Python Pandas replace multiple values – 15 examples

In this Python Pandas tutorial, we will learn how to replace multiple values in Pandas DataFrame using Python. Also, we will cover these topics.

• Pandas replace multiple values in a column based on condition
• Pandas replace multiple values in column
• Pandas replace multiple values with one value
• Pandas replace multiple values regex
• Pandas replace multiple values with nan
• Pandas replace multiple values in one column
• Pandas replace multiple row values
• Pandas replace specific values in multiple columns
• Pandas replace multiple values in multiple columns based on condition
• Pandas replace multiple values at once
• Pandas replace multiple characters at once
• Pandas replace multiple string values
• Pandas replace multiple characters in string
• Pandas str.replace multiple values
• Pandas remove multiple values from list
• Pandas find and replace multiple values

Pandas replace mutiple values

• In this Program, we will discuss how to replace multiple values in Pandas Python.
• To replace multiple values in a DataFrame we can apply the method DataFrame.replace(). In Pandas DataFrame replace method is used to replace values within a dataframe object.
• In Python, we can use this technique to replace multiple columns and this method is also used for replacing a regex, dictionary, and series from the Pandas DataFrame.

Syntax:

Here is the Syntax of DataFrame.replace() method

DataFrame.replace
                 (
                  to_replace=None,
                  value=None,
                  inplace=False,
                  limit=None,
                  regex=False,
                  method='pad'
                 )

• It consists of a few parameters
  • to_replace: This parameter specifies the value which we want to replace.
  • Value: By default its value is none and it specifies the new replacement value.
  • inplace: If the condition is True then it replaces and will return None. By default its value is False.
  • limit: It specifies the maximum size gap and by default its value is None.
  • regex: This parameter check the condition if it is true then to_replace must be string.
  • method: It is a method to use for replacement values.

Example:

Let’s take an example and check how to replace multiple values in DataFrame

import pandas as pd

new_data = pd.DataFrame([
	[18,-78, 47],
	[19, 84, -92],
    [94, 73, 668],
    [809, 719, -356],
	[113,115,119]],
	columns=['m', 'o', 'p'])

new_result = new_data.replace({'m':{94:19, 809:123}, 'p':{-92:63, -356:189}})
print(new_result)

In the above program, we will use the replace() method to replace the value in Dataframe. In this example, we will replace 94 with 19 and 809 with 123 in column ‘m’. Similarly, we will replace the value in column ‘p’.

Here is the execution of the following given code

Read: Python Pandas Drop Rows

Pandas replace multiple values in a column based on condition

• Let us see how to replace multiple values in a column based on condition.
• By using DataFrame.loc method we can perform this particular task. In Pandas DataFrame the loc() method is used to specify the name of the columns and rows that we need to filter out. So we can also filter the data by using the loc() method.
• It is a unique method and retrieves rows from a Pandas DataFramem and this method takes only index labels. When using the loc method on a Pandas DataFrame we have specified which rows and columns we want to adjust in the format.

Syntax:

DataFrame.loc()

Example:

import pandas as pd

new_val = pd.DataFrame([
	[89, 17, 15],
	[115, 178, -5],
	[114, 190, 824]],
	columns=['val1', 'val2', 'val3'])

new_val.loc[(new_val.val1 < 100), 'val1'] = 0
print(new_val)

In the above code, we have replaced those values in the column ‘val1′ which satisfies the condition. In this example, we have declared a condition if the column value is less than 100 then it will be replaced from DataFrame.

Here is the implementation of the following given code

By using NumPy.where function

In Python to replace values in columns based on condition, we can use the method numpy. where(). In Python, this method will help the user to return the indices of elements from a numpy array after filtering based on a given condition.

Syntax:

Here is the Syntax of numpy.where() method

numpy.where(condition[,x,y])

Note: x and y parameter specifies the values from which to choose and always return the array with elements

Example:

import pandas as pd
import numpy as np

new_data = pd.DataFrame([
	[18, 278, 897],
	[55, 77, 91],
	[678, 456, 118]],
	columns=['z', 'u', 't'])

new_data['u'] = np.where((new_data.u < 200), 44, new_data.u)
print(new_data)

In the above code, we have used the numpy.where() method and we have replaced those values in the column ‘u’ which satisfies the condition. In this example, we have declared a condition if the column value is less than 200 then it will be replaced by 44.

Here is the implementation of the following given code

Another approach to replace multiple values in a column based on condition by using DataFrame.where() function.

In this method, we are going to specify the column name which values have to be replaced, and also we will replace the existing values in the given column based on the condition.

