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[Montserrat Labrada] List Comprehension Lab #80

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179 changes: 152 additions & 27 deletions lab-list-comprehensions/your-code/main.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -29,10 +29,21 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": []
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50]\n"
]
}
],
"source": [
"list=[i for i in range (1,51)]\n",
"print(list)"
]
},
{
"cell_type": "markdown",
Expand All @@ -43,10 +54,21 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": []
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40, 42, 44, 46, 48, 50, 52, 54, 56, 58, 60, 62, 64, 66, 68, 70, 72, 74, 76, 78, 80, 82, 84, 86, 88, 90, 92, 94, 96, 98, 100, 102, 104, 106, 108, 110, 112, 114, 116, 118, 120, 122, 124, 126, 128, 130, 132, 134, 136, 138, 140, 142, 144, 146, 148, 150, 152, 154, 156, 158, 160, 162, 164, 166, 168, 170, 172, 174, 176, 178, 180, 182, 184, 186, 188, 190, 192, 194, 196, 198, 200]\n"
]
}
],
"source": [
"list_even=[i for i in range (2, 201) if i%2==0]\n",
"print(list_even)"
]
},
{
"cell_type": "markdown",
Expand Down Expand Up @@ -75,10 +97,34 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": []
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[0.84062117, 0.48006452, 0.7876326, 0.77109654], [0.44409793, 0.09014516, 0.81835917, 0.87645456], [0.7066597, 0.09610873, 0.41247947, 0.57433389], [0.29960807, 0.42315023, 0.34452557, 0.4751035], [0.17003563, 0.46843998, 0.92796258, 0.69814654], [0.41290051, 0.19561071, 0.16284783, 0.97016248], [0.71725408, 0.87702738, 0.31244595, 0.76615487], [0.20754036, 0.57871812, 0.07214068, 0.40356048], [0.12149553, 0.53222417, 0.9976855, 0.12536346], [0.80930099, 0.50962849, 0.94555126, 0.33364763]]\n",
"[0.84062117, 0.48006452, 0.7876326, 0.77109654, 0.44409793, 0.09014516, 0.81835917, 0.87645456, 0.7066597, 0.09610873, 0.41247947, 0.57433389, 0.29960807, 0.42315023, 0.34452557, 0.4751035, 0.17003563, 0.46843998, 0.92796258, 0.69814654, 0.41290051, 0.19561071, 0.16284783, 0.97016248, 0.71725408, 0.87702738, 0.31244595, 0.76615487, 0.20754036, 0.57871812, 0.07214068, 0.40356048, 0.12149553, 0.53222417, 0.9976855, 0.12536346, 0.80930099, 0.50962849, 0.94555126, 0.33364763]\n"
]
}
],
"source": [
"a = np.array([[0.84062117, 0.48006452, 0.7876326 , 0.77109654],\n",
" [0.44409793, 0.09014516, 0.81835917, 0.87645456],\n",
" [0.7066597 , 0.09610873, 0.41247947, 0.57433389],\n",
" [0.29960807, 0.42315023, 0.34452557, 0.4751035 ],\n",
" [0.17003563, 0.46843998, 0.92796258, 0.69814654],\n",
" [0.41290051, 0.19561071, 0.16284783, 0.97016248],\n",
" [0.71725408, 0.87702738, 0.31244595, 0.76615487],\n",
" [0.20754036, 0.57871812, 0.07214068, 0.40356048],\n",
" [0.12149553, 0.53222417, 0.9976855 , 0.12536346],\n",
" [0.80930099, 0.50962849, 0.94555126, 0.33364763]])\n",
"list_numpy=a.tolist()\n",
"print(list_numpy)\n",
"numpy_elements= [value for tercia in list_numpy for value in tercia]\n",
"print(numpy_elements)"
]
},
{
"cell_type": "markdown",
Expand All @@ -89,10 +135,21 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": []
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0.84062117, 0.7876326, 0.77109654, 0.81835917, 0.87645456, 0.7066597, 0.57433389, 0.92796258, 0.69814654, 0.97016248, 0.71725408, 0.87702738, 0.76615487, 0.57871812, 0.53222417, 0.9976855, 0.80930099, 0.50962849, 0.94555126]\n"
]
}
],
"source": [
"greater_elements=[value for tercia in list_numpy for value in tercia if value >=0.5]\n",
"print(greater_elements)"
]
},
{
"cell_type": "markdown",
Expand All @@ -103,9 +160,18 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 7,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[[0.55867166, 0.06210792, 0.08147297], [0.82579068, 0.91512478, 0.06833034]], [[0.05440634, 0.65857693, 0.30296619], [0.06769833, 0.96031863, 0.51293743]], [[0.09143215, 0.71893382, 0.45850679], [0.58256464, 0.59005654, 0.56266457]], [[0.71600294, 0.87392666, 0.11434044], [0.8694668, 0.65669313, 0.10708681]], [[0.07529684, 0.46470767, 0.47984544], [0.65368638, 0.14901286, 0.23760688]]]\n",
