text 快速浏览Pandas for Data Science
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"from https://towardsdatascience.com/quick-dive-into-pandas-for-data-science-cc1c1a80d9c4"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Creating Series from Python List"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"countries = ['USA', 'Brazil', 'Japan', 'France']\n",
"data = [100, 200, 300, 400]"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"USA 100\n",
"Brazil 200\n",
"Japan 300\n",
"France 400\n",
"dtype: int64"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.Series(data, index=countries)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Creating Series from a NumPy Array"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([100, 200, 300, 400])"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np_arr = np.array(data)\n",
"np_arr"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 100\n",
"1 200\n",
"2 300\n",
"3 400\n",
"dtype: int64"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.Series(np_arr)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Creating Series from Python Dictionary"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'a': 50, 'b': 60, 'c': 30, 'd': 100}"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dictionary = {'a': 50, 'b': 60, 'c': 30, 'd': 100}\n",
"dictionary"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"a 50\n",
"b 60\n",
"c 30\n",
"d 100\n",
"dtype: int64"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.Series(dictionary)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Grabbing information from Series"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"series1 = pd.Series([1, 2, 3, 4], ['London', 'HongKong', 'Mumbai', 'Bangladesh'])\n",
"series1['London']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Performing Arithmetic operations on Series"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"series1 = pd.Series([2, 4, 6, 8], ['London', 'HongKong', 'Mumbai', 'Bangladesh'])\n",
"series2 = pd.Series([1, 3, 5, 7], ['London', 'Sao Paulo', 'Mumbai', 'Delhi'])"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Bangladesh NaN\n",
"Delhi NaN\n",
"HongKong NaN\n",
"London 1.0\n",
"Mumbai 1.0\n",
"Sao Paulo NaN\n",
"dtype: float64"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"series1 - series2"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Bangladesh NaN\n",
"Delhi NaN\n",
"HongKong NaN\n",
"London 3.0\n",
"Mumbai 11.0\n",
"Sao Paulo NaN\n",
"dtype: float64"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"series1 + series2"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Bangladesh NaN\n",
"Delhi NaN\n",
"HongKong NaN\n",
"London 2.0\n",
"Mumbai 30.0\n",
"Sao Paulo NaN\n",
"dtype: float64"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"series1 * series2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Creating DataFrame from a dictionary of Series"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Age</th>\n",
" <th>Name</th>\n",
" <th>Nationality</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>a</th>\n",
" <td>28</td>\n",
" <td>Marcelo</td>\n",
" <td>US</td>\n",
" </tr>\n",
" <tr>\n",
" <th>b</th>\n",
" <td>27</td>\n",
" <td>Gabriel</td>\n",
" <td>Brazil</td>\n",
" </tr>\n",
" <tr>\n",
" <th>c</th>\n",
" <td>38</td>\n",
" <td>Raul</td>\n",
" <td>China</td>\n",
" </tr>\n",
" <tr>\n",
" <th>d</th>\n",
" <td>24</td>\n",
" <td>NaN</td>\n",
" <td>Bangladesh</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Age Name Nationality\n",
"a 28 Marcelo US\n",
"b 27 Gabriel Brazil\n",
"c 38 Raul China\n",
"d 24 NaN Bangladesh"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = {'Name': pd.Series(['Marcelo', 'Gabriel', 'Raul'], index=['a', 'b', 'c']),\n",
" 'Age': pd.Series(['28', '27', '38', '24'], index=['a', 'b', 'c', 'd']),\n",
" 'Nationality': pd.Series(['US', 'Brazil', 'China', 'Bangladesh'], index=['a', 'b', 'c', 'd'])\n",
" }\n",
"pd.DataFrame(df)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Creating DataFrame from a dictionary of list"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>age</th>\n",
" <th>name</th>\n",
" <th>year</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Lagos</th>\n",
" <td>30</td>\n",
" <td>George</td>\n",
" <td>2012</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Dubai</th>\n",
" <td>24</td>\n",
" <td>Ann</td>\n",
" <td>2012</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Mumbai</th>\n",
" <td>50</td>\n",
" <td>Tino</td>\n",
" <td>2015</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Accra</th>\n",
" <td>21</td>\n",
" <td>Charles</td>\n",
" <td>2020</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Yuma</th>\n",
" <td>23</td>\n",
" <td>Phil</td>\n",
" <td>2014</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" age name year\n",
"Lagos 30 George 2012\n",
"Dubai 24 Ann 2012\n",
"Mumbai 50 Tino 2015\n",
