Reindexing pandas Series And Dataframes
Series
/* Import Modules */
import pandas as pd
import numpy as np/* 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'])
brushFireRiskBisbee 34
Douglas 23
Sierra Vista 12
Tombstone 23
dtype: int64
/* Reindex the series and create a new series variable */
brushFireRiskReindexed = brushFireRisk.reindex(['Tombstone', 'Douglas', 'Bisbee', 'Sierra Vista', 'Barley', 'Tucson'])
brushFireRiskReindexedTombstone 23.0
Douglas 23.0
Bisbee 34.0
Sierra Vista 12.0
Barley NaN
Tucson NaN
dtype: float64
/* Reindex the series and fill in any missing indexes as 0 */
brushFireRiskReindexed = brushFireRisk.reindex(['Tombstone', 'Douglas', 'Bisbee', 'Sierra Vista', 'Barley', 'Tucson'], fill_value = 0)
brushFireRiskReindexedTombstone 23
Douglas 23
Bisbee 34
Sierra Vista 12
Barley 0
Tucson 0
dtype: int64
DataFrames
/* 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| county | reports | year | |
|---|---|---|---|
| 0 | Cochice | 4 | 2012 |
| 1 | Pima | 24 | 2012 |
| 2 | Santa Cruz | 31 | 2013 |
| 3 | Maricopa | 2 | 2014 |
| 4 | Yuma | 3 | 2014 |
/* Change the order (the index) of the rows */
df.reindex([4, 3, 2, 1, 0])| county | reports | year | |
|---|---|---|---|
| 4 | Yuma | 3 | 2014 |
| 3 | Maricopa | 2 | 2014 |
| 2 | Santa Cruz | 31 | 2013 |
| 1 | Pima | 24 | 2012 |
| 0 | Cochice | 4 | 2012 |
/* Change the order (the index) of the columns */
columnsTitles = ['year', 'reports', 'county']
df.reindex(columns=columnsTitles)| year | reports | county | |
|---|---|---|---|
| 0 | 2012 | 4 | Cochice |
| 1 | 2012 | 24 | Pima |
| 2 | 2013 | 31 | Santa Cruz |
| 3 | 2014 | 2 | Maricopa |
| 4 | 2014 | 3 | Yuma |
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