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

Descriptive statistics include those that summarize the central tendency, dispersion, and shape of a datasetâ€™s distribution.

- Measure of central tendency
- Measure of spread/dispersion
- Measure of symmetry [ will save this for the future post]

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. Letâ€™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=pd.read_csv("bmi.csv")
df
```

**DataFrame**

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

`Mean, Median, Mode`

are measures of central tendency.

- 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

**Formula to calculate mean**

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