"Accra 21 Charles 2020\n",
"Yuma 23 Phil 2014"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data = {'name': ['George', 'Ann', 'Tino', 'Charles', 'Phil'],\n",
" 'age': [30, 24, 50, 21, 23],\n",
" 'year': [2012, 2012, 2015, 2020, 2014]\n",
" }\n",
"data_frame = pd.DataFrame(data, index=['Lagos', 'Dubai', 'Mumbai', 'Accra', 'Yuma'])\n",
"data_frame"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Selecting columns from DataFrames"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Lagos George\n",
"Dubai Ann\n",
"Mumbai Tino\n",
"Accra Charles\n",
"Yuma Phil\n",
"Name: name, dtype: object"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_frame['name']"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Lagos George\n",
"Dubai Ann\n",
"Mumbai Tino\n",
"Accra Charles\n",
"Yuma Phil\n",
"Name: name, dtype: object"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_frame.name"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"pandas.core.series.Series"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(data_frame.name)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Selecting multiple columns"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>name</th>\n",
" <th>year</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Lagos</th>\n",
" <td>George</td>\n",
" <td>2012</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Dubai</th>\n",
" <td>Ann</td>\n",
" <td>2012</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Mumbai</th>\n",
" <td>Tino</td>\n",
" <td>2015</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Accra</th>\n",
" <td>Charles</td>\n",
" <td>2020</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Yuma</th>\n",
" <td>Phil</td>\n",
" <td>2014</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" name year\n",
"Lagos George 2012\n",
"Dubai Ann 2012\n",
"Mumbai Tino 2015\n",
"Accra Charles 2020\n",
"Yuma Phil 2014"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_frame[['name', 'year']]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Creating a new columns from a Series"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>age</th>\n",
" <th>name</th>\n",
" <th>year</th>\n",
" <th>points</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Lagos</th>\n",
" <td>30</td>\n",
" <td>George</td>\n",
" <td>2012</td>\n",
" <td>674</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Dubai</th>\n",
" <td>24</td>\n",
" <td>Ann</td>\n",
" <td>2012</td>\n",
" <td>3435</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Mumbai</th>\n",
" <td>50</td>\n",
" <td>Tino</td>\n",
" <td>2015</td>\n",
" <td>34</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Accra</th>\n",
" <td>21</td>\n",
" <td>Charles</td>\n",
" <td>2020</td>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Yuma</th>\n",
" <td>23</td>\n",
" <td>Phil</td>\n",
" <td>2014</td>\n",
" <td>222</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" age name year points\n",
"Lagos 30 George 2012 674\n",
"Dubai 24 Ann 2012 3435\n",
"Mumbai 50 Tino 2015 34\n",
"Accra 21 Charles 2020 10\n",
"Yuma 23 Phil 2014 222"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_frame['points'] = pd.Series([674, 3435, 34, 10, 222], index=['Lagos', 'Dubai', 'Mumbai', 'Accra', 'Yuma'])\n",
"data_frame"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Removing rows/columns from a DataFrame"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Delete column"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>age</th>\n",
" <th>name</th>\n",
" <th>points</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Lagos</th>\n",
" <td>30</td>\n",
" <td>George</td>\n",
" <td>674</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Dubai</th>\n",
" <td>24</td>\n",
" <td>Ann</td>\n",
" <td>3435</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Mumbai</th>\n",
" <td>50</td>\n",
" <td>Tino</td>\n",
" <td>34</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Accra</th>\n",
" <td>21</td>\n",
" <td>Charles</td>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Yuma</th>\n",
" <td>23</td>\n",
" <td>Phil</td>\n",
" <td>222</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" age name points\n",
"Lagos 30 George 674\n",
"Dubai 24 Ann 3435\n",
"Mumbai 50 Tino 34\n",
"Accra 21 Charles 10\n",
"Yuma 23 Phil 222"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_frame.drop('year', axis=1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Delete row"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>age</th>\n",
" <th>name</th>\n",
" <th>year</th>\n",
" <th>points</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Lagos</th>\n",
" <td>30</td>\n",
" <td>George</td>\n",
" <td>2012</td>\n",
" <td>674</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Dubai</th>\n",
" <td>24</td>\n",
" <td>Ann</td>\n",
" <td>2012</td>\n",
" <td>3435</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Accra</th>\n",
" <td>21</td>\n",
