diff --git a/your-code/pandas_1.ipynb b/your-code/pandas_1.ipynb
index a6c64554..86bad821 100644
--- a/your-code/pandas_1.ipynb
+++ b/your-code/pandas_1.ipynb
@@ -18,7 +18,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -35,7 +35,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -44,11 +44,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0     5.7\n",
+      "1    75.2\n",
+      "2    74.4\n",
+      "3    84.0\n",
+      "4    66.5\n",
+      "5    66.3\n",
+      "6    55.8\n",
+      "7    75.7\n",
+      "8    29.1\n",
+      "9    43.7\n",
+      "dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "series = pd.Series(lst)\n",
+    "print(series)"
    ]
   },
   {
@@ -62,11 +81,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 25,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "74.4\n"
+     ]
+    }
+   ],
    "source": [
-    "# your code here"
+    "third_value = series[2]\n",
+    "print(third_value)"
    ]
   },
   {
@@ -78,7 +106,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -96,11 +124,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "      0     1     2     3     4\n",
+      "0  53.1  95.0  67.5  35.0  78.4\n",
+      "1  61.3  40.8  30.8  37.8  87.6\n",
+      "2  20.6  73.2  44.2  14.6  91.8\n",
+      "3  57.4   0.1  96.1   4.2  69.5\n",
+      "4  83.6  20.5  85.4  22.8  35.9\n",
+      "5  49.0  69.0   0.1  31.8  89.1\n",
+      "6  23.3  40.7  95.0  83.8  26.9\n",
+      "7  27.6  26.4  53.8  88.8  68.5\n",
+      "8  96.6  96.4  53.4  72.4  50.1\n",
+      "9  73.7  39.0  43.2  81.6  34.7\n"
+     ]
+    }
+   ],
+   "source": [
+    "df = pd.DataFrame(b)\n",
+    "print(df)"
    ]
   },
   {
@@ -112,7 +159,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -130,11 +177,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   score_1  score_2  score_3  score_4  score_5\n",
+      "0     53.1     95.0     67.5     35.0     78.4\n",
+      "1     61.3     40.8     30.8     37.8     87.6\n",
+      "2     20.6     73.2     44.2     14.6     91.8\n",
+      "3     57.4      0.1     96.1      4.2     69.5\n",
+      "4     83.6     20.5     85.4     22.8     35.9\n"
+     ]
+    }
+   ],
+   "source": [
+    "b_names = [\"score_1\", \"score_2\", \"score_3\", \"score_4\", \"score_5\"]\n",
+    "\n",
+    "df.columns = b_names\n",
+    "print(df.head())"
    ]
   },
   {
@@ -146,11 +209,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 30,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   score_1  score_3  score_5\n",
+      "0     53.1     67.5     78.4\n",
+      "1     61.3     30.8     87.6\n",
+      "2     20.6     44.2     91.8\n",
+      "3     57.4     96.1     69.5\n",
+      "4     83.6     85.4     35.9\n"
+     ]
+    }
+   ],
    "source": [
-    "# your code here"
+    "subset_df = df[[\"score_1\", \"score_3\", \"score_5\"]]\n",
+    "print(subset_df.head())"
    ]
   },
   {
@@ -162,11 +239,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 31,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "56.95000000000001\n"
+     ]
+    }
+   ],
    "source": [
-    "# your code here"
+    "avg_score3 = df[\"score_3\"].mean()\n",
+    "print(avg_score3)"
    ]
   },
   {
@@ -178,11 +264,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 32,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "88.8\n"
+     ]
+    }
+   ],
    "source": [
-    "# your code here"
+    "max_score4 = df[\"score_4\"].max()\n",
+    "print(max_score4)"
    ]
   },
   {
@@ -194,11 +289,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 33,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "40.75\n"
+     ]
+    }
+   ],
    "source": [
-    "# your code here"
+    "median_score2 = df[\"score_2\"].median()\n",
+    "print(median_score2)"
    ]
   },
   {
@@ -210,7 +314,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -231,11 +335,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                          Description  Quantity  UnitPrice  Revenue\n",
+      "0              LUNCH BAG APPLE DESIGN         1       1.65     1.65\n",
+      "1  SET OF 60 VINTAGE LEAF CAKE CASES         24       0.55    13.20\n",
+      "2         RIBBON REEL STRIPES DESIGN          1       1.65     1.65\n",
+      "3   WORLD WAR 2 GLIDERS ASSTD DESIGNS      2880       0.18   518.40\n",
+      "4    PLAYING CARDS JUBILEE UNION JACK         2       1.25     2.50\n",
