Python has excellent libraries for data visualization. a mixture of Pandas, numpy and matplotlib can help in creating in nearly all kinds of visualizations charts. during this blog we’ll start with watching some simple chart and therefore the various properties of the chart.

Creating a Chart

We use numpy library to make the specified numbers to be mapped for creating the chart and therefore the pyplot method in matplotlib to draws the particular chart.

import numpy as np
import matplotlib.pyplot as plt
x = np.arange(0,10)
y = x ^ 2
#Simple Plot
plt.plot(x,y)

Its output is as follows −

py 2

Labling the Axes

We can apply labels to the axes also as a title for the chart using appropriate methods from the library as shown below.

import numpy as np
import matplotlib.pyplot as plt
x = np.arange(0,10)
y = x ^ 2
#Labeling the Axes and Title
plt.title("Graph Drawing")
plt.xlabel("Time")
plt.ylabel("Distance")
#Simple Plot
plt.plot(x,y)

Its output is as follows −

3.1 4

Formatting Line type and Colour

The style also as colour for the road within the chart are often specified using appropriate methods from the library as shown below.

import numpy as np
import matplotlib.pyplot as plt
x = np.arange(0,10)
y = x ^ 2
#Labeling the Axes and Title
plt.title("Graph Drawing")
plt.xlabel("Time")
plt.ylabel("Distance")
# Formatting the road colors
plt.plot(x,y,'r')
# Formatting the road type
plt.plot(x,y,'>')

Its output is as follows −

py 1

Saving the Chart File

The chart are often saved in several image file formats using appropriate methods from the library as shown below.

import numpy as np
import matplotlib.pyplot as plt
x = np.arange(0,10)
y = x ^ 2
#Labeling the Axes and Title
plt.title("Graph Drawing")
plt.xlabel("Time")
plt.ylabel("Distance")
# Formatting the road colors
plt.plot(x,y,'r')
# Formatting the road type
plt.plot(x,y,'>')
# save in pdf formats
plt.savefig('timevsdist.pdf', format='pdf')

The above code creates the pdf enter the default path of the python environment.

Python – Chart Styling

The charts created in python can have further styling by using some appropriate methods from the libraries used for charting. during this lesson we’ll see the implementation of Annotation, legends and chart background. we’ll still use the code from the last chapter and modify it to feature these styles to the chart.

Adding Annotations

Many times, we’d like to annotate the chart by highlighting the precise locations of the chart. within the below example we indicate the sharp change in values within the chart by adding annotations at those points.

import numpy as np
from matplotlib import pyplot as plt
x = np.arange(0,10)
y = x ^ 2
z = x ^ 3
t = x ^ 4
# Labeling the Axes and Title
plt.title("Graph Drawing")
plt.xlabel("Time")
plt.ylabel("Distance")
plt.plot(x,y)
#Annotate
plt.annotate(xy=[2,1], s='Second Entry')
plt.annotate(xy=[4,6], s='Third Entry')

Its output is as follows −

3.4

Adding Legends

We sometimes need a chart with multiple lines being plotted. Use of legend represents the meaning related to each line. within the below chart we’ve 3 lines with appropriate legends.

import numpy as np
from matplotlib import pyplot as plt
x = np.arange(0,10)
y = x ^ 2
z = x ^ 3
t = x ^ 4
# Labeling the Axes and Title
plt.title("Graph Drawing")
plt.xlabel("Time")
plt.ylabel("Distance")
plt.plot(x,y)
#Annotate
plt.annotate(xy=[2,1], s='Second Entry')
plt.annotate(xy=[4,6], s='Third Entry')
# Adding Legends
plt.plot(x,z)
plt.plot(x,t)
plt.legend(['Race1', 'Race2','Race3'], loc=4)

Its output is as follows −

3.5

Chart presentation Style

We can modify the presentation sort of the chart by using different methods from the design package.

import numpy as np
from matplotlib import pyplot as plt
x = np.arange(0,10)
y = x ^ 2
z = x ^ 3
t = x ^ 4
# Labeling the Axes and Title
plt.title("Graph Drawing")
plt.xlabel("Time")
plt.ylabel("Distance")
plt.plot(x,y)
#Annotate
plt.annotate(xy=[2,1], s='Second Entry')
plt.annotate(xy=[4,6], s='Third Entry')
# Adding Legends
plt.plot(x,z)
plt.plot(x,t)
plt.legend(['Race1', 'Race2','Race3'], loc=4)
#Style the background
plt.style.use('fast')
plt.plot(x,z)

Its output is as follows −

3.6

So, this brings us to the end of blog. This Tecklearn ‘Create a Chart in Python’ blog helps you with commonly asked questions if you are looking out for a job in Python Programming. If you wish to learn Python and build a career in Data Science domain, then check out our interactive, Python with Data Science Training, that comes with 24*7 support to guide you throughout your learning period. Please find the link for course details:

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