" <td>Charles</td>\n",
" <td>2020</td>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Yuma</th>\n",
" <td>23</td>\n",
" <td>Phil</td>\n",
" <td>2014</td>\n",
" <td>222</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" age name year points\n",
"Lagos 30 George 2012 674\n",
"Dubai 24 Ann 2012 3435\n",
"Accra 21 Charles 2020 10\n",
"Yuma 23 Phil 2014 222"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_frame.drop('Mumbai', axis=0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Delete definitively"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
"data_frame.drop('Mumbai', axis=0, inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>age</th>\n",
" <th>name</th>\n",
" <th>year</th>\n",
" <th>points</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Lagos</th>\n",
" <td>30</td>\n",
" <td>George</td>\n",
" <td>2012</td>\n",
" <td>674</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Dubai</th>\n",
" <td>24</td>\n",
" <td>Ann</td>\n",
" <td>2012</td>\n",
" <td>3435</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Accra</th>\n",
" <td>21</td>\n",
" <td>Charles</td>\n",
" <td>2020</td>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Yuma</th>\n",
" <td>23</td>\n",
" <td>Phil</td>\n",
" <td>2014</td>\n",
" <td>222</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" age name year points\n",
"Lagos 30 George 2012 674\n",
"Dubai 24 Ann 2012 3435\n",
"Accra 21 Charles 2020 10\n",
"Yuma 23 Phil 2014 222"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_frame"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Selecting Rows in a DataFrame"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"age 24\n",
"name Ann\n",
"year 2012\n",
"points 3435\n",
"Name: Dubai, dtype: object"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_frame.loc['Dubai']"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"age 23\n",
"name Phil\n",
"year 2014\n",
"points 222\n",
"Name: Yuma, dtype: object"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_frame.iloc[3]"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>age</th>\n",
" <th>name</th>\n",
" <th>year</th>\n",
" <th>points</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Lagos</th>\n",
" <td>30</td>\n",
" <td>George</td>\n",
" <td>2012</td>\n",
" <td>674</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Dubai</th>\n",
" <td>24</td>\n",
" <td>Ann</td>\n",
" <td>2012</td>\n",
" <td>3435</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" age name year points\n",
"Lagos 30 George 2012 674\n",
"Dubai 24 Ann 2012 3435"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_frame.iloc[[0, 1]]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### To select the subset of a row and column"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'George'"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_frame.loc['Lagos', 'name']"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>name</th>\n",
" <th>points</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Lagos</th>\n",
" <td>George</td>\n",
" <td>674</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Accra</th>\n",
" <td>Charles</td>\n",
" <td>10</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" name points\n",
"Lagos George 674\n",
"Accra Charles 10"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_frame.loc[['Lagos', 'Accra'], ['name', 'points']]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Conditional selection"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [],
"source": [
"data_frame = pd.DataFrame(data=np.random.randn(5, 4),\n",
" index=['A', 'B', 'C', 'D', 'E'],\n",
" columns=['W', 'X', 'Y', 'Z'])"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>W</th>\n",
" <th>X</th>\n",
" <th>Y</th>\n",
" <th>Z</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>C</th>\n",
" <td>0.345001</td>\n",
" <td>0.170481</td>\n",
" <td>1.536828</td>\n",
" <td>-1.985748</td>\n",
" </tr>\n",
" <tr>\n",
" <th>D</th>\n",
" <td>1.517225</td>\n",
" <td>0.899145</td>\n",
" <td>1.345495</td>\n",
" <td>0.127641</td>\n",
" </tr>\n",
" <tr>\n",
" <th>E</th>\n",
" <td>0.671808</td>\n",
" <td>1.059403</td>\n",
" <td>-0.516939</td>\n",
" <td>-0.491974</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" W X Y Z\n",
"C 0.345001 0.170481 1.536828 -1.985748\n",
"D 1.517225 0.899145 1.345495 0.127641\n",
"E 0.671808 1.059403 -0.516939 -0.491974"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_frame[data_frame['W'] > 0]"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"C 0.170481\n",
"D 0.899145\n",
"E 1.059403\n",
"Name: X, dtype: float64"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_frame[data_frame['W'] > 0]['X']"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>X</th>\n",
" <th>Y</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>C</th>\n",
" <td>0.170481</td>\n",
" <td>1.536828</td>\n",
" </tr>\n",
" <tr>\n",
" <th>D</th>\n",
