Plot with numerical variables and categorical variables
Change column color
Create a horizontal bar chart
plt.figure(figsize = (8,6)) # set the figure size
performance = [70,95, 61, 88,82] # y_axis values
student = ['Mavin','Lily','Tim','Ben','Julian'] # x_axis values
y_pos = np.arange(len(student)) # calculate number of "student"
plt.bar(y_pos,performance,color = 'orange') # create bars, set the orange color
plt.xticks(y_pos,student) #Create names on the x-axis
plt.show()
2. Horizontal Column Chart
# Create horizontal bars
plt.barh(y_pos, height) # just add a 'h' (horizontal)
plt.yticks(y_pos,student) # we need to set students'name on the y-axisStacked Column Chart
3. Stacked Column Chart
What to learn:
The difference of Stacked Column Chart and Grouped Column Chart
Plot two groups of bars on one figure
Choose the proper chart
labels =['G1', 'G2', 'G3', 'G4']
male_avg = [1.80,1.75, 1.84,1.7]
female_avg =[1.75, 1.6, 1.70, 1.53]
width =0.4 # set the width of the bars
fig, ax = plt.subplots()
ax.bar(labels, male_avg, width, label ='Male')
ax.bar(labels, female_avg, width, bottom= male_avg,
label='Female')
ax.set_ylabel('Average Height')
ax.set_title('Height by group and gender')
ax.legend()
plt.show()
Obviously, this is a bad choice. The stacked bar can't show the trends nor differences clearly. Meanwhile. the stack makes y_axis value non-sense. Nobody grows to 3.5 meters!
So we should alternate to the grouped bar chart.
4. Grouped Column Chart
width =0.4
x = np.arange(len(labels)) # the label locations
fig, ax = plt.subplots()
rects1 = ax.bar(x - width/2, male_avg, width, label='Male')
rects2 = ax.bar(x + width/2, female_avg, width, label='Female')
ax.set_xticks(x)
ax.set_xticklabels(labels) # set the categorical variable on x_axis
ax.set_ylabel('Average Height')
ax.set_title('Height by group and gender')
ax.legend()
plt.show()
