + "7464 | \n", + "12.04 | \n", + "4.10 | \n", + "
| 4 | \n", + "Gràcia | \n", + "121347 | \n", + "Neutral | \n", + "Neutral | \n", + "Neutral | \n", + "Neutral | \n", + "Low | \n", + "4310 | \n", + "6.95 | \n", + "3.55 | \n", + "
| 5 | \n", + "Horta-Guinardó | \n", + "168751 | \n", + "Low | \n", + "Neutral | \n", + "Low | \n", + "Neutral | \n", + "Low | \n", + "4544 | \n", + "7.33 | \n", + "2.69 | \n", + "
| 6 | \n", + "Les Corts | \n", + "82033 | \n", + "Low | \n", + "Low | \n", + "High | \n", + "High | \n", + "Low | \n", + "2420 | \n", + "3.90 | \n", + "2.95 | \n", + "
| 7 | \n", + "Nou Barris | \n", + "166579 | \n", + "Neutral | \n", + "Neutral | \n", + "Low | \n", + "Low | \n", + "Neutral | \n", + "5261 | \n", + "8.49 | \n", + "3.16 | \n", + "
| 8 | \n", + "Sant Andreu | \n", + "147594 | \n", + "Neutral | \n", + "Neutral | \n", + "Low | \n", + "Low | \n", + "Neutral | \n", + "3622 | \n", + "5.84 | \n", + "2.45 | \n", + "
| 9 | \n", + "Sarrià-Sant Gervasi | \n", + "149279 | \n", + "Low | \n", + "Neutral | \n", + "High | \n", + "Neutral | \n", + "Neutral | \n", + "3802 | \n", + "6.13 | \n", + "2.55 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " dist mort_rate facilities_rate crime_rate\n",
+ "0 Ciutat Vella 1002.95 0.37 272.956283\n",
+ "1 Eixample 794.48 0.36 285.104537\n",
+ "2 Gràcia 805.10 0.25 47.698402\n",
+ "3 Horta-Guinardó 871.53 0.21 48.562169\n",
+ "4 Les Corts 677.30 0.52 45.927682\n",
+ "5 Nou Barris 885.38 0.12 53.226506\n",
+ "6 Sant Andreu 819.19 0.20 65.751116\n",
+ "7 Sant Martí 777.40 0.23 138.307475\n",
+ "8 Sants-Montjuïc 827.70 0.19 124.746346\n",
+ "9 Sarrià-Sant Gervasi 821.88 0.31 57.082605"
+ ]
+ },
+ "execution_count": 156,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "dist3"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 153,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Index(['dist', 'population', 'net_density(hab/ha)', 'mort_rate', 'rent_price',\n",
+ " 'facilities_sum', 'crime_rate', 'num_immi', '%immi_total_immi',\n",
+ " '%immi_total_dist'],\n",
+ " dtype='object')"
+ ]
+ },
+ "execution_count": 153,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "new_df1_good.columns"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 164,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | dist | \n", + "mort_rate | \n", + "facilities_rate | \n", + "crime_rate | \n", + "
|---|---|---|---|---|
| 0 | \n", + "Ciutat Vella | \n", + "1002.95 | \n", + "0.37 | \n", + "272.956283 | \n", + "
| 1 | \n", + "Eixample | \n", + "794.48 | \n", + "0.36 | \n", + "285.104537 | \n", + "
| 2 | \n", + "Gràcia | \n", + "805.10 | \n", + "0.25 | \n", + "47.698402 | \n", + "
| 3 | \n", + "Horta-Guinardó | \n", + "871.53 | \n", + "0.21 | \n", + "48.562169 | \n", + "
| 4 | \n", + "Les Corts | \n", + "677.30 | \n", + "0.52 | \n", + "45.927682 | \n", + "
| 5 | \n", + "Nou Barris | \n", + "885.38 | \n", + "0.12 | \n", + "53.226506 | \n", + "
| 6 | \n", + "Sant Andreu | \n", + "819.19 | \n", + "0.20 | \n", + "65.751116 | \n", + "
| 7 | \n", + "Sant Martí | \n", + "777.40 | \n", + "0.23 | \n", + "138.307475 | \n", + "
| 8 | \n", + "Sants-Montjuïc | \n", + "827.70 | \n", + "0.19 | \n", + "124.746346 | \n", + "
| 9 | \n", + "Sarrià-Sant Gervasi | \n", + "821.88 | \n", + "0.31 | \n", + "57.082605 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " dist %immi_total_immi %immi_total_dist\n",
+ "0 Ciutat Vella 16.25 9.93\n",
+ "1 Eixample 20.15 4.69\n",
+ "2 Sant Martí 12.90 3.39\n",
+ "3 Sants-Montjuïc 12.04 4.10\n",
+ "4 Gràcia 6.95 3.55\n",
+ "5 Horta-Guinardó 7.33 2.69\n",
+ "6 Les Corts 3.90 2.95\n",
+ "7 Nou Barris 8.49 3.16\n",
+ "8 Sant Andreu 5.84 2.45\n",
+ "9 Sarrià-Sant Gervasi 6.13 2.55"
+ ]
+ },
+ "execution_count": 164,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "new_df1_good2 = new_df1_good[['dist', '%immi_total_immi','%immi_total_dist']]\n",
+ "new_df1_good2"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 165,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "crime_immi_compare = pd.merge(new_df1_good2, dist3, on=\"dist\", how=\"outer\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 168,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | dist | \n", + "%immi_total_immi | \n", + "%immi_total_dist | \n", + "
|---|---|---|---|
| 0 | \n", + "Ciutat Vella | \n", + "16.25 | \n", + "9.93 | \n", + "
| 1 | \n", + "Eixample | \n", + "20.15 | \n", + "4.69 | \n", + "
| 2 | \n", + "Sant Martí | \n", + "12.90 | \n", + "3.39 | \n", + "
| 3 | \n", + "Sants-Montjuïc | \n", + "12.04 | \n", + "4.10 | \n", + "
| 4 | \n", + "Gràcia | \n", + "6.95 | \n", + "3.55 | \n", + "
| 5 | \n", + "Horta-Guinardó | \n", + "7.33 | \n", + "2.69 | \n", + "
| 6 | \n", + "Les Corts | \n", + "3.90 | \n", + "2.95 | \n", + "
| 7 | \n", + "Nou Barris | \n", + "8.49 | \n", + "3.16 | \n", + "
| 8 | \n", + "Sant Andreu | \n", + "5.84 | \n", + "2.45 | \n", + "
| 9 | \n", + "Sarrià-Sant Gervasi | \n", + "6.13 | \n", + "2.55 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " dist %immi_total_immi %immi_total_dist mort_rate \\\n",
+ "0 Ciutat Vella 16.25 9.93 1002.95 \n",
+ "1 Eixample 20.15 4.69 794.48 \n",
+ "2 Sant Martí 12.90 3.39 777.40 \n",
+ "3 Sants-Montjuïc 12.04 4.10 827.70 \n",
+ "4 Gràcia 6.95 3.55 805.10 \n",
+ "5 Horta-Guinardó 7.33 2.69 871.53 \n",
+ "6 Les Corts 3.90 2.95 677.30 \n",
+ "7 Nou Barris 8.49 3.16 885.38 \n",
+ "8 Sant Andreu 5.84 2.45 819.19 \n",
+ "9 Sarrià-Sant Gervasi 6.13 2.55 821.88 \n",
+ "\n",
+ " facilities_rate crime_rate \n",
+ "0 0.37 272.956283 \n",
+ "1 0.36 285.104537 \n",
+ "2 0.23 138.307475 \n",
+ "3 0.19 124.746346 \n",
+ "4 0.25 47.698402 \n",
+ "5 0.21 48.562169 \n",
+ "6 0.52 45.927682 \n",
+ "7 0.12 53.226506 \n",
+ "8 0.20 65.751116 \n",
+ "9 0.31 57.082605 "
+ ]
+ },
+ "execution_count": 168,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "crime_immi_compare"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "choices = ['High', 'Neutral']\n",
+ "conditions5 = [(Population.rent_price >= 5), ((Population.rent_price < 5) & (Population.rent_price > 3))]\n",
+ "Population.rent_price = np.select(conditions5, choices, default='Low')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 169,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "crime_immi_compare.to_csv(\"../datasets/Data_filtered/comparison_table.csv\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.2"
+ },
+ "toc": {
+ "base_numbering": 1,
+ "nav_menu": {},
+ "number_sections": true,
+ "sideBar": true,
+ "skip_h1_title": false,
+ "title_cell": "Table of Contents",
+ "title_sidebar": "Contents",
+ "toc_cell": false,
+ "toc_position": {},
+ "toc_section_display": true,
+ "toc_window_display": false
