[ Python 範例代碼 ] Matplotlib - 產生 normal distribution 的 Histogram

使用 np.random.normal 產生 random 的 normal distribution 數字, 接著使用 np.histogram 計算繪製 histogram 所需要的資料: 

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1. import numpy as np  
2. import random as rd  
3. from matplotlib import pyplot as plt   
4.   
5. # 產生 normal distribution 的數字  
6. random_nums = np.random.normal(scale=20, size=10000)  
7. random_ints = np.round(random_nums)  
8.   
9. # 計算 histogram 的相關數字  
10. hist, bins = np.histogram(random_ints, bins=20)  
11.   
12. # 繪圖  
13. plt.rcParams['figure.figsize'] = [15, 7]  
14. plt.hist(random_ints, bins)   
15. plt.title("histogram")  
16. plt.xticks(bins)  
17. plt.show()  

[ Python 常見問題 ] Plot a histogram using Matplotlib in Python with a list of data?

Source From Here 
Question 
I am trying to plot a histogram using the matplotlib.hist() function but I am not sure how to do it. I have a list: 

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1. probability = [0.3602150537634409, 0.42028985507246375,   
2.   0.373117033603708, 0.36813186813186816, 0.32517482517482516,   
3.   0.4175257731958763, 0.41025641025641024, 0.39408866995073893,   
4.   0.4143222506393862, 0.34, 0.391025641025641, 0.3130841121495327,   
5.   0.35398230088495575]  

and a list of names(strings). 

How do I make the probability as my y-value of each bar and names as x-values? 

How-To 
If you want a histogram, you don't need to attach any 'names' to x-values, as on x-axis you would have bins: 

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1. import matplotlib.pyplot as plt  
2. import numpy as np  
3. %matplotlib inline  
4.   
5. x = np.random.normal(size = 1000)  
6. # density: If True, the first element of the return tuple will be the counts normalized to form a probability density  
7. plt.hist(x, density=True, bins=30)  
8. plt.ylabel('Probability');  

 

However, if you have limited number of data points, and you want a bar plot, then you may attach labels to x-axis: 

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1. x = np.arange(3)  
2. plt.bar(x, height= [1,2,3])  
3. plt.xticks(x, ['a','b','c'])  

 

Supplement 
* Generate data and plot a simple histogram

[ Python 範例代碼 ] Metplotlib - Bar chart

Source From Here 
Preface 
Matplotlib may be used to create bar charts. You might like the Matplotlib gallery. The course below is all about data visualization: 

* Data Visualization with Python and Matplotlib

Bar chart code 
The code below creates a bar chart: 

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1. %matplotlib inline  
2. import matplotlib.pyplot as plt; plt.rcdefaults()  
3. import numpy as np  
4. import matplotlib.pyplot as plt  
5.   
6. objects = ('Python', 'C++', 'Java', 'Perl', 'Scala', 'Lisp')  
7. y_pos = np.arange(len(objects))  
8. performance = [10,8,6,4,2,1]  
9.   
10. plt.bar(y_pos, performance, align='center', alpha=0.5)  
11. plt.xticks(y_pos, objects)  
12. plt.ylabel('Usage')  
13. plt.title('Programming language usage')  
14.   
15. plt.show()  

 

Matplotlib charts can be horizontal, to create a horizontal bar chart: 

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1. import matplotlib.pyplot as plt; plt.rcdefaults()  
2. import numpy as np  
3. import matplotlib.pyplot as plt  
4.   
5. objects = ('Python', 'C++', 'Java', 'Perl', 'Scala', 'Lisp')  
6. y_pos = np.arange(len(objects))  
7. performance = [10,8,6,4,2,1]  
8.   
9. plt.barh(y_pos, performance, align='center', alpha=0.5)  
10. plt.yticks(y_pos, objects)  
11. plt.xlabel('Usage')  
12. plt.title('Programming language usage')  
13.   
14. plt.show()  

 

More on bar charts 
You can compare two data series using this Matplotlib code: 

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1. import numpy as np  
2. import matplotlib.pyplot as plt  
3.   
4. # data to plot  
5. n_groups = 4  
6. means_frank = (90, 55, 40, 65)  
7. means_guido = (85, 62, 54, 20)  
8.   
9. # create plot  
10. fig, ax = plt.subplots()  
11. index = np.arange(n_groups)  
12. bar_width = 0.35  
13. opacity = 0.8  
14.   
15. rects1 = plt.bar(index, means_frank, bar_width,  
16.                  alpha=opacity,  
17.                  color='b',  
18.                  label='Frank')  
19.   
20. rects2 = plt.bar(index + bar_width, means_guido, bar_width,  
21.                  alpha=opacity,  
22.                  color='g',  
23.                  label='Guido')  
24.   
25. plt.xlabel('Person')  
26. plt.ylabel('Scores')  
27. plt.title('Scores by person')  
28. plt.xticks(index + bar_width, ('A', 'B', 'C', 'D'))  
29. plt.legend()  
30.   
31. plt.tight_layout()  
32. plt.show()  

 

範例代碼 matplotlib_barchart.ipynb