How to SELECT ROWs of a Pandas DataFrame with multiple filters in Python

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How to SELECT ROWs of a Pandas DataFrame with multiple filters in Python

Selecting rows of a Pandas DataFrame with multiple filters in Python can be done by using the bitwise operator ‘&’ or ‘|’. These operators allow you to combine multiple filters to select specific rows from a DataFrame.

First, you need to import the Pandas library and create a DataFrame. For example, you can create a DataFrame with some sample data.

import pandas as pd

data = {'product': ['Apple', 'Banana', 'Cherry', 'Date', 'Eggplant'],

'price': [1.2, 2.3, 2.5, 1.7, 2.0],

'quantity': [3, 5, 2, 4, 8]}

df = pd.DataFrame(data)

Next, you can use the ‘&’ or ‘|’ operator to combine multiple filters and select specific rows from the DataFrame.

For example, if you want to select the rows where the ‘product’ column is ‘Apple’ and the ‘quantity’ column is greater than 3, you can use the following code:

df[(df['product'] == 'Apple') & (df['quantity'] > 3)]

Or you can use the ‘|’ operator to select the rows where the ‘product’ column is ‘Apple’ or the ‘quantity’ column is greater than 3:

df[(df['product'] == 'Apple') | (df['quantity'] > 3)]

By using the ‘&’ or ‘|’ operator, you can easily select rows of a Pandas DataFrame with multiple filters in Python. This can be useful for data analysis, as it allows you to select specific rows based on multiple criteria. It can also be used to filter and clean data in an organized manner.

 

In this Learn through Codes example, you will learn: How to SELECT ROWs of a Pandas DataFrame with multiple filters in Python.



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