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/your-project/merging_database.ipynb b/your-project/merging_database.ipynb
new file mode 100644
index 0000000..b7656ed
--- /dev/null
+++ b/your-project/merging_database.ipynb
@@ -0,0 +1,2569 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "from functools import reduce"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Importing data\n",
+ "\n",
+ "immi = pd.read_csv(\"../datasets/Data_filtered/immigrants-by-nationality.csv\")\n",
+ "immi = immi[[\"Dist\", \"Nbh\", \"Nationality\", \"Number\"]]\n",
+ "immi.columns = ['Dist', 'Nbh', 'Nationality', 'num_immi']\n",
+ "immi_original = immi.copy() \n",
+ "\n",
+ "mort = pd.read_excel(\"../datasets/Data_filtered/mortality_rate.xlsx\")\n",
+ "mort_original = mort.copy() \n",
+ "\n",
+ "rent = pd.read_excel(\"../datasets/Data_filtered/rental_price.xlsx\")\n",
+ "rent_original = rent.copy()\n",
+ "\n",
+ "crime = pd.read_excel(\"../datasets/Data_filtered/crime.xlsx\")\n",
+ "crime_original = crime.copy()\n",
+ "\n",
+ "#no\n",
+ "bars = pd.read_csv(\"../datasets/Data_filtered/bars.csv\", index_col=0)\n",
+ "bars_original = bars.copy()\n",
+ "\n",
+ "#no\n",
+ "children_places = pd.read_csv(\"../datasets/Data_filtered/children.csv\", index_col=0)\n",
+ "children_places_original = children_places.copy()\n",
+ "\n",
+ "#tot 1s\n",
+ "cinema = pd.read_csv(\"../datasets/Data_filtered/cinema.csv\", index_col=0)\n",
+ "cinema_original = cinema.copy()\n",
+ "\n",
+ "#no\n",
+ "education = pd.read_csv(\"../datasets/Data_filtered/education.csv\", index_col=0)\n",
+ "education_original = education.copy()\n",
+ "\n",
+ "#no\n",
+ "education_kids = pd.read_csv(\"../datasets/Data_filtered/educationkids.csv\", index_col=0)\n",
+ "education_kids = education_kids.copy()\n",
+ "\n",
+ "#no\n",
+ "hospital = pd.read_csv(\"../datasets/Data_filtered/hospital.csv\", index_col=0)\n",
+ "hospital = hospital.copy()\n",
+ "\n",
+ "#no\n",
+ "library = pd.read_csv(\"../datasets/Data_filtered/library.csv\", index_col=0)\n",
+ "library = library.copy()\n",
+ "\n",
+ "#no\n",
+ "park = pd.read_csv(\"../datasets/Data_filtered/park.csv\", index_col=0)\n",
+ "park = park.copy()\n",
+ "\n",
+ "population = pd.read_csv(\"../datasets/Data_filtered/population.csv\", index_col=0)\n",
+ "population = population.copy()\n",
+ "\n",
+ "rental = pd.read_csv(\"../datasets/Data_filtered/rental.csv\", index_col=0)\n",
+ "rental = rental.copy()\n",
+ "\n",
+ "#no\n",
+ "sport = pd.read_csv(\"../datasets/Data_filtered/sport.csv\", index_col=0)\n",
+ "sport = sport.copy() \n",
+ "\n",
+ "tenants = pd.read_csv(\"../datasets/Data_filtered/tenants.csv\", index_col=0)\n",
+ "tenants = tenants.copy() "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Grouping immigrants by Nbh and Dist, independent of country of origin\n",
+ "\n",
+ "immi_nbh = immi.groupby([\"Nbh\",\"Dist\"]).num_immi.sum().reset_index()\n",
+ "immi_nbh\n",
+ "\n",
+ "immi_nbh.to_csv(\"../datasets/Data_filtered/immi_nbh.csv\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Excluding Spain from immi dataset\n",
+ "\n",
+ "immi = pd.read_csv(\"../datasets/Data_filtered/immigrants-by-nationality.csv\")\n",
+ "immi = immi[[\"Dist\", \"Nbh\", \"Nationality\", \"Number\"]]\n",
+ "immi.columns = ['Dist', 'Nbh', 'Nationality', 'num_immi']\n",
+ "immi_original = immi.copy() \n",
+ "\n",
+ "\n",
+ "immi_foreign = immi[immi[\"Nationality\"]!=\"Spain\"]\n",
+ "immi_foreign.to_csv(\"../datasets/Data_filtered/immi_foreign.csv\")\n",
+ "\n",
+ "# Grouping immigrants by Nbh and Dist, independent of country of origin\n",
+ "\n",
+ "immi_foreign_nbh = immi_foreign.groupby([\"Nbh\",\"Dist\"]).num_immi.sum().reset_index()\n",
+ "immi_foreign_nbh\n",
+ "immi_foreign_nbh.to_csv(\"../datasets/Data_filtered/immi_foreign_nbh.csv\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "immi.to_csv(\"../datasets/Data_filtered/immi.csv\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Naming Nbh and Dist all the same\n",
+ "\n",
+ "rental.columns = ['Nbh', 'rent_price']\n",
+ "tenants.columns = ['Nbh', 'avg_occupation']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | dist | \n", + "%immi_total_immi | \n", + "%immi_total_dist | \n", + "mort_rate | \n", + "facilities_rate | \n", + "crime_rate | \n", + "
|---|---|---|---|---|---|---|
| 0 | \n", + "Ciutat Vella | \n", + "16.25 | \n", + "9.93 | \n", + "1002.95 | \n", + "0.37 | \n", + "272.956283 | \n", + "
| 1 | \n", + "Eixample | \n", + "20.15 | \n", + "4.69 | \n", + "794.48 | \n", + "0.36 | \n", + "285.104537 | \n", + "
| 2 | \n", + "Sant Martí | \n", + "12.90 | \n", + "3.39 | \n", + "777.40 | \n", + "0.23 | \n", + "138.307475 | \n", + "
| 3 | \n", + "Sants-Montjuïc | \n", + "12.04 | \n", + "4.10 | \n", + "827.70 | \n", + "0.19 | \n", + "124.746346 | \n", + "
| 4 | \n", + "Gràcia | \n", + "6.95 | \n", + "3.55 | \n", + "805.10 | \n", + "0.25 | \n", + "47.698402 | \n", + "
| 5 | \n", + "Horta-Guinardó | \n", + "7.33 | \n", + "2.69 | \n", + "871.53 | \n", + "0.21 | \n", + "48.562169 | \n", + "
| 6 | \n", + "Les Corts | \n", + "3.90 | \n", + "2.95 | \n", + "677.30 | \n", + "0.52 | \n", + "45.927682 | \n", + "
| 7 | \n", + "Nou Barris | \n", + "8.49 | \n", + "3.16 | \n", + "885.38 | \n", + "0.12 | \n", + "53.226506 | \n", + "
| 8 | \n", + "Sant Andreu | \n", + "5.84 | \n", + "2.45 | \n", + "819.19 | \n", + "0.20 | \n", + "65.751116 | \n", + "
| 9 | \n", + "Sarrià-Sant Gervasi | \n", + "6.13 | \n", + "2.55 | \n", + "821.88 | \n", + "0.31 | \n", + "57.082605 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Nbh Bars Children_places Cinemas_Theatres Schools \\\n",
+ "0 Horta 4.0 9.0 3.0 11.0 \n",
+ "1 Navas 2.0 3.0 NaN 6.0 \n",
+ "2 Pedralbes 1.0 7.0 NaN 8.0 \n",
+ "3 Sant Andreu 3.0 22.0 5.0 22.0 \n",
+ "4 Sant Antoni 9.0 8.0 4.0 11.0 \n",
+ ".. ... ... ... ... ... \n",
+ "70 les Roquetes NaN 6.0 NaN 4.0 \n",
+ "71 les Tres Torres NaN 5.0 2.0 2.0 \n",
+ "72 la Clota NaN NaN NaN NaN \n",
+ "73 la Dreta de l'Eixample NaN NaN NaN NaN \n",
+ "74 el Poble Sec NaN NaN NaN NaN \n",
+ "\n",
+ " Pre-Schools Hospitals Libraries_Theatres Park_Gardens Sport_Centers \\\n",
+ "0 7.0 3.0 5.0 3.0 2.0 \n",
+ "1 3.0 NaN NaN NaN NaN \n",
+ "2 41.0 NaN 10.0 9.0 1.0 \n",
+ "3 17.0 5.0 2.0 5.0 3.0 \n",
+ "4 11.0 1.0 5.0 7.0 1.0 \n",
+ ".. ... ... ... ... ... \n",
+ "70 2.0 1.0 1.0 2.0 NaN \n",
+ "71 4.0 5.0 1.0 3.0 NaN \n",
+ "72 NaN NaN NaN 2.0 NaN \n",
+ "73 NaN NaN NaN NaN NaN \n",
+ "74 NaN NaN NaN NaN NaN \n",
+ "\n",
+ " Population Net_density(hab/ha) avg_occupation \n",
+ "0 26715.0 422.0 2.5 \n",
+ "1 22129.0 984.0 2.5 \n",
+ "2 12076.0 147.0 2.9 \n",
+ "3 57183.0 746.0 2.4 \n",
+ "4 38345.0 928.0 2.3 \n",
+ ".. ... ... ... \n",
+ "70 15590.0 856.0 2.7 \n",
+ "71 16667.0 391.0 3.0 \n",
+ "72 610.0 100.0 2.3 \n",
