Data Visualization with matplotlib

Plotting Curves

• Process of presenting data in the form of graphs or charts
• Helps to understand large and complex amounts of data very easily

Matplotlib:

• Matplotlib is a low-level library of python which is used for data visualization
• Matplotlib utilities presentd under the pyplot submodule and can be imported using the alias

Syntax

import matplotlib.pyplot as plt

Example 1

Draw a line from position (0,0) to (10,100)

• Plot() function is used to draw points in a figure
• By default, it draws line from one point to other

This function takes the parameters as:

• Parameter 1: an array containing the points on x-axis
• Parameter 2: an array containing the points on y-axis
• To draw a line from (0,0 to (10,100:
  • Create two arrays: X_array should have points in x axis and y_array should have points in y axus
  • Here, x_array: 0, 10 Y_array: 0,100

.

import matplotlib.pyplot as pl
import numpy as np
x_axis= np.array([0,10]) # create x axis using array
y_axis = np.array([0,100])
pl.plot(x_axis,y_axis)
pl.show()

Plotting Without line

To plot only the markers

• A string like notation parameter ‘o’ can be used  which means rings

Example: Draw two points in the diagram one at position (1,5) and one in (10,20)

.

import matplotlib.pyplot as pl
import numpy as np
x_axis= np.array([1,10]) # create x axis using array
y_axis = np.array([5,20])
pl.plot(x_axis,y_axis,'o')
pl.show()

Plotting Multiple points

Example 3: Draw a line from (1,3) (5,2),(8,9)

.

import matplotlib.pyplot as pl
import numpy as np
x_axis = np.array([1,5,8])
y_axis = np.array([3,2,9])
pl.plot(x_axis,y_axis)
pl.show()

• If X axis is not provided, then default value of x is assigned till the length of the y axis.
• Default values of x start from 0

.

import matplotlib.pyplot as plt
import numpy as np
yaxis = np.array([5,1,4,8])
plt.plot(yaxis)
plt.show()

Markers

Line

The line value can be specified as:

Color

Marker Size

• The keyword argument markersize can be specified
• It can also be specified as ms

Example

markersize = 20
(or)
ms= 20

Marker Edge color

• The keyword argument markeredgecolor can be used
• It can also be specified as mec

markeredgecolor = ‘r’
(or)
mec=‘r’

Marker Face color

• The keyword argument markerfacecolor can be specified
• It can also be specified as mfc

markerfacecolor = ‘r’
(or)
mfc=‘r’

Example

.

import matplotlib.pyplot as plt
import numpy as np
yaxis = np.array([5,1,4,8])
plt.plot(yaxis,'p:b',mec='r',mfc='g',ms=20)
plt.show()

color = ‘r’
(or)
c=‘r’

Line Width

• The keyword argument linewidth can be specified
• In short, lw can be specified

linewidth = 18.6
(or)
lw= 17

.

import matplotlib.pyplot as plt
import numpy as np
yaxis = np.array([5,1,4,8])
plt.plot(yaxis,ls='dashdot', c='hotpink', lw='5.5',marker='H',markeredgecolor='g')
plt.show()

Plotting Multiple Lines

• Include as many plot() functions
• One plot() function for each line

import matplotlib.pyplot as plt
import numpy as np
yaxis = np.array([5,1,4,8]) #dimension of yaxis and yaxis1 must be same
yaxis1 = np.array( [2,9,7,4])
plt.plot(yaxis)
plt.plot(yaxis1)
plt.show()

Alternately, include both x and y axis for each line in a single plot() function

import matplotlib.pyplot as plt
import numpy as np
x1 = np.array([2,3,7,4])
x2= np.array([2,3,7,4])
yaxis = np.array([5,1,4,8]) #dimension of yaxis and yaxis1 must be same
yaxis1 = np.array( [2,9,7,4])
plt.plot(x1,yaxis,x2,yaxis1,marker='s')
plt.show()

Labels, Title

plt.xlabel(“Height”)
plt.ylabel(“Weight”)

plt.title(“Height vs.Weight”)

legend()

• Gives explanation about each wedge (observation)
• It has the parameter loc

Which takes the values:

Example 8:

The following data gives the amount of loans (in crores) disbursed by a bank during some years

• a) Represent the above data with the help of line graph
• b) Give the label for x as year and y as Loan (in crores) with font size as 15, color as blue and family as serif
• c) Represent the marker as square and size as 5.
• d) Also set the edge and face color of the marker as green and red respectively
• e) Give the title as Loan Disbursed in left with font size as 15, color as blue and family as serif
• f) The property of the line is dashed line with orange color

import matplotlib.pyplot as plt
import numpy as np
year = np.array([1992,1993,1994,1995,1996])
Loan = np.array([28,33,55,55,80])
plt.plot(year,Loan,marker="s",ms=5,mec="g", mfc='r',linestyle="dashed",c='orange')
font1 = {'family':'serif','color':'blue','fontsize':15}
plt.xlabel("Year",fontdict=font1)
plt.ylabel("Loan",fontdict=font1)
plt.title("Loan Disbursed",loc='left',fontdict=font1)

Grid

• grid() function is used to add grid lines to the plot

Syntax

plt.grid()

Parameter axis is used to specify which grid lines to display

• Values taken by axis parameters are ‘x’, ‘y’ and ‘both. Default value is ‘both

Properties of the grid line

• Inside the grid() function:
  • color=‘colorname’, linestyle=‘anylinestyle’,linewidth=number can be specified

import matplotlib.pyplot as plt
import numpy as np
a = np.array([20,30,40,50])
b= np.array([60.1, 45,34,67])
plt.plot(a,b)
plt.grid(axis='x',color = 'orange',linestyle='dashdot', linewidth=3.5)
plt.show()

Subplot

• subplot() function takes three arguments that describes the layout of the figure
• The layout is organized in rows and columns, which are represented by first and second argument
• The third argument represents the index of the current plot

plt.subplot(1,2,1)

import matplotlib.pyplot as plt
import numpy as np
a = np.array([20,30,40,50])
b= np.array([60.1, 45,34,67])
x1 = np.array([2,3,7,4])
yaxis = np.array([5,1,4,8]) #dimension of yaxis and yaxis1 must be same

plt.subplot(1,2,1)
plt.plot(a,b,marker='s')
plt.subplot(1,2,2)
plt.plot(x1,yaxis,marker='s')
plt.show()

Creating Line graph for DataFrame

The sale of electric bulb on different days of a week is shown below

• a) Represent the above data with the help of line graph
• b) Give the label for x as days and y as sales
• c) Represent the marker as square and size as 5
• e) Give the title as Sales

import matplotlib.pyplot as plt
import pandas as pd
sales = pd.DataFrame({'Days':['Monday','Tuesday','Wednesday','Thursday','Friday','Saturday','Sunday'],
                     'Sales':[12,16,8,10,14,8,18],
                     'profit':[100,50,200,300,40,50,10]}
                    )
plt.plot(sales["Days"],sales["Sales"],markersize=5,marker='s')
plt.xlabel('Days')
plt.ylabel('Sales')
plt.title('Sales')
plt.show()

Views: 1