Python Matplotlib Bar Plot
In this article, you will learn about Python Matplotlib Bar Plot.
Bar plot represents data in rectangular bar with heights proportional to the values that they represent. It is used to compare things between different groups or to track changes over time. It shows the relationship between a numeric and a categorical variable. It can also display values for several levels of grouping. The bars can be plotted vertically or horizontally. The bar graphs are best when the big changes in data over time. Matplotlib provides bar() method to plot bar graph.
Syntax of Bar Plot
matplotlib.pyplot.bar(x, height, width, bottom, align)x - It specifies the sequence of scalars. The x coordinates of bar.
height - It specifies scalar or sequence of scalars. The height of the bars.
width (optional)- The width of the bars(by default 0.8).
bottom (optional)- The y coordinate(s) of the bars bases.
align (optional)- Alignment of the bars to the x coordinates.
Basic Example of Bar Graph
Here is a very basic bar plot -
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_axes([0,0,1,1])
ax.bar([1, 2, 3, 4, 5, 6], [4, 3, 7, 9, 5, 2])
plt.show()The above code returns the following output -
Bar Plot Horizontally
The barh() function is used to make a horizontal bar plot. The bars are positioned at y with the given alignment. Here is a simple example that plots the temperature of different cities -
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_axes([0,0,1,1])
city = ['Dhanbad', 'Bokaro', 'Ranchi', 'Koderma', 'Giridih']
temp = [35,37,32,33,38]
ax.barh(city,temp,0.3)
plt.show()
Set Label Name, Title & Color of the Bar Plot
The above bar plots are very basic. So, in the given example, you will learn to add the label name, title of the plot and to set the color of the bar. Here, we have also used the ggplot style sheet. The ggplot is a plotting system for Python based on R's ggplot2 and the Grammar of Graphics. It is built for making professional looking and to plot quickly with minimal code.
import matplotlib.pyplot as plt
plt.style.use('ggplot')
city = ['Dhanbad', 'Bokaro', 'Ranchi', 'Koderma', 'Giridih']
temp = [35,37,32,33,38]
x_pos = [i for i, _ in enumerate(city)]
plt.bar(city,temp,0.3,color='red')
plt.ylabel("Temperature")
plt.xlabel("City")
plt.title("Temperature of Jhar Popular Cities")
plt.show()
Multiple Bar Plot
A multiple bar plot is a plot that has multiple bars for each category. In this, we can compare as many sets of data you want. The process for creating a multiple bar graph is same as creating any other bar graph, only will have to set different colors to represent different sets of data.
import matplotlib.pyplot as plt
import numpy as np
plt.style.use('ggplot')
state = ['Delhi', 'MP', 'UP', 'Tamil', 'Assam']
women_champion = [35,37,32,33,38]
men_champion = [30,22,40,42,35]
inv = np.arange(5)
x_pos = [i for i, _ in enumerate(state)]
plt.bar(inv,women_champion,0.25,color='red')
plt.bar(inv + 0.25,men_champion,0.25,color='blue')
plt.ylabel("Champions")
plt.xlabel("States")
plt.title("Champions of Different States")
plt.xticks(inv + 0.25 / 2, state)
plt.show()The above code returns the following output -
Matplotlib Stacked Bar Chart
A stacked bar chart is a visual representation of data. It uses the length of two or more stacked bars to represent the components of a total quantitative value across a range of different categorical values. Here is a very simple example of a stacked bar chart -
import matplotlib.pyplot as plt
import numpy as np
plt.style.use('ggplot')
state = ['Delhi', 'MP', 'UP', 'Tamil', 'Assam']
gold_champion = np.array([32, 15, 12, 29, 25])
silver_champion = np.array([35, 20, 28, 21, 10])
bronze_champion = np.array([40, 35, 29, 10, 27])
inv = np.arange(5)
inv = [i for i, _ in enumerate(state)]
plt.bar(inv,gold_champion,0.25,color='red',bottom=silver_champion+bronze_champion)
plt.bar(inv,silver_champion,0.25,color='blue',bottom=bronze_champion)
plt.bar(inv,bronze_champion,0.25,color='green')
plt.ylabel("Champions")
plt.xlabel("States")
plt.title("Champions of Different States")
plt.xticks(inv, state)
plt.show()
The output of this code is shown in the following image -
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