How do you implement a bar chart in python?

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A bar plot or bar chart is a graph that represents the category of data with rectangular bars with lengths and heights that is proportional to the values which they represent. The bar plots can be plotted horizontally or vertically. A bar chart describes the comparisons between the discrete categories. One of the axis of the plot represents the specific categories being compared, while the other axis represents the measured values corresponding to those categories.
 

Creating a bar plot

The matplotlib API in Python provides the bar() function which can be used in MATLAB style use or as an object-oriented API. The syntax of the bar() function to be used with the axes is as follows:-

plt.bar(x, height, width, bottom, align)

The function creates a bar plot bounded with a rectangle depending on the given parameters. Following is a simple example of the bar plot, which represents the number of students enrolled in different courses of an institute. 
 

Python3

import numpy as np

import matplotlib.pyplot as plt

data = {'C':20, 'C++':15, 'Java':30,

        'Python':35}

courses = list(data.keys())

values = list(data.values())

fig = plt.figure(figsize = (10, 5))

plt.bar(courses, values, color ='maroon',

        width = 0.4)

plt.xlabel("Courses offered")

plt.ylabel("No. of students enrolled")

plt.title("Students enrolled in different courses")

plt.show()

Output- 
 

Here plt.bar(courses, values, color=’maroon’) is used to specify that the bar chart is to be plotted by using the courses column as the X-axis, and the values as the Y-axis. The color attribute is used to set the color of the bars(maroon in this case).plt.xlabel(“Courses offered”) and plt.ylabel(“students enrolled”) are used to label the corresponding axes.plt.title() is used to make a title for the graph.plt.show() is used to show the graph as output using the previous commands.
 

Customizing the bar plot

Python3

import pandas as pd

from matplotlib import pyplot as plt

data = pd.read_csv(r"cars.csv")

data.head()

df = pd.DataFrame(data)

name = df['car'].head(12)

price = df['price'].head(12)

fig = plt.figure(figsize =(10, 7))

plt.bar(name[0:10], price[0:10])

plt.show()

Output:
 

It is observed in the above bar graph that the X-axis ticks are overlapping each other thus it cannot be seen properly. Thus by rotating the X-axis ticks, it can be visible clearly. That is why customization in bar graphs is required.

Python3

import pandas as pd

from matplotlib import pyplot as plt

data = pd.read_csv(r"cars.csv")

data.head()

df = pd.DataFrame(data)

name = df['car'].head(12)

price = df['price'].head(12)

fig, ax = plt.subplots(figsize =(16, 9))

ax.barh(name, price)

for s in ['top', 'bottom', 'left', 'right']:

    ax.spines[s].set_visible(False)

ax.xaxis.set_ticks_position('none')

ax.yaxis.set_ticks_position('none')

ax.xaxis.set_tick_params(pad = 5)

ax.yaxis.set_tick_params(pad = 10)

ax.grid(b = True, color ='grey',

        linestyle ='-.', linewidth = 0.5,

        alpha = 0.2)

ax.invert_yaxis()

for i in ax.patches:

    plt.text(i.get_width()+0.2, i.get_y()+0.5,

             str(round((i.get_width()), 2)),

             fontsize = 10, fontweight ='bold',

             color ='grey')

ax.set_title('Sports car and their price in crore',

             loc ='left', )

fig.text(0.9, 0.15, 'Jeeteshgavande30', fontsize = 12,

         color ='grey', ha ='right', va ='bottom',

         alpha = 0.7)

plt.show()

Output:
 

There are many more Customizations available for bar plots.
 

Multiple bar plots

Multiple bar plots are used when comparison among the data set is to be done when one variable is changing. We can easily convert it as a stacked area bar chart, where each subgroup is displayed by one on top of the others. It can be plotted by varying the thickness and position of the bars. Following bar plot shows the number of students passed in the engineering branch:

Python3

import numpy as np

import matplotlib.pyplot as plt

barWidth = 0.25

fig = plt.subplots(figsize =(12, 8))

IT = [12, 30, 1, 8, 22]

ECE = [28, 6, 16, 5, 10]

CSE = [29, 3, 24, 25, 17]

br1 = np.arange(len(IT))

br2 = [x + barWidth for x in br1]

br3 = [x + barWidth for x in br2]

plt.bar(br1, IT, color ='r', width = barWidth,

        edgecolor ='grey', label ='IT')

plt.bar(br2, ECE, color ='g', width = barWidth,

        edgecolor ='grey', label ='ECE')

plt.bar(br3, CSE, color ='b', width = barWidth,

        edgecolor ='grey', label ='CSE')

plt.xlabel('Branch', fontweight ='bold', fontsize = 15)

plt.ylabel('Students passed', fontweight ='bold', fontsize = 15)

plt.xticks([r + barWidth for r in range(len(IT))],

        ['2015', '2016', '2017', '2018', '2019'])

plt.legend()

plt.show()

Output:
 

Stacked bar plot

Stacked bar plots represent different groups on top of one another. The height of the bar depends on the resulting height of the combination of the results of the groups. It goes from the bottom to the value instead of going from zero to value. The following bar plot represents the contribution of boys and girls in the team.
 

Python3

import numpy as np

import matplotlib.pyplot as plt

N = 5

boys = (20, 35, 30, 35, 27)

girls = (25, 32, 34, 20, 25)

boyStd = (2, 3, 4, 1, 2)

girlStd = (3, 5, 2, 3, 3)

ind = np.arange(N)  

width = 0.35 

fig = plt.subplots(figsize =(10, 7))

p1 = plt.bar(ind, boys, width, yerr = boyStd)

p2 = plt.bar(ind, girls, width,

             bottom = boys, yerr = girlStd)

plt.ylabel('Contribution')

plt.title('Contribution by the teams')

plt.xticks(ind, ('T1', 'T2', 'T3', 'T4', 'T5'))

plt.yticks(np.arange(0, 81, 10))

plt.legend((p1[0], p2[0]), ('boys', 'girls'))

plt.show()

Output-