Syntax:

DataFrame.where
               (
                cond,
                other=nan,
                inplace=False,
                axis=None,
                Level=None,
                error='raise',
                try_cast=Nodefault.no_default
               )

Source Code:

import pandas as pd

data = pd.DataFrame([
	[76, 119,168],
	[346, 345, 156],
	[99, 145, 908]],
	columns=['Col1', 'Col2', 'Col3'])

data['Col3'].where(~(data.Col3 < 200), other=76, inplace=True)
print(data)

Here is the output of the following given code

Read: How to Convert Pandas DataFrame to a Dictionary

Pandas replace multiple values in column

• Let us see how to replace multiple values in a specific column using Python Pandas.
• In this example, we will see how to replace multiple values inside the column. To do this task we will use the Python inbuilt function DataFrame.replace.
• By using DataFrame.replace() method we will replace multiple values with multiple new strings or text for an individual DataFrame column. This method searches the entire Pandas DataFrame and replaces every specified value.

Source Code:

import pandas as pd                                                                                                                                

Country_name = pd.DataFrame({'Country_name':['Germany', 'England', 'France', 'England', 'Germany']})                                                                    
b= Country_name.replace({'Country_name':{'England' : 'China', 'Germany' : 'japan' }})                                                                                         

print(b)

In this program, we are going to specify the column name ‘Country_name’ which values have to be replaced, In the above code we have declared specific values which we want to replace with the new value.

Here is the Screenshot of the following given code

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Pandas replace multiple values with one value

• Here we can see how to replace multiple values with one value.
• In this program, we will replace multiple strings with one specific string. To perform this particular task we can apply the combination of replacing and dict.fromkeys() method.
• In Python, the dict. fromkeys() is a built-in function that declares a new dictionary with keys from the given sequence and values.

Syntax:

Here is the Syntax of fromkeys() method

dict.fromkeys(seq[,value])

• It consists of few parameters
  • seq: This parameter specifies the list of all values.
  • Value: This is an optional paramter and by default its value is none.

Example:

import pandas as pd                                                                                                                                

Employee_name = pd.DataFrame({'Employee_name':['john','george','Micheal','oliva']})                                                                    
print(Employee_name)
b= Employee_name.replace(dict.fromkeys(['john','george','Micheal','oliva'], 'Micheal'))
print("After replacing the values:")
print(b)

In the above program, we have replaced all the strings with the ‘Micheal’ string. Once you will print ‘b’ the output will display only ‘Micheal’ values in the DataFrame.

You can refer to the below Screenshot

Read: Python convert DataFrame to list

Pandas replace multiple values regex

• In this program, we will discuss how to replace multiple values in Pandas DataFrame by using the regex method.
• Here we can see how to replace the string that matches the regular expression. In Python to replace a string using regular expression using the replace() method.
• In Python, regular expressions are used to match character combinations in a given string and it also defines a search pattern. The Python module ‘re’ provides regular expression.

Syntax:

DataFrame.replace
                 (
                  to_replace=None,
                  value=None,
                  inplace=False,
                  limit=None,
                  regex=False,
                  method='pad'
                 )

Source Code:

import pandas as pd                                                                                                                                

df = pd.DataFrame({'City':['Ger-many', 'Eng-land', 'Fr-ance', 'Eng-land', 'Ger-many']})  
new_value = {
   'CITY': {
      r'(G.*Ge|Germany.*)': 'Ger-many',
      r'E[ng]*[oo]*.*': 'Fraan ce'}
}

b= df.replace(new_value, regex=True, inplace=True)
print(b)

Here is the Screenshot of the following given code

Read: Python Pandas DataFrame Iterrows

Pandas replace multiple values with nan

• Here we can see how to replace multiple values with nan.
• In this Program, we will understand how to replace values with nan values. To do this task we will apply the concept of DataFrame.replace() method.
• In Python nan stands for Not a Number and it represents the missing values in the DataFrame and these are the special values in Python.

Source Code:

import pandas as pd
import numpy as np

new_data = pd.DataFrame([
	[76,889, 156],
	[19, 84, 467],
    [94, 73, 321],
    [116, 609,557],
	[156,864,467]],
	columns=['u', 'o', 'z'])

new_result = new_data.replace({'o':{84:np.nan, 609:np.nan}, 'u':{94:np.nan, 156:np.nan}})
print(new_result)

In the above program, we will use the replace() method to replace the value in Dataframe. In this example, we will replace 84 with nan and 809 with nan in column ‘o’. Similarly, we will replace the value in column ‘u’.

Here is the execution of the following given code

Read: Count Rows in Pandas DataFrame

Pandas replace multiple values in one column

• In this program, we will discuss how to replace multiple values in one specific column.
• To perform this task we will use the method DataFrame.replace() and check to replace() method will solve this problem. In this example, we will replace ‘Country_name’ values with the new integer value under the ‘Country_name’ column.
• In Pandas DataFrame replace method is used to replace values within a dataframe object.

