new

notebooks/02_03_b.ipynb

@@ -51,17 +51,78 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   PassengerID  Survived  Pclass  \\\n",
+      "0            1         0       3   \n",
+      "1            2         1       1   \n",
+      "2            3         1       3   \n",
+      "3            4         1       1   \n",
+      "4            5         0       3   \n",
+      "\n",
+      "                                                Name     Sex   Age  SibSp  \\\n",
+      "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
+      "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
+      "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
+      "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
+      "4                           Allen, Mr. William Henry    male  35.0      0   \n",
+      "\n",
+      "   Parch            Ticket     Fare Cabin Embarked  \n",
+      "0      0         A/5 21171   7.2500   NaN        S  \n",
+      "1      0          PC 17599  71.2833   C85        C  \n",
+      "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
+      "3      0            113803  53.1000  C123        S  \n",
+      "4      0            373450   8.0500   NaN        S  \n"
+     ]
+    }
+   ],
+   "source": [
+    "url = '/workspaces/python-for-data-science-and-machine-learning-essential-training-part-2-3016934/data/titanic-training-data.csv'\n",
+    "titanic_training = pd.read_csv(url)\n",
+    "titanic_training.columns = ['PassengerID','Survived','Pclass','Name','Sex','Age','SibSp','Parch','Ticket','Fare','Cabin','Embarked']\n",
+    "print(titanic_training.head())"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 891 entries, 0 to 890\n",
+      "Data columns (total 12 columns):\n",
+      " #   Column       Non-Null Count  Dtype  \n",
+      "---  ------       --------------  -----  \n",
+      " 0   PassengerID  891 non-null    int64  \n",
+      " 1   Survived     891 non-null    int64  \n",
+      " 2   Pclass       891 non-null    int64  \n",
+      " 3   Name         891 non-null    object \n",
+      " 4   Sex          891 non-null    object \n",
+      " 5   Age          714 non-null    float64\n",
+      " 6   SibSp        891 non-null    int64  \n",
+      " 7   Parch        891 non-null    int64  \n",
+      " 8   Ticket       891 non-null    object \n",
+      " 9   Fare         891 non-null    float64\n",
+      " 10  Cabin        204 non-null    object \n",
+      " 11  Embarked     889 non-null    object \n",
+      "dtypes: float64(2), int64(5), object(5)\n",
+      "memory usage: 83.7+ KB\n",
+      "None\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(titanic_training.info())"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -87,7 +148,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -101,7 +162,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.13"
+   "version": "3.12.1"
   }
  },
  "nbformat": 4,