using series to filter dataframe row

To filter a dataframe row using a series, you can use the boolean indexing technique. Here's an example:

import pandas as pd

# create a sample dataframe
df = pd.DataFrame({'Name': ['John', 'Jane', 'Mike', 'Kate'],
                   'Age': [25, 30, 22, 28],
                   'Gender': ['M', 'F', 'M', 'F']})

# create a series to filter the rows
filter_series = pd.Series([True, False, True, False])

# filter the dataframe using the series
filtered_df = df[filter_series]

# print the filtered dataframe
print(filtered_df)

Output:

   Name  Age Gender
0  John   25      M
2  Mike   22      M

In this example, we created a sample dataframe with three columns - Name, Age, and Gender. We also created a series with boolean values to filter the rows. We then used boolean indexing to filter the rows of the dataframe based on the values in the series. The resulting filtered dataframe contains only the rows where the corresponding value in the series is True.