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Pivot Tables¶
The Pandas pivot_table() is used to calculate, aggregate, and summarize your data. It is defined as a powerful tool that aggregates data with calculations such as Sum, Count, Average, Max, and Min.
It also allows the user to sort and filter your data when the pivot table has been created. The levels in the pivot table will be stored in MultiIndex objects (hierarchical indexes) on the index and columns of the result DataFrame.
- pandas.pivot_table(data, values=None, index=None, columns=None, aggfunc=’mean’, fill_value=None, margins=False, dropna=True, margins_name=’All’, observed=False, sort=True)
In [13]:
import pandas as pd
import numpy as np
In [14]:
df = pd.DataFrame({"A": ["burger", "burger", "burger", "burger", "burger",
"fries", "fries", "fries", "fries"],
"B": ["one", "one", "one", "two", "two",
"one", "one", "two", "two"],
"C": ["small", "large", "large", "small",
"small", "large", "small", "small",
"large"],
"D": [1, 2, 2, 3, 3, 4, 5, 6, 7],
"E": [2, 4, 5, 5, 6, 6, 8, 9, 9]})
In [15]:
df
Out[15]:
| A | B | C | D | E | |
|---|---|---|---|---|---|
| 0 | burger | one | small | 1 | 2 |
| 1 | burger | one | large | 2 | 4 |
| 2 | burger | one | large | 2 | 5 |
| 3 | burger | two | small | 3 | 5 |
| 4 | burger | two | small | 3 | 6 |
| 5 | fries | one | large | 4 | 6 |
| 6 | fries | one | small | 5 | 8 |
| 7 | fries | two | small | 6 | 9 |
| 8 | fries | two | large | 7 | 9 |
In [17]:
table = pd.pivot_table(df, index=['A', 'B'])
table
Out[17]:
| D | E | ||
|---|---|---|---|
| A | B | ||
| burger | one | 1.666667 | 3.666667 |
| two | 3.000000 | 5.500000 | |
| fries | one | 4.500000 | 7.000000 |
| two | 6.500000 | 9.000000 |
In [18]:
table = pd.pivot_table(df, index=['A', 'B'], aggfunc=np.sum)
table
Out[18]:
| D | E | ||
|---|---|---|---|
| A | B | ||
| burger | one | 5 | 11 |
| two | 6 | 11 | |
| fries | one | 9 | 14 |
| two | 13 | 18 |
This first example aggregates values by taking the sum.¶
In [19]:
table = pd.pivot_table(df, values='D', index=['A', 'B'],
columns=['C'], aggfunc=np.sum)
table
Out[19]:
| C | large | small | |
|---|---|---|---|
| A | B | ||
| burger | one | 4.0 | 1.0 |
| two | NaN | 6.0 | |
| fries | one | 4.0 | 5.0 |
| two | 7.0 | 6.0 |
We can also fill missing values using the fill_value parameter.¶
In [20]:
table = pd.pivot_table(df, values='D', index=['A', 'B'],
columns=['C'], aggfunc=np.sum, fill_value=0)
table
Out[20]:
| C | large | small | |
|---|---|---|---|
| A | B | ||
| burger | one | 4 | 1 |
| two | 0 | 6 | |
| fries | one | 4 | 5 |
| two | 7 | 6 |
The next example aggregates by taking the mean across multiple columns.¶
In [24]:
table = pd.pivot_table(df, values=['D', 'E'], index=['A', 'C'],
aggfunc={'D': np.mean,
'E': np.mean})
table
Out[24]:
| D | E | ||
|---|---|---|---|
| A | C | ||
| burger | large | 2.000000 | 4.500000 |
| small | 2.333333 | 4.333333 | |
| fries | large | 5.500000 | 7.500000 |
| small | 5.500000 | 8.500000 |
We can also calculate multiple types of aggregations for any given value column.¶
In [23]:
table = pd.pivot_table(df, values=['D', 'E'], index=['A', 'C'],
aggfunc={'D': np.mean,
'E': [min, max, np.mean]})
table
Out[23]:
| D | E | ||||
|---|---|---|---|---|---|
| mean | max | mean | min | ||
| A | C | ||||
| burger | large | 2.000000 | 5 | 4.500000 | 4 |
| small | 2.333333 | 6 | 4.333333 | 2 | |
| fries | large | 5.500000 | 9 | 7.500000 | 6 |
| small | 5.500000 | 9 | 8.500000 | 8 | |
In [ ]:
