{ "cells": [ { "cell_type": "code", "execution_count": 12, "id": "15f3fd92", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "from seaborn import countplot" ] }, { "cell_type": "code", "execution_count": 2, "id": "62c59abc", "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
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891 rows × 12 columns

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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", "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": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv('titanic.csv')\n", "df" ] }, { "cell_type": "code", "execution_count": 4, "id": "1b87dbfa", "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
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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 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": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "code", "execution_count": 5, "id": "e8211d09", "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": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.describe()" ] }, { "cell_type": "code", "execution_count": 6, "id": "3e3ad060", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(891, 12)" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.shape" ] }, { "cell_type": "code", "execution_count": 7, "id": "12d012d3", "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": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.isnull().sum()" ] }, { "cell_type": "code", "execution_count": 16, "id": "3184abf8", "metadata": {}, "outputs": [], "source": [ "df['Age'] = df['Age'].fillna(np.mean(df['Age']))" ] }, { "cell_type": "code", "execution_count": 18, "id": "982dc15b", "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", "Cabin 687\n", "Embarked 2\n", "dtype: int64" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.isnull().sum()" ] }, { "cell_type": "code", "execution_count": null, "id": "e23271a1", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 22, "id": "340d25f2", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n", " 'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n", " dtype='object')\n" ] } ], "source": [ "print(df.columns)" ] }, { "cell_type": "code", "execution_count": 24, "id": "072f3992", "metadata": {}, "outputs": [], "source": [ "df['fare'] = pd.to_numeric(df['Fare'], errors='coerce')\n" ] }, { "cell_type": "code", "execution_count": 26, "id": "44cb17e2", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "\n", "# Plotting histogram for ticket prices ('fare')\n", "plt.figure(figsize=(10, 6)) # Set the figure size\n", "\n", "# Create a histogram of ticket prices ('fare')\n", "sns.histplot(data=df, x='fare', bins=30, kde=True)\n", "\n", "# Customize the plot labels and title\n", "plt.title('Distribution of Ticket Prices')\n", "plt.xlabel('Fare')\n", "plt.ylabel('Frequency')\n", "\n", "# Show the plot\n", "plt.show()\n" ] }, { "cell_type": "code", "execution_count": null, "id": "cfa6ed9d", "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 }