# Calculate the Mean in Python

## Your goal

You need to compute the mean of numerical data in Python.

## Step-by-step tutorial

There are different ways to do this, depending on whether you're working with a list or a Pandas DataFrame.

### Approach 1: List data

If you have a Python list, you can use the statistics module from the Python Standard Library:

>>> import statistics
>>> statistics.mean([3, 4, 3, 5, 10])
5

### Approach 2: Pandas DataFrame

If on the other hand you have a Pandas DataFrame, we can use its mean method:

>>> import pandas as pd
>>> precip = pd.read_csv("precip-central-park.csv")
>>> precip
YEAR   JAN   FEB   MAR   APR   MAY   JUN   JUL   AUG   SEP   OCT   NOV   DEC  ANNUAL
0    1869  2.53  6.87  4.61  1.39  4.15  4.40  3.20  1.76  2.81  6.48  2.03  5.02   45.25
1    1870  4.41  2.83  3.33  5.11  1.83  2.82  3.76  3.07  2.52  4.97  2.42  2.18   39.25
2    1871  2.07  2.72  5.54  3.03  4.04  7.05  5.57  5.60  2.34  7.50  3.56  2.24   51.26
3    1872  1.88  1.29  3.74  2.29  2.68  2.93  7.83  6.29  2.95  3.35  4.08  3.18   42.49
4    1873  5.34  3.80  2.09  4.16  3.69  1.28  4.61  9.56  3.14  2.73  4.63  2.96   47.99
..    ...   ...   ...   ...   ...   ...   ...   ...   ...   ...   ...   ...   ...     ...
146  2015  5.23  2.04  4.72  2.08  1.86  4.79  3.98  2.35  3.28  3.91  2.01  4.72   40.97
147  2016  4.41  4.40  1.17  1.61  3.75  2.60  7.02  1.97  2.79  4.15  5.41  2.89   42.17
148  2017  4.83  2.48  5.25  3.84  6.38  4.76  4.19  3.34  2.00  4.18  1.58  2.21   45.04
149  2018  2.18  5.83  5.17  5.78  3.53  3.11  7.45  8.59  6.19  3.59  7.62  6.51   65.55
150  2019  3.58  3.14  3.87  4.55  6.82  5.46  5.77  3.70  0.95  6.15  1.95  7.09   53.03

[151 rows x 14 columns]
>>> precip.mean()
YEAR      1944.000000
JAN          3.500861
FEB          3.339470
MAR          3.999735
APR          3.716358
MAY          3.717947
JUN          3.684040
JUL          4.328212
AUG          4.394834
SEP          3.827682
OCT          3.717815
NOV          3.554106
DEC          3.661523
ANNUAL      45.445894
dtype: float64

In the output above, we see the mean of each DataFrame column.

We can also calculate the mean for an individual column, which is a Pandas Series:

>>> precip["ANNUAL"].mean()
45.4458940397351