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Matplotlib.pyplot.barh() function in Python
  • Last Updated : 23 Dec, 2020

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 Horizontal bar plot

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

Syntax: matplotlib.pyplot.barh(y, width, height=0.8, left=None, *, align=’center’, **kwargs)

Some of the positional and optional parameters of the above function are described below:

Parameters Description
Co-ordinates of the Y bars.
width Scalar or array like, denotes the width of the bars.
height Scalar or array like, denotes the height of the bars(default value is 0.8).
left Scalar or sequence of scalars, denotes X co-ordinates on the left sides of the bars(default value is 0).
align {‘center’, ‘edge’}aligns the base of the Y co-ordinates(default value is center).
color Scalar or array like, denotes the color of the bars.
edgecolor Scalar or array like, denotes the edge color of the bars.
linewidth Scalar or array like, denotes the width of the bar edges.
tick_label Scalar or array like, denotes the tick labels of the bars(default value is None).

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



Example 1:

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import numpy as np
import matplotlib.pyplot as plt
  
  
# creating the dataset
data = {'C': 20, 'C++': 15, 'Java': 30,
        'Python': 35}
  
courses = list(data.keys())
values = list(data.values())
  
fig = plt.figure(figsize=(10, 5))
  
# creating the bar plot
plt.barh(courses, values, color='maroon')
  
plt.xlabel("Courses offered")
plt.ylabel("No. of students enrolled")
plt.title("Students enrolled in different courses")
plt.show()

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Output :

Here plt.barh(courses, values, color=’maroon’) is used to specify that the bar chart is to be plotted by using the courses column as the Y-axis, and the values as the X-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.

Example 2:

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import pandas as pd
from matplotlib import pyplot as plt
  
# Read CSV into pandas
data = pd.read_csv(r"Downloads/cars1.csv")
data.head()
df = pd.DataFrame(data)
  
name = df['car'].head(12)
price = df['price'].head(12)
  
# Figure Size
fig, ax = plt.subplots(figsize=(16, 9))
  
# Horizontal Bar Plot
ax.barh(name, price)
  
# Remove axes splines
for s in ['top', 'bottom', 'left', 'right']:
    ax.spines[s].set_visible(False)
  
# Remove x, y Ticks
ax.xaxis.set_ticks_position('none')
ax.yaxis.set_ticks_position('none')
  
# Add padding between axes and labels
ax.xaxis.set_tick_params(pad=5)
ax.yaxis.set_tick_params(pad=10)
  
# Add x, y gridlines
ax.grid(b=True, color='grey',
        linestyle='-.', linewidth=0.5,
        alpha=0.2)
  
# Show top values
ax.invert_yaxis()
  
# Add annotation to bars
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')
  
# Add Plot Title
ax.set_title('Sports car and their price in crore',
             loc='left', )
  
# Add Text watermark
fig.text(0.9, 0.15, 'Jeeteshgavande30', fontsize=12,
         color='grey', ha='right', va='bottom',
         alpha=0.7)
  
# Show Plot
plt.show()

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Output:


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