Course Content

Pandas First Steps

## Pandas First Steps

# Describing the Data

Pandas offers the handy `mean()`

method 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()`

method, 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 method in pandas is `describe()`

. Here's how to use it:

This method 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.

Task

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.

Task

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?

# Describing the Data

Pandas offers the handy `mean()`

method 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()`

method, 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 method in pandas is `describe()`

. Here's how to use it:

This method 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.

Task

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.

Task

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?

# Describing the Data

Pandas offers the handy `mean()`

method 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()`

method, 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 method in pandas is `describe()`

. Here's how to use it:

This method 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.

Task

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.

Task

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?

`mean()`

method 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:

`mode()`

method, 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 method in pandas is `describe()`

. Here's how to use it:

This method 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.

Task

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.