{ "cells": [ { "cell_type": "markdown", "id": "05f393b9", "metadata": {}, "source": [ "# Practical No 1\n", "Data Wrangling I\n", "\n", "Perform the following operations using Python on any open source dataset (e.g., data.csv)\n", "1. Import all the required Python Libraries.\n", "2. Locate an open source data from the web (e.g., https://www.kaggle.com). Provide a clear \n", " description of the data and its source (i.e., URL of the web site).\n", "3. Load the Dataset into pandas dataframe.\n", "4. Data Preprocessing: check for missing values in the data using pandas isnull(), describe() \n", "function to get some initial statistics. Provide variable descriptions. Types of variables etc. \n", "Check the dimensions of the data frame.\n", "5. Data Formatting and Data Normalization: Summarize the types of variables by checking \n", "the data types (i.e., character, numeric, integer, factor, and logical) of the variables in the \n", "data set. If variables are not in the correct data type, apply proper type conversions.\n", "6. Turn categorical variables into quantitative variables in Python.\n", "\n", "In addition to the codes and outputs, explain every operation that you do in the above steps and \n", "explain everything that you do to import/read/scrape the data set." ] }, { "cell_type": "code", "execution_count": 1, "id": "a4115d06", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns" ] }, { "cell_type": "code", "execution_count": 3, "id": "e8d2c07a", "metadata": {}, "outputs": [], "source": [ "df = pd.read_csv('titanic_train.csv')" ] }, { "cell_type": "code", "execution_count": 4, "id": "99388c45", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNS
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C
2313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250NaNS
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S
4503Allen, Mr. William Henrymale35.0003734508.0500NaNS
.......................................
88688702Montvila, Rev. Juozasmale27.00021153613.0000NaNS
88788811Graham, Miss. Margaret Edithfemale19.00011205330.0000B42S
88888903Johnston, Miss. Catherine Helen \"Carrie\"femaleNaN12W./C. 660723.4500NaNS
88989011Behr, Mr. Karl Howellmale26.00011136930.0000C148C
89089103Dooley, Mr. Patrickmale32.0003703767.7500NaNQ
\n", "

891 rows × 12 columns

\n", "
" ], "text/plain": [ " 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", "886 887 0 2 \n", "887 888 1 1 \n", "888 889 0 3 \n", "889 890 1 1 \n", "890 891 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", "886 Montvila, Rev. Juozas male 27.0 0 \n", "887 Graham, Miss. Margaret Edith female 19.0 0 \n", "888 Johnston, Miss. Catherine Helen \"Carrie\" female NaN 1 \n", "889 Behr, Mr. Karl Howell male 26.0 0 \n", "890 Dooley, Mr. Patrick male 32.0 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", ".. ... ... ... ... ... \n", "886 0 211536 13.0000 NaN S \n", "887 0 112053 30.0000 B42 S \n", "888 2 W./C. 6607 23.4500 NaN S \n", "889 0 111369 30.0000 C148 C \n", "890 0 370376 7.7500 NaN Q \n", "\n", "[891 rows x 12 columns]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "code", "execution_count": 7, "id": "96f4d6e5", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNS
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C
2313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250NaNS
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S
4503Allen, Mr. William Henrymale35.0003734508.0500NaNS
\n", "
" ], "text/plain": [ " 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 " ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ " # It's showing top 5 result\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 8, "id": "45227176", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
88688702Montvila, Rev. Juozasmale27.00021153613.00NaNS
88788811Graham, Miss. Margaret Edithfemale19.00011205330.00B42S
88888903Johnston, Miss. Catherine Helen \"Carrie\"femaleNaN12W./C. 660723.45NaNS
88989011Behr, Mr. Karl Howellmale26.00011136930.00C148C
89089103Dooley, Mr. Patrickmale32.0003703767.75NaNQ
\n", "
