![Creating Array
import numpy as np
L=[1,2,3,4]
a=np.array(L) makes ‘a’ array from List ‘L’.
print( type(a) ) numpy.ndarray
print(a.shape ) (1,)
print(a.itemsize ) 4
print(a.dtype) dtype(‘int32’)](https://image.slidesharecdn.com/16-230131135937-d95f4b3e/85/16-Data-VIsualization-using-PyPlot-pdf-7-320.jpg)
![Bar Chart
import matplotlib.pyplot as pl
a=[1,2,3,4,5]
b=[1,4,9,16,25]
pl.bar(a,b)
pl.xlabel(‘N)
pl.ylabel(‘N*N’)
pl.show( )
Default width of bars : 0.8 units](https://image.slidesharecdn.com/16-230131135937-d95f4b3e/85/16-Data-VIsualization-using-PyPlot-pdf-14-320.jpg)
![Styling bars
a=[‘Grapes’, ‘Banana’, ‘Apple’, ‘Guava’]
plt.bar(a,b,width = [0.2, 0.5, 0.3, 0.8] )
plt.bar(a,b,color = [‘g’, ‘r’, ‘b’, ‘black’] )
Color & width are applied in left to right order, but the bars are plotted in
sorted order.](https://image.slidesharecdn.com/16-230131135937-d95f4b3e/85/16-Data-VIsualization-using-PyPlot-pdf-15-320.jpg)
![Styling bars
V =[[5,25,45,20], [4,23,49,17], [6,22,47,19]]
X=np.arange(4)
plt.bar(X, V[0], color=‘b’, width=0.25,label=‘a’)
plt.bar(X+0.25, V[1], color=‘g’, width=0.25,label=‘b’)
plt.bar(X+0.50, V[2], color=‘r’, width=0.25,label=‘c’)
plt.legend(loc='upper left')
Color & width are applied in left to right order, but the bars are plotted in
sorted order.](https://image.slidesharecdn.com/16-230131135937-d95f4b3e/85/16-Data-VIsualization-using-PyPlot-pdf-17-320.jpg)
![Horizontal Bar Chart
import matplotlib.pyplot as plt
a=[‘Grapes’, ‘Banana’, ‘Apple’, ‘Guava’]
x=[4,1,2,5]
plt.barh(a, x)](https://image.slidesharecdn.com/16-230131135937-d95f4b3e/85/16-Data-VIsualization-using-PyPlot-pdf-18-320.jpg)
![Pie Chart
C=[4,1,8,9]
D=[A1, B1, A2, B2]
plt.axis(‘equal’)
plt.pie(C,labels=D)
Colors may differ. For circular shape.](https://image.slidesharecdn.com/16-230131135937-d95f4b3e/85/16-Data-VIsualization-using-PyPlot-pdf-19-320.jpg)
![CLR=[‘green’, ‘blue’, ‘red’, ‘yellow’]
E=[0,0,0.1,0.15]
pl.pie(C, colors=CLR, autopct=‘%2.2f%%’, explode=E)
Styling Pie Charts
Colors may differ.](https://image.slidesharecdn.com/16-230131135937-d95f4b3e/85/16-Data-VIsualization-using-PyPlot-pdf-20-320.jpg)
![Styling Pie Chart
C=[4,1,8,9]
D=[A1, B1, A2, B2]
plt.pie(C,labels=D, autopct= ‘%1.1f%%’)
40.9%
18.2%
4.5%
36.4%
Colors may differ.
‘%[FLAG][WIDTH] . [PRECISION]type’](https://image.slidesharecdn.com/16-230131135937-d95f4b3e/85/16-Data-VIsualization-using-PyPlot-pdf-21-320.jpg)
![‘%[FLAG][WIDTH] . [PRECISION]type’
It’s a special string which defines the structure of a string.
WIDTH specifies the min. number of characters in the string.
PRECISION is the number of digits till which rounding off takes place after decimal.
type specifies the datatype. Single % specifies it’s a special string,
That’s why we use %%.](https://image.slidesharecdn.com/16-230131135937-d95f4b3e/85/16-Data-VIsualization-using-PyPlot-pdf-22-320.jpg)
![Customizing Chart
plt.title(‘Student’s data’)
X= [0,1,2,3]
Y= [5,25,45,20]
plt.xlim(3.5,-0.5)
plt.ylim(-50,50)
plt.title(‘Student’s data’)
plt.plot(X,Y)](https://image.slidesharecdn.com/16-230131135937-d95f4b3e/85/16-Data-VIsualization-using-PyPlot-pdf-24-320.jpg)
![Customizing Chart
plt.title(‘Student’s data’)
X= [0,1,2,3]
Y= [5,25,45,20]
plt.xticks([0,1,2,3],[‘a’, ‘b’, ‘c’, ‘d’])
plt.bar(X,Y)
plt.title(‘Student’s data’)](https://image.slidesharecdn.com/16-230131135937-d95f4b3e/85/16-Data-VIsualization-using-PyPlot-pdf-25-320.jpg)
16. Data VIsualization using PyPlot.pdf
- 1. Algorithmic Efficiency Intro, Complexity Finding Complexities Best, Worst & Average Cases -> By Apni Kaksha <-
- 2. Data Data is one of the most valuable things today. All Big companies stores data, which helps in diagnosis, focused services and many more.