"[0.55867166, 0.06210792, 0.08147297, 0.82579068, 0.91512478, 0.06833034, 0.05440634, 0.65857693, 0.30296619, 0.06769833, 0.96031863, 0.51293743, 0.09143215, 0.71893382, 0.45850679, 0.58256464, 0.59005654, 0.56266457, 0.71600294, 0.87392666, 0.11434044, 0.8694668, 0.65669313, 0.10708681, 0.07529684, 0.46470767, 0.47984544, 0.65368638, 0.14901286, 0.23760688]\n"
]
}
],
"source": [
"b = np.array([[[0.55867166, 0.06210792, 0.08147297],\n",
" [0.82579068, 0.91512478, 0.06833034]],\n",
Expand All @@ -120,7 +186,12 @@
" [0.8694668 , 0.65669313, 0.10708681]],\n",
"\n",
" [[0.07529684, 0.46470767, 0.47984544],\n",
" [0.65368638, 0.14901286, 0.23760688]]])"
" [0.65368638, 0.14901286, 0.23760688]]])\n",
"list_numpy2=b.tolist()\n",
"print(list_numpy2)\n",
"\n",
"numpy_elements2= [valor for sixvalues in list_numpy2 for three in sixvalues for valor in three]\n",
"print(numpy_elements2)\n"
]
},
{
Expand Down Expand Up @@ -153,10 +224,21 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": []
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['../data/r\\\\sample_file_0.txt', '../data/r\\\\sample_file_1.txt', '../data/r\\\\sample_file_2.txt', '../data/r\\\\sample_file_3.txt', '../data/r\\\\sample_file_4.txt', '../data/r\\\\sample_file_5.txt', '../data/r\\\\sample_file_6.txt', '../data/r\\\\sample_file_7.txt', '../data/r\\\\sample_file_8.txt', '../data/r\\\\sample_file_9.txt']\n"
]
}
],
"source": [
"CSV_files=[os.path.join(\"../data/r\", file) for file in os.listdir(\"../data/\") if file.endswith(\"txt\")]\n",
"print(CSV_files)"
]
},
{
"cell_type": "markdown",
Expand All @@ -167,10 +249,42 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": []
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" 0 1 2 3 4 5 6 \\\n",
"0 0.734751 0.195362 0.734309 0.598184 0.763433 0.263434 0.868066 \n",
"1 0.772607 0.445391 0.249642 0.787922 0.598583 0.827238 0.624126 \n",
"2 0.226428 0.268764 0.694262 0.622335 0.063843 0.122683 0.815625 \n",
"3 0.362748 0.495430 0.113876 0.594149 0.612522 0.625204 0.864050 \n",
"4 0.033415 0.340433 0.464971 0.363737 0.025815 0.434129 0.415163 \n",
"\n",
" 7 8 9 10 11 12 13 \\\n",
"0 0.058092 0.753502 0.587513 0.311608 0.178356 0.182922 0.147631 \n",
"1 0.601524 0.688753 0.338870 0.081595 0.471474 0.267443 0.453351 \n",
"2 0.584542 0.032594 0.589775 0.764350 0.650973 0.565705 0.691784 \n",
"3 0.260279 0.528873 0.168043 0.715929 0.677014 0.175735 0.632370 \n",
"4 0.892210 0.381701 0.415264 0.790801 0.696930 0.819751 0.944029 \n",
"\n",
" 14 15 16 17 18 19 \n",
"0 0.391188 0.816049 0.749068 0.293260 0.937828 0.880858 \n",
"1 0.800716 0.045749 0.683793 0.389789 0.016787 0.503695 \n",
"2 0.265223 0.739031 0.560394 0.334802 0.517694 0.646110 \n",
"3 0.926715 0.085675 0.120525 0.141746 0.771144 0.489660 \n",
"4 0.869965 0.041723 0.819140 0.676051 0.109349 0.872947 \n"
]
}
],
"source": [
"rutas=['../data/sample_file_0.txt']\n",
"dfs=[pd.read_csv(ruta) for ruta in rutas]\n",
"data=pd.concat(dfs)\n",
"print(data)"
]
},
{
"cell_type": "markdown",
Expand All @@ -181,10 +295,21 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": []
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0.3627479843783245, 0.3404325433462202, 0.4649714815238164, 0.4341291641280539, 0.4152638711413228, 0.267442803433069, 0.0856753003374878, 0.3348015507267528]\n"
]
}
],
"source": [
"med=[median for median in dfs[0].median() if median <=0.48]\n",
"print(med)"
]
},
{
"cell_type": "markdown",
Expand Down Expand Up @@ -217,7 +342,7 @@
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
Expand All @@ -231,9 +356,9 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.7"
"version": "3.9.12"
}
},
"nbformat": 4,
"nbformat_minor": 2
"nbformat_minor": 4
}