Source Code:

import pandas as pd                                                                                                                                

Country_name = pd.DataFrame({'Country_name':['China', 'Malaysia', 'Newzealand', 'Paris', 'Bangladesh']})                                                                    
m= Country_name.replace({'Country_name':{'Malaysia' : 56, 'Paris' : 778 }})                                                                                         

print(m)

In the above program, we want to replace multiple values with multiple new values for an individual or specific Pandas DataFrame column.

Here is the output of the following given code.

Read: Python Pandas Write DataFrame to Excel

Pandas replace multiple row values

• Let us see how to replace multiple row values in Pandas DataFrame.
• By using DataFrame.loc method we can perform this particular task. In Pandas DataFrame the loc() method is used to specify the name of the columns and rows that we need to filter out. So we can also filter the data by using the loc() method.
• In Python, this method retrieves rows from a Pandas DataFrame and it is also used with a boolean array.

Example:

import pandas as pd

new_data = pd.DataFrame([
	[187, 634, 138],
	[779, 908, 346],
	[459, 709, 116]],
	columns=['new_val1', 'new_val2', 'new_val3'])

new_data.loc[(new_data.new_val2 < 800), 'new_val2'] = 92
print(new_data)

In the above code, we have replaced those values in the column ‘new_val2′ which satisfies the condition. In this example, we have declared a condition if the column value is less than 800 then it will be replaced from DataFrame.

You can refer to the below Screenshot

Read: Get index Pandas Python

Pandas replace specific values in multiple columns

• In this program, we will discuss how to replace specific values in multiple columns.
• In this example, the keys specify the column values and we want to replace specific values which is available in the DataFrame.
• Here we can use the Pandas DataFrame.replace() function to change or modify multiple column values. To do this task first create data by using sample function from the random module and then create two lists in which data has been stored.

Source Code:

import pandas as pd 
from random import sample

new_val = ["Rose", "Lilly","Jasmine","Lotus"]
my_val1 = sample(new_val,4)
my_val2 = sample(new_val,4)
my_val3 = sample(new_val,4)
df = pd.DataFrame({"col1":my_val1,
              "col2":my_val2,
              "col3":my_val3,
             })
print(df)
b= df.replace({"Rose":"Banana",
            "Lilly":"Apple",
            "Jasmine":"Oranges",
            "Lotus":"Grapes"})
print(b)

In the above example, we have created a DataFrame object and inserts three columns into it. Now we want to replace the values of all given specific columns.

Here is the implementation of the following given code

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Pandas replace multiple values in multiple columns based on condition

• In this Program, we will discuss how to replace multiple values in multiple columns that are based on the condition.
• By using Pandas.Series.map() method we can solve this task. This method is used to map the values from two given series that have a specific column and the end column of the series should be the same as the index column.

Syntax:

Here is the Syntax of Pandas.Series.map() method

Series.map(arg, na_action=None)

Example:

import pandas as pd 

new_df = pd.DataFrame(dict(
    new_val=['values {}'.format(m) for m in [8, 9, 6, 9, 8]],
    Other=range(5)
))
print(new_df)
dct = {'value 1': 6789,
       'value 2': 95678,
       'value 3': 399456}
b= new_df.assign(new_val=new_df.new_val.map(dct))
print(b)

Here is the Screenshot of the following given code

Read: How to Set Column as Index in Python Pandas

Pandas replace multiple values at once

• Let us see how to replace multiple values at once in a Pandas DataFrame.
• To perform this task we can use the DataFrame.replace() method for replacing multiple values at once.
• In this example, we have created multiple columns ‘val1’ and ‘val2’ in which we have passed the string values and then created a DataFrame object and assign columns name in the list.

Source Code:

import pandas as pd

new_val = {'Val1':  ['Rose','Rose','Lilly','Jasmine','Jasmine','Rose','Lilly','Lilly'],
          'Val2': ['Australia','Germany','Germany','Australia','Germany','Germany','Australia','Australia']
         }

df = pd.DataFrame(new_val, columns= ['Val1','Val2'])
df['Val1'] = df['Val1'].replace(['Lilly'],'Lotus')
df['Val2'] = df['Val2'].replace(['Germany'],'China')

print (df)

In the above program, once you will print ‘df’ then the output will display ‘Lotus’ value instead of ‘Lilly’. Similarly in the second column, the ‘Germany’ value has been replaced with ‘China’.

Here is the Screenshot of the following given code

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Pandas replace multiple string values

• Here we can see how to replace multiple string values in Pandas DataFrame.
• In Python string is a collection of characters and a character is simply a symbol and the string can be denoted within the single or double-quotes. Now we want to replace multiple strings values in a Pandas DataFrame.
• To do this task we will use the concept of replace() method. This method replaces all the occurrences of a string with a new string.

