Okay, we’ve covered some basics on matplotlib syntax and seen how graphs are made and altered. Now let’s make a chart ourselves! In the next few exercises we’ll focus on making a line chart to compare temperatures in cities around the world.

As we saw already, the function for a line chart is .plot(). As always, we need to call matplotlib (as plt) first, so to make a line chart we’ll use plt.plot().

The line graph function has parameters for x and y data: plt.plot(x, y).

• x is the data for the x-axis – in a line chart, this is very often a measure of time
• y is the data for y-axis – generally things we want to view over time, like interest rates, the price of a slice of pizza, or population size in a country

Depending on our data setup, x and y could be written directly into the function, pulled from a named array, or a column of a dataframe. You can see live examples of each of these in the Jupyter notebook where it says Narrative Examples.

# make a line chart from direct data
plt.plot(['Mon', 'Tues', 'Weds', 'Thurs', 'Fri', 'Sat', 'Sun'], [540, 500, 490, 590, 680, 710, 610])

# make a line chart using named arrays
day = ['Mon', 'Tues', 'Weds', 'Thurs', 'Fri', 'Sat', 'Sun']
sales_totals = [540, 500, 490, 590, 680, 710, 610]
plt.plot(day, sales_totals)

# make a line chart with columns from a dataframe
df = pd.read_csv('restaurant_data.csv')
plt.plot(df.day, df.sales_totals)
# or
plt.plot(df['day'], df['sales totals'])

To add multiple lines to a chart, we simply call plt.plot() again with different y data. Matplotlib will keep adding lines to the same instance of plt until we clear the plot using the “clear the figure” command: plt.clf().

(Side note: matplotlib’s official documentation is the perfect place to answer questions about which parameters a function will take.)