Code Explanation

This code snippet is written in Python and is designed to manipulate and analyze a DataFrame using the Pandas library. The code performs two main tasks: filtering rows based on a condition and calculating the mean of a specific column.

Code Breakdown

Initial Setup

Before running the provided code, it is assumed that the following have been completed:

• The Pandas library has been imported.
• A DataFrame named df has been created, which contains at least one column labeled 'Age'.

Step 1: Filter Rows

filtered_df = df[df['Age'] > 25]

Explanation

• df: This is the original DataFrame containing various columns, including 'Age'.
• df['Age'] > 25: This expression creates a boolean Series (a list of True/False values) indicating which rows have an 'Age' value greater than 25.
• df[...]: Using the boolean Series, this syntax filters the DataFrame, returning only those rows where the condition is True.
• filtered_df: This variable stores the new DataFrame that contains only the rows where the age is greater than 25.

Step 2: Print Filtered DataFrame

print(filtered_df)

Explanation

• This line prints the contents of filtered_df to the console, allowing the user to see the filtered results.

Step 3: Calculate Mean Age

mean_age = df['Age'].mean()

Explanation

• df['Age']: This accesses the 'Age' column of the original DataFrame.
• .mean(): This method calculates the arithmetic mean (average) of all values in the 'Age' column.
• mean_age: This variable stores the computed mean value.

Step 4: Print Mean Age

print("Mean Age:", mean_age)

Explanation

• This line outputs the mean age to the console, formatting the text to include the label "Mean Age:" followed by the calculated average.

Key Concepts

• Pandas: A powerful data manipulation and analysis library for Python, primarily used for data handling in DataFrame format.
• DataFrame: A two-dimensional, labeled data structure in Pandas, capable of holding various data types.
• Filtering: The process of selecting a subset of data based on specified conditions.
• Mean Calculation: A statistical function that determines the average value of a set of numbers.

Alternative Examples

Example 1: Filter Age Less than 30

filtered_df = df[df['Age'] < 30]
print(filtered_df)

Explanation

This example filters the DataFrame to include only those rows where the age is less than 30.

Example 2: Calculate Mean Age for Filtered Data

mean_age_filtered = filtered_df['Age'].mean()
print("Mean Age of Filtered Data:", mean_age_filtered)

Explanation

Here, the mean age is calculated from the previously filtered DataFrame, allowing for analysis of a specific subset of data.

Conclusion

The provided code snippet effectively demonstrates basic data manipulation techniques using the Pandas library. It filters rows based on a condition and calculates statistical measures, which is crucial for data analysis. For further exploration of data manipulation and analysis, consider engaging with comprehensive resources available through the Enterprise DNA Platform.