{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
namesexagefare
0Allen, Miss. Elisabeth Waltonfemale29.0000211.3375
1Allison, Master. Hudson Trevormale0.9167151.5500
2Allison, Miss. Helen Lorainefemale2.0000151.5500
3Allison, Mr. Hudson Joshua Creightonmale30.0000151.5500
4Allison, Mrs. Hudson J C (Bessie Waldo Daniels)female25.0000151.5500
5Anderson, Mr. Harrymale48.000026.5500
\n", "
" ], "text/plain": [ " name sex age fare\n", "0 Allen, Miss. Elisabeth Walton female 29.0000 211.3375\n", "1 Allison, Master. Hudson Trevor male 0.9167 151.5500\n", "2 Allison, Miss. Helen Loraine female 2.0000 151.5500\n", "3 Allison, Mr. Hudson Joshua Creighton male 30.0000 151.5500\n", "4 Allison, Mrs. Hudson J C (Bessie Waldo Daniels) female 25.0000 151.5500\n", "5 Anderson, Mr. Harry male 48.0000 26.5500" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Read the csv file\n", "titanic_df = pd.read_csv('titanic.csv')\n", "\n", "#It's a big file so let's extract a small data out of it\n", "df = titanic_df.loc[[0,1,2,3,4,5],['name','sex','age','fare']]\n", "df" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(6, 4)" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Size of dataframe\n", "df.shape" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
012345
nameAllen, Miss. Elisabeth WaltonAllison, Master. Hudson TrevorAllison, Miss. Helen LoraineAllison, Mr. Hudson Joshua CreightonAllison, Mrs. Hudson J C (Bessie Waldo Daniels)Anderson, Mr. Harry
sexfemalemalefemalemalefemalemale
age290.91672302548
fare211.338151.55151.55151.55151.5526.55
\n", "
" ], "text/plain": [ " 0 1 \\\n", "name Allen, Miss. Elisabeth Walton Allison, Master. Hudson Trevor \n", "sex female male \n", "age 29 0.9167 \n", "fare 211.338 151.55 \n", "\n", " 2 3 \\\n", "name Allison, Miss. Helen Loraine Allison, Mr. Hudson Joshua Creighton \n", "sex female male \n", "age 2 30 \n", "fare 151.55 151.55 \n", "\n", " 4 5 \n", "name Allison, Mrs. Hudson J C (Bessie Waldo Daniels) Anderson, Mr. Harry \n", "sex female male \n", "age 25 48 \n", "fare 151.55 26.55 " ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Transposing a dataframe\n", "df.T" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
namesexfare
0Allen, Miss. Elisabeth Waltonfemale211.3375
1Allison, Master. Hudson Trevormale151.5500
2Allison, Miss. Helen Lorainefemale151.5500
3Allison, Mr. Hudson Joshua Creightonmale151.5500
4Allison, Mrs. Hudson J C (Bessie Waldo Daniels)female151.5500
5Anderson, Mr. Harrymale26.5500
\n", "
" ], "text/plain": [ " name sex fare\n", "0 Allen, Miss. Elisabeth Walton female 211.3375\n", "1 Allison, Master. Hudson Trevor male 151.5500\n", "2 Allison, Miss. Helen Loraine female 151.5500\n", "3 Allison, Mr. Hudson Joshua Creighton male 151.5500\n", "4 Allison, Mrs. Hudson J C (Bessie Waldo Daniels) female 151.5500\n", "5 Anderson, Mr. Harry male 26.5500" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Dropping a column\n", "#axis=1 is for column\n", "df.drop(['age'], axis=1)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
namesexagefare
0Allen, Miss. Elisabeth Waltonfemale29.0000211.3375
1Allison, Master. Hudson Trevormale0.9167151.5500
2Allison, Miss. Helen Lorainefemale2.0000151.5500
3Allison, Mr. Hudson Joshua Creightonmale30.0000151.5500
4Allison, Mrs. Hudson J C (Bessie Waldo Daniels)female25.0000151.5500
5Anderson, Mr. Harrymale48.000026.5500
\n", "
" ], "text/plain": [ " name sex age fare\n", "0 Allen, Miss. Elisabeth Walton female 29.0000 211.3375\n", "1 Allison, Master. Hudson Trevor male 0.9167 151.5500\n", "2 Allison, Miss. Helen Loraine female 2.0000 151.5500\n", "3 Allison, Mr. Hudson Joshua Creighton male 30.0000 151.5500\n", "4 Allison, Mrs. Hudson J C (Bessie Waldo Daniels) female 25.0000 151.5500\n", "5 Anderson, Mr. Harry male 48.0000 26.5500" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#If you dont'put inplace the orginical df remains same\n", "df" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
namesexagefare
0Allen, Miss. Elisabeth Waltonfemale29.0211.3375
2Allison, Miss. Helen Lorainefemale2.0151.5500
3Allison, Mr. Hudson Joshua Creightonmale30.0151.5500
4Allison, Mrs. Hudson J C (Bessie Waldo Daniels)female25.0151.5500
5Anderson, Mr. Harrymale48.026.5500
\n", "
