Subset of columns and filter Pandas

Subset of columns and filter Pandas

In Pandas, you can easily select a subset of columns from a DataFrame and then apply filtering using the selected columns. Here's how you can do it:

Assuming you have a DataFrame named df and you want to select specific columns and then apply filtering:

import pandas as pd # Sample DataFrame data = { "Name": ["Alice", "Bob", "Charlie"], "Age": [25, 30, 22], "Gender": ["Female", "Male", "Male"], } df = pd.DataFrame(data) # Select a subset of columns selected_columns = ["Name", "Age"] subset_df = df[selected_columns] # Apply filtering to the subset of columns filtered_df = subset_df[subset_df["Age"] > 22] print(filtered_df)

In this example, we first select a subset of columns ("Name" and "Age") by indexing the DataFrame with the list of column names. Then, we apply filtering to the subset DataFrame to keep only the rows where the "Age" column is greater than 22.

The output will be:

 Name Age 0 Alice 25 1 Bob 30

Keep in mind that when you select a subset of columns, you get a new DataFrame that contains only the selected columns. You can then apply various operations, including filtering, on the subset DataFrame.

Examples

  1. "How to select a subset of columns in a Pandas DataFrame?"

    • Use DataFrame.loc[] or DataFrame.iloc[] to select specific columns by name or index.
    import pandas as pd data = { 'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9] } df = pd.DataFrame(data) subset_df = df[['A', 'C']] print(subset_df) # A C # 0 1 7 # 1 2 8 # 2 3 9

  2. "How to filter rows in Pandas based on a condition?"

    • Use boolean indexing to filter rows that meet a certain condition.
    df = pd.DataFrame({ 'A': [10, 20, 30], 'B': [15, 25, 35] }) filtered_df = df[df['A'] > 15] print(filtered_df) # A B # 1 20 25 # 2 30 35

  3. "How to select subset of columns and rows in Pandas?"

    • Use DataFrame.loc[] to select rows and specific columns at the same time.
    data = { 'Name': ['Alice', 'Bob', 'Charlie'], 'Age': [25, 30, 35], 'City': ['New York', 'Los Angeles', 'Chicago'] } df = pd.DataFrame(data) subset_df = df.loc[1:, ['Name', 'City']] print(subset_df) # Name City # 1 Bob Los Angeles # 2 Charlie Chicago

  4. "How to filter rows based on multiple conditions in Pandas?"

    • Use multiple boolean conditions with & or | to filter rows.
    df = pd.DataFrame({ 'Name': ['Alice', 'Bob', 'Charlie', 'David'], 'Age': [25, 30, 35, 40], 'City': ['New York', 'Los Angeles', 'Chicago', 'Houston'] }) filtered_df = df[(df['Age'] > 25) & (df['City'] == 'Chicago')] print(filtered_df) # Name Age City # 2 Charlie 35 Chicago

  5. "How to select a subset of columns using a variable list in Pandas?"

    • Use a list of column names stored in a variable to select a subset of columns.
    columns_to_select = ['Name', 'Age'] subset_df = df[columns_to_select] print(subset_df) # Name Age # 0 Alice 25 # 1 Bob 30 # 2 Charlie 35

  6. "How to filter rows based on a string pattern in Pandas?"

    • Use str.contains() to filter rows that match a specific string pattern.
    df = pd.DataFrame({ 'Name': ['Alice', 'Bob', 'Charlie', 'David'], 'City': ['New York', 'Los Angeles', 'Chicago', 'Houston'] }) filtered_df = df[df['City'].str.contains('New')] print(filtered_df) # Name City # 0 Alice New York

  7. "How to subset rows by specific indices in Pandas?"

    • Use DataFrame.iloc[] to select rows by index positions.
    subset_df = df.iloc[1:3] print(subset_df) # Name Age City # 1 Bob 30 Los Angeles # 2 Charlie 35 Chicago

  8. "How to subset a DataFrame by dropping specific columns in Pandas?"

    • Use drop() to remove specific columns and create a subset of the DataFrame.
    subset_df = df.drop(columns=['City']) print(subset_df) # Name Age # 0 Alice 25 # 1 Bob 30 # 2 Charlie 35

  9. "How to filter rows based on multiple column values in Pandas?"

    • Use a combination of boolean conditions to filter rows based on multiple column values.
    df = pd.DataFrame({ 'Name': ['Alice', 'Bob', 'Charlie', 'David'], 'Age': [25, 30, 35, 40], 'City': ['New York', 'Los Angeles', 'Chicago', 'Houston'] }) filtered_df = df[(df['Age'] > 25) & (df['Name'] == 'Charlie')] print(filtered_df) # Name Age City # 2 Charlie 35 Chicago

  10. "How to filter rows based on a list of values in Pandas?"

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