" ], "text/plain": [ " PassengerId Survived Pclass Name \\\n", "886 887 0 2 Montvila, Rev. Juozas \n", "887 888 1 1 Graham, Miss. Margaret Edith \n", "888 889 0 3 Johnston, Miss. Catherine Helen \"Carrie\" \n", "889 890 1 1 Behr, Mr. Karl Howell \n", "890 891 0 3 Dooley, Mr. Patrick \n", "\n", " Sex Age SibSp Parch Ticket Fare Cabin Embarked \n", "886 male 27.0 0 0 211536 13.00 NaN S \n", "887 female 19.0 0 0 112053 30.00 B42 S \n", "888 female NaN 1 2 W./C. 6607 23.45 NaN S \n", "889 male 26.0 0 0 111369 30.00 C148 C \n", "890 male 32.0 0 0 370376 7.75 NaN Q " ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# It's showing bottom 5 result\n", "df.tail() " ] }, { "cell_type": "code", "execution_count": 9, "id": "7b3c7333", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "PassengerId 0\n", "Survived 0\n", "Pclass 0\n", "Name 0\n", "Sex 0\n", "Age 177\n", "SibSp 0\n", "Parch 0\n", "Ticket 0\n", "Fare 0\n", "Cabin 687\n", "Embarked 2\n", "dtype: int64" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Calculating the Null values\n", "df.isnull().sum()" ] }, { "cell_type": "code", "execution_count": 10, "id": "c26fa763", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.heatmap(df.isnull(),yticklabels=False,cbar=False,cmap='viridis')" ] }, { "cell_type": "code", "execution_count": 11, "id": "6f2482c2", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "177" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Calculating the Null values in AGE Coloumns\n", "df['Age'].isnull().sum()" ] }, { "cell_type": "code", "execution_count": 12, "id": "1d291504", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "687" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Calculating the Null values in Cabin Columns\n", "df['Cabin'].isnull().sum()" ] }, { "cell_type": "code", "execution_count": 13, "id": "40e7eed7", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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PassengerIdSurvivedPclassAgeSibSpParchFare
count891.000000891.000000891.000000714.000000891.000000891.000000891.000000
mean446.0000000.3838382.30864229.6991180.5230080.38159432.204208
std257.3538420.4865920.83607114.5264971.1027430.80605749.693429
min1.0000000.0000001.0000000.4200000.0000000.0000000.000000
25%223.5000000.0000002.00000020.1250000.0000000.0000007.910400
50%446.0000000.0000003.00000028.0000000.0000000.00000014.454200
75%668.5000001.0000003.00000038.0000001.0000000.00000031.000000
max891.0000001.0000003.00000080.0000008.0000006.000000512.329200
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" ], "text/plain": [ " PassengerId Survived Pclass Age SibSp \\\n", "count 891.000000 891.000000 891.000000 714.000000 891.000000 \n", "mean 446.000000 0.383838 2.308642 29.699118 0.523008 \n", "std 257.353842 0.486592 0.836071 14.526497 1.102743 \n", "min 1.000000 0.000000 1.000000 0.420000 0.000000 \n", "25% 223.500000 0.000000 2.000000 20.125000 0.000000 \n", "50% 446.000000 0.000000 3.000000 28.000000 0.000000 \n", "75% 668.500000 1.000000 3.000000 38.000000 1.000000 \n", "max 891.000000 1.000000 3.000000 80.000000 8.000000 \n", "\n", " Parch Fare \n", "count 891.000000 891.000000 \n", "mean 0.381594 32.204208 \n", "std 0.806057 49.693429 \n", "min 0.000000 0.000000 \n", "25% 0.000000 7.910400 \n", "50% 0.000000 14.454200 \n", "75% 0.000000 31.000000 \n", "max 6.000000 512.329200 " ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# get some initial statistics\n", "df.describe()" ] }, { "cell_type": "code", "execution_count": 14, "id": "139d27b0", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\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" ] } ], "source": [ "# getting some information about dataset\n", "df.info()" ] }, { "cell_type": "code", "execution_count": 15, "id": "9ed8c66e", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "PassengerId int64\n", "Survived int64\n", "Pclass int64\n", "Name object\n", "Sex object\n", "Age float64\n", "SibSp int64\n", "Parch int64\n", "Ticket object\n", "Fare float64\n", "Cabin object\n", "Embarked object\n", "dtype: object" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# finding data types\n", "df.dtypes" ] }, { "cell_type": "code", "execution_count": 