Boxplots are a measure of how well distributed the data in a data set is. It divides the data set into three quartiles. This graph represents the minimum, maximum, median, first quartile and third quartile in the data set. It is also useful in comparing the distribution of data across data sets by drawing boxplots for each of them.

Drawing a Box Plot

Boxplot can be drawn calling Series.box.plot() and DataFrame.box.plot(), or DataFrame.boxplot() to visualize the distribution of values within each column.
For instance, here is a boxplot representing five trials of 10 observations of a uniform random variable on [0,1).
import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.rand(10, 5), columns=[‘A’, ‘B’, ‘C’, ‘D’, ‘E’])
df.plot.box(grid=’True’)

Its output is as follows −

p 16

Python – Heat Maps

A heatmap contains values representing various shades of the same colour for each value to be plotted. Usually the darker shades of the chart represent higher values than the lighter shade. For a very different value a completely different colour can also be used.
The below example is a two-dimensional plot of values which are mapped to the indices and columns of the chart.
from pandas import DataFrame
import matplotlib.pyplot as plt

data=[{2,3,4,1},{6,3,5,2},{6,3,5,4},{3,7,5,4},{2,8,1,5}]
Index= [‘I1’, ‘I2′,’I3′,’I4′,’I5’]
Cols = [‘C1’, ‘C2’, ‘C3′,’C4’]
df = DataFrame(data, index=Index, columns=Cols)

plt.pcolor(df)
plt.show()

Its output is as follows −

p 17

Python – Scatter Plots

Scatterplots show many points plotted in the Cartesian plane. Each point represents the values of two variables. One variable is chosen in the horizontal axis and another in the vertical axis.

Drawing a Scatter Plot

Scatter plot can be created using the DataFrame.plot.scatter() methods.
import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.rand(50, 4), columns=[‘a’, ‘b’, ‘c’, ‘d’])
df.plot.scatter(x=’a’, y=’b’)

Its output is as follows −

p 18 1

Python – Bubble Charts

Bubble charts display data as a cluster of circles. The required data to create bubble chart needs to have the xy coordinates, size of the bubble and the colour of the bubbles. The colours can be supplied by the library itself.

Drawing a Bubble Chart

Bubble chart can be created using the DataFrame.plot.scatter() methods.
import matplotlib.pyplot as plt
import numpy as np

# create data
x = np.random.rand(40)
y = np.random.rand(40)
z = np.random.rand(40)
colors = np.random.rand(40)
# use the scatter function
plt.scatter(x, y, s=z*1000,c=colors)
plt.show()

Its output is as follows −

p 19

Python – 3D Charts

Python is also capable of creating 3d charts. It involves adding a subplot to an existing two-dimensional plot and assigning the projection parameter as 3d.

Drawing a 3D Plot

3dPlot is drawn by mpl_toolkits.mplot3d to add a subplot to an existing 2d plot.
from mpl_toolkits.mplot3d import axes3d
import matplotlib.pyplot as plt

chart = plt.figure()
chart3d = chart.add_subplot(111, projection=’3d’)

# Create some test data.
X, Y, Z = axes3d.get_test_data(0.08)

# Plot a wireframe.
chart3d.plot_wireframe(X, Y, Z, color=’r’,rstride=15, cstride=10)

plt.show()

Its output is as follows −

p 20 1

So, this brings us to the end of blog. This Tecklearn ‘How to draw different Charts 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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