In this post, we'll look at a couple of statistics functions in Python. These statistics functions are part of the Python Standard Library in the **statistics** module. The four functions we'll use in this post are common in statistics:

- mean - average value
- median - middle value
- mode - most often value
- standard deviation - spread of values

To access Python's **statistics** functions, we need to import the functions from the **statistics module** using the statement:

```
from statistics import mean, median, mode, stdev
```

After the import statement, the functions `mean()`

, `median()`

, `mode()`

and `stdev()`

(standard deviation) can be used. Since the **statistics** module is part of the Python Standard Library, no external packages need to be installed.

Let's imagine we have a data set of 5 test scores. The test scores are `60`

, `83`

, `91`

and `100`

. These test scores can be stored in a Python list. Python lists are defined with square brackets `[ ]`

. Elements in Python lists are separated with commas.

```
from statistics import mean, median, mode, stdev
test_scores = [60 , 83, 83, 91, 100]
```

### Calculate the mean¶

To calculate the mean, or average of our test scores, use the **statistics** module's `mean()`

function.

```
mean(test_scores)
```

### Calculate the median¶

To calculate the median, or middle value of our test scores, use the statistics module's `median()`

function.

If there are an odd number of values, `median()`

returns the middle value. If there are an even number of values `median()`

returns an average of the two middle values.

```
median(test_scores)
83
```

### Calculate the mode¶

To calculate the mode, or most often value of our test scores, use the statistics module's `mode()`

function.

If there is more than one number which occurs most often, `mode()`

returns an error.

```
>>> mode([1, 1, 2, 2, 3])
StatisticsError: no unique mode; found 2 equally common values
```

If there is no value that occurs most often (all the values are unique or occur the same number of times), `mode()`

also returns an error.

```
>>> mode([1,2,3])
StatisticsError: no unique mode; found 3 equally common values
```

```
mode(test_scores)
```

### Calculate the standard deviation¶

To calculate the standard deviation, or spread of the test scores, use the statistics module's `stdev()`

function. A large standard deviation indicates the data is spread out; a small standard deviation indicates the data is clustered close together.

```
stdev(test_scores)
```

Alternatively, we can import the whole **statistics** module at once (all the functions in the **staticsitics** module) using the the line:

```
import statistics
```

Then to use the functions from the module, we need to call the names `statistics.mean()`

, `statistics.median()`

, `statistics.mode()`

, and `statistics.stdev()`

. See below:

```
import statistics
test_scores = [60 , 83, 83, 91, 100]
```

```
statistics.mean(test_scores)
```

```
statistics.median(test_scores)
```

```
statistics.mode(test_scores)
```

```
statistics.stdev(test_scores)
```

## Summary¶

The **statistics** module is part of the Python Standard Library. To use **statistics** module functions, you first have to import the functions with the line `from statistics import <function_name>`

where `<function_name>`

is the name of the function you want to use. Then you can call the `<function_name>()`

and pass in a list of values.

The following functions are part of Python's **statistics** module:

statistics module function | name | description | example | result |
---|---|---|---|---|

`mean()` |
mean | mean or average | `mean([1,4,5,5])` |
`3.75` |

`median()` |
median | middle value | `median([1,4,5,5])` |
`4.5` |

`mode()` |
mode | most often | `mode([1,4,5,5])` |
`5` |

`stdev()` |
standard deviation | spread of data | `stdev([1,4,5,5])` |
`1.892` |

```
```