Five-Point-Summarization

The following Repository Displays the use of five point Summary and as well as mean and mode using the Pandas and Numpy library with the help of linnerud dataset which is an inbuilt dataset in sklearn library. We have used Google colab for the illustration purpose.

What is Five Point Summarization?

Five Point Summarization is used to measure the spread of data using the five measures of calculation. It can also be reffered to as the partition of attribute into five parts which are as below:

1. min- Indicates minimum value of attribute.
2. 25%- Indicates value below which 25% of data values exists.
3. median or 50%- Indicates the median or middle value of attribute after sorting.
4. 75%- Indicates value below which 75% of data values exists.
5. max-indicates maximum value of attribute.

Python Code Explanation

We have implemnted five point summarization in the python programming language with the help of modeules indicated below:

1. Pandas- To perform DataFrame Operations.
2. Numpy- To perform Percentile Operations.
3. sklearn- To import linnerud Dataset.

We have used min(), max(), std(), mode(), mean(), median(), and mode() functions to calculate them respectively.