As a Python developer or data analyst, you might encounter the need to apply a function to each row of a DataFrame. Suppose you have a DataFrame with sales data and want to apply a complex discount strategy to each row, calculating the final price. The input is a DataFrame with columns for ‘item’, ‘quantity’, and ‘price’, and the desired output is the same DataFrame with an additional ‘discounted_price’ column.
Method 1: Using DataFrame.apply()
Method 1 utilizes the DataFrame.apply() method in pandas to apply a function along the axis of the DataFrame. This approach allows the function to access each row presented as a Series and perform row-wise operations.
Here’s an example:
import pandas as pd
# Define a function that applies a discount strategy
def discount_strategy(row):
if row['quantity'] > 20:
return row['price'] * 0.9 # 10% discount
else:
return row['price']
# Create a DataFrame
df = pd.DataFrame({
'item': ['widget', 'gadget', 'thingamajig'],
'quantity': [10, 25, 15],
'price': [100, 200, 150]
})
# Apply the function to each row
df['discounted_price'] = df.apply(discount_strategy, axis=1)
Output:
item quantity price discounted_price
0 widget 10 100 100.0
1 gadget 25 200 180.0
2 thingamajig 15 150 150.0
This code defines a discount strategy and applies it to the ‘price’ column based on the ‘quantity’ of items sold. The apply() method executes the function for each row across the DataFrame, and the result is stored in a new ‘discounted_price’ column.
Method 2: Using DataFrame.apply() with lambda functions
Method 2 leverages lambda functions within DataFrame.apply(). It allows for concise, on-the-fly function definitions without the need for a separate function declaration.
Here’s an example:
# Use a lambda function to apply a discount df['discounted_price'] = df.apply(lambda row: row['price'] * 0.85 if row['quantity'] > 20 else row['price'], axis=1)
Output:
item quantity price discounted_price
0 widget 10 100 100.0
1 gadget 25 200 170.0
2 thingamajig 15 150 150.0
This snippet uses a lambda function to apply a 15% discount if the quantity is greater than 20. The concise nature of lambda expressions makes them ideal for simple operations applied across DataFrame rows.
Method 3: Using DataFrame.iterrows()
Method 3 uses the DataFrame.iterrows() function, which iterates over DataFrame rows as (index, Series) pairs. This method is straightforward and provides row-by-row processing.
Here’s an example:
# Iterate over rows and apply a discount
for index, row in df.iterrows():
df.loc[index, 'discounted_price'] = row['price'] * 0.95 if row['quantity'] > 20 else row['price']
Output:
item quantity price discounted_price
0 widget 10 100 100.0
1 gadget 25 200 190.0
2 thingamajig 15 150 150.0
The iterrows() function allows iterating through each row, and the discount is applied individually with an if-else condition. This method directly modifies the DataFrame while iterating, which is sometimes less efficient for large DataFrames.
Method 4: Using vectorized operations with pandas
Method 4 employs vectorized operations provided by pandas, which are designed to be efficient and fast. They work by applying a function or calculation to entire columns without explicitly writing a loop.
Here’s an example:
# Apply a discount with vectorized operations df['discounted_price'] = where(df['quantity'] > 20, df['price'] * 0.8, df['price'])
Output:
item quantity price discounted_price
0 widget 10 100 100.0
1 gadget 25 200 160.0
2 thingamajig 15 150 150.0
The where() function from pandas applies a discount to all the applicable rows at once. This vectorized approach is significantly faster than iterating through rows for large datasets.
Bonus One-Liner Method 5: Using list comprehensions
Bonus Method 5 uses a Python list comprehension to achieve row-wise application of functions. List comprehensions are a Pythonic way of building lists and can be used to create a new column based on conditions.
Here’s an example:
# Use a list comprehension for applying a discount df['discounted_price'] = [x * 0.8 if q > 20 else x for q, x in zip(df['quantity'], df['price'])]
Output:
item quantity price discounted_price
0 widget 10 100 100.0
1 gadget 25 200 160.0
2 thingamajig 15 150 150.0
The list comprehension iterates over the paired ‘quantity’ and ‘price’ columns with a zip function, applying the discount strategy. This one-liner approach is both readable and efficient, provided the expressions within are not too complex.
Summary/Discussion
- Method 1: DataFrame.apply(). Good for complex functions. But can be slower than other methods for larger DataFrames.
- Method 2: DataFrame.apply() with lambda. Ideal for simple, one-off functions that don’t warrant a separate function definition. Limited by the complexity of what can be cleanly expressed in a lambda.
- Method 3: DataFrame.iterrows(). Simple and intuitive, but potentially inefficient for large datasets due to its iterative nature.
- Method 4: Vectorized operations. Very fast for large datasets. Best for simple arithmetic operations; however, they might not be suitable for more complex row-wise logic.
- Bonus Method 5: List comprehensions. Pythonic and efficient for medium-sized DataFrames, but can become unwieldy with very complex logic.