Title: Reindexing pandas Series And Dataframes
Slug: pandas_dataframe_reindexing
Summary: Reindexing pandas Series And Dataframes
Date: 2016-05-01 12:00
Category: Python
Tags: Data Wrangling
Authors: Chris Albon

Series



In [1]:

    
# Import Modules
import pandas as pd
import numpy as np



In [2]:

    
# Create a pandas series of the risk of fire in Southern Arizona
brushFireRisk = pd.Series([34, 23, 12, 23], index = ['Bisbee', 'Douglas', 'Sierra Vista', 'Tombstone'])
brushFireRisk









    Out[2]:





Bisbee          34
Douglas         23
Sierra Vista    12
Tombstone       23
dtype: int64



In [3]:

    
# Reindex the series and create a new series variable
brushFireRiskReindexed = brushFireRisk.reindex(['Tombstone', 'Douglas', 'Bisbee', 'Sierra Vista', 'Barley', 'Tucson'])
brushFireRiskReindexed









    Out[3]:





Tombstone       23.0
Douglas         23.0
Bisbee          34.0
Sierra Vista    12.0
Barley           NaN
Tucson           NaN
dtype: float64



In [4]:

    
# Reindex the series and fill in any missing indexes as 0
brushFireRiskReindexed = brushFireRisk.reindex(['Tombstone', 'Douglas', 'Bisbee', 'Sierra Vista', 'Barley', 'Tucson'], fill_value = 0)
brushFireRiskReindexed









    Out[4]:





Tombstone       23
Douglas         23
Bisbee          34
Sierra Vista    12
Barley           0
Tucson           0
dtype: int64

DataFrames



In [5]:

    
# Create a dataframe
data = {'county': ['Cochice', 'Pima', 'Santa Cruz', 'Maricopa', 'Yuma'], 
        'year': [2012, 2012, 2013, 2014, 2014], 
        'reports': [4, 24, 31, 2, 3]}
df = pd.DataFrame(data)
df



In [6]:

    
# Change the order (the index) of the rows
df.reindex([4, 3, 2, 1, 0])



In [7]:

    
# Change the order (the index) of the columns
columnsTitles = ['year', 'reports', 'county']
df.reindex(columns=columnsTitles)

	county	reports	year
0	Cochice	4	2012
1	Pima	24	2012
2	Santa Cruz	31	2013
3	Maricopa	2	2014
4	Yuma	3	2014