" <td>0.899145</td>\n",
" <td>1.345495</td>\n",
" </tr>\n",
" <tr>\n",
" <th>E</th>\n",
" <td>1.059403</td>\n",
" <td>-0.516939</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" X Y\n",
"C 0.170481 1.536828\n",
"D 0.899145 1.345495\n",
"E 1.059403 -0.516939"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_frame[data_frame['W'] > 0][['X', 'Y']]"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>W</th>\n",
" <th>X</th>\n",
" <th>Y</th>\n",
" <th>Z</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>E</th>\n",
" <td>0.671808</td>\n",
" <td>1.059403</td>\n",
" <td>-0.516939</td>\n",
" <td>-0.491974</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" W X Y Z\n",
"E 0.671808 1.059403 -0.516939 -0.491974"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_frame[(data_frame['W'] > 0) & (data_frame['X'] > 1)]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Reseting the Index of a DataFrame"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>index</th>\n",
" <th>W</th>\n",
" <th>X</th>\n",
" <th>Y</th>\n",
" <th>Z</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>A</td>\n",
" <td>-0.379437</td>\n",
" <td>0.236841</td>\n",
" <td>0.142426</td>\n",
" <td>-0.148394</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>B</td>\n",
" <td>-0.513635</td>\n",
" <td>2.581328</td>\n",
" <td>-1.391317</td>\n",
" <td>0.362272</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>C</td>\n",
" <td>0.345001</td>\n",
" <td>0.170481</td>\n",
" <td>1.536828</td>\n",
" <td>-1.985748</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>D</td>\n",
" <td>1.517225</td>\n",
" <td>0.899145</td>\n",
" <td>1.345495</td>\n",
" <td>0.127641</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>E</td>\n",
" <td>0.671808</td>\n",
" <td>1.059403</td>\n",
" <td>-0.516939</td>\n",
" <td>-0.491974</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" index W X Y Z\n",
"0 A -0.379437 0.236841 0.142426 -0.148394\n",
"1 B -0.513635 2.581328 -1.391317 0.362272\n",
"2 C 0.345001 0.170481 1.536828 -1.985748\n",
"3 D 1.517225 0.899145 1.345495 0.127641\n",
"4 E 0.671808 1.059403 -0.516939 -0.491974"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_frame.reset_index()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Setting the Index of a DataFrame"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### First, we create a new Column 'ID'"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>W</th>\n",
" <th>X</th>\n",
" <th>Y</th>\n",
" <th>Z</th>\n",
" <th>ID</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>A</th>\n",
" <td>-0.379437</td>\n",
" <td>0.236841</td>\n",
" <td>0.142426</td>\n",
" <td>-0.148394</td>\n",
" <td>df1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>B</th>\n",
" <td>-0.513635</td>\n",
" <td>2.581328</td>\n",
" <td>-1.391317</td>\n",
" <td>0.362272</td>\n",
" <td>df2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>C</th>\n",
" <td>0.345001</td>\n",
" <td>0.170481</td>\n",
" <td>1.536828</td>\n",
" <td>-1.985748</td>\n",
" <td>df3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>D</th>\n",
" <td>1.517225</td>\n",
" <td>0.899145</td>\n",
" <td>1.345495</td>\n",
" <td>0.127641</td>\n",
" <td>df4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>E</th>\n",
" <td>0.671808</td>\n",
" <td>1.059403</td>\n",
" <td>-0.516939</td>\n",
" <td>-0.491974</td>\n",
" <td>df5</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" W X Y Z ID\n",
"A -0.379437 0.236841 0.142426 -0.148394 df1\n",
"B -0.513635 2.581328 -1.391317 0.362272 df2\n",
"C 0.345001 0.170481 1.536828 -1.985748 df3\n",
"D 1.517225 0.899145 1.345495 0.127641 df4\n",
"E 0.671808 1.059403 -0.516939 -0.491974 df5"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_frame['ID'] = ['df1', 'df2', 'df3', 'df4', 'df5']\n",
"data_frame"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Now, we will set the 'ID' Column to be out index "
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>W</th>\n",
" <th>X</th>\n",
" <th>Y</th>\n",
" <th>Z</th>\n",
" </tr>\n",
" <tr>\n",
" <th>ID</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>df1</th>\n",
" <td>-0.379437</td>\n",
" <td>0.236841</td>\n",
" <td>0.142426</td>\n",
" <td>-0.148394</td>\n",
" </tr>\n",
" <tr>\n",
" <th>df2</th>\n",
" <td>-0.513635</td>\n",
" <td>2.581328</td>\n",
" <td>-1.391317</td>\n",
" <td>0.362272</td>\n",
" </tr>\n",
" <tr>\n",
" <th>df3</th>\n",
" <td>0.345001</td>\n",
" <td>0.170481</td>\n",
" <td>1.536828</td>\n",
" <td>-1.985748</td>\n",
" </tr>\n",
" <tr>\n",
" <th>df4</th>\n",
" <td>1.517225</td>\n",
" <td>0.899145</td>\n",
" <td>1.345495</td>\n",
" <td>0.127641</td>\n",
" </tr>\n",
" <tr>\n",
" <th>df5</th>\n",
" <td>0.671808</td>\n",
" <td>1.059403</td>\n",
" <td>-0.516939</td>\n",
" <td>-0.491974</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" W X Y Z\n",
"ID \n",
"df1 -0.379437 0.236841 0.142426 -0.148394\n",
