Nicely done! You've built many interesting bar charts. Now it's time to add some information to make the plots informative.

Like, in the previous chapters, we can use the `.set()` function applied to the `Axes` object to set the following parameters: `xlabel`, `ylabel` for **labels on the axes**, `xlim`, `ylim` to **limit values on the axes** (this parameters must have list/tuple as a value), `title` for **chart title**. Also, to improve the readability we can add values above (or right/left) to each bar. To do it, we should save in separate variable each call of `.bar()` (or `.barh()`) function, and then call the `.bar_label()` applied to `Axes` object, passing saved `.bar()` function call as the first argument, and `padding` as the second (optional, the distance between bar and text). For example,

# Import library
import matplotlib.pyplot as plt

# Create data for chart
subjects = ['Math', 'Literature', 'History', 'Physics', 'Arts']
grades = [95, 76, 83, 92, 68]

# Create Axes and Figure objects
fig, ax = plt.subplots()

# Initialize bar chart
bar1 = ax.bar(subjects, grades)

# Add labels above bars
ax.bar_label(bar1, padding = 0)

# Display the plot
plt.show()

In this section, we will be introduced to the very basics of matplotlib - creating empty Figure and Axes, how to build a line plot, display multiple lines on one plot, add a legend, customize line type, and so on...

In this section, we will learn about other chart types, such as bar plots, histograms, box plots, how to set necessary parameters and customize them.

In this section, we will learn the scatter plots - one of the easiest ways to discover the relationships in your data.

Visualization in Python with matplotlib

Customizing Bar Charts