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• Post category:Pandas

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 !!