+ "73 44052.0 387.0 2.4 \n",
+ "74 40228.0 1043.0 2.5 \n",
+ "\n",
+ "[75 rows x 13 columns]"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Creating list with all dataframes\n",
+ "# , rental missing --- hi ha moltes files jo el trauria\n",
+ "\n",
+ "\n",
+ "\n",
+ "# Adding datasets that have only Nbh\n",
+ "data_frames1 = [bars, children_places, cinema, education, education_kids, hospital, library, park, sport, population, tenants]\n",
+ "df_merged1 = reduce(lambda left,right: pd.merge(left,right,on=[\"Nbh\"], how='outer'), data_frames1)\n",
+ "df_merged1"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | Nbh | \n", + "Bars | \n", + "Children_places | \n", + "Cinemas_Theatres | \n", + "Schools | \n", + "Pre-Schools | \n", + "Hospitals | \n", + "Libraries_Theatres | \n", + "Park_Gardens | \n", + "Sport_Centers | \n", + "Population | \n", + "Net_density(hab/ha) | \n", + "avg_occupation | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "Horta | \n", + "4.0 | \n", + "9.0 | \n", + "3.0 | \n", + "11.0 | \n", + "7.0 | \n", + "3.0 | \n", + "5.0 | \n", + "3.0 | \n", + "2.0 | \n", + "26715.0 | \n", + "422.0 | \n", + "2.5 | \n", + "
| 1 | \n", + "Navas | \n", + "2.0 | \n", + "3.0 | \n", + "NaN | \n", + "6.0 | \n", + "3.0 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "22129.0 | \n", + "984.0 | \n", + "2.5 | \n", + "
| 2 | \n", + "Pedralbes | \n", + "1.0 | \n", + "7.0 | \n", + "NaN | \n", + "8.0 | \n", + "41.0 | \n", + "NaN | \n", + "10.0 | \n", + "9.0 | \n", + "1.0 | \n", + "12076.0 | \n", + "147.0 | \n", + "2.9 | \n", + "
| 3 | \n", + "Sant Andreu | \n", + "3.0 | \n", + "22.0 | \n", + "5.0 | \n", + "22.0 | \n", + "17.0 | \n", + "5.0 | \n", + "2.0 | \n", + "5.0 | \n", + "3.0 | \n", + "57183.0 | \n", + "746.0 | \n", + "2.4 | \n", + "
| 4 | \n", + "Sant Antoni | \n", + "9.0 | \n", + "8.0 | \n", + "4.0 | \n", + "11.0 | \n", + "11.0 | \n", + "1.0 | \n", + "5.0 | \n", + "7.0 | \n", + "1.0 | \n", + "38345.0 | \n", + "928.0 | \n", + "2.3 | \n", + "
| ... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "
| 70 | \n", + "les Roquetes | \n", + "NaN | \n", + "6.0 | \n", + "NaN | \n", + "4.0 | \n", + "2.0 | \n", + "1.0 | \n", + "1.0 | \n", + "2.0 | \n", + "NaN | \n", + "15590.0 | \n", + "856.0 | \n", + "2.7 | \n", + "
| 71 | \n", + "les Tres Torres | \n", + "NaN | \n", + "5.0 | \n", + "2.0 | \n", + "2.0 | \n", + "4.0 | \n", + "5.0 | \n", + "1.0 | \n", + "3.0 | \n", + "NaN | \n", + "16667.0 | \n", + "391.0 | \n", + "3.0 | \n", + "
| 72 | \n", + "la Clota | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "2.0 | \n", + "NaN | \n", + "610.0 | \n", + "100.0 | \n", + "2.3 | \n", + "
| 73 | \n", + "la Dreta de l'Eixample | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "44052.0 | \n", + "387.0 | \n", + "2.4 | \n", + "
| 74 | \n", + "el Poble Sec | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "40228.0 | \n", + "1043.0 | \n", + "2.5 | \n", + "
75 rows × 13 columns
\n", + "\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Nbh Dist num_immi \\\n",
+ "0 Baró de Viver Sant Andreu 92.0 \n",
+ "1 Can Baró Horta-Guinardó 473.0 \n",
+ "2 Can Peguera Nou Barris 57.0 \n",
+ "3 Canyelles Nou Barris 140.0 \n",
+ "4 Ciutat Meridiana Nou Barris 838.0 \n",
+ ".. ... ... ... \n",
+ "71 les Corts Les Corts 2276.0 \n",
+ "72 les Roquetes Nou Barris 920.0 \n",
+ "73 les Tres Torres Sarrià-Sant Gervasi 702.0 \n",
+ "74 el Poble Sec - Parc Montjuïc Sants-Montjuïc NaN \n",
+ "75 la Marina del Prat Vermell - Zona Franca Sants-Montjuïc NaN \n",
+ "\n",
+ " mort_rate rent_price \n",
+ "0 1067.6 2.830 \n",
+ "1 861.0 2.859 \n",
+ "2 968.0 2.330 \n",
+ "3 804.9 2.330 \n",
+ "4 793.9 1.498 \n",
+ ".. ... ... \n",
+ "71 720.4 4.821 \n",
+ "72 869.4 1.885 \n",
+ "73 708.5 5.689 \n",
+ "74 883.9 3.936 \n",
+ "75 1139.9 3.760 \n",
+ "\n",
+ "[76 rows x 5 columns]"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Adding datasets that have Nbh and Dist\n",
+ "\n",
+ "data_frames2 = [immi_nbh, mort, rent]\n",
+ "df_merged2 = reduce(lambda left,right: pd.merge(left,right,on=[\"Nbh\", \"Dist\"], how='outer'), data_frames2)\n",
+ "df_merged2\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | Nbh | \n", + "Dist | \n", + "num_immi | \n", + "mort_rate | \n", + "rent_price | \n", + "
|---|---|---|---|---|---|
| 0 | \n", + "Baró de Viver | \n", + "Sant Andreu | \n", + "92.0 | \n", + "1067.6 | \n", + "2.830 | \n", + "
| 1 | \n", + "Can Baró | \n", + "Horta-Guinardó | \n", + "473.0 | \n", + "861.0 | \n", + "2.859 | \n", + "
| 2 | \n", + "Can Peguera | \n", + "Nou Barris | \n", + "57.0 | \n", + "968.0 | \n", + "2.330 | \n", + "
| 3 | \n", + "Canyelles | \n", + "Nou Barris | \n", + "140.0 | \n", + "804.9 | \n", + "2.330 | \n", + "
| 4 | \n", + "Ciutat Meridiana | \n", + "Nou Barris | \n", + "838.0 | \n", + "793.9 | \n", + "1.498 | \n", + "
| ... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "
| 71 | \n", + "les Corts | \n", + "Les Corts | \n", + "2276.0 | \n", + "720.4 | \n", + "4.821 | \n", + "
| 72 | \n", + "les Roquetes | \n", + "Nou Barris | \n", + "920.0 | \n", + "869.4 | \n", + "1.885 | \n", + "
| 73 | \n", + "les Tres Torres | \n", + "Sarrià-Sant Gervasi | \n", + "702.0 | \n", + "708.5 | \n", + "5.689 | \n", + "
| 74 | \n", + "el Poble Sec - Parc Montjuïc | \n", + "Sants-Montjuïc | \n", + "NaN | \n", + "883.9 | \n", + "3.936 | \n", + "
| 75 | \n", + "la Marina del Prat Vermell - Zona Franca | \n", + "Sants-Montjuïc | \n", + "NaN | \n", + "1139.9 | \n", + "3.760 | \n", + "
76 rows × 5 columns
\n", + "\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Nbh Dist num_immi \\\n",
+ "0 Baró de Viver Sant Andreu 92.0 \n",
+ "1 Can Baró Horta-Guinardó 473.0 \n",
+ "2 Can Peguera Nou Barris 57.0 \n",
+ "3 Canyelles Nou Barris 140.0 \n",
+ "4 Ciutat Meridiana Nou Barris 838.0 \n",
+ ".. ... ... ... \n",
+ "71 les Corts Les Corts 2276.0 \n",
+ "72 les Roquetes Nou Barris 920.0 \n",
+ "73 les Tres Torres Sarrià-Sant Gervasi 702.0 \n",
+ "74 el Poble Sec - Parc Montjuïc Sants-Montjuïc NaN \n",
+ "75 la Marina del Prat Vermell - Zona Franca Sants-Montjuïc NaN \n",
+ "\n",
+ " mort_rate rent_price num_crimes \n",
+ "0 1067.6 2.830 10657.0 \n",
+ "1 861.0 2.859 7871.0 \n",
+ "2 968.0 2.330 8627.0 \n",
+ "3 804.9 2.330 8627.0 \n",
+ "4 793.9 1.498 8627.0 \n",
+ ".. ... ... ... \n",
+ "71 720.4 4.821 7444.0 \n",
+ "72 869.4 1.885 8627.0 \n",
+ "73 708.5 5.689 9252.0 \n",
+ "74 883.9 3.936 20219.0 \n",
+ "75 1139.9 3.760 20219.0 \n",
+ "\n",
+ "[76 rows x 6 columns]"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Adding datasets that have Nbh and Dist (df_merged2) with dataset that has only Dist\n",
+ "\n",
+ "df_merged3 = pd.merge(df_merged2, crime, how=\"left\", on=\"Dist\")\n",