"df2 -0.513635 2.581328 -1.391317 0.362272\n",
"df3 0.345001 0.170481 1.536828 -1.985748\n",
"df4 1.517225 0.899145 1.345495 0.127641\n",
"df5 0.671808 1.059403 -0.516939 -0.491974"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_frame.set_index('ID')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Multi-level index (MultiIndex) and Index Hierarchy"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[('0 Level', 21),\n",
" ('0 Level', 22),\n",
" ('0 Level', 23),\n",
" ('A Level', 21),\n",
" ('A Level', 22),\n",
" ('A Level', 23)]"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"outside = ['0 Level', '0 Level', '0 Level', 'A Level', 'A Level', 'A Level']\n",
"inside = [21, 22, 23, 21, 22, 23]\n",
"\n",
"indexes = list(zip(outside, inside))\n",
"indexes"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Using MultiIndex.from_tuples() function, we will create a multi-leve index"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"MultiIndex(levels=[['0 Level', 'A Level'], [21, 22, 23]],\n",
" labels=[[0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 1, 2]])"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"indexes = pd.MultiIndex.from_tuples(indexes)\n",
"indexes"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Finally, let's convert out multi-leve data into a DataFrame"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th></th>\n",
" <th>A</th>\n",
" <th>B</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"3\" valign=\"top\">0 Level</th>\n",
" <th>21</th>\n",
" <td>-0.713027</td>\n",
" <td>1.287223</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>1.357808</td>\n",
" <td>-0.777819</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>0.283293</td>\n",
" <td>1.388174</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"3\" valign=\"top\">A Level</th>\n",
" <th>21</th>\n",
" <td>-0.867373</td>\n",
" <td>-1.388114</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>-0.421166</td>\n",
" <td>0.142394</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>0.240072</td>\n",
" <td>2.179087</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A B\n",
"0 Level 21 -0.713027 1.287223\n",
" 22 1.357808 -0.777819\n",
" 23 0.283293 1.388174\n",
"A Level 21 -0.867373 -1.388114\n",
" 22 -0.421166 0.142394\n",
" 23 0.240072 2.179087"
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_frame = pd.DataFrame(np.random.randn(6, 2), index=indexes, columns=['A', 'B'])\n",
"data_frame"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Let's grab everything undex '0 Level'"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>A</th>\n",
" <th>B</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>-0.713027</td>\n",
" <td>1.287223</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>1.357808</td>\n",
" <td>-0.777819</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>0.283293</td>\n",
" <td>1.388174</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A B\n",
"21 -0.713027 1.287223\n",
"22 1.357808 -0.777819\n",
"23 0.283293 1.388174"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_frame.loc['0 Level']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### We can go futher to grab the data in '21'"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"A -0.713027\n",
"B 1.287223\n",
"Name: 21, dtype: float64"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_frame.loc['0 Level'].loc[21]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### We can give them names using .index.names"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th></th>\n",
" <th>A</th>\n",
" <th>B</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Levels</th>\n",
" <th>Num</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"3\" valign=\"top\">0 Level</th>\n",
" <th>21</th>\n",
" <td>-0.713027</td>\n",
" <td>1.287223</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>1.357808</td>\n",
" <td>-0.777819</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>0.283293</td>\n",
" <td>1.388174</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"3\" valign=\"top\">A Level</th>\n",
" <th>21</th>\n",
" <td>-0.867373</td>\n",
" <td>-1.388114</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>-0.421166</td>\n",
" <td>0.142394</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>0.240072</td>\n",
" <td>2.179087</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A B\n",
"Levels Num \n",
"0 Level 21 -0.713027 1.287223\n",
" 22 1.357808 -0.777819\n",
" 23 0.283293 1.388174\n",
"A Level 21 -0.867373 -1.388114\n",
" 22 -0.421166 0.142394\n",
" 23 0.240072 2.179087"
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_frame.index.names = ['Levels', 'Num']\n",
"data_frame"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"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": "3.6.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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