Pandas Technique: Pivot Table

Pivot table in pandas is an excellent tool to summarize one or more numeric variable based on two other categorical variables. Pivot tables in pandas are popularly seen in MS Excel files. In python, Pivot tables of pandas dataframes can be created using the command: pandas. pivot_table.

import pandas as pd
 import numpy as np

df = pd.read_csv('Srt_dta.csv')
 df

 Here we calculated the mean of individual weight(kg) with color
 values: column to aggregate, optional
 (If an array is passed, it must be the same length as the data.
 The list can contain any of the other types (except list). Keys to group by on the pivot table index. If an array is passed, it is being used as the same manner as column values.)
 index: column, Grouper, array, or list of the previous

df.pivot_table(values='Weight(kg)', index='Color')

df.pivot_table(values='Weight(kg)', index='Color', aggfunc=np.median)

 aggfunc : function, list of functions, dict, default numpy.mean

 If list of functions passed, the resulting pivot table will have hierarchical columns whose top level are the function names (inferred from the function objects themselves) If dict is passed, the key is column to aggregate and value is function or list of functions.

df.pivot_table(values='Weight(kg)', index='Color', aggfunc=[np.mean, np.median])

 columns: column, Grouper, array, or list of the previous

 If an array is passed, it must be the same length as the data. The list can contain any of the other types (except list). Keys to group by on the pivot table column. If an array is passed, it is being used as the same manner as column values.

df.pivot_table(values='Weight(kg)', index='Color', columns='Breed')

 fill_value: scalar, default None

 Value to replace missing values with (in the resulting pivot table, after aggregation).

df.pivot_table(values='Weight(kg)', index='Color', columns='Breed', fill_value=0)

 margins: bool, default False

 Add all row / columns (e.g. for subtotal / grand totals)

df.pivot_table(values='Weight(kg)', index='Color', columns='Breed', fill_value=0, margins=True)