Simultaneous Replacement

The method described in the previous chapter allows you to replace specific values in one column 'manually'. But we need to perform replacements in 4 columns, which means we need to repeat the actions at least 3 more times.

However, pandas predicted that task, too. Let's consider the method that allows to perform replacement for all dataframe columns.

df.where(condition, other = values_to_replace, inplace = False)


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df.where(condition, other = values_to_replace, inplace = False)

Explanation: condition is the first parameter, if True, then keeps original values, if False, then replaces them by values specified in the other parameter. inplace - if True, then rewrites the data. If you want to 'revert' the condition to opposite, place the ~ symbol in front of it. For instance, let's replace all the zeros with the word null.

# Importing the library

import pandas as pd

# Reading the file

df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/f2947b09-5f0d-4ad9-992f-ec0b87cd4b3f/data1.csv')

# Replace 0s by words 'null'

df = df.where(~(df == 0), other = 'null')

print(df)


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# Importing the library
import pandas as pd

# Reading the file
df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/f2947b09-5f0d-4ad9-992f-ec0b87cd4b3f/data1.csv')
# Replace 0s by words 'null'
df = df.where(~(df == 0), other = 'null')
print(df)

As you can see, there are many 'null's appeared in the dataframe. If you remove the ~ symbol within the .where() method, then all values but 0 will be replaced to 'null'.

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Seksjon 1. Kapittel 7

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