How to calculate skewness and kurtosis using pandas?
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# How to calculate skewness and kurtosis using pandas?

This recipe helps you calculate skewness and kurtosis using pandas

## Recipe Objective

Skewness is a measure of asymmetry of a distribution. Kurtosis describes the peakedness of the distribution.

So this recipe is a short example on How to calculate skewness and kurtosis using pandas. Let's get started.

## Step 1 - Import the library

``` import pandas as pd import seaborn as sb ```

Let's pause and look at these imports. Pandas is generally used for performing mathematical operation and preferably over arrays. Seaborn is just used in here to import dataset.

## Step 2 - Setup the Data

``` df = sb.load_dataset('tips') ```

Here we have imported tips dataset from seaborn library.

## Step 3 - Calculating

``` print(df['total_bill'].astype(float).skew()) print(df['total_bill'].astype(float).kurt()) ```

Using skew() and kurt() function we have drawn the skewness and kurtosis of total_bill distribution.

## Step 4 - Let's look at our dataset now

Once we run the above code snippet, we will see:

```1.1332130376158205
1.2184840156638854
```

Results of our calculation can be seen in here.

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