+      "5                      POPCORN HOLDER         7       0.85     5.95\n",
+      "6      BOX OF VINTAGE ALPHABET BLOCKS         1      11.95    11.95\n",
+      "7                       PARTY BUNTING         4       4.95    19.80\n",
+      "8            JAZZ HEARTS ADDRESS BOOK        10       0.19     1.90\n",
+      "9       SET OF 4 SANTA PLACE SETTINGS        48       1.25    60.00\n"
+     ]
+    }
+   ],
+   "source": [
+    "orders_df = pd.DataFrame(orders)\n",
+    "print(orders_df)"
    ]
   },
   {
@@ -247,11 +370,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total Quantity Ordered: 2978\n",
+      "Total Revenue Generated: 637.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "total_quantity = orders_df[\"Quantity\"].sum()\n",
+    "total_revenue = orders_df[\"Revenue\"].sum()\n",
+    "\n",
+    "print(\"Total Quantity Ordered:\", total_quantity)\n",
+    "print(\"Total Revenue Generated:\", total_revenue)"
    ]
   },
   {
@@ -263,11 +399,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Most Expensive Item Price 11.95\n",
+      "Least Expensive Item Price 0.18\n",
+      "Price Difference 11.77\n"
+     ]
+    }
+   ],
+   "source": [
+    "max_price = orders_df[\"UnitPrice\"].max()\n",
+    "min_price = orders_df[\"UnitPrice\"].min()\n",
+    "\n",
+    "price_difference = max_price - min_price\n",
+    "\n",
+    "print(\"Most Expensive Item Price\", max_price)\n",
+    "print(\"Least Expensive Item Price\", min_price)\n",
+    "print(\"Price Difference\", price_difference)"
    ]
   },
   {
@@ -279,12 +432,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [],
    "source": [
     "# Run this code:\n",
-    "admissions = pd.read_csv('../Admission_Predict.csv')"
+    "admissions = pd.read_csv(r'C:\\Users\\Timoteo\\OneDrive\\Documents\\Iron_Hack\\Week_2\\Labs\\D2\\lab-pandas-en\\Admission_Predict.csv')"
    ]
   },
   {
@@ -296,11 +449,129 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Serial No.</th>\n",
+       "      <th>GRE Score</th>\n",
+       "      <th>TOEFL Score</th>\n",
+       "      <th>University Rating</th>\n",
+       "      <th>SOP</th>\n",
+       "      <th>LOR</th>\n",
+       "      <th>CGPA</th>\n",
+       "      <th>Research</th>\n",
+       "      <th>Chance of Admit</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>337</td>\n",
+       "      <td>118</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4.5</td>\n",
+       "      <td>4.5</td>\n",
+       "      <td>9.65</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.92</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>316</td>\n",
+       "      <td>104</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>8.00</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>322</td>\n",
+       "      <td>110</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>8.67</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>314</td>\n",
+       "      <td>103</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>8.21</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>330</td>\n",
+       "      <td>115</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4.5</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>9.34</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.90</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Serial No.  GRE Score  TOEFL Score  University Rating  SOP  LOR   CGPA  \\\n",
+       "0           1        337          118                  4  4.5   4.5  9.65   \n",
+       "1           2        316          104                  3  3.0   3.5  8.00   \n",
+       "2           3        322          110                  3  3.5   2.5  8.67   \n",
+       "3           4        314          103                  2  2.0   3.0  8.21   \n",
+       "4           5        330          115                  5  4.5   3.0  9.34   \n",
+       "\n",
+       "   Research  Chance of Admit   \n",
+       "0         1              0.92  \n",
+       "1         1              0.72  \n",
+       "2         1              0.80  \n",
+       "3         0              0.65  \n",
+       "4         1              0.90  "
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "admissions.head()"
    ]
   },
   {
@@ -312,11 +583,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Serial No.           0\n",
+      "GRE Score            0\n",
+      "TOEFL Score          0\n",