+ "df_merged3"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | Nbh | \n", + "Dist | \n", + "num_immi | \n", + "mort_rate | \n", + "rent_price | \n", + "num_crimes | \n", + "
|---|---|---|---|---|---|---|
| 0 | \n", + "Baró de Viver | \n", + "Sant Andreu | \n", + "92.0 | \n", + "1067.6 | \n", + "2.830 | \n", + "10657.0 | \n", + "
| 1 | \n", + "Can Baró | \n", + "Horta-Guinardó | \n", + "473.0 | \n", + "861.0 | \n", + "2.859 | \n", + "7871.0 | \n", + "
| 2 | \n", + "Can Peguera | \n", + "Nou Barris | \n", + "57.0 | \n", + "968.0 | \n", + "2.330 | \n", + "8627.0 | \n", + "
| 3 | \n", + "Canyelles | \n", + "Nou Barris | \n", + "140.0 | \n", + "804.9 | \n", + "2.330 | \n", + "8627.0 | \n", + "
| 4 | \n", + "Ciutat Meridiana | \n", + "Nou Barris | \n", + "838.0 | \n", + "793.9 | \n", + "1.498 | \n", + "8627.0 | \n", + "
| ... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "
| 71 | \n", + "les Corts | \n", + "Les Corts | \n", + "2276.0 | \n", + "720.4 | \n", + "4.821 | \n", + "7444.0 | \n", + "
| 72 | \n", + "les Roquetes | \n", + "Nou Barris | \n", + "920.0 | \n", + "869.4 | \n", + "1.885 | \n", + "8627.0 | \n", + "
| 73 | \n", + "les Tres Torres | \n", + "Sarrià-Sant Gervasi | \n", + "702.0 | \n", + "708.5 | \n", + "5.689 | \n", + "9252.0 | \n", + "
| 74 | \n", + "el Poble Sec - Parc Montjuïc | \n", + "Sants-Montjuïc | \n", + "NaN | \n", + "883.9 | \n", + "3.936 | \n", + "20219.0 | \n", + "
| 75 | \n", + "la Marina del Prat Vermell - Zona Franca | \n", + "Sants-Montjuïc | \n", + "NaN | \n", + "1139.9 | \n", + "3.760 | \n", + "20219.0 | \n", + "
76 rows × 6 columns
\n", + "\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Nbh Bars Children_places Cinemas_Theatres Schools Pre-Schools \\\n",
+ "0 Horta 4.0 9.0 3.0 11.0 7.0 \n",
+ "1 Navas 2.0 3.0 NaN 6.0 3.0 \n",
+ "2 Pedralbes 1.0 7.0 NaN 8.0 41.0 \n",
+ "3 Sant Andreu 3.0 22.0 5.0 22.0 17.0 \n",
+ "4 Sant Antoni 9.0 8.0 4.0 11.0 11.0 \n",
+ "\n",
+ " Hospitals Libraries_Theatres Park_Gardens Sport_Centers Population \\\n",
+ "0 3.0 5.0 3.0 2.0 26715.0 \n",
+ "1 NaN NaN NaN NaN 22129.0 \n",
+ "2 NaN 10.0 9.0 1.0 12076.0 \n",
+ "3 5.0 2.0 5.0 3.0 57183.0 \n",
+ "4 1.0 5.0 7.0 1.0 38345.0 \n",
+ "\n",
+ " Net_density(hab/ha) avg_occupation Dist num_immi mort_rate \\\n",
+ "0 422.0 2.5 Horta-Guinardó 1127.0 805.5 \n",
+ "1 984.0 2.5 Sant Andreu 988.0 696.0 \n",
+ "2 147.0 2.9 Les Corts 764.0 661.6 \n",
+ "3 746.0 2.4 Sant Andreu 1965.0 763.0 \n",
+ "4 928.0 2.3 Eixample 2490.0 737.3 \n",
+ "\n",
+ " rent_price num_crimes \n",
+ "0 2.970 7871.0 \n",
+ "1 3.029 10657.0 \n",
+ "2 6.340 7444.0 \n",
+ "3 3.210 10657.0 \n",
+ "4 4.591 46210.0 "
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Merging all datasets (df_3 already contains df_1 and df_2)\n",
+ "\n",
+ "df = pd.merge(df_merged1, df_merged3, how=\"left\", on=\"Nbh\")\n",
+ "df.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Renaming columns\n",
+ "\n",
+ "df.columns = ['nbh', 'bars', 'children_places', 'cinemas_theatres', 'schools',\n",
+ " 'pre-schools', 'hospitals', 'libraries_theatres', 'park_gardens',\n",
+ " 'sport_centers', 'population', 'net_density(hab/ha)', 'avg_occupation',\n",
+ " 'dist', 'num_immi', 'mort_rate', 'rent_price', 'num_crimes']"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Basic analysis"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | Nbh | \n", + "Bars | \n", + "Children_places | \n", + "Cinemas_Theatres | \n", + "Schools | \n", + "Pre-Schools | \n", + "Hospitals | \n", + "Libraries_Theatres | \n", + "Park_Gardens | \n", + "Sport_Centers | \n", + "Population | \n", + "Net_density(hab/ha) | \n", + "avg_occupation | \n", + "Dist | \n", + "num_immi | \n", + "mort_rate | \n", + "rent_price | \n", + "num_crimes | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "Horta | \n", + "4.0 | \n", + "9.0 | \n", + "3.0 | \n", + "11.0 | \n", + "7.0 | \n", + "3.0 | \n", + "5.0 | \n", + "3.0 | \n", + "2.0 | \n", + "26715.0 | \n", + "422.0 | \n", + "2.5 | \n", + "Horta-Guinardó | \n", + "1127.0 | \n", + "805.5 | \n", + "2.970 | \n", + "7871.0 | \n", + "
| 1 | \n", + "Navas | \n", + "2.0 | \n", + "3.0 | \n", + "NaN | \n", + "6.0 | \n", + "3.0 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "22129.0 | \n", + "984.0 | \n", + "2.5 | \n", + "Sant Andreu | \n", + "988.0 | \n", + "696.0 | \n", + "3.029 | \n", + "10657.0 | \n", + "
| 2 | \n", + "Pedralbes | \n", + "1.0 | \n", + "7.0 | \n", + "NaN | \n", + "8.0 | \n", + "41.0 | \n", + "NaN | \n", + "10.0 | \n", + "9.0 | \n", + "1.0 | \n", + "12076.0 | \n", + "147.0 | \n", + "2.9 | \n", + "Les Corts | \n", + "764.0 | \n", + "661.6 | \n", + "6.340 | \n", + "7444.0 | \n", + "
| 3 | \n", + "Sant Andreu | \n", + "3.0 | \n", + "22.0 | \n", + "5.0 | \n", + "22.0 | \n", + "17.0 | \n", + "5.0 | \n", + "2.0 | \n", + "5.0 | \n", + "3.0 | \n", + "57183.0 | \n", + "746.0 | \n", + "2.4 | \n", + "Sant Andreu | \n", + "1965.0 | \n", + "763.0 | \n", + "3.210 | \n", + "10657.0 | \n", + "
| 4 | \n", + "Sant Antoni | \n", + "9.0 | \n", + "8.0 | \n", + "4.0 | \n", + "11.0 | \n", + "11.0 | \n", + "1.0 | \n", + "5.0 | \n", + "7.0 | \n", + "1.0 | \n", + "38345.0 | \n", + "928.0 | \n", + "2.3 | \n", + "Eixample | \n", + "2490.0 | \n", + "737.3 | \n", + "4.591 | \n", + "46210.0 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " nbh bars children_places cinemas_theatres schools pre-schools \\\n",
+ "0 Horta 4.0 9.0 3.0 11.0 7.0 \n",
+ "1 Navas 2.0 3.0 NaN 6.0 3.0 \n",
+ "2 Pedralbes 1.0 7.0 NaN 8.0 41.0 \n",
+ "3 Sant Andreu 3.0 22.0 5.0 22.0 17.0 \n",
+ "4 Sant Antoni 9.0 8.0 4.0 11.0 11.0 \n",
+ "\n",
+ " hospitals libraries_theatres park_gardens sport_centers population \\\n",
+ "0 3.0 5.0 3.0 2.0 26715.0 \n",
+ "1 NaN NaN NaN NaN 22129.0 \n",
+ "2 NaN 10.0 9.0 1.0 12076.0 \n",
+ "3 5.0 2.0 5.0 3.0 57183.0 \n",
+ "4 1.0 5.0 7.0 1.0 38345.0 \n",
+ "\n",
+ " net_density(hab/ha) avg_occupation dist num_immi mort_rate \\\n",
+ "0 422.0 2.5 Horta-Guinardó 1127.0 805.5 \n",
+ "1 984.0 2.5 Sant Andreu 988.0 696.0 \n",
+ "2 147.0 2.9 Les Corts 764.0 661.6 \n",
+ "3 746.0 2.4 Sant Andreu 1965.0 763.0 \n",
+ "4 928.0 2.3 Eixample 2490.0 737.3 \n",
+ "\n",
+ " rent_price num_crimes \n",
+ "0 2.970 7871.0 \n",
+ "1 3.029 10657.0 \n",
+ "2 6.340 7444.0 \n",
+ "3 3.210 10657.0 \n",
+ "4 4.591 46210.0 "
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "| \n", + " | nbh | \n", + "bars | \n", + "children_places | \n", + "cinemas_theatres | \n", + "schools | \n", + "pre-schools | \n", + "hospitals | \n", + "libraries_theatres | \n", + "park_gardens | \n", + "sport_centers | \n", + "population | \n", + "net_density(hab/ha) | \n", + "avg_occupation | \n", + "dist | \n", + "num_immi | \n", + "mort_rate | \n", + "rent_price | \n", + "num_crimes | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "Horta | \n", + "4.0 | \n", + "9.0 | \n", + "3.0 | \n", + "11.0 | \n", + "7.0 | \n", + "3.0 | \n", + "5.0 | \n", + "3.0 | \n", + "2.0 | \n", + "26715.0 | \n", + "422.0 | \n", + "2.5 | \n", + "Horta-Guinardó | \n", + "1127.0 | \n", + "805.5 | \n", + "2.970 | \n", + "7871.0 | \n", + "
