Exploring Matplotlib’s Architecture

To review the concepts covered in this step, please refer to the Understanding Matplotlib’s Architecture module of the Getting Started with Matplotlib course.

Understanding Matplotlib’s architecture is important because it forms the foundation of how we create and customize plots. This step will focus on creating figures, adding text elements, and customizing colors.

Let's dive into the world of Matplotlib and explore its architecture. In this step, you'll create a figure using both the scripting and artist layers of Matplotlib. You'll also learn how to add text elements to your plot and customize colors using various methods such as basic color names, single letter color codes, and hexadecimal color codes. Tools you'll use include the plt.figure(), plt.plot(), and plt.subplots() functions.

Task 1.1: Creating a Plot using Scripting Layer

Create a plot using the scripting layer of Matplotlib. Use the plt.plot() function to accomplish this.

import matplotlib.pyplot as plt

# Create a plot using plt.plot()
plt.plot(2, 2, marker='o')

Task 1.2: Creating a Plot using Artist Layer

Create a plot using the artist layer of Matplotlib. Create a figure with plt.figure() followed by the axis and plot.

# Create a figure
fig = plt.figure()
ax = fig.subplots()
ax.plot(2, 2, marker='o')

Task 1.3: Adding Text to a Plot

Add a title, xlabel and ylabel to a plot. Set the title's font size to 14 and color to red.

# Create a plot
plt.plot([0, 1], [0, 1])

# Add text
plt.xlabel('X-axis Label')
plt.ylabel('Y-axis Label')
plt.title('My Customized Title', fontsize=14, color='red')

Task 1.4: Customizing Colors using Basic Color Names

Customize the color of a plot using basic color names. Create a plot using plt.plot() and set the color to 'red'.

import matplotlib.pyplot as plt

# Create a plot with color 'red'
plt.plot([0, 1], [0, 1], color='red')

Task 1.5: Customizing Colors using Single Letter Color Codes

Customize the color of a plot using single letter color codes. Create a plot using plt.plot() and set the color to 'g' (green).

import matplotlib.pyplot as plt

# Create a plot with color 'g'
plt.plot([0, 1], [0, 1], color='g')

Task 1.6: Customizing Colors using Hexadecimal Color Codes

Customize the color of a plot using hexadecimal color codes. Create a plot using plt.plot() and set the color to '#FFD700' (gold).

import matplotlib.pyplot as plt

# Create a plot with color '#FFD700'
plt.plot([0, 1], [0, 1], color='#FFD700')

Building Line Charts

To review the concepts covered in this step, please refer to the Building Line Charts module of the Getting Started with Matplotlib course.

Building line charts is important because they provide a clear view of data trends over time. This step will focus on creating and customizing line charts using real-world data.

Now that you've got a grasp on Matplotlib's architecture, let's put that knowledge to use by creating some line charts. In this step, you'll use the plt.plot() function to create a simple line chart. You'll also learn how to group records by a specific column and count the number of occurrences. Additionally, you'll customize the appearance of your line chart using markers and line styles.

Task 2.1: Importing Necessary Libraries

Before we start with the actual task, we need to import the necessary libraries. Import pandas for data manipulation and matplotlib for data visualization.

import pandas as pd
import matplotlib.pyplot as plt

Task 2.2: Loading the Dataset

Load the dataset 'Marketing_data.csv' into a pandas DataFrame. Display the first few rows of the data.

df = pd.read_csv('Marketing_data.csv')
df.head()

Task 2.3: Grouping and Getting Cost Info

Use the provided code to group the records by the 'Age' column and record the average cost, recorded in the 'Amount' column, for each age.

age_spend_average = df.groupby('Age').Amount.mean()

Task 2.4: Creating a Line Chart

Create a line chart to visualize the average cost as age increases. Use 'Age' as the x-axis and 'Cost' as the y-axis.

plt.plot(age_spend_average.index, age_spend_average.values)

Task 2.5: Customizing the Line Chart

Recreate your plot from Task 2.4 and customize the line chart by adding a title, x-label, y-label, and grid. Also, change the line style to dashed.

plt.plot(age_spend_average.index, age_spend_average.values, '--')
plt.title('Average Spend Amount per Age')
plt.xlabel('Age')
plt.ylabel('Average Spend ($)')
plt.grid(True)

Working with Scatter Plots

To review the concepts covered in this step, please refer to the Working with Scatter Plots module of the Getting Started with Matplotlib course.

