Python: using .iterrows() to create columns

Python: using .iterrows() to create columns

The .iterrows() method in Pandas allows you to iterate through a DataFrame row by row, yielding each row as a Pandas Series containing the row's data. However, using .iterrows() to create new columns is not efficient and can lead to performance issues, especially for large DataFrames. It's generally recommended to avoid using .iterrows() for creating new columns and instead use vectorized operations provided by Pandas.

If you have specific operations you want to perform on each row to create new columns, you should aim to leverage Pandas' built-in functions for better performance. Here's an example of using .iterrows() to create new columns:

import pandas as pd # Create a sample DataFrame data = {'A': [1, 2, 3], 'B': [4, 5, 6]} df = pd.DataFrame(data) # Use iterrows to create new columns for index, row in df.iterrows(): df.at[index, 'C'] = row['A'] + row['B'] df.at[index, 'D'] = row['A'] * row['B'] print(df)

However, this approach is not efficient, especially for large DataFrames, due to the overhead of iterating through rows and performing assignments. Instead, you can achieve the same result using vectorized operations:

df['C'] = df['A'] + df['B'] df['D'] = df['A'] * df['B'] print(df)

In this example, vectorized operations directly create the new columns 'C' and 'D' without the need for explicit iteration.

If you have more complex operations that cannot be easily vectorized, consider using the .apply() function or other methods like list comprehensions. These can be more efficient than .iterrows() for creating new columns.

Examples

  1. "How to create new DataFrame columns using .iterrows() in Python?"

    • This query explains how to create new columns in a DataFrame using the .iterrows() method.
    import pandas as pd df = pd.DataFrame({ 'name': ['Alice', 'Bob', 'Charlie'], 'age': [25, 30, 35] }) for index, row in df.iterrows(): df.loc[index, 'age_group'] = 'Adult' if row['age'] >= 18 else 'Minor' print(df) # Output: # name age age_group # 0 Alice 25 Adult # 1 Bob 30 Adult # 2 Charlie 35 Adult

  2. "Python: Using .iterrows() to derive new columns based on existing ones?"

    • This query demonstrates how to derive new columns from existing ones using .iterrows().
    import pandas as pd df = pd.DataFrame({ 'product': ['A', 'B', 'C'], 'price': [100, 200, 300] }) for index, row in df.iterrows(): df.loc[index, 'price_with_tax'] = row['price'] * 1.1 # Adding 10% tax print(df) # Output: # product price price_with_tax # 0 A 100 110.0 # 1 B 200 220.0 # 2 C 300 330.0

  3. "How to add a new column using .iterrows() and external data?"

    • This query describes adding a new column to a DataFrame based on external data using .iterrows().
    import pandas as pd df = pd.DataFrame({ 'city': ['New York', 'Los Angeles', 'Chicago'] }) population_data = { 'New York': 8_336_817, 'Los Angeles': 3_979_576, 'Chicago': 2_693_976 } for index, row in df.iterrows(): df.loc[index, 'population'] = population_data.get(row['city'], None) print(df) # Output: # city population # 0 New York 8336817 # 1 Los Angeles 3979576 # 2 Chicago 2693976

  4. "Using .iterrows() to calculate a new column based on conditions?"

    • This query explains how to create a new column by applying conditions to existing data.
    import pandas as pd df = pd.DataFrame({ 'employee': ['John', 'Doe', 'Smith'], 'hours_worked': [35, 45, 40] }) for index, row in df.iterrows(): df.loc[index, 'overtime'] = 'Yes' if row['hours_worked'] > 40 else 'No' print(df) # Output: # employee hours_worked overtime # John 35 No # Doe 45 Yes # Smith 40 No

  5. "Python: Using .iterrows() to create cumulative sum columns?"

    • This query shows how to create a new column that reflects a cumulative sum using .iterrows().
    import pandas as pd df = pd.DataFrame({ 'month': ['January', 'February', 'March'], 'revenue': [1000, 1500, 1200] }) cumulative_sum = 0 for index, row in df.iterrows(): cumulative_sum += row['revenue'] df.loc[index, 'cumulative_revenue'] = cumulative_sum print(df) # Output: # month revenue cumulative_revenue # January 1000 1000 # February 1500 2500 # March 1200 3700

  6. "How to create a calculated column in pandas using .iterrows()?"

    • This query demonstrates creating a calculated column by applying a formula in a loop with .iterrows().
    import pandas as pd df = pd.DataFrame({ 'length': [10, 15, 20], 'width': [5, 10, 15] }) for index, row in df.iterrows(): df.loc[index, 'area'] = row['length'] * row['width'] print(df) # Output: # length width area # 10 5 50 # 15 10 150 # 20 15 300

  7. "Python: Using .iterrows() to create a column based on external API data?"

    • This query shows how to add a column to a DataFrame using .iterrows() and data from an external API.
    import pandas as pd import requests df = pd.DataFrame({ 'country': ['USA', 'Canada', 'Mexico'] }) for index, row in df.iterrows(): response = requests.get(f"https://restcountries.com/v3.1/name/{row['country']}") country_data = response.json() df.loc[index, 'capital'] = country_data[0]['capital'][0] print(df) # Output: # country capital # USA Washington, D.C. # Canada Ottawa # Mexico Mexico City

  8. "Using .iterrows() to create a rolling average column in pandas?"

    • This query explains how to calculate a rolling average to create a new column using .iterrows().
    import pandas as pd df = pd.DataFrame({ 'week': [1, 2, 3, 4, 5], 'sales': [100, 200, 150, 250, 300] }) window = 3 # Rolling window of 3 for index, row in df.iterrows(): if index >= window - 1: df.loc[index, 'rolling_avg'] = df['sales'][index - window + 1: index + 1].mean() print(df) # Output: # week sales rolling_avg # 1 100 NaN # 2 200 NaN # 3 150 150.0 # 4 250 200.0 # 5 300 233.3

  9. "How to add a derived column in pandas using .iterrows()?"

    • This query demonstrates adding a derived column based on existing data using .iterrows().
    import pandas as pd df = pd.DataFrame({ 'employee': ['John', 'Doe', 'Jane'], 'salary': [50000, 60000, 70000] }) for index, row in df.iterrows(): df.loc[index, 'salary_with_bonus'] = row['salary'] * 1.10 # 10% bonus print(df) # Output: # employee salary salary_with_bonus # John 50000 55000 # Doe 60000 66000 # Jane 70000 77000

  10. "Python: Using .iterrows() to create a rank-based column?"

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