Pandas isin() function exists in both DataFrame & Series which is used to check if the object contains the elements from list, Series, Dict. It returns the same as the caller object of booleans indicating if each row cell/element is in values.
When it is called on DataFrame it returns a Pandas DataFrame with a boolean value representing whether it has a value or not. When it is called on Pandas Series, it returns a Series of booleans indicating if each element is in values.
pandas isin() Key Points –
- isin() function exists in both DataFrame and Series.
- It returns the same object as the caller with boolean values.
- Represented as True when value present, otherwise False.
- By using Series.isin(), you can filter the DataFrame.
1. pandas isin() Syntax
Following is the syntax of the isin() function. This takes any thing from iterable, Series, and Dict values as parameters.
# Syntax of isin() Function
isin(values)
Values parameter accept the following.
values– iterable, Series, DataFrame or dict
Let’s create a DataFrame and learn how to use isin() function with examples.
# Create a pandas DataFrame.
import pandas as pd
df = pd.DataFrame({
'Courses' :['Spark','Python','Java'],
'Fee' :[22000,25000,23000,],
'Duration':['30days','50days','30days']
})
print(df)
# Outputs
# Courses Fee Duration
#0 Spark 22000 30days
#1 Python 25000 50days
#2 Java 23000 30days
2. Series.isin() Example
pandas Series.isin() function is used to filter the DataFrame rows that contain a list of values. When it is called on Series, it returns a Series of booleans indicating if each element is in values, True when present, False when not. You can pass this series to the DataFrame to filter the rows.
2.1. Using Single Value
The below example checks each element of the Courses column contains the value Spark, when present it represents True. Returns rows that have Spark in column Courses.
# Specific Value
df2=df[df['Courses'].isin(['Spark'])]
print(df2)
# Outputs
# Courses Fee Duration
#0 Spark 22000 30days
2.1. isin() with List of Values
This checks if the column Courses contains in the list of values by using Pandas isin(). It returns rows where Courses contains Spark, Java.
# List of Values
df2=df[df['Courses'].isin(['Spark','Java'])]
print(df2)
# Outputs
# Courses Fee Duration
#0 Spark 22000 30days
#2 Java 23000 30days
3. DataFrame.isin() Example
Below are examples of how to use DataFrame.isin() function.
3.1 isin() with list of values
When a python list is passed as a parameter value to the Pandas DataFrame.isin() function, it checks whether each cell value from DataFrame is present in the list, if found, shows True otherwise False (When a value is not present). The resultant DataFrame just contains boolean values.
# isin() with list of values
print(df.isin(['Spark','Python',23000,'50days']))
# Outputs
# Courses Fee Duration
#0 True False False
#1 True False True
#2 False True False
3.2 Using Dict
The above example doesn’t check values in a specific DataFrame column, In order to check the values in a specific column use the Python Dictionary object as param. When a python Dict is passed as a param to the isin(), you should have a column name as the key and elements you wanted to check as Dict value. With this, you can check values in multiple columns.
# check by column name
print(df.isin({'Courses': ['Spark', 'Python']}))
# Outputs
# Courses Fee Duration
#0 True False False
#1 True False False
#2 False False False
3.3 Using another DataFrame
You can also check with another DataFrame.
# Checks in another DataFrame
df2 = pd.DataFrame({
'Courses' :['C++','Python',],
'Fee' :[23000,25000,],
'Duration':['30days','55days']
})
print(df.isin(df2))
# Outputs
# Courses Fee Duration
#0 False False True
#1 True True False
#2 False False False
4. Complete Example of DataFrame & Series isin()
# Create a pandas DataFrame.
import pandas as pd
import numpy as np
df = pd.DataFrame({
'Courses' :['Spark','Python','Java'],
'Fee' :[22000,25000,23000,],
'Duration':['30days','50days','30days']
})
print(df)
# List of values
print(df.isin(['Spark','Python',23000,'50days']))
# check by column
print(df.isin({'Courses': ['Spark', 'Python',23000]}))
# Using DataFrame
df2 = pd.DataFrame({
'Courses' :['C++','Python',],
'Fee' :[23000,25000,],
'Duration':['30days','55days']
})
print(df.isin(df2))
# single value
df2=df[df['Courses'].isin(['Spark'])]
print(df2)
# multiple values
df2=df[df['Courses'].isin(['Spark','Java'])]
print(df2)
5. Conclusion
In this article, you have learned isin() function exists in both DataFrame & Series which is used to check if the object contains the elements from a list, Series, and Dict.
