Describing the Data | Analyzing the Data

Course Content

Pandas First Steps

## Describing the Data

Pandas offers the handy `mean()` function that calculates the average of all values for each column. Here's how you can utilize it:

To determine the average value for a specific column, you can follow this method:

Pandas also provides the `mode()` function, which identifies the most frequently occurring value in each column. See the example below:

To find the mode for a particular column, you can do this:

Another useful function in pandas is `describe()`. Here's how to use it:

This function provides an overview of various metrics from the dataset, including:

• Total number of entries;
• Mean or average value;
• Standard deviation;
• The minimum and maximum values;
• The 25th, 50th (median), and 75th percentiles.

Retrieve the following from the dataframe named `data_frame`:

• Total entries for each column;
• Average value for each column;
• Standard deviation for each column;
• Minimum value for each column;
• Maximum value for each column;
• 25th, 50th, and 75th percentiles for each column.

Everything was clear?

Section 3. Chapter 11

Course Content

Pandas First Steps

## Describing the Data

Pandas offers the handy `mean()` function that calculates the average of all values for each column. Here's how you can utilize it:

To determine the average value for a specific column, you can follow this method:

Pandas also provides the `mode()` function, which identifies the most frequently occurring value in each column. See the example below:

To find the mode for a particular column, you can do this:

Another useful function in pandas is `describe()`. Here's how to use it:

This function provides an overview of various metrics from the dataset, including:

• Total number of entries;
• Mean or average value;
• Standard deviation;
• The minimum and maximum values;
• The 25th, 50th (median), and 75th percentiles.

Retrieve the following from the dataframe named `data_frame`: