Python has excellent libraries for data visualization. A combination of Pandas, numpy and matplotlib can help in creating in nearly all types of visualizations charts. In this chapter we will get started with looking at some simple chart and the various properties of the chart.

Creating a Chart

We use numpy library to create the required numbers to be mapped for creating the chart and the pyplot method in matplotlib to draws the actual 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 −

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Labling the Axes

We can apply labels to the axes as well 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 −

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Formatting Line type and Colour

The style as well as colour for the line in the chart can be 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 line colors
plt.plot(x,y,’r’)

# Formatting the line type
plt.plot(x,y,’>’)
Its output is as follows −

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Saving the Chart File

The chart can be saved in different 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 line colors
plt.plot(x,y,’r’)

# Formatting the line type
plt.plot(x,y,’>’)

# save in pdf formats
plt.savefig(‘timevsdist.pdf’, format=’pdf’)
The above code creates the pdf file in 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. In this lesson we will see the implementation of Annotation, legends and chart background. We will continue to use the code from the last chapter and modify it to add these styles to the chart.

Adding Annotations

Many times, we need to annotate the chart by highlighting the specific locations of the chart. In the below example we indicate the sharp change in values in 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 −

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Adding Legends

We sometimes need a chart with multiple lines being plotted. Use of legend represents the meaning associated with each line. In the below chart we have 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 −

P 10

Chart presentation Style

We can modify the presentation style of the chart by using different methods from the style 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)

#Style the background
plt.style.use(‘fast’)
plt.plot(x,z)
Its output is as follows −

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So, this brings us to the end of blog. This Tecklearn ‘Creating 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 Python Programming domain, then check out our interactive, Python with Data Science Training, that comes with 24*7 support to guide you throughout your learning period.

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