# Exploring Descriptive Statistics Using Pandas and Seaborn Exploring Descriptive Statistics Using Pandas and Seaborn. Quantitative approach and Visual approach

## Descriptive Statistics in Python

Descriptive statistics include those that summarize the central tendency, dispersion, and shape of a dataset’s distribution.

1. Measure of central tendency
3. Measure of symmetry [ will save this for the future post]

## Dataset

Imported all the libraries needed for statistical plots and created a dataframe from the dataset given in `bmi.csv `file.

This dataset contains Height, Weight, Age, BMI, and Gender columns. Let’s calculate descriptive statistics for this dataset.

The code used in this project is available as a Jupyter Notebook on GitHub.

``````import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
% matplotlib inline
df`````` DataFrame

## Measure of Central Tendency

Measure of central tendency is used to describe the middle/center value of the data.

`Mean, Median, Mode` are measures of central tendency.

### 1. Mean

• Mean is the `average value` of the dataset.
• Mean is calculated by adding all values in the dataset divided by the number of values in the dataset.
• We can calculate the mean for only numerical variables

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