How to Select rows in Pandas DataFrame Based on Conditions

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To select rows in a Pandas DataFrame based on a condition or a set of conditions, you can use the loc or iloc methods. Here are the steps:

  1. Define the condition or set of conditions as a Boolean expression.
  2. Use the loc method to select rows based on labels, or the iloc method to select rows based on positions.
  3. Pass the Boolean expression as an argument to the loc or iloc method to select the rows that meet the condition.

Here’s an example:

import pandas as pd

# create a DataFrame
data = {'name': ['John', 'Alice', 'Bob', 'Mary'],
        'age': [25, 30, 35, 40],
        'gender': ['M', 'F', 'M', 'F']}
df = pd.DataFrame(data)

# select rows where age is greater than 30
condition = df['age'] > 30
selected_rows = df.loc[condition]

print(selected_rows)

Output:

   name  age gender
2   Bob   35      M
3  Mary   40      F

In this example, we created a condition where the age is greater than 30. We then passed the condition to the loc method to select the rows where the condition is True, which in this case are rows 2 and 3.

Conclusion:

In conclusion, selecting rows in a Pandas DataFrame based on conditions is a fundamental operation in data analysis. You can use the loc or iloc method to select rows based on labels or positions, respectively. The key is to define the condition or set of conditions as a Boolean expression, and then pass it as an argument to the loc or iloc method to select the rows that meet the condition. This operation allows you to filter and manipulate data, and is an essential step in any data analysis or machine learning pipeline.