Pandas Series.isin() Function

Pandas Series.isin() function is used to check whether values are contained in the given Series object or not. It returns a Series of booleans indicating True when present, and False when not. In this article, I will explain the syntax of Series.isin() function, its parameters, and how to check whether values are contained in a given Series object with examples.

1. Quick Examples of Series.isin() Function

If you are in a hurry, below are some quick examples of the Pandas Series.isin() function.

# Below are quick examples.

# Example 1: Use series.isin() function 
# To Single Value
ser2 =  ser.isin([20])

# Example 2: Use series.isin() function
# To multiple values
ser2 = ser.isin([20, 35])

# Example 3: Use ~ operator
ser2 = ~ser.isin([20, 58])

2. Syntax of Series.isin()

Following is the syntax of Series.isin() function.

# Syntax of Series.isin() 
Series.isin(values)

Values parameter accepts set or list-like.

3. Use Series.isin() Function to Single Value

You can use Series.isin() function to check single value is contained in the given Series object or not. For example, isin() function returns a Series of booleans indicating True when present, and False when not.

Now, let’s create pandas series using a list of values and use the max() function.

import pandas as pd
import numpy as np

# Create a Series
ser = pd.Series([15, 20, 58, 10, 35, 49, 20, 15])
print(ser)

Yields below output.

# Output:
0    15
1    20
2    58
3    10
4    35
5    49
6    20
7    15
dtype: int64

Let’s use the isin() now.

# Use series.isin() function 
# To Single Value
ser2 =  ser.isin([20])
print(ser2)

Yields below output.

# Output:
0    False
1     True
2    False
3    False
4    False
5    False
6     True
7    False
dtype: bool

4. Use Series.isin() Function to Multiple Values

Similarly, you can also use Series.isin() function to check whether multiple values are contained in the given Series object or not. For example, Use isin() function has returned an object containing boolean values. All values have been mapped to True if it is present in the list else False.

# Use series.isin() function
# To multiple values
ser2 = ser.isin([20, 35])
print(ser2)

Yields below output. Observe that the values at index 1, 4, and 6 are matched to the given sequence of values, while the remaining are not.

# Output:
0    False
1     True
2    False
3    False
4     True
5    False
6     True
7    False
dtype: bool

6. Use ~ Operator

# Use ~ operator
ser2 = ~ser.isin([20, 58])
print(ser2)

Yields below output.

# Output:
0     True
1    False
2    False
3     True
4     True
5     True
6    False
7     True
dtype: bool

7. Complete Example of Series.isin() Function

import pandas as pd
import numpy as np

# Create a Series
ser = pd.Series([15, 20, 58, 10, 35, 49, 20, 15])
print(ser)

# Use series.isin() function 
# To Single Value
ser2 =  ser.isin([20])
print(ser2)

# Use series.isin() function
# To multiple values
ser2 = ser.isin([20, 35])
print(ser2)

# Use ~ operator
ser2 = ~ser.isin([20, 58])
print(ser2)

8. Conclusion

In this article, I have explained the pandas Series isin() function that returns a Series of booleans indicating True when present, and False when not for each value of the Series.

Happy Learning !!

Related Articles

• Pandas Series.max() Function
• Pandas Series.mean() Function
• Pandas Series.fillna() Function
• How to Reshape Pandas Series
• How to Rename a Pandas Series
• Pandas Get Floor or Ceil of Series
• How to Convert List to Pandas Series
• Pandas Remove Elements From Series
• Create a Set From a Series in Pandas
• Check Values of Pandas Series is Unique
• Pretty Print Pandas DataFrame or Series
• Add Column Name to Pandas Series
• Pandas Rolling Sum
• How To Get Value From Pandas Series?

References

• https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.isin.html#pandas.DataFrame.isin