| 1 | \n", + "Navas | \n", + "2.0 | \n", + "3.0 | \n", + "NaN | \n", + "6.0 | \n", + "3.0 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "22129.0 | \n", + "984.0 | \n", + "2.5 | \n", + "Sant Andreu | \n", + "988.0 | \n", + "696.0 | \n", + "3.029 | \n", + "10657.0 | \n", + "
| 2 | \n", + "Pedralbes | \n", + "1.0 | \n", + "7.0 | \n", + "NaN | \n", + "8.0 | \n", + "41.0 | \n", + "NaN | \n", + "10.0 | \n", + "9.0 | \n", + "1.0 | \n", + "12076.0 | \n", + "147.0 | \n", + "2.9 | \n", + "Les Corts | \n", + "764.0 | \n", + "661.6 | \n", + "6.340 | \n", + "7444.0 | \n", + "
| 3 | \n", + "Sant Andreu | \n", + "3.0 | \n", + "22.0 | \n", + "5.0 | \n", + "22.0 | \n", + "17.0 | \n", + "5.0 | \n", + "2.0 | \n", + "5.0 | \n", + "3.0 | \n", + "57183.0 | \n", + "746.0 | \n", + "2.4 | \n", + "Sant Andreu | \n", + "1965.0 | \n", + "763.0 | \n", + "3.210 | \n", + "10657.0 | \n", + "
| 4 | \n", + "Sant Antoni | \n", + "9.0 | \n", + "8.0 | \n", + "4.0 | \n", + "11.0 | \n", + "11.0 | \n", + "1.0 | \n", + "5.0 | \n", + "7.0 | \n", + "1.0 | \n", + "38345.0 | \n", + "928.0 | \n", + "2.3 | \n", + "Eixample | \n", + "2490.0 | \n", + "737.3 | \n", + "4.591 | \n", + "46210.0 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " bars children_places cinemas_theatres schools pre-schools \\\n",
+ "count 30.000000 72.000000 39.000000 71.000000 71.000000 \n",
+ "mean 11.966667 7.083333 4.076923 8.802817 10.352113 \n",
+ "std 13.857609 4.369388 4.515381 5.866661 9.774747 \n",
+ "min 1.000000 1.000000 1.000000 1.000000 1.000000 \n",
+ "25% 2.000000 4.000000 1.000000 4.000000 4.500000 \n",
+ "50% 5.000000 7.000000 3.000000 8.000000 8.000000 \n",
+ "75% 12.750000 9.000000 4.500000 12.000000 13.000000 \n",
+ "max 45.000000 22.000000 19.000000 25.000000 58.000000 \n",
+ "\n",
+ " hospitals libraries_theatres park_gardens sport_centers \\\n",
+ "count 53.000000 55.000000 63.000000 48.000000 \n",
+ "mean 2.358491 4.763636 4.682540 1.812500 \n",
+ "std 1.710762 6.119077 4.234366 1.142156 \n",
+ "min 1.000000 1.000000 1.000000 1.000000 \n",
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+ "50% 2.000000 2.000000 3.000000 1.000000 \n",
+ "75% 3.000000 5.000000 7.000000 2.000000 \n",
+ "max 8.000000 28.000000 24.000000 5.000000 \n",
+ "\n",
+ " population net_density(hab/ha) avg_occupation num_immi \\\n",
+ "count 73.000000 73.000000 73.000000 73.000000 \n",
+ "mean 22202.863014 682.643836 2.519178 1333.219178 \n",
+ "std 14622.058303 290.236128 0.188309 1088.180582 \n",
+ "min 610.000000 32.000000 2.200000 32.000000 \n",
+ "25% 10401.000000 499.000000 2.400000 603.000000 \n",
+ "50% 20487.000000 710.000000 2.500000 1073.000000 \n",
+ "75% 30584.000000 888.000000 2.500000 1791.000000 \n",
+ "max 58180.000000 1490.000000 3.000000 5400.000000 \n",
+ "\n",
+ " mort_rate rent_price num_crimes \n",
+ "count 71.000000 71.000000 73.000000 \n",
+ "mean 835.756338 3.600028 16848.986301 \n",
+ "std 141.164841 1.193082 12712.509278 \n",
+ "min 641.500000 1.498000 7444.000000 \n",
+ "25% 751.350000 2.766500 7871.000000 \n",
+ "50% 795.400000 3.296000 9252.000000 \n",
+ "75% 868.750000 4.365000 22417.000000 \n",
+ "max 1479.200000 6.402000 46210.000000 "
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.describe()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | bars | \n", + "children_places | \n", + "cinemas_theatres | \n", + "schools | \n", + "pre-schools | \n", + "hospitals | \n", + "libraries_theatres | \n", + "park_gardens | \n", + "sport_centers | \n", + "population | \n", + "net_density(hab/ha) | \n", + "avg_occupation | \n", + "num_immi | \n", + "mort_rate | \n", + "rent_price | \n", + "num_crimes | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | \n", + "30.000000 | \n", + "72.000000 | \n", + "39.000000 | \n", + "71.000000 | \n", + "71.000000 | \n", + "53.000000 | \n", + "55.000000 | \n", + "63.000000 | \n", + "48.000000 | \n", + "73.000000 | \n", + "73.000000 | \n", + "73.000000 | \n", + "73.000000 | \n", + "71.000000 | \n", + "71.000000 | \n", + "73.000000 | \n", + "
| mean | \n", + "11.966667 | \n", + "7.083333 | \n", + "4.076923 | \n", + "8.802817 | \n", + "10.352113 | \n", + "2.358491 | \n", + "4.763636 | \n", + "4.682540 | \n", + "1.812500 | \n", + "22202.863014 | \n", + "682.643836 | \n", + "2.519178 | \n", + "1333.219178 | \n", + "835.756338 | \n", + "3.600028 | \n", + "16848.986301 | \n", + "
| std | \n", + "13.857609 | \n", + "4.369388 | \n", + "4.515381 | \n", + "5.866661 | \n", + "9.774747 | \n", + "1.710762 | \n", + "6.119077 | \n", + "4.234366 | \n", + "1.142156 | \n", + "14622.058303 | \n", + "290.236128 | \n", + "0.188309 | \n", + "1088.180582 | \n", + "141.164841 | \n", + "1.193082 | \n", + "12712.509278 | \n", + "
| min | \n", + "1.000000 | \n", + "1.000000 | \n", + "1.000000 | \n", + "1.000000 | \n", + "1.000000 | \n", + "1.000000 | \n", + "1.000000 | \n", + "1.000000 | \n", + "1.000000 | \n", + "610.000000 | \n", + "32.000000 | \n", + "2.200000 | \n", + "32.000000 | \n", + "641.500000 | \n", + "1.498000 | \n", + "7444.000000 | \n", + "
| 25% | \n", + "2.000000 | \n", + "4.000000 | \n", + "1.000000 | \n", + "4.000000 | \n", + "4.500000 | \n", + "1.000000 | \n", + "1.000000 | \n", + "2.000000 | \n", + "1.000000 | \n", + "10401.000000 | \n", + "499.000000 | \n", + "2.400000 | \n", + "603.000000 | \n", + "751.350000 | \n", + "2.766500 | \n", + "7871.000000 | \n", + "
| 50% | \n", + "5.000000 | \n", + "7.000000 | \n", + "3.000000 | \n", + "8.000000 | \n", + "8.000000 | \n", + "2.000000 | \n", + "2.000000 | \n", + "3.000000 | \n", + "1.000000 | \n", + "20487.000000 | \n", + "710.000000 | \n", + "2.500000 | \n", + "1073.000000 | \n", + "795.400000 | \n", + "3.296000 | \n", + "9252.000000 | \n", + "
| 75% | \n", + "12.750000 | \n", + "9.000000 | \n", + "4.500000 | \n", + "12.000000 | \n", + "13.000000 | \n", + "3.000000 | \n", + "5.000000 | \n", + "7.000000 | \n", + "2.000000 | \n", + "30584.000000 | \n", + "888.000000 | \n", + "2.500000 | \n", + "1791.000000 | \n", + "868.750000 | \n", + "4.365000 | \n", + "22417.000000 | \n", + "
| max | \n", + "45.000000 | \n", + "22.000000 | \n", + "19.000000 | \n", + "25.000000 | \n", + "58.000000 | \n", + "8.000000 | \n", + "28.000000 | \n", + "24.000000 | \n", + "5.000000 | \n", + "58180.000000 | \n", + "1490.000000 | \n", + "3.000000 | \n", + "5400.000000 | \n", + "1479.200000 | \n", + "6.402000 | \n", + "46210.000000 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " bars children_places cinemas_theatres schools \\\n",