16, "id": "c12ad65d", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(891, 12)" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# finding dimensions of the data frame\n", "df.shape" ] }, { "cell_type": "code", "execution_count": 20, "id": "edc39058", "metadata": {}, "outputs": [], "source": [ "def impute_age(cols):\n", " Age = cols[0]\n", " Pclass = cols[1]\n", " \n", " if pd.isnull(Age):\n", " \n", " if Pclass == 1:\n", " return 37\n", " \n", " elif Pclass == 2:\n", " return 29\n", " \n", " else:\n", " return 24\n", " \n", " else:\n", " return Age" ] }, { "cell_type": "code", "execution_count": 21, "id": "a25f1c60", "metadata": {}, "outputs": [], "source": [ "# applying the function\n", "df['Age'] = df[['Age','Pclass']].apply(impute_age,axis=1)" ] }, { "cell_type": "code", "execution_count": 22, "id": "1b98fb93", "metadata": {}, "outputs": [], "source": [ "# dropping cabin column\n", "df.drop('Cabin',axis=1,inplace=True)" ] }, { "cell_type": "code", "execution_count": 23, "id": "75098f42", "metadata": {}, "outputs": [], "source": [ "df.dropna(inplace=True)" ] }, { "cell_type": "code", "execution_count": 24, "id": "92da4204", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareEmbarked
0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500S
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C
2313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250S
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000S
4503Allen, Mr. William Henrymale35.0003734508.0500S
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" ], "text/plain": [ " 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 Embarked \n", "0 0 A/5 21171 7.2500 S \n", "1 0 PC 17599 71.2833 C \n", "2 0 STON/O2. 3101282 7.9250 S \n", "3 0 113803 53.1000 S \n", "4 0 373450 8.0500 S " ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "code", "execution_count": 25, "id": "86fcc464", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareEmbarked
88688702Montvila, Rev. Juozasmale27.00021153613.00S
88788811Graham, Miss. Margaret Edithfemale19.00011205330.00S
88888903Johnston, Miss. Catherine Helen \"Carrie\"female24.012W./C. 660723.45S
88989011Behr, Mr. Karl Howellmale26.00011136930.00C
89089103Dooley, Mr. Patrickmale32.0003703767.75Q
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" ], "text/plain": [ " PassengerId Survived Pclass Name \\\n", "886 887 0 2 Montvila, Rev. Juozas \n", "887 888 1 1 Graham, Miss. Margaret Edith \n", "888 889 0 3 Johnston, Miss. Catherine Helen \"Carrie\" \n", "889 890 1 1 Behr, Mr. Karl Howell \n", "890 891 0 3 Dooley, Mr. Patrick \n", "\n", " Sex Age SibSp Parch Ticket Fare Embarked \n", "886 male 27.0 0 0 211536 13.00 S \n", "887 female 19.0 0 0 112053 30.00 S \n", "888 female 24.0 1 2 W./C. 6607 23.45 S \n", "889 male 26.0 0 0 111369 30.00 C \n", "890 male 32.0 0 0 370376 7.75 Q " ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.tail()" ] }, { "cell_type": "code", "execution_count": 26, "id": "2abecf6b", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "PassengerId 0\n", "Survived 0\n", "Pclass 0\n", "Name 0\n", "Sex 0\n", "Age 0\n", "SibSp 0\n", "Parch 0\n", "Ticket 0\n", "Fare 0\n", "Embarked 0\n", "dtype: int64" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.isnull().sum()" ] }, { "cell_type": "code", "execution_count": 27, "id": "ce750d69", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "PassengerId int64\n", "Survived int64\n", "Pclass int64\n", "Name object\n", "Sex object\n", "Age float64\n", "SibSp int64\n", "Parch int64\n", "Ticket object\n", "Fare float64\n", "Embarked object\n", "dtype: object" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.dtypes" ] }, { "cell_type": "code", "execution_count": 28, "id": "049c0826", "metadata": {}, "outputs": [], "source": [ "# data type conversion\n", "df['Age'] = df['Age'].astype('int')" ] }, { "cell_type": "code", "execution_count": 29, "id": "1bd3107a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "PassengerId int64\n", "Survived int64\n", "Pclass int64\n", "Name object\n", "Sex object\n", "Age int32\n", "SibSp int64\n", "Parch int64\n", "Ticket object\n", "Fare float64\n", "Embarked