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.
Swipe to show code editor
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.
Switch to desktop for real-world practiceContinue from where you are using one of the options belowThanks for your feedback!
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.
Swipe to show code editor
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.
Switch to desktop for real-world practiceContinue from where you are using one of the options belowThanks for your feedback!
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.
Swipe to show code editor
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.
Switch to desktop for real-world practiceContinue from where you are using one of the options belowThanks for your feedback!
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.
Swipe to show code editor
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.
Switch to desktop for real-world practiceContinue from where you are using one of the options belowSwitch to desktop for real-world practiceContinue from where you are using one of the options below