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In the previous post, we have learnt the basics of using the matplotlib module for data visualization. Also, we have only learnt how to draw a line plot.

In this post, we will learn to draw other plots including bar plot, pie plot, scatter plot, box plot, and histogram.

Bar Plot

Use matplotlib.pyplot.bar() to create bar plots. Find out more details in the official documentation.

from matplotlib import pyplot as plt
from matplotlib.pyplot import figure
import numpy as np

# Defines the chart font style
font = {'family' : 'Times New Roman',
        'weight' : 'bold',
        'size'   : 18}

# includes the chart font style
plt.rc('font', **font)

# You can also define like this
# plt.rcParams["font.family"] = "Times New Roman"

# To define figure size
figure(num=None, figsize=(12, 6))

# Defines X-axis labels and Y-axis values
x_axis_labels = ['0', '1', '2', '3', '4', '5']
y_axis_values = [670, 520, 240, 135, 96, 567]

# Creating n-dimensional array with evenly spaced values

# Input bar values
# Define the bar styles with width, color, and legend labels
plt.bar(y_pos + 0, y_axis_values, width=0.5, color = 'c', label='legend title')
# For Horizontal bars
# plt.barh(y_pos + 0, y_axis_values, color = 'c', label='legend title')

# Define X-axis labels
plt.xticks(y_pos, x_axis_labels)
# plt.xticks(y_pos, x_axis_labels, rotation='vertical')

# Defines best position of the legend in the figure

# Defines X and Y axis labels
plt.ylabel('This is Y-axis label')
plt.xlabel('This is X-axis label')

# Defines plot title
plt.title("This is the Graph Title")

# Show the plot

# To save the figure as pdf/png/jpg, use plt.savefig
# plt.savefig('Figure_name.pdf', dpi=300)

You can check more customized bar plots in my other post:

Creating Bar Charts using Python Matplotlib

Pie Plot

Use matplotlib.pyplot.pie() to create pie plots. Find out more details in the official documentation.

colors = ["#1f77b4", "#ff7f0e", "#2ca02c", "#d62728", "#8c564b"]
labels = 'x', 'y', 'z', 'w'
sizes = [5, 10, 20, 30]
explode = (0, 0, 0, 0.1)  # is used to explode a slice

fig1, ax = plt.subplots()
ax.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%',
        shadow=True, startangle=180)


Scatter Plot

Use matplotlib.pyplot.scatter() to create scatter plots. Find out more details in the official documentation.

from matplotlib import pyplot as plt
import numpy as np

# gaussian data generation
mu_vec1 = np.array([0,0])
cov_mat1 = np.array([[2,0],[0,2]])

x1 = np.random.multivariate_normal(mu_vec1, cov_mat1, 100)
x2 = np.random.multivariate_normal(mu_vec1+0.2, cov_mat1+0.2, 100)
x3 = np.random.multivariate_normal(mu_vec1+0.4, cov_mat1+0.4, 100)

plt.scatter(x1[:,0], x1[:,1], marker='^', 
            color='blue', alpha=0.4, label='x1 data')
plt.scatter(x2[:,0], x1[:,1], marker='o', 
            color='green', alpha=0.4, label='x2 data')
plt.scatter(x3[:,0], x1[:,1], marker='x', 
            color='red', alpha=0.4, label='x3 data')
plt.ylabel('put your y-label')
plt.xlabel('put your x-label')
plt.legend(loc='best') # leave empty for best, default = `best`


Box Plot

Use matplotlib.pyplot.boxplot() to create box plots. Find out more details in the official documentation.

all_data = [np.random.normal(0, std, 100) for std in range(1, 4)]

fig = plt.figure(figsize=(8,6))

            notch=False, # box instead of notch shape 
            sym='rs',    # red squares for outliers
            vert=True)   # False means horizontal

plt.xticks([y+1 for y in range(len(all_data))], ['x1', 'x2', 'x3'])
plt.xlabel('put your x-label')


You can plot histogram using the matplotlib.pyplot.hist() method. Find out more details in the official documentation.

import random

data1 = [random.gauss(15,10) for i in range(500)]  
data2 = [random.gauss(5,15) for i in range(500)]  
bins = np.arange(-40, 40, 1.5)
plt.xlim([min(data1+data2)-5, max(data1+data2)+5])

plt.hist(data1, bins=bins, alpha=0.2, label='class a')
plt.hist(data2, bins=bins, alpha=0.2, label='class b')
plt.title('Random Gaussian data for Example Histogram')
plt.xlabel('put your x-label')
plt.ylabel('put your y-label')

That’s it for now. In the later posts, I will include code snippets for stylish data visualization using seaborn and interactive visualization using plotly modules.

For accessing all data science in python related posts, check this post:

Collection of Data Science in Python Posts in my Blog.

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