How to Replace Values in Column Based on Condition in Pandas?

In this article, we are going to discuss the various methods to replace the values in the columns of a dataset in pandas with conditions. This can be done by many methods let’s see all of those methods in detail.

Replace Values in Column Based on Condition Using  dataframe.loc[] function

With this method, we can access a group of rows or columns with a condition or a boolean array. If we can access it we can also manipulate the values, Yes! this is our first method by the dataframe.loc[] function in pandas we can access a column and change its values with a condition.

Now, we are going to change all the “male” to 1 in the gender column.

Syntax: df.loc[ df[“column_name”] == “some_value”, “column_name”] = “value”

some_value = The value that needs to be replaced

value = The value that should be placed instead.

Note: You can also use other operators to construct the condition to change numerical values.

Example: The code imports the Pandas and NumPy libraries, builds a DataFrame (‘df’) from a dictionary (‘Student’) holding student data, and then changes the value of the ‘gender’ column from “male” to “1” before printing the modified DataFrame.

Output:

 Name  gender  math score test preparation
0    John       1          50             none
1     Jay       1         100        completed
2  sachin       1          70             none
3  Geetha  female          80        completed
4  Amutha  female          75        completed
5  ganesh       1          40             none

Replace Values in Column Based on Condition Using NumPy.where() function

Another method we are going to see is with the NumPy library. NumPy is a very popular library used for calculations with 2d and 3d arrays. It gives us a very useful method where() to access the specific rows or columns with a condition. We can also use this function to change a specific value of the columns. 

This numpy.where() function should be written with the condition followed by the value if the condition is true and a value if the condition is false. Now, we are going to change all the “female” to 0 and “male” to 1 in the gender column.

syntax: df[“column_name”] = np.where(df[“column_name”]==”some_value”, value_if_true, value_if_false)

Example: The code imports the Pandas and NumPy libraries, builds a DataFrame called “df” from a dictionary called “student” that contains student data, and uses the NumPy np.where function to change the values of the “gender” column from “female” to “0” and “male” to 1. It then outputs the altered DataFrame.

Output:

Name  gender  math score test preparation
0    John       1          50             none
1     Jay       1         100        completed
2  sachin       1          70             none
3  Geetha       0          80        completed
4  Amutha       0          75        completed
5  ganesh       1          40             none

Replace Values in Column Based on Condition Using pandas masking function

Pandas masking function is made for replacing the values of any row or a column with a condition. Now using this masking condition we are going to change all the “female” to 0 in the gender column.

syntax: df[‘column_name’].mask( df[‘column_name’] == ‘some_value’, value , inplace=True )

Example: The code imports the Pandas and NumPy libraries, builds a DataFrame named “df” from a dictionary named “student” containing student data, then uses the Pandas mask function to replace the value “female” in the “gender” column with 0 before printing the modified DataFrame. It also includes a line that has been commented out to show how to conditionally replace the values in the “math score” column with “good” for scores higher than or equal to 60.

Output:

Name gender  math score test preparation
0    John   male          50             none
1     Jay   male         100        completed
2  sachin   male          70             none
3  Geetha      0          80        completed
4  Amutha      0          75        completed
5  ganesh   male          40             none