Vectorize conditional assignment in pandas dataframe

Vectorize conditional assignment in pandas dataframe

You can use the numpy library's np.where() function to vectorize conditional assignment in a Pandas DataFrame. This function allows you to assign values to a column based on a condition without using explicit loops. Here's how you can do it:

Let's assume you have a DataFrame named df and you want to create a new column 'new_column' based on a condition applied to another column 'old_column'. Here's how you can vectorize the conditional assignment:

import pandas as pd import numpy as np # Create a sample DataFrame data = {'old_column': [10, 15, 20, 25, 30]} df = pd.DataFrame(data) # Define the condition and values condition = df['old_column'] > 20 true_value = 'high' false_value = 'low' # Use np.where() to assign values based on the condition df['new_column'] = np.where(condition, true_value, false_value) print(df)

In this example, the condition is defined as df['old_column'] > 20, which checks whether each element in 'old_column' is greater than 20. The true_value is 'high', and the false_value is 'low'. The np.where() function assigns 'high' to the 'new_column' for rows where the condition is True, and 'low' for rows where the condition is False.

The resulting DataFrame will look like this:

 old_column new_column 0 10 low 1 15 low 2 20 low 3 25 high 4 30 high

By using np.where(), you can efficiently apply conditional assignments to DataFrame columns without using explicit loops.

Examples

  1. Search Query: "How to use loc for conditional assignment in pandas DataFrame?"

    Description: The loc method in pandas allows conditional assignment based on a specified condition. This example demonstrates using loc to assign values to a DataFrame column conditionally.

    import pandas as pd # Sample DataFrame df = pd.DataFrame({ 'Name': ['Alice', 'Bob', 'Charlie', 'David'], 'Score': [85, 92, 78, 70] }) # Conditional assignment: set 'Passed' based on 'Score' df.loc[df['Score'] >= 80, 'Passed'] = 'Yes' df.loc[df['Score'] < 80, 'Passed'] = 'No' print(df)

  2. Search Query: "Using np.where for conditional assignment in pandas DataFrame"

    Description: The np.where function is a vectorized method to apply a condition and assign values accordingly. This example demonstrates how to use np.where for conditional assignment.

    import pandas as pd import numpy as np # Sample DataFrame df = pd.DataFrame({ 'Age': [22, 25, 30, 40], 'City': ['NYC', 'SF', 'LA', 'Chicago'] }) # Conditional assignment with np.where df['Is_Adult'] = np.where(df['Age'] >= 18, 'Yes', 'No') print(df)

  3. Search Query: "How to use apply for conditional assignment in pandas DataFrame?"

    Description: The apply method allows you to run a function on DataFrame columns or rows. This example demonstrates using apply to assign values conditionally based on a function.

    import pandas as pd # Sample DataFrame df = pd.DataFrame({ 'Temperature': [36.6, 38.0, 37.5, 39.1] }) # Conditional assignment with apply def fever_status(temp): return 'Fever' if temp >= 37 else 'Normal' df['Status'] = df['Temperature'].apply(fever_status) print(df)

  4. Search Query: "Using assign for conditional assignment in pandas DataFrame"

    Description: The assign method creates new columns in a DataFrame. This example shows how to use assign for conditional assignment with lambda functions.

    import pandas as pd # Sample DataFrame df = pd.DataFrame({ 'Salary': [50000, 60000, 75000, 90000] }) # Conditional assignment with assign and lambda df = df.assign( Tax_Bracket=lambda x: 'High' if x['Salary'] > 70000 else 'Low' ) print(df)

  5. Search Query: "How to use mask for conditional assignment in pandas DataFrame?"

    Description: The mask method replaces values where a condition is true. This example demonstrates using mask to assign values conditionally based on a condition.

    import pandas as pd # Sample DataFrame df = pd.DataFrame({ 'Price': [10, 20, 30, 40] }) # Conditional assignment with mask df['Discounted'] = df['Price'].mask(df['Price'] > 25, 'Yes').fillna('No') print(df)

  6. Search Query: "Using map for conditional assignment in pandas DataFrame"

    Description: The map method applies a function or a dictionary to a Series for conditional assignment. This example shows how to use map with a dictionary for conditional assignment.

    import pandas as pd # Sample DataFrame df = pd.DataFrame({ 'Department': ['HR', 'IT', 'Finance', 'Sales'] }) # Using map for conditional assignment with a dictionary department_heads = { 'HR': 'Alice', 'IT': 'Bob', 'Finance': 'Charlie', 'Sales': 'David' } df['Department_Head'] = df['Department'].map(department_heads) print(df)

  7. Search Query: "How to use replace for conditional assignment in pandas DataFrame?"

    Description: The replace method replaces values in a DataFrame based on a mapping. This example demonstrates how to use replace for conditional assignment.

    import pandas as pd # Sample DataFrame df = pd.DataFrame({ 'Grade': ['A', 'B', 'C', 'A'] }) # Using replace for conditional assignment grade_description = { 'A': 'Excellent', 'B': 'Good', 'C': 'Average' } df['Description'] = df['Grade'].replace(grade_description) print(df)

  8. Search Query: "Using query for conditional assignment in pandas DataFrame"

    Description: The query method allows you to filter a DataFrame using a string-based condition. This example demonstrates using query and loc to assign values based on a condition.

    import pandas as pd # Sample DataFrame df = pd.DataFrame({ 'Age': [25, 35, 45, 55] }) # Using query to filter and assign df.loc[df.query("Age > 40").index, 'Category'] = 'Senior' print(df)

  9. Search Query: "Vectorized conditional assignment with multiple conditions in pandas DataFrame"

    Description: Using multiple conditions for conditional assignment requires bitwise operations (&, |). This example shows how to use multiple conditions for assignment.

    import pandas as pd # Sample DataFrame df = pd.DataFrame({ 'Height': [150, 160, 170, 180], 'Weight': [50, 60, 70, 80] }) # Vectorized conditional assignment with multiple conditions df['Body_Type'] = df.apply( lambda row: 'Athletic' if row['Height'] > 160 and row['Weight'] > 60 else 'Regular', axis=1 ) print(df)

  10. Search Query: "Using merge for conditional assignment in pandas DataFrame"

    Description: The merge method combines DataFrames based on a common key, allowing conditional assignment based on other DataFrames. This example demonstrates using merge to add information based on a condition.

    import pandas as pd # DataFrames to merge df1 = pd.DataFrame({ 'ID': [1, 2, 3], 'Name': ['Alice', 'Bob', 'Charlie'] }) df2 = pd.DataFrame({ 'ID': [2, 3, 4], 'Age': [30, 25, 35] }) # Using merge to conditionally assign merged_df = df1.merge(df2, on='ID', how='left') print(merged_df)

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