{
"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": [
"
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" Allison, Master. Hudson Trevor | \n",
" male | \n",
" 0.9167 | \n",
" 151.5500 | \n",
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\n",
" \n",
" 2 | \n",
" Allison, Miss. Helen Loraine | \n",
" female | \n",
" 2.0000 | \n",
" 151.5500 | \n",
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\n",
" \n",
" 3 | \n",
" Allison, Mr. Hudson Joshua Creighton | \n",
" male | \n",
" 30.0000 | \n",
" 151.5500 | \n",
"
\n",
" \n",
" 4 | \n",
" Allison, Mrs. Hudson J C (Bessie Waldo Daniels) | \n",
" female | \n",
" 25.0000 | \n",
" 151.5500 | \n",
"
\n",
" \n",
" 5 | \n",
" Anderson, Mr. Harry | \n",
" male | \n",
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" 26.5500 | \n",
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"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",
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" | \n",
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" 4 | \n",
" 5 | \n",
"
\n",
" \n",
" \n",
" \n",
" name | \n",
" Allen, Miss. Elisabeth Walton | \n",
" Allison, Master. Hudson Trevor | \n",
" Allison, Miss. Helen Loraine | \n",
" Allison, Mr. Hudson Joshua Creighton | \n",
" Allison, Mrs. Hudson J C (Bessie Waldo Daniels) | \n",
" Anderson, Mr. Harry | \n",
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" male | \n",
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"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",
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" | \n",
" name | \n",
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" Allen, Miss. Elisabeth Walton | \n",
" female | \n",
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" 1 | \n",
" Allison, Master. Hudson Trevor | \n",
" male | \n",
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" 2 | \n",
" Allison, Miss. Helen Loraine | \n",
" female | \n",
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\n",
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" 3 | \n",
" Allison, Mr. Hudson Joshua Creighton | \n",
" male | \n",
" 151.5500 | \n",
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" 4 | \n",
" Allison, Mrs. Hudson J C (Bessie Waldo Daniels) | \n",
" female | \n",
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\n",
" \n",
" 5 | \n",
" Anderson, Mr. Harry | \n",
" male | \n",
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"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",
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" | \n",
" name | \n",
" sex | \n",
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" fare | \n",
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" \n",
" \n",
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" Allen, Miss. Elisabeth Walton | \n",
" female | \n",
" 29.0000 | \n",
" 211.3375 | \n",
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" male | \n",
" 0.9167 | \n",
" 151.5500 | \n",
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" 2 | \n",
" Allison, Miss. Helen Loraine | \n",
" female | \n",
" 2.0000 | \n",
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" 3 | \n",
" Allison, Mr. Hudson Joshua Creighton | \n",
" male | \n",
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" \n",
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" Allison, Mrs. Hudson J C (Bessie Waldo Daniels) | \n",
" female | \n",
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" 151.5500 | \n",
"
\n",
" \n",
" 5 | \n",
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" name sex age fare\n",
"0 Allen, Miss. Elisabeth Walton female 29.0000 211.3375\n",
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"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": [
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\n",
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" 3 | \n",
" Allison, Mr. Hudson Joshua Creighton | \n",
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" female | \n",
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\n",
" \n",
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" name sex age fare\n",
"0 Allen, Miss. Elisabeth Walton female 29.0 211.3375\n",
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"3 Allison, Mr. Hudson Joshua Creighton male 30.0 151.5500\n",
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"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
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"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",
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\n",
" \n",
" 4 | \n",
" Allison, Mrs. Hudson J C (Bessie Waldo Daniels) | \n",
" female | \n",
" 25.0000 | \n",
" 151.5500 | \n",
" 35.0000 | \n",
"
\n",
" \n",
" 5 | \n",
" Anderson, Mr. Harry | \n",
" male | \n",
" 48.0000 | \n",
" 26.5500 | \n",
" 58.0000 | \n",
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"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",
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"5 58.0000 "
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"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": {
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" \n",
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" | \n",
" name | \n",
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\n",
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" 70.0000 | \n",
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\n",
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" female | \n",
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" 35.0000 | \n",
" 60.0000 | \n",
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\n",
" \n",
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"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",
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"0 39.0000 68.0000 \n",
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"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": [
{
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" 70.0000 | \n",
" 300.000 | \n",
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" female | \n",
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" 35.0000 | \n",
" 60.0000 | \n",
" 250.000 | \n",
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\n",
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" male | \n",
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\n",
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"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",
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"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"
]
}
],
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