If you’ve worked with pandas, you’re probably familiar with the apply method. This method allows you to apply a function to each element in a pandas DataFrame or Series. However, sometimes we need to apply more complex functions that cannot be written in a single line. This is where lambda functions come in handy.
In this article, we’ll explore how to apply lambda functions to a pandas DataFrame to perform more complex operations.
Consider the following Pandas DataFrame:
import pandas as pd
df = pd.DataFrame({
'A': [1, 2, 3, 4],
'B': [5, 6, 7, 8],
'C': [9, 10, 11, 12]
})
If we want to apply a lambda function to the values in column ‘A’, we can use the ‘apply’ method as follows:
Using Lambda Functions with apply()
Sure, here are some better examples:
Consider the following Pandas DataFrame:
import pandas as pd
df = pd.DataFrame({
'A': [1, 2, 3, 4],
'B': [5, 6, 7, 8],
'C': [9, 10, 11, 12]
})
If we want to apply a lambda function to the values in column ‘A’, we can use the ‘apply’ method as follows:
Using apply method with lambda function:
df['A'] = df['A'].apply(lambda x: x ** 2) print(df)
Output:
A B C 0 1 5 9 1 4 6 10 2 9 7 11 3 16 8 12
Similarly, we can use the ‘applymap’ method to apply a lambda function to all the values in the DataFrame:
Using applymap method with lambda function:
df = df.applymap(lambda x: x ** 2) print(df)
Output:
A B C 0 1 25 81 1 16 36 100 2 81 49 121 3 256 64 144
We can also use the ‘map’ method to apply a lambda function to a specific column:
Using map method with lambda function:
df['B'] = df['B'].map(lambda x: x / 5) print(df)
Output:
A B C 0 1 5.0 81 1 16 7.2 100 2 81 9.8 121 3 256 12.8 144
We can also use the ‘apply’ method to apply a lambda function to a specific row:
Using apply method with axis=1:
df['sum'] = df.apply(lambda row: row['A'] + row['B'] + row['C'], axis=1) print(df)
Output:
A B C sum 0 1 5.0 81 87.0 1 16 7.2 100 123.2 2 81 9.8 121 211.8 3 256 12.8 144 412.8
Code Example #1
import pandas as pd
# Create a sample dataframe
df = pd.DataFrame({'Name': ['Alice', 'Bob', 'Charlie', 'David', 'Ella'],
'Age': [25, 30, 35, 40, 45],
'Salary': [50000, 60000, 70000, 80000, 90000]})
# Apply a lambda function to create a new column
df['Salary with Bonus'] = df['Salary'].apply(lambda x: x * 1.1 if x < 70000 else x * 1.05)
# Apply a lambda function to filter rows
df_filtered = df[df.apply(lambda x: x['Age'] > 30 and x['Salary'] > 60000, axis=1)]
# Apply a lambda function to groupby and aggregate data
df_grouped = df.groupby(lambda x: 'Young' if x < 30 else 'Old').agg({'Salary': lambda x: x.mean()})
# Print the final dataframes
print("Dataframe with new Salary with Bonus column:\n", df)
print("Dataframe filtered by Age and Salary:\n", df_filtered)
print("Dataframe grouped by Age and aggregated by Salary:\n", df_grouped)
Output:
Dataframe with new Salary with Bonus column:
Name Age Salary Salary with Bonus
0 Alice 25 50000 55000.00
1 Bob 30 60000 66000.00
2 Charlie 35 70000 73500.00
3 David 40 80000 84000.00
4 Ella 45 90000 94500.00
Dataframe filtered by Age and Salary:
Name Age Salary Salary with Bonus
3 David 40 80000 84000.0
4 Ella 45 90000 94500.0
Dataframe grouped by Age and aggregated by Salary:
Salary
Old 78333.333333
Young 55000.000000
Code Example #2
import pandas as pd
# create a sample dataframe
df = pd.DataFrame({
'name': ['Alice', 'Bob', 'Charlie', 'David'],
'age': [25, 30, 35, 40],
'income': [50000, 70000, 90000, 110000],
'gender': ['F', 'M', 'M', 'M']
})
# apply a lambda function to calculate the tax for each person
df['tax'] = df['income'].apply(lambda x: x * 0.1)
# apply a lambda function to categorize people into different age groups
df['age_group'] = df['age'].apply(lambda x: 'young' if x < 30 else 'middle-aged' if x < 40 else 'old')
# apply a lambda function to assign a binary code to each gender
df['gender_code'] = df['gender'].apply(lambda x: 1 if x == 'M' else 0)
print(df)
name age income gender tax age_group gender_code
0 Alice 25 50000 F 5000.0 young 0
1 Bob 30 70000 M 7000.0 middle-aged 1
2 Charlie 35 90000 M 9000.0 middle-aged 1
3 David 40 110000 M 11000.0 old 1
Last Updated on May 16, 2023 by admin