## Use pandas

# How to measure Kurtosis in Python pandas

# How to measure Kurtosis in Python pandas

Kurtosis! It's a neat statistical measure that tells you how different from a normal distribution a given set of data is. In particular it measures if data are heavy-tailed or light tailed when compared to a normal distribution.

The lower the number is, the less outliers exist in the data. The higher it is, the more outliers exist.

Let's take a look at the kurtosis for the `price`

column in the following .csv of housing data.

## homes_sorted.csv

Address | Price | Bedrooms |
---|---|---|

992 Settled St | 823,049 | 4 |

1506 Guido St | 784,049 | 3 |

247 Fort St | 299,238 | 3 |

132 Walrus Ave | 299,001 | 2 |

491 Python St | 293,923 | 4 |

4981 Anytown Rd | 199,000 | 4 |

938 Zeal Rd | 148,398 | 2 |

123 Main St | 99,000 | 1 |

## How to measure kurtosis with Python pandas

`import pandas as pddf = pd.read_csv('/Users/kennethcassel/homes_sorted.csv')df['price'].kurtosis()`

## Output: `-0.29610470855022797`

## Conclusion:

It's super easy to analyze data to find kurtosis using python pandas.

Our dataset had a low kurtosis measurement. A normal distribution is 3. Anything below 1 is considered a light-tailed set of data. Anything higher than 1 is heavy tailed.

🐼 Get pandas recipes straight to your inbox!

Join other Data Scientists/Analysts/Engineers in learning pandas deeper. No spam!