To choose columns and rows based on a keyword in a specific column (let's call it "column1") in a DataFrame in Python, you can use the Pandas library. Here are the steps:

1. Import the necessary libraries:

   import pandas as pd

2. Load or create your DataFrame. You can either read data from a file or create a DataFrame from scratch. Here's an example of creating a DataFrame from a dictionary:

   data = {

       "column1": ["apple", "banana", "cherry", "date"],

       "column2": [10, 20, 30, 40],

       "column3": [100, 200, 300, 400]

   }

   df = pd.DataFrame(data)

3. Filter rows based on the keyword in "column1":

   keyword = "banana"  # Replace with your desired keyword

   filtered_df = df[df["column1"] == keyword]

   This code creates a new DataFrame `filtered_df` that contains only the rows where "column1" is equal to the specified keyword.

4. Optionally, choose specific columns:

   If you also want to select specific columns from the filtered DataFrame, you can do so by providing a list of column names:

   selected_columns = ["column2", "column3"]  # Replace with your desired column names

   final_df = filtered_df[selected_columns]

   Now, `final_df` contains only the rows where "column1" is equal to the specified keyword, and it includes only the selected columns.

Here's the complete code with all the steps:

import pandas as pd

data = {

    "column1": ["apple", "banana", "cherry", "date"],

    "column2": [10, 20, 30, 40],

    "column3": [100, 200, 300, 400]

}

df = pd.DataFrame(data)

keyword = "banana"

filtered_df = df[df["column1"] == keyword]

selected_columns = ["column2", "column3"]

final_df = filtered_df[selected_columns]

print(final_df)

This code will print the DataFrame `final_df`, which contains only the rows where "column1" is "banana," and it includes only "column2" and "column3."

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