PLOTTING WITH PYPLOT I - BAR GRAPHS AND SCATTER PLOTS (CLASS - XII) (PART-2)

PLOTTING WITH PYPLOT I - BAR GRAPHS AND SCATTER PLOTS

CHAPTER - 3           (PART -2)

CLASS - XII

Applying Various Settings in plot() Function

The plot() function allows you specify multiple settings for your chart/graph such as:

Color (line color/marker color), marker type, marker size, and so forth.

Changing Line Color: To change line color, you can specify the color code next to the data being plotted in plot() function as shown below:

<matplotlib>.plot(<data1>, [,data2], <color code>)

You can use color codes as ‘r’ for red, ‘y’ for yellow, ‘g’ for green ‘b’ for blue.

Example:

import numpy as np

import matplotlib.pyplot as plt

x = np.arange(0, 10, 0.1)

y = np.cos(x)

z = np.sin(x)

plt.plot(x, y, 'g')           #color code green for ndarray a

plt.plot(x, z, 'r')            #color code green for ndarray b

plt.show()

OUTPUT

Note: If you skip the color information in plot(), Python will plot multiple lines in the same plot with different colors but these colors are decided internally by Python.

Different Color Code in Python

S. No.	Character	Color
1	‘b’	Blue
2	‘r’	Red
3	‘m’	Magenta
4	‘g’	Green
5	‘k’	Black
6	‘y’	Yellow
7	‘w’	White
8	‘c’	Cyan

 

To change the line style, you can add following optional argument in plot() function:

linestyle or ls = [‘solid’ | ‘dashed’ , ‘dashdot’ , ‘dotted’]

Changing Marker Type, Size and Color: To change marker type, its size and color, you can give following additional optional arguments in plot() function:

marker = <valid marker type>, markersize - <in points>, markeredgecolor = <valid color>

Table of Marker Types for Plotting

Marker	Description	Marker	Description	Marker	Description
‘ . ‘	point marker	‘s’	square marker	‘3’	tri_left marker
‘ , ‘	Pixel marker	‘p’	pentagon marker	‘4’	tri_right marker
‘o’	Circle marker	‘*’	Star marker	‘v’	triangle_down
‘+’	plus	‘h’	hexagon1	‘^’	triangle_up
‘x’	X marker	‘H’	Hexagon2	‘<’	triangle_left
‘D’	diamond	‘1’	tri_down	‘>’	triangle_right
‘d’	thin_diamond	‘2’	tri_up	‘|’, ‘ _ ‘	vline, hline markers

 Example

import numpy as np

import matplotlib.pyplot as plt

a = [1,2,3,4]

b = [2,4,6,8]

plt.plot(a,b, 'k', marker = 'o', markersize = 8, markeredgecolor = 'yellow')

plt.show()

OUTPUT

Scatter Chart: The scatter chart is a graph of plotted points on two axes that show the relationship between two sets of data.

It can be created through two functions of pyplot library:

(i) plot() function

(ii) scatter() function

In plot() function, whenever you specify market type/style, whether with color or without color, and do not give linestyle argument, plot() will create the scatter chart.

So with marker type specified plot() creates the scatter chart in the absence of linestyle.

Example of different scatter chart:

Q. Create an array in the range 1 to 20 with values 1.25 apart. Another array contains the log values of the elements in first array.

Solution: According to question

import numpy as np import matplotlib.pyplot as plt a = np.arange(1, 20, 1.15) b = np.log(a) print(a)	import numpy as np import matplotlib.pyplot as plt a = np.arange(1, 20, 1.15) b = np.log(a) print(b)
OUTPUT [ 1.    2.15  3.3   4.45  5.6   6.75  7.9   9.05 10.2  11.35 12.5  13.65  14.8  15.95 17.1  18.25 19.4 ]	OUTPUT 0.         0.76546784 1.19392247 1.4929041  1.7227666  1.9095425  2.06686276 2.20276476 2.32238772 2.42921774 2.52572864 2.61373952  2.69462718 2.76945883 2.83907846 2.90416508 2.96527307]

 

(i) Create a plot of first vs second array; specify the x-axis (containing first array’s values) title as ‘Random Values’ and y-axis title as ‘Logarithm Values’.

Solution:

import numpy as np import matplotlib.pyplot as plt a = np.arange(1, 20, 1.15) b = np.log(a) plt.plot(a,b) plt.xlabel('Random Values') plt.ylabel('Logarithm Values') plt.show()

OUTPUT

(ii) Create a third array that stores the COS values of first array and then plot both the second and third arrays vs first array. The cos values should be plotted with a dashdotted line.

import numpy as np import matplotlib.pyplot as plt a = np.arange(1, 20, 1.15) b = np.log(a) c = np.cos(a) plt.plot(a,b) plt.plot(a,c, linestyle = 'dashdot') plt.show()

OUTPUT

(iii) Change the marker type as a circle with blue color in second array.

import numpy as np import matplotlib.pyplot as plt a = np.arange(1, 20, 1.15) b = np.log(a) c = np.cos(a) plt.plot(a,b) plt.plot(a,c,'bo', linestyle = 'dashdot') plt.show()

OUTPUT

 (iv) Create scatter chart as this: second array data points as blur small diamonds, third array data points as black circles.

import numpy as np import matplotlib.pyplot as plt a = np.arange(1, 20, 1.15) b = np.log(a) c = np.cos(a) plt.plot(a,b, 'bd') plt.plot(a,c,'ro')   plt.show()

OUTPUT

Specifying varying colors and size for data points: Scatter() allows you to specify different size and colors for data points.

Example:

import numpy as np

import matplotlib.pyplot as plt

ar1 = np.linspace(-1,1,5)        # ar1 with 5 data points created

ar2 = np.exp(ar1)                    # ar2 has 5 data points created

x = ['r', 'b', 'm', 'g', 'k']             # x is a sequence of color

y = [20, 60, 100, 45, 25]          # y is a sequence of sizecolor

plt.scatter(ar1, ar2, c=x, s=y)

plt.show()

OUTPUT

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Thank You!!!