---
title: Virgil - Intro To Pandas Seaborn - S71 Pivot Table
tags: Virgil, LearnWorld, IntroPandasSeaborn
---
<a target="_blank" href="https://colab.research.google.com/drive/17fdmTUJHaIRGn3jKZ0mIGfIDNHAQZOGh"><img src="https://www.tensorflow.org/images/colab_logo_32px.png" />Run in Google Colab</a>

## Pivot Table

A pivot table is a table of statistics that summarizes the data of a more extensive table. This summary might include sums, averages, or other statistics, which the pivot table groups together in a meaningful way.

OFFICIAL DOCUMENTATION ▸ https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.pivot_table.html

EXPLANATION ▸ https://pbpython.com/pandas-pivot-table-explained.html

df.groupby(['day', 'sex'])['total_bill'].mean()

day sex
Fri Female 14.145556
Male 19.857000
Sat Female 19.680357
Male 20.802542
Sun Female 19.872222
Male 21.887241
Thur Female 16.715312
Male 18.714667
Name: total_bill, dtype: float64

# Finding average pledged by states and main_categories
pd.pivot_table(data=df, index='day', columns='sex', values='tip', aggfunc='sum')

<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}

.dataframe tbody tr th {
vertical-align: top;
}

.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th>sex</th>
<th>Female</th>
<th>Male</th>
</tr>
<tr>
<th>day</th>
<th></th>
<th></th>
</tr>
</thead>
<tbody>
<tr>
<th>Fri</th>
<td>25.03</td>
<td>26.93</td>
</tr>
<tr>
<th>Sat</th>
<td>78.45</td>
<td>181.95</td>
</tr>
<tr>
<th>Sun</th>
<td>60.61</td>
<td>186.78</td>
</tr>
<tr>
<th>Thur</th>
<td>82.42</td>
<td>89.41</td>
</tr>
</tbody>
</table>
</div>

# Perform pivot table on multiple value

pd.pivot_table(data=df,
               index='day', 
               columns='sex',
               values=['tip', 'total_bill'],
               aggfunc='mean')

<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}

.dataframe tbody tr th {
vertical-align: top;
}

.dataframe thead tr th {
text-align: left;
}

.dataframe thead tr:last-of-type th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr>
<th></th>
<th colspan="2" halign="left">tip</th>
<th colspan="2" halign="left">total_bill</th>
</tr>
<tr>
<th>sex</th>
<th>Female</th>
<th>Male</th>
<th>Female</th>
<th>Male</th>
</tr>
<tr>
<th>day</th>
<th></th>
<th></th>
<th></th>
<th></th>
</tr>
</thead>
<tbody>
<tr>
<th>Fri</th>
<td>2.781111</td>
<td>2.693000</td>
<td>14.145556</td>
<td>19.857000</td>
</tr>
<tr>
<th>Sat</th>
<td>2.801786</td>
<td>3.083898</td>
<td>19.680357</td>
<td>20.802542</td>
</tr>
<tr>
<th>Sun</th>
<td>3.367222</td>
<td>3.220345</td>
<td>19.872222</td>
<td>21.887241</td>
</tr>
<tr>
<th>Thur</th>
<td>2.575625</td>
<td>2.980333</td>
<td>16.715312</td>
<td>18.714667</td>
</tr>
</tbody>
</table>
</div>

# Perform pivot table on multiple value and multiple aggregation function

pd.pivot_table(data=df, 
               index='day', 
               columns='sex',
               values=['tip', 'total_bill'],
               aggfunc='mean')

# Perform pivot table on two set of values with different aggregation functions

pd.pivot_table(data=df, 
               index='day', 
               columns='sex',
               values=['tip', 'total_bill'],
               aggfunc={'tip':'mean', 'total_bill':'sum'})