- 3. It basically means graphical or visual representation of data & statistics using elements like charts, graphs and maps,etc. We can see patterns, trends, relations, etc. in the data. Data Visualization
- 4. Library It provides many interfaces & functionality for 2D-graphics in various forms. Basically, it’s a high quality plotting library of python. PyPlot is such a module in Matplotlib.
- 5. PyPlot Matplotlib comes pre-installed with anaconda. To run every command we need to write : matplotlib.pyplot.<command> Let’s better do : import matplotlib.pyplot as pl pl.plot(<x>,<y>) Shorter way
- 6. Numpy Numpy is a module of python that offers functions for fast mathematical computation on arrays. Arrays is a named group of homogeneous elements. It’s very similar to lists. All elements are of same datatype. Functionality is different. .
- 7. Creating Array import numpy as np L=[1,2,3,4] a=np.array(L) makes ‘a’ array from List ‘L’. print( type(a) ) numpy.ndarray print(a.shape ) (1,) print(a.itemsize ) 4 print(a.dtype) dtype(‘int32’)
- 8. Creating Arrays import numpy as np a=np.arange(<start>, <stop>, <step>, <dtype>) a=np.arange( 1,9,3,np.float32 ) a=np.linespace(<start>,<stop>,<number of values to be generated>) will generate elements at equal intervals. a=np.arange( 1,9,2 ) ends at 8
- 10. import numpy as np import matplotlib.pyplot as pl a=np.linspace(1,5,6) b=np.log(a) pl.plot(a,b) pl.xlabel(‘This shows on x axis’) pl.ylabel(‘This shows on y axis’) pl.show( ) Plotting using numpy ends at 5
- 11. pl.plot(a-1, b-1, 'b') pl.plot(a, b, 'r') pl.plot(a+1, b+1, linewidth=2, linestyle='dashed', marker='d', markersize=4, markeredgecolor= 'red' ) pl.plot(a+2, b+2, 'bd', linestyle = 'dashdot') Changing style Blue & Diamond
- 13. Charts BAR CHART PIE CHART LINE CHART
- 14. Bar Chart import matplotlib.pyplot as pl a=[1,2,3,4,5] b=[1,4,9,16,25] pl.bar(a,b) pl.xlabel(‘N) pl.ylabel(‘N*N’) pl.show( ) Default width of bars : 0.8 units
- 15. Styling bars a=[‘Grapes’, ‘Banana’, ‘Apple’, ‘Guava’] plt.bar(a,b,width = [0.2, 0.5, 0.3, 0.8] ) plt.bar(a,b,color = [‘g’, ‘r’, ‘b’, ‘black’] ) Color & width are applied in left to right order, but the bars are plotted in sorted order.
- 17. Styling bars V =[[5,25,45,20], [4,23,49,17], [6,22,47,19]] X=np.arange(4) plt.bar(X, V[0], color=‘b’, width=0.25,label=‘a’) plt.bar(X+0.25, V[1], color=‘g’, width=0.25,label=‘b’) plt.bar(X+0.50, V[2], color=‘r’, width=0.25,label=‘c’) plt.legend(loc='upper left') Color & width are applied in left to right order, but the bars are plotted in sorted order.
- 18. Horizontal Bar Chart import matplotlib.pyplot as plt a=[‘Grapes’, ‘Banana’, ‘Apple’, ‘Guava’] x=[4,1,2,5] plt.barh(a, x)
- 19. Pie Chart C=[4,1,8,9] D=[A1, B1, A2, B2] plt.axis(‘equal’) plt.pie(C,labels=D) Colors may differ. For circular shape.
- 20. CLR=[‘green’, ‘blue’, ‘red’, ‘yellow’] E=[0,0,0.1,0.15] pl.pie(C, colors=CLR, autopct=‘%2.2f%%’, explode=E) Styling Pie Charts Colors may differ.
- 21. Styling Pie Chart C=[4,1,8,9] D=[A1, B1, A2, B2] plt.pie(C,labels=D, autopct= ‘%1.1f%%’) 40.9% 18.2% 4.5% 36.4% Colors may differ. ‘%[FLAG][WIDTH] . [PRECISION]type’
- 22. ‘%[FLAG][WIDTH] . [PRECISION]type’ It’s a special string which defines the structure of a string. WIDTH specifies the min. number of characters in the string. PRECISION is the number of digits till which rounding off takes place after decimal. type specifies the datatype. Single % specifies it’s a special string, That’s why we use %%.
- 23. ‘%3d’ - 24 = _24 ‘%05i’ - 24 = 00024 ‘%03d%%’ - 24 = 024% ‘%6.1f’ - 24.2 = _24.20 ‘%3.3f%%’ - 24.2 = 24.200%
- 24. Customizing Chart plt.title(‘Student’s data’) X= [0,1,2,3] Y= [5,25,45,20] plt.xlim(3.5,-0.5) plt.ylim(-50,50) plt.title(‘Student’s data’) plt.plot(X,Y)
- 25. Customizing Chart plt.title(‘Student’s data’) X= [0,1,2,3] Y= [5,25,45,20] plt.xticks([0,1,2,3],[‘a’, ‘b’, ‘c’, ‘d’]) plt.bar(X,Y) plt.title(‘Student’s data’)