" ], "text/plain": [ " name sex age fare\n", "0 Allen, Miss. Elisabeth Walton female 29.0 211.3375\n", "2 Allison, Miss. Helen Loraine female 2.0 151.5500\n", "3 Allison, Mr. Hudson Joshua Creighton male 30.0 151.5500\n", "4 Allison, Mrs. Hudson J C (Bessie Waldo Daniels) female 25.0 151.5500\n", "5 Anderson, Mr. Harry male 48.0 26.5500" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Dropping a row\n", "#axis=0 for row\n", "df.drop([1], axis=0)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
namesexagefarebig_age
0Allen, Miss. Elisabeth Waltonfemale29.0000211.337539.0000
1Allison, Master. Hudson Trevormale0.9167151.550010.9167
2Allison, Miss. Helen Lorainefemale2.0000151.550012.0000
3Allison, Mr. Hudson Joshua Creightonmale30.0000151.550040.0000
4Allison, Mrs. Hudson J C (Bessie Waldo Daniels)female25.0000151.550035.0000
5Anderson, Mr. Harrymale48.000026.550058.0000
\n", "
" ], "text/plain": [ " name sex age fare \\\n", "0 Allen, Miss. Elisabeth Walton female 29.0000 211.3375 \n", "1 Allison, Master. Hudson Trevor male 0.9167 151.5500 \n", "2 Allison, Miss. Helen Loraine female 2.0000 151.5500 \n", "3 Allison, Mr. Hudson Joshua Creighton male 30.0000 151.5500 \n", "4 Allison, Mrs. Hudson J C (Bessie Waldo Daniels) female 25.0000 151.5500 \n", "5 Anderson, Mr. Harry male 48.0000 26.5500 \n", "\n", " big_age \n", "0 39.0000 \n", "1 10.9167 \n", "2 12.0000 \n", "3 40.0000 \n", "4 35.0000 \n", "5 58.0000 " ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Scalar addition\n", "df['big_age'] = df['age'] + 10\n", "df" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
namesexagefarebig_agebigger_age
0Allen, Miss. Elisabeth Waltonfemale29.0000211.337539.000068.0000
1Allison, Master. Hudson Trevormale0.9167151.550010.916711.8334
2Allison, Miss. Helen Lorainefemale2.0000151.550012.000014.0000
3Allison, Mr. Hudson Joshua Creightonmale30.0000151.550040.000070.0000
4Allison, Mrs. Hudson J C (Bessie Waldo Daniels)female25.0000151.550035.000060.0000
5Anderson, Mr. Harrymale48.000026.550058.0000106.0000
\n", "
" ], "text/plain": [ " name sex age fare \\\n", "0 Allen, Miss. Elisabeth Walton female 29.0000 211.3375 \n", "1 Allison, Master. Hudson Trevor male 0.9167 151.5500 \n", "2 Allison, Miss. Helen Loraine female 2.0000 151.5500 \n", "3 Allison, Mr. Hudson Joshua Creighton male 30.0000 151.5500 \n", "4 Allison, Mrs. Hudson J C (Bessie Waldo Daniels) female 25.0000 151.5500 \n", "5 Anderson, Mr. Harry male 48.0000 26.5500 \n", "\n", " big_age bigger_age \n", "0 39.0000 68.0000 \n", "1 10.9167 11.8334 \n", "2 12.0000 14.0000 \n", "3 40.0000 70.0000 \n", "4 35.0000 60.0000 \n", "5 58.0000 106.0000 " ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#we can add two columns\n", "#Think it as matrix addition\n", "df['bigger_age'] = df['age'] + df['big_age']\n", "df" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
namesexagefarebig_agebigger_agebiggest_age
0Allen, Miss. Elisabeth Waltonfemale29.0000211.337539.000068.0000290.000
1Allison, Master. Hudson Trevormale0.9167151.550010.916711.83349.167
2Allison, Miss. Helen Lorainefemale2.0000151.550012.000014.000020.000
3Allison, Mr. Hudson Joshua Creightonmale30.0000151.550040.000070.0000300.000
4Allison, Mrs. Hudson J C (Bessie Waldo Daniels)female25.0000151.550035.000060.0000250.000
5Anderson, Mr. Harrymale48.000026.550058.0000106.0000480.000
\n", "
" ], "text/plain": [ " name sex age fare \\\n", "0 Allen, Miss. Elisabeth Walton female 29.0000 211.3375 \n", "1 Allison, Master. Hudson Trevor male 0.9167 151.5500 \n", "2 Allison, Miss. Helen Loraine female 2.0000 151.5500 \n", "3 Allison, Mr. Hudson Joshua Creighton male 30.0000 151.5500 \n", "4 Allison, Mrs. Hudson J C (Bessie Waldo Daniels) female 25.0000 151.5500 \n", "5 Anderson, Mr. Harry male 48.0000 26.5500 \n", "\n", " big_age bigger_age biggest_age \n", "0 39.0000 68.0000 290.000 \n", "1 10.9167 11.8334 9.167 \n", "2 12.0000 14.0000 20.000 \n", "3 40.0000 70.0000 300.000 \n", "4 35.0000 60.0000 250.000 \n", "5 58.0000 106.0000 480.000 " ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Scalar multiplication\n", "df['biggest_age'] = df['age']*10\n", "df" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.6.3" } }, "nbformat": 4, "nbformat_minor": 2 }