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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

### Conclusion

Well that's it for this post! Thanks for following along in this article and if you have any questions or concerns please feel free to post a comment in this post and I will get back to you when I find the time.

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