+ "bars 1.000000 -0.102707 0.709313 0.193809 \n",
+ "children_places -0.102707 1.000000 -0.032304 0.560562 \n",
+ "cinemas_theatres 0.709313 -0.032304 1.000000 0.318014 \n",
+ "schools 0.193809 0.560562 0.318014 1.000000 \n",
+ "pre-schools 0.171637 0.269314 0.303047 0.533480 \n",
+ "hospitals 0.057727 0.214586 -0.054959 0.452514 \n",
+ "libraries_theatres 0.771140 0.008574 0.633089 0.259619 \n",
+ "park_gardens 0.055703 0.433556 0.276051 0.528749 \n",
+ "sport_centers 0.180134 0.328197 0.321942 0.491636 \n",
+ "population 0.259253 0.600762 0.404353 0.809047 \n",
+ "net_density(hab/ha) 0.052216 0.217517 0.037718 0.100823 \n",
+ "avg_occupation -0.167796 -0.033285 -0.151583 -0.245823 \n",
+ "num_immi 0.708781 0.393416 0.663233 0.603175 \n",
+ "mort_rate 0.506183 -0.309516 0.129834 -0.344282 \n",
+ "rent_price 0.261483 -0.018694 0.194731 0.315475 \n",
+ "num_crimes 0.520150 0.106860 0.236951 0.140247 \n",
+ "\n",
+ " pre-schools hospitals libraries_theatres park_gardens \\\n",
+ "bars 0.171637 0.057727 0.771140 0.055703 \n",
+ "children_places 0.269314 0.214586 0.008574 0.433556 \n",
+ "cinemas_theatres 0.303047 -0.054959 0.633089 0.276051 \n",
+ "schools 0.533480 0.452514 0.259619 0.528749 \n",
+ "pre-schools 1.000000 0.385839 0.571432 0.545387 \n",
+ "hospitals 0.385839 1.000000 0.037207 0.262427 \n",
+ "libraries_theatres 0.571432 0.037207 1.000000 0.397716 \n",
+ "park_gardens 0.545387 0.262427 0.397716 1.000000 \n",
+ "sport_centers 0.079800 0.202860 0.003136 0.391060 \n",
+ "population 0.403899 0.306432 0.213359 0.566760 \n",
+ "net_density(hab/ha) -0.065525 -0.385390 -0.066193 -0.037354 \n",
+ "avg_occupation -0.010794 0.070037 -0.043643 0.141457 \n",
+ "num_immi 0.407366 0.166799 0.569822 0.495059 \n",
+ "mort_rate -0.287845 -0.088532 0.145671 -0.328160 \n",
+ "rent_price 0.517587 0.374856 0.394171 0.429743 \n",
+ "num_crimes 0.163265 -0.145565 0.530310 0.241821 \n",
+ "\n",
+ " sport_centers population net_density(hab/ha) \\\n",
+ "bars 0.180134 0.259253 0.052216 \n",
+ "children_places 0.328197 0.600762 0.217517 \n",
+ "cinemas_theatres 0.321942 0.404353 0.037718 \n",
+ "schools 0.491636 0.809047 0.100823 \n",
+ "pre-schools 0.079800 0.403899 -0.065525 \n",
+ "hospitals 0.202860 0.306432 -0.385390 \n",
+ "libraries_theatres 0.003136 0.213359 -0.066193 \n",
+ "park_gardens 0.391060 0.566760 -0.037354 \n",
+ "sport_centers 1.000000 0.378546 0.062624 \n",
+ "population 0.378546 1.000000 0.398740 \n",
+ "net_density(hab/ha) 0.062624 0.398740 1.000000 \n",
+ "avg_occupation -0.241096 -0.300605 -0.357094 \n",
+ "num_immi 0.266286 0.869310 0.360960 \n",
+ "mort_rate -0.003124 -0.405142 -0.304675 \n",
+ "rent_price 0.154026 0.331823 -0.239659 \n",
+ "num_crimes -0.056487 0.426884 0.301385 \n",
+ "\n",
+ " avg_occupation num_immi mort_rate rent_price \\\n",
+ "bars -0.167796 0.708781 0.506183 0.261483 \n",
+ "children_places -0.033285 0.393416 -0.309516 -0.018694 \n",
+ "cinemas_theatres -0.151583 0.663233 0.129834 0.194731 \n",
+ "schools -0.245823 0.603175 -0.344282 0.315475 \n",
+ "pre-schools -0.010794 0.407366 -0.287845 0.517587 \n",
+ "hospitals 0.070037 0.166799 -0.088532 0.374856 \n",
+ "libraries_theatres -0.043643 0.569822 0.145671 0.394171 \n",
+ "park_gardens 0.141457 0.495059 -0.328160 0.429743 \n",
+ "sport_centers -0.241096 0.266286 -0.003124 0.154026 \n",
+ "population -0.300605 0.869310 -0.405142 0.331823 \n",
+ "net_density(hab/ha) -0.357094 0.360960 -0.304675 -0.239659 \n",
+ "avg_occupation 1.000000 -0.253046 0.154147 -0.017004 \n",
+ "num_immi -0.253046 1.000000 -0.180493 0.409336 \n",
+ "mort_rate 0.154147 -0.180493 1.000000 -0.145057 \n",
+ "rent_price -0.017004 0.409336 -0.145057 1.000000 \n",
+ "num_crimes -0.223715 0.678115 0.047283 0.424728 \n",
+ "\n",
+ " num_crimes \n",
+ "bars 0.520150 \n",
+ "children_places 0.106860 \n",
+ "cinemas_theatres 0.236951 \n",
+ "schools 0.140247 \n",
+ "pre-schools 0.163265 \n",
+ "hospitals -0.145565 \n",
+ "libraries_theatres 0.530310 \n",
+ "park_gardens 0.241821 \n",
+ "sport_centers -0.056487 \n",
+ "population 0.426884 \n",
+ "net_density(hab/ha) 0.301385 \n",
+ "avg_occupation -0.223715 \n",
+ "num_immi 0.678115 \n",
+ "mort_rate 0.047283 \n",
+ "rent_price 0.424728 \n",
+ "num_crimes 1.000000 "
+ ]
+ },
+ "execution_count": 17,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.corr()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "bars 11.966667\n",
+ "children_places 7.083333\n",
+ "cinemas_theatres 4.076923\n",
+ "schools 8.802817\n",
+ "pre-schools 10.352113\n",
+ "hospitals 2.358491\n",
+ "libraries_theatres 4.763636\n",
+ "park_gardens 4.682540\n",
+ "sport_centers 1.812500\n",
+ "population 22202.863014\n",
+ "net_density(hab/ha) 682.643836\n",
+ "avg_occupation 2.519178\n",
+ "num_immi 1333.219178\n",
+ "mort_rate 835.756338\n",
+ "rent_price 3.600028\n",
+ "num_crimes 16848.986301\n",
+ "dtype: float64"
+ ]
+ },
+ "execution_count": 20,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.mean()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Adding new columns"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | bars | \n", + "children_places | \n", + "cinemas_theatres | \n", + "schools | \n", + "pre-schools | \n", + "hospitals | \n", + "libraries_theatres | \n", + "park_gardens | \n", + "sport_centers | \n", + "population | \n", + "net_density(hab/ha) | \n", + "avg_occupation | \n", + "num_immi | \n", + "mort_rate | \n", + "rent_price | \n", + "num_crimes | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| bars | \n", + "1.000000 | \n", + "-0.102707 | \n", + "0.709313 | \n", + "0.193809 | \n", + "0.171637 | \n", + "0.057727 | \n", + "0.771140 | \n", + "0.055703 | \n", + "0.180134 | \n", + "0.259253 | \n", + "0.052216 | \n", + "-0.167796 | \n", + "0.708781 | \n", + "0.506183 | \n", + "0.261483 | \n", + "0.520150 | \n", + "
| children_places | \n", + "-0.102707 | \n", + "1.000000 | \n", + "-0.032304 | \n", + "0.560562 | \n", + "0.269314 | \n", + "0.214586 | \n", + "0.008574 | \n", + "0.433556 | \n", + "0.328197 | \n", + "0.600762 | \n", + "0.217517 | \n", + "-0.033285 | \n", + "0.393416 | \n", + "-0.309516 | \n", + "-0.018694 | \n", + "0.106860 | \n", + "
| cinemas_theatres | \n", + "0.709313 | \n", + "-0.032304 | \n", + "1.000000 | \n", + "0.318014 | \n", + "0.303047 | \n", + "-0.054959 | \n", + "0.633089 | \n", + "0.276051 | \n", + "0.321942 | \n", + "0.404353 | \n", + "0.037718 | \n", + "-0.151583 | \n", + "0.663233 | \n", + "0.129834 | \n", + "0.194731 | \n", + "0.236951 | \n", + "