Example:

import pandas as pd                                                                                                                                

Fruit_name = pd.DataFrame({'Fruit_name':['Mangoes', 'Apple', 'Grapes', 'Litchi', 'Cherry']})                                                                    
m =Fruit_name.replace({'Fruit_name':{'Apple' : 'Banana', 'Grapes' : 'Pears' }})                                                                                         

print("Replace string values:",m)

In the above code, we want to replace multiple string values with multiple new string values for an individual DataFrame column. In this example, we will replace the ‘Apple’ string value with ‘Banana’.

You can refer to the below Screenshot

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Pandas replace multiple characters in string

• Here we can see how to replace multiple characters in a string by using DataFrame.
• In this example, we will apply Python inbuilt functions that is replace() and join(). In Python, the join() method is used to append a string with the iterable object and returns a new string by appending all of the elements which are available in the list.

Example:

import pandas as pd

df = pd.DataFrame({'High_level': ['Python language', 'Java language', 'Ruby language', 'C++ language']})

new_val = '|'.join(['Java', 'Ruby'])

df['High_level'] = df['High_level'].str.replace(new_val, 'Programming')
print(df)

In the above example, we have created a dataframe object ‘df’ and within this object, we have passed a key-value pair elements. In Pandas Dataframe the key element has been considered as a column name. In this program, we want to replace multiple characters in a given string. To do this task first we will create a list and assign replacing elements by using the join() method.

Here is the implementation of the following given code

Read: Python Pandas CSV Tutorial

Pandas str.replace multiple values

• In this Program, we will discuss how to replace multiple values in a string by using the str.replace() method.
• Here we can use the replace() method of the string datatype to replace value into a different value.
• In Python str.replace() method will help the user to replace matching elements of the old text in the given string replace with the new value.

Syntax:

str.replace(old,new [,count])

• It consists of few parameters
  • old: This parameter specifies character or text that should replaced from DataFrame.
  • new: A new string or text that will replace the old text.
  • Count: This is an optional parameter

Source Code:

import pandas as pd                                                                                                                                

Employee_name = pd.DataFrame({'Employee_name':['Chris', 'Hayden', 'Potter', 'William', 'George']})                                                                    
c= Employee_name.replace({'Employee_name':{'Chris' : 'Hemsworth', 'Potter' : 'Adam' }})                                                                                         

print(c)

In the above code first, we have imported the Pandas module and then create a dataframe object ‘Employee_name’. Now we have to use replace() method to return a new string. Once you will print the ‘c’ then the output will display the new strings in the Pandas DataFrame.

You can refer to the below Screenshot

Read: Missing Data in Pandas in Python

Pandas replace multiple values from list

• Let us see how to replace a multiple value from given list in Pandas DataFrame.
• Here we can apply the concept of DataFrame.replace() method and it will help the user to replace multiple values from list.

Source Code:

import pandas as pd
import numpy as np

new_data = pd.DataFrame([
	[168,489, 136],
	[134, 378, 996],
    [152, 73, 321],
    [116, 609,557],
	[156,864,467]],
	columns=['l', 'm', 'n'])

new_result = new_data.replace({'m':{378:960, 609:11}, 'n':{996:109, 557:338}})
print(new_result)

In the above code, we have to use the replace() method to replace the value in Dataframe. In this example, we will replace 378 with 960 and 609 with 11 in column ‘m’. Similarly, we will replace the value in column ‘n’.

Here is the Output of the following given code

Also, Read: Check If DataFrame is Empty in Python Pandas

Pandas find and replace multiple values

• Here we can see how to find and replace multiple values in Pandas DataFrame.
• By using np.where() function we can perform this task and this method will help the user to return the indices of elements from a numpy array after filtering based on a given condition.

Example:

import pandas as pd
import numpy as np

new_val = pd.DataFrame([
	[18, 278, 897],
	[55, 77, 91],
	[678, 456, 118]],
	columns=['l', 'o', 'p'])

new_val['o'] = np.where((new_val.o < 200), 56, new_val.o)
print(new_val)

Here is the Screenshot of the following given code

In this Pandas tutorial, we have learned how to replace multiple values in Pandas DataFrame using Python. Also, we have covered these topics.

• Pandas replace multiple values in a column based on condition
• Pandas replace multiple values in column
• Pandas replace multiple values with one value
• Pandas replace multiple values regex
• Pandas replace multiple values with nan
• Pandas replace multiple values in one column
• Pandas replace multiple row values
• Pandas replace specific values in multiple columns
• Pandas replace multiple values in multiple columns based on condition
• Pandas replace multiple values at once
• Pandas replace multiple characters at once
• Pandas replace multiple string values
• Pandas replace multiple characters in string
• Pandas str.replace multiple values
• Pandas remove multiple values from list
• Pandas find and replace multiple values