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Basic Overview of Summary Functions using Pandas

Hello everyone, in this blog post I'll cover some Summary Functions that the Pandas Python Data Analysis Library offers. Let's get started!

List of Summary Functions

Describe Function

The describe() function is used to obtain a informative statistical summary of a given Pandas DataFrame. The data that is displayed if the DataFrame contains numerical columns include the following(which is shown only the numerical columns):

  • count - Amount of not-null values
  • mean - Average value of the column values
  • std - Standard deviation of the column values
  • 25% - Shows the value of the 25th percentile
  • 50% - Shows the value of the 50th percentile
  • 75% - Shows the value of the 75th percentile
  • max- Maximum value contained in the column values

Note: The percentile value of the column data indicate how many of the values that are less than a given percentile. A percentile is a value on a scale of 100 that indicates the percent of a dataset that is equal to or below it

Info Function

The Info() function is used to display information about the DataFrame that it is used on. The information provided by it include:

  • Number of columns in the DataFrame
  • Column labels
  • Column data types
  • Memory usage
  • Range index
  • Number of cells in each column

Note: The info() function does not have a return value

Value Counts Function

The value_counts() function returns a series containing counts of unique values.

The output object will be ordered in descending fashion. This means the first element is the most frequently-occurring element.

Note: this function excludes NA values by default


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