Statistical concepts with examples, formula, and python code. The describe() function computes a summary of statistics pertaining to the DataFrame columns. This function gives the mean, std and IQR values. And, function excludes the character columns and given summary about numeric columns.

**Estimate of Location**

- Mean
- Trimmed Mean
- Weighted Mean
- Median
- Mode

2.** Estimate of Variability**

- Deviation
- Mean Absolute Deviation
- Median Absolute Deviation
- Variance
- Standard Deviation
- Interquartile Range

3.** Correlation**

We will be using simple product details dataset which contains Product ID, Cost Price, and Selling Price to demonstrate various statistical methods.

Most of the statistical methods can be done with Pandas except trimmed mean(scipy) and weighted mean(numpy). Reading product data into a data frame called ‘_products_’. Seaborn is a graphical plotting library.

```
import numpy as np
import pandas as pd
from scipy import stats
import seaborn as sns
products = pd.read_csv('products.csv')
```

When there are several distinct values it is often very helpful to see an estimate of where the data is located or centered. It is also referred to as **Measure of Central Tendency**. Let’s see different ways of measurement.

**Mean**

The most basic estimate of location is the mean of data, simply an **average** of the values. That is the sum of all the values divided by the total number of values.

Example

Values: 10, 11 , 1, 20, 13

Mean = (10+11+1+20+13)/5 = 11

The mean is symbolized as ‘x-bar’, n is the total number of values.

```
products['Cost Price'].mean()
Output: 94.2
```

statistics statistical-analysis data-science pandas data-analysis

Learn to group the data and summarize in several different ways, to use aggregate functions, data transformation, filter, map.

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These statistical tests allow researchers to make inferences because they can show whether an observed pattern is due to intervention or chance. There is a wide range of statistical tests.