| schools | \n", + "0.193809 | \n", + "0.560562 | \n", + "0.318014 | \n", + "1.000000 | \n", + "0.533480 | \n", + "0.452514 | \n", + "0.259619 | \n", + "0.528749 | \n", + "0.491636 | \n", + "0.809047 | \n", + "0.100823 | \n", + "-0.245823 | \n", + "0.603175 | \n", + "-0.344282 | \n", + "0.315475 | \n", + "0.140247 | \n", + "
| pre-schools | \n", + "0.171637 | \n", + "0.269314 | \n", + "0.303047 | \n", + "0.533480 | \n", + "1.000000 | \n", + "0.385839 | \n", + "0.571432 | \n", + "0.545387 | \n", + "0.079800 | \n", + "0.403899 | \n", + "-0.065525 | \n", + "-0.010794 | \n", + "0.407366 | \n", + "-0.287845 | \n", + "0.517587 | \n", + "0.163265 | \n", + "
| hospitals | \n", + "0.057727 | \n", + "0.214586 | \n", + "-0.054959 | \n", + "0.452514 | \n", + "0.385839 | \n", + "1.000000 | \n", + "0.037207 | \n", + "0.262427 | \n", + "0.202860 | \n", + "0.306432 | \n", + "-0.385390 | \n", + "0.070037 | \n", + "0.166799 | \n", + "-0.088532 | \n", + "0.374856 | \n", + "-0.145565 | \n", + "
| libraries_theatres | \n", + "0.771140 | \n", + "0.008574 | \n", + "0.633089 | \n", + "0.259619 | \n", + "0.571432 | \n", + "0.037207 | \n", + "1.000000 | \n", + "0.397716 | \n", + "0.003136 | \n", + "0.213359 | \n", + "-0.066193 | \n", + "-0.043643 | \n", + "0.569822 | \n", + "0.145671 | \n", + "0.394171 | \n", + "0.530310 | \n", + "
| park_gardens | \n", + "0.055703 | \n", + "0.433556 | \n", + "0.276051 | \n", + "0.528749 | \n", + "0.545387 | \n", + "0.262427 | \n", + "0.397716 | \n", + "1.000000 | \n", + "0.391060 | \n", + "0.566760 | \n", + "-0.037354 | \n", + "0.141457 | \n", + "0.495059 | \n", + "-0.328160 | \n", + "0.429743 | \n", + "0.241821 | \n", + "
| sport_centers | \n", + "0.180134 | \n", + "0.328197 | \n", + "0.321942 | \n", + "0.491636 | \n", + "0.079800 | \n", + "0.202860 | \n", + "0.003136 | \n", + "0.391060 | \n", + "1.000000 | \n", + "0.378546 | \n", + "0.062624 | \n", + "-0.241096 | \n", + "0.266286 | \n", + "-0.003124 | \n", + "0.154026 | \n", + "-0.056487 | \n", + "
| population | \n", + "0.259253 | \n", + "0.600762 | \n", + "0.404353 | \n", + "0.809047 | \n", + "0.403899 | \n", + "0.306432 | \n", + "0.213359 | \n", + "0.566760 | \n", + "0.378546 | \n", + "1.000000 | \n", + "0.398740 | \n", + "-0.300605 | \n", + "0.869310 | \n", + "-0.405142 | \n", + "0.331823 | \n", + "0.426884 | \n", + "
| net_density(hab/ha) | \n", + "0.052216 | \n", + "0.217517 | \n", + "0.037718 | \n", + "0.100823 | \n", + "-0.065525 | \n", + "-0.385390 | \n", + "-0.066193 | \n", + "-0.037354 | \n", + "0.062624 | \n", + "0.398740 | \n", + "1.000000 | \n", + "-0.357094 | \n", + "0.360960 | \n", + "-0.304675 | \n", + "-0.239659 | \n", + "0.301385 | \n", + "
| avg_occupation | \n", + "-0.167796 | \n", + "-0.033285 | \n", + "-0.151583 | \n", + "-0.245823 | \n", + "-0.010794 | \n", + "0.070037 | \n", + "-0.043643 | \n", + "0.141457 | \n", + "-0.241096 | \n", + "-0.300605 | \n", + "-0.357094 | \n", + "1.000000 | \n", + "-0.253046 | \n", + "0.154147 | \n", + "-0.017004 | \n", + "-0.223715 | \n", + "
| num_immi | \n", + "0.708781 | \n", + "0.393416 | \n", + "0.663233 | \n", + "0.603175 | \n", + "0.407366 | \n", + "0.166799 | \n", + "0.569822 | \n", + "0.495059 | \n", + "0.266286 | \n", + "0.869310 | \n", + "0.360960 | \n", + "-0.253046 | \n", + "1.000000 | \n", + "-0.180493 | \n", + "0.409336 | \n", + "0.678115 | \n", + "
| mort_rate | \n", + "0.506183 | \n", + "-0.309516 | \n", + "0.129834 | \n", + "-0.344282 | \n", + "-0.287845 | \n", + "-0.088532 | \n", + "0.145671 | \n", + "-0.328160 | \n", + "-0.003124 | \n", + "-0.405142 | \n", + "-0.304675 | \n", + "0.154147 | \n", + "-0.180493 | \n", + "1.000000 | \n", + "-0.145057 | \n", + "0.047283 | \n", + "
| rent_price | \n", + "0.261483 | \n", + "-0.018694 | \n", + "0.194731 | \n", + "0.315475 | \n", + "0.517587 | \n", + "0.374856 | \n", + "0.394171 | \n", + "0.429743 | \n", + "0.154026 | \n", + "0.331823 | \n", + "-0.239659 | \n", + "-0.017004 | \n", + "0.409336 | \n", + "-0.145057 | \n", + "1.000000 | \n", + "0.424728 | \n", + "
| num_crimes | \n", + "0.520150 | \n", + "0.106860 | \n", + "0.236951 | \n", + "0.140247 | \n", + "0.163265 | \n", + "-0.145565 | \n", + "0.530310 | \n", + "0.241821 | \n", + "-0.056487 | \n", + "0.426884 | \n", + "0.301385 | \n", + "-0.223715 | \n", + "0.678115 | \n", + "0.047283 | \n", + "0.424728 | \n", + "1.000000 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " nbh bars children_places cinemas_theatres schools \\\n",
+ "0 Horta 4.0 9.0 3.0 11.0 \n",
+ "1 Navas 2.0 3.0 NaN 6.0 \n",
+ "2 Pedralbes 1.0 7.0 NaN 8.0 \n",
+ "3 Sant Andreu 3.0 22.0 5.0 22.0 \n",
+ "4 Sant Antoni 9.0 8.0 4.0 11.0 \n",
+ ".. ... ... ... ... ... \n",
+ "70 les Roquetes NaN 6.0 NaN 4.0 \n",
+ "71 les Tres Torres NaN 5.0 2.0 2.0 \n",
+ "72 la Clota NaN NaN NaN NaN \n",
+ "73 la Dreta de l'Eixample NaN NaN NaN NaN \n",
+ "74 el Poble Sec NaN NaN NaN NaN \n",
+ "\n",
+ " pre-schools hospitals libraries_theatres park_gardens sport_centers \\\n",
+ "0 7.0 3.0 5.0 3.0 2.0 \n",
+ "1 3.0 NaN NaN NaN NaN \n",
+ "2 41.0 NaN 10.0 9.0 1.0 \n",
+ "3 17.0 5.0 2.0 5.0 3.0 \n",
+ "4 11.0 1.0 5.0 7.0 1.0 \n",
+ ".. ... ... ... ... ... \n",
+ "70 2.0 1.0 1.0 2.0 NaN \n",
+ "71 4.0 5.0 1.0 3.0 NaN \n",
+ "72 NaN NaN NaN 2.0 NaN \n",
+ "73 NaN NaN NaN NaN NaN \n",
+ "74 NaN NaN NaN NaN NaN \n",
+ "\n",
+ " ... num_crimes children_places_pop cinemas_theatres_pop schools_pop \\\n",
+ "0 ... 7871.0 0.336889 0.112296 0.411754 \n",
+ "1 ... 10657.0 0.135569 NaN 0.271137 \n",
+ "2 ... 7444.0 0.579662 NaN 0.662471 \n",
+ "3 ... 10657.0 0.384730 0.087439 0.384730 \n",
+ "4 ... 46210.0 0.208632 0.104316 0.286869 \n",
+ ".. ... ... ... ... ... \n",
+ "70 ... 8627.0 0.384862 NaN 0.256575 \n",
+ "71 ... 9252.0 0.299994 0.119998 0.119998 \n",
+ "72 ... 7871.0 NaN NaN NaN \n",
+ "73 ... 46210.0 NaN NaN NaN \n",
+ "74 ... 20219.0 NaN NaN NaN \n",
+ "\n",
+ " pre-schools_pop hospitals_pop libraries_theatres_pop park_gardens_pop \\\n",
+ "0 0.262025 0.112296 0.187161 0.112296 \n",
+ "1 0.135569 NaN NaN NaN \n",
+ "2 3.395164 NaN 0.828089 0.745280 \n",
+ "3 0.297291 0.087439 0.034975 0.087439 \n",
+ "4 0.286869 0.026079 0.130395 0.182553 \n",
+ ".. ... ... ... ... \n",
+ "70 0.128287 0.064144 0.064144 0.128287 \n",
+ "71 0.239995 0.299994 0.059999 0.179996 \n",
+ "72 NaN NaN NaN 3.278689 \n",
+ "73 NaN NaN NaN NaN \n",
+ "74 NaN NaN NaN NaN \n",
+ "\n",
+ " sport_centers_pop facilities_pop \n",
+ "0 0.074864 0.168445 \n",
+ "1 NaN NaN \n",
+ "2 0.082809 NaN \n",
+ "3 0.052463 0.139902 \n",
+ "4 0.026079 0.120615 \n",
+ ".. ... ... \n",
+ "70 NaN NaN \n",
+ "71 NaN NaN \n",
+ "72 NaN NaN \n",
+ "73 NaN NaN \n",
+ "74 NaN NaN \n",
+ "\n",
+ "[75 rows x 27 columns]"
+ ]
+ },
+ "execution_count": 21,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Facilities per 10,000 (?) habitants\n",
+ "\n",