object\n", "dtype: object" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.dtypes" ] }, { "cell_type": "code", "execution_count": 30, "id": "dcd386fb", "metadata": {}, "outputs": [], "source": [ "# data types conversion\n", "df['Age'] = df['Age'].round(0).astype('int')" ] }, { "cell_type": "code", "execution_count": 31, "id": "532679c0", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "PassengerId int64\n", "Survived int64\n", "Pclass int64\n", "Name object\n", "Sex object\n", "Age int32\n", "SibSp int64\n", "Parch int64\n", "Ticket object\n", "Fare float64\n", "Embarked object\n", "dtype: object" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.dtypes" ] }, { "cell_type": "code", "execution_count": 32, "id": "2e3d4d5b", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareEmbarked
0103Braund, Mr. Owen Harrismale2210A/5 211717.2500S
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female3810PC 1759971.2833C
2313Heikkinen, Miss. Lainafemale2600STON/O2. 31012827.9250S
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female351011380353.1000S
4503Allen, Mr. William Henrymale35003734508.0500S
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" ], "text/plain": [ " 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 1 \n", "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38 1 \n", "2 Heikkinen, Miss. Laina female 26 0 \n", "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35 1 \n", "4 Allen, Mr. William Henry male 35 0 \n", "\n", " Parch Ticket Fare Embarked \n", "0 0 A/5 21171 7.2500 S \n", "1 0 PC 17599 71.2833 C \n", "2 0 STON/O2. 3101282 7.9250 S \n", "3 0 113803 53.1000 S \n", "4 0 373450 8.0500 S " ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "code", "execution_count": 33, "id": "cbebfe6d", "metadata": {}, "outputs": [], "source": [ "# Converting Categorical Variables to Quantitative Variables\n", "cat = pd.get_dummies(df, columns=['Sex'])" ] }, { "cell_type": "code", "execution_count": 34, "id": "22b4c147", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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PassengerIdSurvivedPclassNameAgeSibSpParchTicketFareEmbarkedSex_femaleSex_male
0103Braund, Mr. Owen Harris2210A/5 211717.2500SFalseTrue
1211Cumings, Mrs. John Bradley (Florence Briggs Th...3810PC 1759971.2833CTrueFalse
2313Heikkinen, Miss. Laina2600STON/O2. 31012827.9250STrueFalse
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)351011380353.1000STrueFalse
4503Allen, Mr. William Henry35003734508.0500SFalseTrue
\n", "
" ], "text/plain": [ " 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 Age SibSp Parch \\\n", "0 Braund, Mr. Owen Harris 22 1 0 \n", "1 Cumings, Mrs. John Bradley (Florence Briggs Th... 38 1 0 \n", "2 Heikkinen, Miss. Laina 26 0 0 \n", "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) 35 1 0 \n", "4 Allen, Mr. William Henry 35 0 0 \n", "\n", " Ticket Fare Embarked Sex_female Sex_male \n", "0 A/5 21171 7.2500 S False True \n", "1 PC 17599 71.2833 C True False \n", "2 STON/O2. 3101282 7.9250 S True False \n", "3 113803 53.1000 S True False \n", "4 373450 8.0500 S False True " ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cat.head()" ] }, { "cell_type": "code", "execution_count": 36, "id": "2ed0fff4", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 False\n", "1 True\n", "2 True\n", "3 True\n", "4 False\n", " ... \n", "886 False\n", "887 True\n", "888 True\n", "889 False\n", "890 False\n", "Name: Sex_female, Length: 889, dtype: bool" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Female = 0\n", "cat[\"Sex_female\"]" ] }, { "cell_type": "code", "execution_count": 37, "id": "59c7190d", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 True\n", "1 False\n", "2 False\n", "3 False\n", "4 True\n", " ... \n", "886 True\n", "887 False\n", "888 False\n", "889 True\n", "890 True\n", "Name: Sex_male, Length: 889, dtype: bool" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Male = 1\n", "cat['Sex_male']" ] }, { "cell_type": "code", "execution_count": null, "id": "c47a7d39", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.11.5" } }, "nbformat": 4, "nbformat_minor": 5 }