Here is how to select rows from a DataFrame based on column values with Pandas in Python.

import pandas as pd

# Create a DataFrame with some example data
df = pd.DataFrame({"A": [1, 2, 3, 4, 5], "B": [6, 7, 8, 9, 10]})

# Select rows where the value in column A is greater than 2
selected_rows = df.loc[df["A"] > 2]

# Print the selected rows
print(selected_rows) 

Copy & Run

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To select rows from a Pandas DataFrame based on column values, you can use the DataFrame.loc method. This method allows you to specify the rows you want to select based on a boolean expression that evaluates to True or False for each row.

In the above example, we create a DataFrame with some example data. Then, we use the DataFrame.loc method to select the rows where the value in column A is greater than 2. The DataFrame.loc method returns a new DataFrame containing only the selected rows. Finally, we print the selected rows to the console.

The DataFrame.loc method is very powerful and can be used to select rows based on a wide range of criteria. For example, you can use it to select rows based on multiple column values, combine multiple boolean expressions, or even use custom functions to evaluate each row.

Overall, the DataFrame.loc method is the recommended way to select rows from a Pandas DataFrame based on column values. It allows you to specify a boolean expression that evaluates to True or False for each row, and returns a new DataFrame containing only the selected rows.

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