+ "df[\"children_places_pop\"] = df[\"children_places\"]/df[\"population\"]*1000\n",
+ "df[\"cinemas_theatres_pop\"] = df[\"cinemas_theatres\"]/df[\"population\"]*1000\n",
+ "df[\"schools_pop\"] = df[\"schools\"]/df[\"population\"]*1000\n",
+ "df[\"pre-schools_pop\"] = df[\"pre-schools\"]/df[\"population\"]*1000\n",
+ "df[\"hospitals_pop\"] = df[\"hospitals\"]/df[\"population\"]*1000\n",
+ "df[\"libraries_theatres_pop\"] = df[\"libraries_theatres\"]/df[\"population\"]*1000\n",
+ "df[\"park_gardens_pop\"] = df[\"park_gardens\"]/df[\"population\"]*1000\n",
+ "df[\"sport_centers_pop\"] = df[\"sport_centers\"]/df[\"population\"]*1000\n",
+ "df[\"facilities_pop\"] = (df[\"children_places_pop\"] +\n",
+ " df[\"cinemas_theatres_pop\"]+\n",
+ " df[\"schools_pop\"]+\n",
+ " df[\"hospitals_pop\"]+\n",
+ " df[\"libraries_theatres_pop\"]+\n",
+ " df[\"park_gardens_pop\"]+\n",
+ " df[\"sport_centers_pop\"])/8\n",
+ "\n",
+ "df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | nbh | \n", + "bars | \n", + "children_places | \n", + "cinemas_theatres | \n", + "schools | \n", + "pre-schools | \n", + "hospitals | \n", + "libraries_theatres | \n", + "park_gardens | \n", + "sport_centers | \n", + "... | \n", + "num_crimes | \n", + "children_places_pop | \n", + "cinemas_theatres_pop | \n", + "schools_pop | \n", + "pre-schools_pop | \n", + "hospitals_pop | \n", + "libraries_theatres_pop | \n", + "park_gardens_pop | \n", + "sport_centers_pop | \n", + "facilities_pop | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "Horta | \n", + "4.0 | \n", + "9.0 | \n", + "3.0 | \n", + "11.0 | \n", + "7.0 | \n", + "3.0 | \n", + "5.0 | \n", + "3.0 | \n", + "2.0 | \n", + "... | \n", + "7871.0 | \n", + "0.336889 | \n", + "0.112296 | \n", + "0.411754 | \n", + "0.262025 | \n", + "0.112296 | \n", + "0.187161 | \n", + "0.112296 | \n", + "0.074864 | \n", + "0.168445 | \n", + "
| 1 | \n", + "Navas | \n", + "2.0 | \n", + "3.0 | \n", + "NaN | \n", + "6.0 | \n", + "3.0 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "... | \n", + "10657.0 | \n", + "0.135569 | \n", + "NaN | \n", + "0.271137 | \n", + "0.135569 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "
| 2 | \n", + "Pedralbes | \n", + "1.0 | \n", + "7.0 | \n", + "NaN | \n", + "8.0 | \n", + "41.0 | \n", + "NaN | \n", + "10.0 | \n", + "9.0 | \n", + "1.0 | \n", + "... | \n", + "7444.0 | \n", + "0.579662 | \n", + "NaN | \n", + "0.662471 | \n", + "3.395164 | \n", + "NaN | \n", + "0.828089 | \n", + "0.745280 | \n", + "0.082809 | \n", + "NaN | \n", + "
| 3 | \n", + "Sant Andreu | \n", + "3.0 | \n", + "22.0 | \n", + "5.0 | \n", + "22.0 | \n", + "17.0 | \n", + "5.0 | \n", + "2.0 | \n", + "5.0 | \n", + "3.0 | \n", + "... | \n", + "10657.0 | \n", + "0.384730 | \n", + "0.087439 | \n", + "0.384730 | \n", + "0.297291 | \n", + "0.087439 | \n", + "0.034975 | \n", + "0.087439 | \n", + "0.052463 | \n", + "0.139902 | \n", + "
| 4 | \n", + "Sant Antoni | \n", + "9.0 | \n", + "8.0 | \n", + "4.0 | \n", + "11.0 | \n", + "11.0 | \n", + "1.0 | \n", + "5.0 | \n", + "7.0 | \n", + "1.0 | \n", + "... | \n", + "46210.0 | \n", + "0.208632 | \n", + "0.104316 | \n", + "0.286869 | \n", + "0.286869 | \n", + "0.026079 | \n", + "0.130395 | \n", + "0.182553 | \n", + "0.026079 | \n", + "0.120615 | \n", + "
| ... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "
| 70 | \n", + "les Roquetes | \n", + "NaN | \n", + "6.0 | \n", + "NaN | \n", + "4.0 | \n", + "2.0 | \n", + "1.0 | \n", + "1.0 | \n", + "2.0 | \n", + "NaN | \n", + "... | \n", + "8627.0 | \n", + "0.384862 | \n", + "NaN | \n", + "0.256575 | \n", + "0.128287 | \n", + "0.064144 | \n", + "0.064144 | \n", + "0.128287 | \n", + "NaN | \n", + "NaN | \n", + "
| 71 | \n", + "les Tres Torres | \n", + "NaN | \n", + "5.0 | \n", + "2.0 | \n", + "2.0 | \n", + "4.0 | \n", + "5.0 | \n", + "1.0 | \n", + "3.0 | \n", + "NaN | \n", + "... | \n", + "9252.0 | \n", + "0.299994 | \n", + "0.119998 | \n", + "0.119998 | \n", + "0.239995 | \n", + "0.299994 | \n", + "0.059999 | \n", + "0.179996 | \n", + "NaN | \n", + "NaN | \n", + "
| 72 | \n", + "la Clota | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "2.0 | \n", + "NaN | \n", + "... | \n", + "7871.0 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "3.278689 | \n", + "NaN | \n", + "NaN | \n", + "
| 73 | \n", + "la Dreta de l'Eixample | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "... | \n", + "46210.0 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "
| 74 | \n", + "el Poble Sec | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "... | \n", + "20219.0 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "
75 rows × 27 columns
\n", + "\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " num_crimes rent_price mort_rate\n",
+ "0 7871.0 2.970 805.5\n",
+ "1 10657.0 3.029 696.0\n",
+ "2 7444.0 6.340 661.6\n",
+ "3 10657.0 3.210 763.0\n",
+ "4 46210.0 4.591 737.3\n",
+ ".. ... ... ...\n",
+ "70 8627.0 1.885 869.4\n",
+ "71 9252.0 5.689 708.5\n",
+ "72 7871.0 2.920 1033.9\n",
+ "73 46210.0 6.332 897.2\n",
+ "74 20219.0 NaN NaN\n",
+ "\n",
+ "[75 rows x 3 columns]"
+ ]
+ },
+ "execution_count": 22,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df[[\"num_crimes\", \"rent_price\", \"mort_rate\"]].astype(float)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df.to_csv(\"../datasets/Data_filtered/complete_dataset.csv\")"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
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+ "language_info": {
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+ "version": "3.8.2"
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+ "number_sections": true,
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+ "skip_h1_title": false,
+ "title_cell": "Table of Contents",
+ "title_sidebar": "Contents",
+ "toc_cell": false,
+ "toc_position": {},
+ "toc_section_display": true,
+ "toc_window_display": false
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
| \n", + " | num_crimes | \n", + "rent_price | \n", + "mort_rate | \n", + "
|---|---|---|---|
| 0 | \n", + "7871.0 | \n", + "2.970 | \n", + "805.5 | \n", + "
| 1 | \n", + "10657.0 | \n", + "3.029 | \n", + "696.0 | \n", + "
| 2 | \n", + "7444.0 | \n", + "6.340 | \n", + "661.6 | \n", + "
| 3 | \n", + "10657.0 | \n", + "3.210 | \n", + "763.0 | \n", + "
| 4 | \n", + "46210.0 | \n", + "4.591 | \n", + "737.3 | \n", + "
| ... | \n", + "... | \n", + "... | \n", + "... | \n", + "
| 70 | \n", + "8627.0 | \n", + "1.885 | \n", + "869.4 | \n", + "
| 71 | \n", + "9252.0 | \n", + "5.689 | \n", + "708.5 | \n", + "
| 72 | \n", + "7871.0 | \n", + "2.920 | \n", + "1033.9 | \n", + "
| 73 | \n", + "46210.0 | \n", + "6.332 | \n", + "897.2 | \n", + "
| 74 | \n", + "20219.0 | \n", + "NaN | \n", + "NaN | \n", + "
75 rows × 3 columns
\n", + "