# Matplotlib Assertions

In this notebook, you'll learn how to use most used matplotlib assertion functions. There is only one assertion function for checking the expected figure with the actual figure.

assert_plt_student_fig_matches_png_fname(student_figure_variable_name, expected_png_fname): Checks if student figure object is equals to expected png image.

Load the utils.py file to use the assertion functions.

exec(open("utils.py").read())

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

# Activities

Now, with activities examples, you'll learn how to use the assertion functions. We have df dataframe that contains the data of high school graduation rates and poverty rates across different U.S. states.

# 1: Plotting and Comparing Socio-Economic Indicators with Matplotlib

Create a visualization featuring two point plots on the same graph:

The first one representing normalized poverty rates, using df['normalized_poverty_rate'] and using a blue marker (that is already provided).

The second one representing normalized high schoool graduation rates, using df['normalized_hs_rate'], and using a red x marker (already provided).

This visualization will enable a comparative analysis of these two crucial socio-economic indicators across different states using Matplotlib.

Your visualization should have a size of (14, 7) and look something like:

# Creating a figure and an axis
fig, ax = plt.subplots(figsize=(14, 7))

# Plotting the data using scatter plots
ax...(..,.., label='High School Graduation Rates', marker='o', color='b')
ax...(..,.. label='Poverty Rates', marker='x', color='r')

# Adding labels and title
ax.set_xlabel('States', fontsize=10, color='blue')
ax.set_ylabel('Normalized Rates')
ax.set_title('Comparison of High School Graduation and Poverty Rates by State')

# Rotating x-axis labels by 90 degrees
ax.set_xticklabels(df['State'], fontsize=8,rotation=90)
# Adding a legend
ax.legend()

Solution:

# Creating a figure and an axis
fig, ax = plt.subplots(figsize=(14, 7))

# Plotting the data using scatter plots
ax.plot(df['State'], df['normalized_hs_rate'], label='High School Graduation Rates', marker='o', color='b')
ax.plot(df['State'], df['normalized_poverty_rate'], label='Poverty Rates', marker='x', color='r')

# Adding labels and title
ax.set_xlabel('States', fontsize=10, color='blue')
ax.set_ylabel('Normalized Rates')
ax.set_title('Comparison of High School Graduation and Poverty Rates by State')

# Rotating x-axis labels by 90 degrees
ax.set_xticklabels(df['State'], fontsize=8,rotation=90)
# Adding a legend
ax.legend()

As we have to assert student variable with expected png image, we will use assert_plt_student_fig_matches_png_fname() function.

This is how you can save the figure and assert it with the expected image.

fig.savefig('activity_solutions_files/expected_plot_1.png')

Assertions:

assert_plt_student_fig_matches_png_fname("fig", 'expected_plot_1.png')

# 2: Scatter Plotting Socio-Economic Indicators

Create a scatter plot to visualize and analyze the relationship between the two key socio-economic indicators: the normalized high school graduation rate (normalized_hs_rate) and the poverty rate (normalized_poverty_rate) across different U.S. states.

Use normalized_hs_rate in the X axis and normalized_poverty_rate in the Y axis. Your plot should look something like:

# Plotting the scatter plot
fig, ax = plt.subplots(figsize=(14, 7))
ax....(..., ...)

# Adding labels and title
ax.set_xlabel('Normalized High School Graduation Rate')
ax.set_ylabel('Normalized Poverty Rate')
ax.set_title('Statewise Comparison of High School Graduation and Poverty Rates')

Solution:

# Plotting the scatter plot
fig, ax = plt.subplots(figsize=(14, 7))
ax.scatter(df['normalized_hs_rate'], df['normalized_poverty_rate'])

# Adding labels and title
ax.set_xlabel('Normalized High School Graduation Rate')
ax.set_ylabel('Normalized Poverty Rate')
ax.set_title('Statewise Comparison of High School Graduation and Poverty Rates')

fig.savefig('activity_solutions_files/expected_plot_2.png')

Assertions:

assert_plt_student_fig_matches_png_fname("fig", 'expected_plot_2.png')