+      "University Rating    0\n",
+      "SOP                  0\n",
+      "LOR                  0\n",
+      "CGPA                 0\n",
+      "Research             0\n",
+      "Chance of Admit      0\n",
+      "dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(admissions.isnull().sum())"
    ]
   },
   {
@@ -328,11 +616,137 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>GRE Score</th>\n",
+       "      <th>TOEFL Score</th>\n",
+       "      <th>University Rating</th>\n",
+       "      <th>SOP</th>\n",
+       "      <th>LOR</th>\n",
+       "      <th>CGPA</th>\n",
+       "      <th>Research</th>\n",
+       "      <th>Chance of Admit</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Serial No.</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>337</td>\n",
+       "      <td>118</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4.5</td>\n",
+       "      <td>4.5</td>\n",
+       "      <td>9.65</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.92</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>316</td>\n",
+       "      <td>104</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>8.00</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>322</td>\n",
+       "      <td>110</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>8.67</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>314</td>\n",
+       "      <td>103</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>8.21</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>330</td>\n",
+       "      <td>115</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4.5</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>9.34</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.90</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            GRE Score  TOEFL Score  University Rating  SOP  LOR   CGPA  \\\n",
+       "Serial No.                                                               \n",
+       "1                 337          118                  4  4.5   4.5  9.65   \n",
+       "2                 316          104                  3  3.0   3.5  8.00   \n",
+       "3                 322          110                  3  3.5   2.5  8.67   \n",
+       "4                 314          103                  2  2.0   3.0  8.21   \n",
+       "5                 330          115                  5  4.5   3.0  9.34   \n",
+       "\n",
+       "            Research  Chance of Admit   \n",
+       "Serial No.                              \n",
+       "1                  1              0.92  \n",
+       "2                  1              0.72  \n",
+       "3                  1              0.80  \n",
+       "4                  0              0.65  \n",
+       "5                  1              0.90  "
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "admissions = admissions.set_index(\"Serial No.\")\n",
+    "admissions.head()"
    ]
   },
   {
@@ -351,13 +765,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 59,
    "metadata": {
     "scrolled": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "101\n"
+     ]
+    }
+   ],
    "source": [
-    "# your code here"
+    "condition = (admissions[\"CGPA\"] > 9) & (admissions[\"Research\"] == 1)\n",
+    "high_cgpa = condition.sum()\n",
+    "print(high_cgpa)\n",
+    "\n"
    ]
   },
   {
@@ -369,17 +794,48 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean Chance of Admit for CGPA > 9 and SOP < 3.5: 0.8019999999999999\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Filter rows based on conditions\n",
+    "new_condition = (admissions[\"CGPA\"] > 9) & (admissions[\"SOP\"] < 3.5)\n",
+    "\n",
+    "# Apply condition to the DataFrame, not to the boolean Series\n",
+    "mean_chance = admissions.loc[new_condition, \"Chance of Admit \"].mean()\n",
+    "\n",
+    "print(\"Mean Chance of Admit for CGPA > 9 and SOP < 3.5:\", mean_chance)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['GRE Score', 'TOEFL Score', 'University Rating', 'SOP', 'LOR ', 'CGPA', 'Research', 'Chance of Admit ']\n"
+     ]
+    }
+   ],
    "source": [
-    "# your code here"
+    "print(admissions.columns.tolist())"
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "base",
    "language": "python",
    "name": "python3"
   },
@@ -393,7 +849,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.13"
+   "version": "3.13.5"
   },
   "toc": {
    "base_numbering": "",