Pandas DataFrame DataFrame.isin() Function

  1. Syntax of pandas.DataFrame.isin(values)
  2. Example Codes: DataFrame.isin() with iterable as the input
  3. Example Codes: DataFrame.isin() with Dictionary as the input
  4. Example Codes: DataFrame.isin() with Series as the input
  5. Example Codes: DataFrame.isin() with DataFrame as the input

pandas.DataFrame.isin(values) function checks whether each element in the caller DataFrame contains the value specified in the input values.

Syntax of pandas.DataFrame.isin(values)

DataFrame.isin(values) 

Parameters

values iterable - list, tuple, set, etc.
Dictionary,
Series
DataFrame

Return

It returns a DataFrame of Booleans of the same dimension of the caller DataFrame indicating whether each element contains the input values.

Example Codes: DataFrame.isin() with iterable as the input

When the Python iterable is the input, Pandas DataFrame.isin() function checks whether each value in the DataFrame contains any value in the iterable.

import pandas as pd

df = pd.DataFrame({'Sales': [100, 200], 'Profit': [200, 400]})

df = df.isin([200, 400])
print(df)

The caller DataFrame is

   Sales  Profit
0    100     200
1    200     400

Output:

   Sales  Profit
0  False    True
1   True    True

Here, 200 and 400 present in the list [200, 400], therefore, the values in the returned DataFrame whose original values are 200 and 400 are True. 100 is not in the list [200, 400], therefore, the value in its position returns False.

Example Codes: DataFrame.isin() with Dictionary as the input

If the input value type is Dictionary, isin() function checks not only the values but also the key. It returns True only when the column name is the same as the key and the cell value contains in the value of the dictionary.

import pandas as pd

df = pd.DataFrame({'Sales': [100, 200], 'Profit': [200, 400]})

df = df.isin({'Sales': [200, 400]})
print(df)

Output:

   Sales  Profit
0  False   False
1   True   False

In the first example, values of the Profit columns are both True but are False in this example because the column name is different from the key in the input dictionary.

It returns True in the Sales column if the value is contained in the value of the dictionary - [200, 400].

Example Codes: DataFrame.isin() with Series as the input

If the input value type is Pandas Series, isin() function checks whether the element per column is the same as the value in the same index of the input Series.

import pandas as pd

df = pd.DataFrame({'Sales': [100, 200], 'Profit': [200, 400]})

valueSeries = pd.Series([200, 400])
print(valueSeries)

df = df.isin(valueSeries)
print(df)

Output:

0    200
1    400
dtype: int64
   Sales  Profit
0  False    True
1  False    True

The elements in the Profit column are the same as the elements in the input Series element-wise, therefore, it returns True for both elements in that column.

Example Codes: DataFrame.isin() with DataFrame as the input

If the input value type is Pandas DataFrame, isin() function checks each element in the caller DataFrame is the same as the element of the input DataFrame at the same position.

It returns True when values are identical, or False in the case of mismatch.

import pandas as pd

df = pd.DataFrame({'Sales': [100, 200], 'Profit': [200, 400]})
print(df)

valueDf = pd.DataFrame({'Sales': [100, 200], 'Profit': [200, 300]})
print(valueDf)

df = df.isin(valueDf)
print(df)

Output:

   Sales  Profit
0    100     200
1    200     400
   Sales  Profit
0    100     200
1    200     300
   Sales  Profit
0   True    True
1   True   False

The value in the position (1, 1) returns False because the values are different between the caller DataFrame and the input DataFrame.

Warning

isin() function checks not only the value element-wise, but it also checks whether the name of the column is identical. It returns False if the column names are different even if the value is the same in these two DataFrames.

Related Article - Pandas DataFrame

  • Pandas DataFrame DataFrame.set_index() Function
  • Pandas DataFrame DataFrame.plot.bar() Function
  • comments powered by Disqus