Scatter plots are important because they allow us to identify relationships between two variables. This step will focus on creating and customizing scatter plots.

In this step, you'll dive into the world of scatter plots. You'll learn how to create scatter plots using both the plt.scatter() and plt.plot() functions. You'll also learn how to identify positive correlations between two variables and how to customize the appearance of your scatter plot, including coloring markers based on another column from the data frame.

After the successful completion of each task, proceed to execute the Jupyter Notebook cell by using the Ctrl/cmd + Enter key combination to enact any necessary changes.

Task 3.1: Importing Necessary Libraries

Import the necessary libraries for data manipulation and visualization, pandas and matplotlib.

import pandas as pd
import matplotlib.pyplot as plt

Task 3.2: Loading the Dataset

Load the dataset 'Marketing_data.csv' into a pandas DataFrame. Display the first few rows.

df = pd.read_csv('Marketing_data.csv')
df.head()

Task 3.3: Creating a Basic Scatter Plot

Create a scatter plot using plt.scatter() function to visualize the relationship between 'Age' and 'Amount'.

plt.scatter(df['Age'], df['Amount'])

Task 3.4: Customizing the Scatter Plot

Customize the scatter plot by adding a title, x-label, and y-label. Also, color the markers based on 'Gender'. Finally, make the points partially transparent with the alpha parameter.

# provided code
colors = ['blue' if gender=='Male' else 'pink' for gender in df['Gender']]

# scatter plot
plt.scatter(df['Age'], df['Amount'], c=colors)
plt.title('Age vs Amount')
plt.xlabel('Age')
plt.ylabel('Amount')

Task 3.5: Creating a Scatter Plot using plt.plot()

Create a basic scatter plot using plt.plot() function to visualize the relationship between 'Age' and 'Amount'.

plt.plot(df['Age'], df['Amount'], marker='o', linestyle='None')

Creating Bar Charts

To review the concepts covered in this step, please refer to the Creating Bar Charts module of the Getting Started with Matplotlib course.

Bar charts are important because they allow us to compare and rank categories. This step will focus on creating and customizing bar charts in interactive mode.

In this final step, you'll learn how to create various types of bar charts including vertical and stacked bar charts. You'll also learn how to customize bar chart properties such as bar color, width, opacity, and alignment. You'll learn to set up interactive mode and how to add labels and data labels to your bar chart. Tools you'll use include the plt.bar(), plt.barh(), and plt.xticks() functions.

Task 4.1: Importing Necessary Libraries

Import the necessary libraries for data manipulation and visualization, pandas and matplotlib.

import pandas as pd
import matplotlib.pyplot as plt

Task 4.2: Loading the Dataset

Load the dataset 'Marketing_data.csv' into a pandas DataFrame. Display the first few rows.

df = pd.read_csv('Marketing_data.csv')
df.head()

Task 4.3: Setting Up Interactive Plots

Use %matplotlib notebook to change matplotlib to interactive mode.

%matplotlib notebook

Task 4.4: Creating a Basic Bar Chart

Create a basic bar chart to visualize the count of each product category in the 'Category' column. Use the df.value_counts() method to get the counts. The x-ticks will overlap -- we'll address this in the next task.

Use the provided code to set an effective figure size.

plt.figure(figsize=(4,4))

plt.bar(df['Category'].value_counts().index, df['Category'].value_counts().values)

Task 4.5: Customizing the Bar Chart

Customize the bar chart by adding a title, x-label, and y-label. Rotate the x-ticks to be more readable with plt.xticks(rotation=30, ha='right').

plt.title('Product Category Counts')
plt.xlabel('Category')
plt.ylabel('Count')
plt.xticks(rotation=30, ha='right')

Task 4.6: Creating a Horizontal Bar Chart

Stop the previous interactive chat session and create a new plot. Make a horizontal bar chart to visualize the count of each product category.

Use the provided code to set an appropriate figure size.

plt.figure(figsize=(4,4))	

plt.barh(df['Category'].value_counts().index, df['Category'].value_counts().values)