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‎07_descriptive_statistics/01_basic_descriptive_statistics.ipynb‎

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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "f6dc47ed-b5c4-4e23-8af3-365fa5aea930",
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"metadata": {},
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"source": [
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"# Basic descriptive statistics\n",
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"The term [descriptive statistics](https://en.wikipedia.org/wiki/Descriptive_statistics) refers to methods that allow summarizing collections of data. To demonstrate the most important methods, we start by defining a dataset first."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "52c2d369-3c34-42f1-860d-4b15aa0a56fb",
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"metadata": {},
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"outputs": [],
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"source": [
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"measurements = [5, 2, 6, 4, 8, 6, 2, 5, 1, 3, 3, 6]"
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]
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},
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{
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"cell_type": "markdown",
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"id": "495cce67-9f45-4b36-842d-404747ba43a1",
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"metadata": {},
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"source": [
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"## Measurements of central tendency\n",
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"We can measure the _location_ of our `measurement` in space using [numpy's statistics functions](https://numpy.org/doc/stable/reference/routines.statistics.html) and Python's [statistics module](https://docs.python.org/3/library/statistics.html)."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "afe45a7b-d925-46b6-b2c3-8fdcbdafae5c",
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import statistics as st"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "ae6d6f1d-da15-4fb3-9021-bfffa18b6e76",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"4.25"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"np.mean(measurements)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "5c8785a5-8f3e-43f3-abb6-ab798d2d0e8e",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"4.5"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"np.median(measurements)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "068fadac-1e6d-4449-b074-6b320ff85bbf",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"6"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"st.mode(measurements)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "b14d6e91-b0b8-4acf-8fa4-616aca040bb8",
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"metadata": {},
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"source": [
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"## Measurements of spread\n",
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"Numpy also allows measuring the spread of `measurements`."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "21cc5cfa-6e48-481b-a9d1-a51e114976b5",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"2.0052015692526606"
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]
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},
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"np.std(measurements)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "90a53582-7f8b-4c46-9360-224ea142acc3",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"4.020833333333333"
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]
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},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"np.var(measurements)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"id": "959c06f8-05eb-481c-9fbd-32b965e7cd40",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"(1, 8)"
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]
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},
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"execution_count": 8,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"np.min(measurements), np.max(measurements)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"id": "b2501f96-4f16-488f-9b9b-74d62309d471",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([2.75, 4.5 , 6. ])"
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]
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},
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"execution_count": 9,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"np.percentile(measurements, [25, 50, 75])"
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]
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},
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{
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"cell_type": "markdown",
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"id": "c5465b23-6818-4456-b07b-42e479431a96",
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"metadata": {},
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"source": [
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"## Exercise\n",
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"Find out if the median of a sample dataset is always a number within the sample. Use these three examples to elaborate on this:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"id": "acc54c8e-5b77-4f56-8bee-c6d0c5a13bbb",
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"metadata": {},
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"outputs": [],
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"source": [
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"example1 = [3, 4, 5]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "0789f045-0782-4587-ab26-7ea96a38b662",
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"id": "bd27b8de-db9b-48f9-b6c9-78dea2958319",
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"metadata": {},
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"outputs": [],
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"source": [
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"example2 = [3, 4, 4, 5]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "1a4cfe20-e174-43e6-8bdd-7bb0d70c2474",
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"id": "9b679e86-3ffb-49ce-ac36-a2bcb2607004",
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"metadata": {},
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"outputs": [],
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"source": [
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"example3 = [3, 4, 5, 6]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "56d926f8-81b9-4b22-9392-12c2fa568adb",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.7"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

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