Jupyter Guide

To get started, open the file on the right entitled "Step 1...". You'll complete each task for Step 1 in that Jupyter Notebook file. Remember, you must run the cells (Ctrl/Cmd(⌘) + Enter) for each task before moving onto the next task in the Jupyter Notebook. Continue until you have completed all tasks in this step. Then when you are ready to move onto the next step, you'll come back and click on the file for the next step until you have completed all tasks in all steps of the lab.

Generating Random Numbers and Creating Histograms

To review the concepts covered in this step, please refer to the Exploring Variable Distributions with Plots module of the Statistical Analysis with Matplotlib course.

Understanding how to generate random numbers and create histograms is important because these skills form the foundation of data analysis and visualization. They allow us to simulate data and understand the distribution of data respectively.

In this step, we will practice generating random numbers using NumPy and creating histograms using Matplotlib. The goal is to familiarize ourselves with the process of generating data and visualizing its distribution. We will use the numpy.random.default_rng object to generate the data and the matplotlib.pyplot.hist function to create the histogram. We'll look at a few different distributions.

Task 1.1: Importing Required Libraries

Import the required libraries for this lab. We will need NumPy for generating random numbers and Matplotlib for creating histograms.

import numpy as np
import matplotlib.pyplot as plt

Task 1.2: Initialize Random Number Generator

Initialize the random number generator using the numpy.random.default_rng function.

rng = np.random.default_rng()

Task 1.3: Generate Normally Distributed Random Numbers

Generate a set of random numbers using the random number generator. Let's generate 1000 numbers from a normal distribution with mean 0 and standard deviation 1.

numbers = rng.normal(0, 1, 1000)

Task 1.4: Create a Histogram of the Normal Random Numbers

Create a histogram of the generated numbers using the matplotlib.pyplot.hist function.

plt.hist(numbers, bins=20)
plt.show()

Task 1.5: Generate Uniformly Sampled Random Numbers

Now let's generate 1000 numbers from a uniform distribution between 0 and 1.

numbers_uniform = rng.uniform(0, 1, 1000)

Task 1.6: Create a Histogram of the Uniform Random Sample

Create a histogram of the generated uniformly sampled numbers using the matplotlib.pyplot.hist function.

plt.hist(numbers_uniform, bins=20)
plt.show()

Exploring Histogram Parameters

To review the concepts covered in this step, please refer to the Exploring Variable Distributions with Plots module of the Statistical Analysis with Matplotlib course.

Exploring the parameters of a histogram is important because it allows us to customize the visualization to better understand the data. It can help us reveal patterns in the data that might not be apparent with the default settings.

In this step, we will practice customizing the bin size and visual elements of a histogram. The goal is to understand how these parameters affect the visualization and what they can tell us about the data. We will use the numpy.random.default_rng object to generate the data and the matplotlib.pyplot.hist function to generate the plots. We'll explore its parameters like bins, color, edgecolor, and rwidth parameters.

Task 2.1: Generate Random Data

Use the numpy.random.default_rng object to generate a set of random data. This data will be used to create the histogram.

import numpy as np

rng = np.random.default_rng()

data = rng.normal(size=1000)

Task 2.2: Create Basic Histogram

Use the matplotlib.pyplot.hist function to create a basic histogram of the data.

import matplotlib.pyplot as plt

plt.hist(data)
plt.show()

Task 2.3: Customize Bin Size

Customize the bin size of the histogram to better understand the distribution of the data. To do this, recreate the histogram from Task 2.2 with a custom value for the bins parameter.

plt.hist(data, bins=50)
plt.show()

Task 2.4: Customize Visual Elements

Now lets customize the color, edge color, and relative width of the bars in the histogram to improve its readability.

plt.hist(data, bins=50, color='red', edgecolor='black', rwidth=0.5)
plt.show()

Creating and Customizing Boxplots

To review the concepts covered in this step, please refer to the Using Different Chart Types for Distributions module of the Statistical Analysis with Matplotlib course.

Creating and customizing boxplots is important because they provide a summary of the data's distribution. They can help us identify outliers, skewness, and other characteristics of the data.

In this step, we will practice creating and customizing boxplots using Matplotlib. The goal is to understand how to interpret a boxplot and how to customize it to better represent the data. We will use the matplotlib.pyplot.boxplot function and its parameters like notch, whis, showmeans, etc. We will use the numpy.random.default_rng object to generate the data for the plots.

Task 3.1: Importing Required Libraries

Import the required libraries for this lab. We will need matplotlib.pyplot for creating the boxplots and numpy for generating the random data.

import matplotlib.pyplot as plt
import numpy as np

Task 3.2: Generating Random Data

Generate a random dataset using the numpy.random.default_rng object. Create an array of 100 random numbers.

rng = np.random.default_rng()
data = rng.uniform(low=0, high=1, size=100)

Task 3.3: Creating a Basic Boxplot

Create a basic boxplot of the data using the matplotlib.pyplot.boxplot function.

plt.boxplot(data)
plt.show()

Task 3.4: Customizing the Boxplot

Customize the boxplot by adding a notch, changing the whisker length, and showing the means. Use the notch, whis, and showmeans parameters of the boxplot function.

plt.boxplot(data, notch=True, whis=1.5, showmeans=True)
plt.show()

Creating Violin Plots and Subplot Mosaics

To review the concepts covered in this step, please refer to the Using Different Chart Types for Distributions module of the Statistical Analysis with Matplotlib course.

Creating violin plots and subplot mosaics is important because they provide more detailed visualizations of the data. Violin plots combine the benefits of histograms and boxplots, while subplot mosaics allow us to compare multiple plots side by side.

In this step, we will practice creating violin plots and subplot mosaics using Matplotlib. The goal is to understand how these visualizations can provide more insights into the data. We will use the plt.violinplot function to create some violin plots and the figure.subplot_mosaic function to create subplots with keys. We'll display two different datasets side by side with data generated randomly using the numpy.random.default_rng object.

Task 4.1: Importing Necessary Libraries

Import the necessary libraries for creating violin plots and subplot mosaics.

import matplotlib.pyplot as plt
import numpy as np

Task 4.2: Generating Random Data

Generate two sets of normally distributed random data using the numpy.random.default_rng object, each with 500 samples. Choose different mean and standard deviations for each dataset.

rng = np.random.default_rng()
data1 = rng.normal(loc=1, scale=0.5, size=500)
data2 = rng.normal(loc=2, scale=2, size=500)

Task 4.3: Creating Violin Plots

Create violin plots for the two sets of data using the plt.violinplot function.

plt.violinplot([data1, data2])
plt.show()

Task 4.4: Constructing Subplot Mosaics for Violin Plots and Histograms

Create a subplot mosaic that arranges violin plots and histograms for two separate datasets in a specific grid pattern. The grid should be organized as follows:

• The top row displays the first dataset: a violin plot followed by a histogram.
• The bottom row shows the second dataset in a similar fashion: a violin plot and then a histogram.

The desired layout is:

[[d1violin, d1hist],
[d2violin, d2hist]]

fig = plt.figure()
axes = fig.subplot_mosaic([
    ['d1violin', 'd1hist'],
    ['d2violin', 'd2hist']
])
axes['d1violin'].violinplot(data1)
axes['d1hist'].hist(data1)
axes['d2violin'].violinplot(data2)
axes['d2hist'].hist(data2)
plt.show()