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Python | Pandas dataframe.replace()

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  • Last Updated : 08 Aug, 2022

Pandas dataframe.replace() function is used to replace a string, regex, list, dictionary, series, number, etc. from a Pandas Dataframe in Python. Every instance of the provided value is replaced after a thorough search of the full DataFrame.

Syntax of dataframe.replace()

Syntax: DataFrame.replace(to_replace=None, value=None, inplace=False, limit=None, regex=False, method=’pad’, axis=None) 

Parameters:

  • to_replace : [str, regex, list, dict, Series, numeric, or None] pattern that we are trying to replace in dataframe. 
  • value : Value to use to fill holes (e.g. 0), alternately a dict of values specifying which value to use for each column (columns not in the dict will not be filled). Regular expressions, strings and lists or dicts of such objects are also allowed. 
  • inplace : If True, in place. Note: this will modify any other views on this object (e.g. a column from a DataFrame). Returns the caller if this is True. 
  • limit : Maximum size gap to forward or backward fill 
  • regex : Whether to interpret to_replace and/or value as regular expressions. If this is True then to_replace must be a string. Otherwise, to_replace must be None because this parameter will be interpreted as a regular expression or a list, dict, or array of regular expressions.
  • method : Method to use when for replacement, when to_replace is a list. 

Returns: filled : NDFrame

Example:

Here, we are replacing 49.50 with 60.

Python3




import pandas as pd
  
df = {
  "Array_1": [49.50, 70],
  "Array_2": [65.1, 49.50]
}
  
data = pd.DataFrame(df)
  
print(data.replace(49.50, 60))

Output:

   Array_1  Array_2
0     60.0     65.1
1     70.0     60.0

Usages of Pandas dataframe.replace()

Here, we are going to see the implementation of dataframe.replace() methods.

 For a link to the CSV file Used in Code, click here 

Python3




# importing pandas as pd
import pandas as pd
  
# Making data frame from the csv file
df = pd.read_csv("nba.csv")
  
# Printing the first 10 rows of the data frame for visualization
df[:10]

Output: 

 

Example 1: 

We are going to replace team “Boston Celtics” with “Omega Warrior” in the ‘df’ Dataframe.

Python3




# this will replace "Boston Celtics" with "Omega Warrior"
df.replace(to_replace="Boston Celtics",
           value="Omega Warrior")

Output: 

 

Example 2:

Replacing more than one value at a time. Using python list as an argument We are going to replace team “Boston Celtics” and “Texas” with “Omega Warrior” in the ‘df’ Dataframe. 

Python3




# importing pandas as pd
import pandas as pd
  
# Making data frame from the csv file
df = pd.read_csv("nba.csv")
  
# this will replace "Boston Celtics" and "Texas" with "Omega Warrior"
df.replace(to_replace=["Boston Celtics", "Texas"],
           value="Omega Warrior")

Output: 

Notice the College column in the first row, “Texas” has been replaced with “Omega Warriors”   

 

Example 3: 

Replace the Nan value in the data frame with the -99999 value. 

Python3




# importing pandas as pd
import pandas as pd
  
# Making data frame from the csv file
df = pd.read_csv("nba.csv")
  
# will replace Nan value in dataframe with value -99999
df.replace(to_replace = np.nan, value =-99999)

Output: 

 

Notice all the Nan value in the data frame has been replaced by -99999. Though for practical purposes we should be careful with what value we are replacing nan value.


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