In Python, What Is The Approach To Choosing Rows From A DataFrame Relying On Column Values?

To select rows from a DataFrame based on column values in Python, you can use boolean indexing. Here are two common ways to achieve this:

  1. Using Boolean Indexing with Square Brackets:
# Import pandas library
import pandas as pd

# Create a DataFrame
data = {'Name': ['John', 'Emma', 'Alex', 'Jane'],
        'Age': [25, 28, 21, 30],
        'City': ['London', 'Paris', 'New York', 'Berlin']}
df = pd.DataFrame(data)

# Select rows where Age is greater than 25
selected_rows = df[df['Age'] > 25]

print(selected_rows)
  1. Using the query() method:
# Import pandas library
import pandas as pd

# Create a DataFrame
data = {'Name': ['John', 'Emma', 'Alex', 'Jane'],
        'Age': [25, 28, 21, 30],
        'City': ['London', 'Paris', 'New York', 'Berlin']}
df = pd.DataFrame(data)

# Select rows where Age is greater than 25
selected_rows = df.query('Age > 25')

print(selected_rows)

Both methods will output the following result:

   Name  Age     City
1  Emma   28    Paris
3  Jane   30   Berlin

In the above examples, we selected the rows where the ‘Age’ column value is greater than 25. Feel free to modify the condition to suit your specific requirements.

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