Pandas DataFrame DataFrame.where() Function

  1. Syntax of pandas.DataFrame.where()
  2. Example Codes: DataFrame.where()
  3. Example Codes: DataFrame.where() to Specify a Value
  4. Example Codes: DataFrame.where() to Use Multiple Conditions

Python Pandas DataFrame.where() function accepts a condition as a parameter and produces results accordingly. It checks the condition for each value of the DataFrame and selects the values that accept the condition. The functionality of it is similar to the if-else statement. The value which does not accept the condition is replaced with a NaN value by default.

Syntax of pandas.DataFrame.where()

DataFrame.where(cond,
                other= NaN,
                inplace= False,
                axis= None,
                level= None,
                errors= 'raise',
                try_cast= False) 

Parameters

This function has several parameters. The default values of all the parameters are mentioned above.

cond It is a Boolean Series or DataFrame, an array-like structure or, a callable. It represents the condition/conditions to check for each value of the DataFrame. If the condition is True then the original value is not replaced. Otherwise, it is replaced by a NaN value.
other It is a scalar, Series/DataFrame, or a callable. It represents the value that will be placed for the original value if the condition is False.
inplace It is a Boolean value. It tells about the operation on data. If True, it makes changes itself.
axis It is an integer value. It tells about the working axis either rows or columns.
level It is an integer value. It tells about the level.
errors It is a string. It tells about the errors. It accepts two options: raise or ignore. If its value is raise then it allows exceptions to be raised. If its value is ignore then it ignores the exceptions and returns the original object if there is an error.
try_cast It is a Boolean value. It casts the output of the function to the original input type if possible.

Return

It returns the changed DataFrame depending upon the condition.

Example Codes: DataFrame.where()

We will learn more about this function by implementing it in the next code example.

import pandas as pd

dataframe=pd.DataFrame({
                        'A': 
                            {0: 60, 
                            1: 100, 
                            2: 80,
                            3: 78,
                            4: 95,
                            5: 45,
                            6: 67,
                            7: 12,
                            8: 23,
                            9: 50},
                        'B': 
                            {0: 90, 
                            1: 75, 
                            2: 82, 
                            3: 64, 
                            4: 45,
                            5: 35,
                            6: 74,
                            7: 52,
                            8: 93,
                            9: 18}
                        })

print(dataframe)

The example DataFrame is,

     A   B
0   60  90
1  100  75
2   80  82
3   78  64
4   95  45
5   45  35
6   67  74
7   12  52
8   23  93
9   50  18

This function has one mandatory parameter i.e cond.

import pandas as pd

dataframe=pd.DataFrame({
                        'A': 
                            {0: 60, 
                            1: 100, 
                            2: 80,
                            3: 78,
                            4: 95,
                            5: 45,
                            6: 67,
                            7: 12,
                            8: 23,
                            9: 50},
                        'B': 
                            {0: 90, 
                            1: 75, 
                            2: 82, 
                            3: 64, 
                            4: 45,
                            5: 35,
                            6: 74,
                            7: 52,
                            8: 93,
                            9: 18}
                        })

dataframe1 = dataframe.where(dataframe>50)
print(dataframe1)

Output:

       A     B
0   60.0  90.0
1  100.0  75.0
2   80.0  82.0
3   78.0  64.0
4   95.0   NaN
5    NaN   NaN
6   67.0  74.0
7    NaN  52.0
8    NaN  93.0
9    NaN   NaN

The values that are not greater than 50 i.e that are not satisfying the condition are replaced by a NaN value.

Example Codes: DataFrame.where() to Specify a Value

import pandas as pd

dataframe=pd.DataFrame({
                        'A': 
                            {0: 60, 
                            1: 100, 
                            2: 80,
                            3: 78,
                            4: 95,
                            5: 45,
                            6: 67,
                            7: 12,
                            8: 23,
                            9: 50},
                        'B': 
                            {0: 90, 
                            1: 75, 
                            2: 82, 
                            3: 64, 
                            4: 45,
                            5: 35,
                            6: 74,
                            7: 52,
                            8: 93,
                            9: 18}
                        })

dataframe1 = dataframe.where(dataframe>50, other= 0)
print(dataframe1)

Output:

     A   B
0   60  90
1  100  75
2   80  82
3   78  64
4   95   0
5    0   0
6   67  74
7    0  52
8    0  93
9    0   0

Here, the values that do not meet the condition are replaced by a user-defined value.

Example Codes: DataFrame.where() to Use Multiple Conditions

import pandas as pd

dataframe=pd.DataFrame({
                        'A': 
                            {0: 60, 
                            1: 100, 
                            2: 80,
                            3: 78,
                            4: 95,
                            5: 45,
                            6: 67,
                            7: 12,
                            8: 23,
                            9: 50},
                        'B': 
                            {0: 90, 
                            1: 75, 
                            2: 82, 
                            3: 64, 
                            4: 45,
                            5: 35,
                            6: 74,
                            7: 52,
                            8: 93,
                            9: 18}
                        })

dataframe1 = dataframe.where((dataframe==80)|(dataframe<50), other= 0)
print(dataframe1)

Output:

    A   B
0   0   0
1   0   0
2  80   0
3   0   0
4   0  45
5  45  35
6   0   0
7  12   0
8  23   0
9   0  18

The returned DataFrame contains the values that meet both of the conditions.

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