Select Rows From List of Values in Pandas DataFrame
Let's learn how to select rows from a list of values in Pandas DataFrame using isin() method.
Using isin() to Select Rows from a List of Values
The isin() function is one of the most commonly used methods for filtering data based on a list of values. Let’s walk through a simple example to illustrate this.
import pandas as pd
data = {
'Name': ['Alice', 'Bob', 'Charlie', 'David'],
'Department': ['HR', 'IT', 'IT', 'Finance']
}
df = pd.DataFrame(data)
departments = ['HR', 'Finance']
filtered_df = df[df['Department'].isin(departments)]
print(filtered_df)
import pandas as pddata = { 'Name': ['Alice', 'Bob', 'Charlie', 'David'], 'Department': ['HR', 'IT', 'IT', 'Finance']}df = pd.DataFrame(data)departments = ['HR', 'Finance']filtered_df = df[df['Department'].isin(departments)]print(filtered_df)Output
Name Department 0 Alice HR 3 David Finance
In this example, the isin() function filters rows where the Department column matches 'HR' or 'Finance'. This is a simple and effective way to filter data in Pandas.
Using isin() to Filter Based on Multiple Conditions
You can also apply multiple conditions using isin() along with logical operators like & (AND) or | (OR). Let us consider a dataframe. Here we will filter out those rows using isin() that satisfy the column named 'Status' as 'Pending' or 'Completed'.
import pandas as pd
data = {
'OrderID': [1011, 1021, 1052],
'Customer': ['Alice', 'Bob', 'Eve'],
'Status': ['Completed', 'Pending', 'Canceled']
}
df = pd.DataFrame(data)
status = ['Completed','Pending']
filtered_df = df[df['Status'].isin(status)]
print(filtered_df)
import pandas as pddata = { 'OrderID': [1011, 1021, 1052], 'Customer': ['Alice', 'Bob', 'Eve'], 'Status': ['Completed', 'Pending', 'Canceled']}df = pd.DataFrame(data)status = ['Completed','Pending']filtered_df = df[df['Status'].isin(status)]print(filtered_df)Output
OrderID Customer Status 0 1011 Alice Completed 1 1021 Bob Pending
Using Negation with isin() to Exclude Rows
You can exclude rows from the DataFrame that match certain values using the negation operator ~ with isin(). Let us consider the following example:
import pandas as pd
# Sample DataFrame
data = {
'Item': ['Carrot', 'Banana', 'Broccoli', 'Orange'],
'Category': ['Vegetable', 'Fruit', 'Vegetable', 'Fruit']
}
df = pd.DataFrame(data)
# Exclude fruits
filtered_df = df[~df['Category'].isin(['Fruit'])]
print(filtered_df)
import pandas as pd# Sample DataFramedata = { 'Item': ['Carrot', 'Banana', 'Broccoli', 'Orange'], 'Category': ['Vegetable', 'Fruit', 'Vegetable', 'Fruit']}df = pd.DataFrame(data)# Exclude fruitsfiltered_df = df[~df['Category'].isin(['Fruit'])]print(filtered_df)Output
Item Category 0 Carrot Vegetable 2 Broccoli Vegetable
Using multiple isin() along with conditional operators
You can filter rows by using multiple isin() methods in combination with the AND (&) operator. This is especially helpful when you want to check multiple columns against different lists of values.
import pandas as pd
# Sample DataFrame
data = {
'Fruit': ['Apple', 'Carrot', 'Banana', 'Grapes'],
'Category': ['Fruit', 'Vegetable', 'Fruit', 'Fruit'],
}
df = pd.DataFrame(data)
# Conditional filtering with AND
filtered_df = df[df['Fruit'].isin(['Apple', 'Banana', 'Carrot']) & df['Category'].isin(['Fruit'])]
print(filtered_df)
import pandas as pd# Sample DataFramedata = { 'Fruit': ['Apple', 'Carrot', 'Banana', 'Grapes'], 'Category': ['Fruit', 'Vegetable', 'Fruit', 'Fruit'],}df = pd.DataFrame(data)# Conditional filtering with ANDfiltered_df = df[df['Fruit'].isin(['Apple', 'Banana', 'Carrot']) & df['Category'].isin(['Fruit'])]print(filtered_df)Output
Fruit Category 0 Apple Fruit 2 Banana Fruit
In this example, we apply the AND condition to ensure that only fruits from the ['Apple', 'Banana', 'Carrot'] list in the Fruit column are included, and that their Category is Fruit. The isin() checks if the value in each column belongs to the specified list, and the logical AND (&) ensures both conditions must be true for the row to be included.
Using query() for Advanced Filtering
The query() method in Pandas is similar to SQL queries. It allows you to filter rows using an in operator, making it ideal for filtering based on a list of values. Let's consider following example, where we filter rows based on specific grades.
import pandas as pd
# Sample DataFrame with student data
data = {
'Student': ['John', 'Emily', 'Sarah', 'Michael'],
'Age': [18, 19, 17, 20],
'Grade': ['A', 'B', 'A', 'C'],
}
df = pd.DataFrame(data)
# Select rows where 'Student' is in a tuple
filtered_df = df.query('Grade in ("A", "C")')
print(filtered_df)
import pandas as pd# Sample DataFrame with student datadata = { 'Student': ['John', 'Emily', 'Sarah', 'Michael'], 'Age': [18, 19, 17, 20], 'Grade': ['A', 'B', 'A', 'C'],}df = pd.DataFrame(data)# Select rows where 'Student' is in a tuplefiltered_df = df.query('Grade in ("A", "C")')print(filtered_df)Output
Student Age Grade 0 John 18 A 2 Sarah 17 A 3 Michael 20 C
In this example, query() filters the DataFrame to only include students with grades 'A' or 'C'. This approach is clean and efficient, especially when dealing with more complex queries involving multiple columns.
Using apply() and Lambda Functions for Complex Filters
For more complex filtering, apply() combined with lambda functions is a versatile option. This method is especially useful when you need to check conditions that cannot be easily captured by isin().
import pandas as pd
# Sample DataFrame with employee data
data = {
'Employee': ['John','Michael', 'David', 'Sophia'],
'Department': ['HR', 'HR', 'Finance', 'IT'],
'Salary': [50000, 65000, 70000, 62000]
}
df = pd.DataFrame(data)
# Select rows where 'Department' is either 'HR' or 'Finance' using apply
filtered_df = df[df['Department'].apply(lambda x: x in ['HR', 'Finance'])]
print(filtered_df)
import pandas as pd# Sample DataFrame with employee datadata = { 'Employee': ['John','Michael', 'David', 'Sophia'], 'Department': ['HR', 'HR', 'Finance', 'IT'], 'Salary': [50000, 65000, 70000, 62000]}df = pd.DataFrame(data)# Select rows where 'Department' is either 'HR' or 'Finance' using applyfiltered_df = df[df['Department'].apply(lambda x: x in ['HR', 'Finance'])]print(filtered_df)Output
Employee Department Salary 0 John HR 50000 1 Michael HR 65000 2 David Finance 70000
In this case, apply() along with a lambda function checks if each value in the Department column matches either 'HR' or 'Finance'. This approach is particularly useful when the filtering condition is complex or involves custom logic that cannot be directly captured by simple methods like isin().
Conclusion
In this article, we explored several techniques for filtering rows in a Pandas DataFrame using a list of values. The most common and efficient approach is the isin() method, which works well for basic filtering. For more complex conditions, methods like query() and apply() with lambda functions provide greater flexibility.
