Python has the ability to manipulate some statistical data and calculate results of various statistical operations using the file “**statistics**“, useful in domain of mathematics.

**Important Average and measure of central location functions** :

**1. mean()** :- This function returns the** mean or average** of the data passed in its arguments. If passed argument is empty,** StatisticsError** is raised.

**2. mode()** :- This function returns the the** number with maximum number of occurrences**. If passed argument is empty,** StatisticsError** is raised.

`# Python code to demonstrate the working of ` `# mean() and mode() ` ` ` `# importing statistics to handle statistical operations ` `import` `statistics ` ` ` `# initializing list ` `li ` `=` `[` `1` `, ` `2` `, ` `3` `, ` `3` `, ` `2` `, ` `2` `, ` `2` `, ` `1` `] ` ` ` `# using mean() to calculate average of list elements ` `print` `(` `"The average of list values is : "` `,end` `=` `"") ` `print` `(statistics.mean(li)) ` ` ` `# using mode() to print maximum occurring of list elements ` `print` `(` `"The maximum occurring element is : "` `,end` `=` `"") ` `print` `(statistics.mode(li)) ` |

Output:

The average of list values is : 2.0 The maximum occurring element is : 2

**3. median()** :- This function is used to calculate the median, i.e** middle element of data.** If passed argument is empty,** StatisticsError** is raised.

**4. median_low()** :- This function returns the median of data in case of odd number of elements, but in case of even number of elements, returns the** lower of two middle **elements. If passed argument is empty,** StatisticsError** is raised.

**5. median_high()** :- This function returns the median of data in case of odd number of elements, but in case of even number of elements, **returns the higher of two middle** elements. If passed argument is empty,** StatisticsError** is raised.

`# Python code to demonstrate the working of ` `# median(), median_low() and median_high() ` ` ` `# importing statistics to handle statistical operations ` `import` `statistics ` ` ` `# initializing list ` `li ` `=` `[` `1` `, ` `2` `, ` `2` `, ` `3` `, ` `3` `, ` `3` `] ` ` ` `# using median() to print median of list elements ` `print` `(` `"The median of list element is : "` `,end` `=` `"") ` `print` `(statistics.median(li)) ` ` ` `# using median_low() to print low median of list elements ` `print` `(` `"The lower median of list element is : "` `,end` `=` `"") ` `print` `(statistics.median_low(li)) ` ` ` `# using median_high() to print high median of list elements ` `print` `(` `"The higher median of list element is : "` `,end` `=` `"") ` `print` `(statistics.median_high(li)) ` |

Output:

The median of list element is : 2.5 The lower median of list element is : 2 The higher median of list element is : 3

**6. median_grouped() **:- This function is used to compute group median, i.e** 50th percentile** of the data. If passed argument is empty,** StatisticsError** is raised.

`# Python code to demonstrate the working of ` `# median_grouped() ` ` ` `# importing statistics to handle statistical operations ` `import` `statistics ` ` ` `# initializing list ` `li ` `=` `[` `1` `, ` `2` `, ` `2` `, ` `3` `, ` `3` `, ` `3` `] ` ` ` `# using median_grouped() to calculate 50th percentile ` `print` `(` `"The 50th percentile of data is : "` `,end` `=` `"") ` `print` `(statistics.median_grouped(li)) ` |

Output:

The 50th percentile of data is : 2.5

Statistical Functions in Python | Set 2 ( Measure of Spread)

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