1. Save success! F:\File_Python\Resources\data_csv_xls\demo_dataset\data_test01.csv
2. <class 'pandas.core.frame.DataFrame'>
3. RangeIndex: 6 entries, 0 to 5
4. Data columns (total 11 columns):
5.  #   Column            Non-Null Count  Dtype  
6. ---  ------            --------------  -----  
7. 0   Name              6 non-null      object
8. 1   Sex               6 non-null      object
9. 2   Age               6 non-null      int64  
10. 3   Age02             5 non-null      float64
11. 4   Capitalisation    6 non-null      object
12. 5   Capitalisation02  6 non-null      object
13. 6   Education         6 non-null      object
14. 7   Company           6 non-null      object
15. 8   StockMarket       6 non-null      object
16. 9   Score             6 non-null      int64  
17. 10  Others            6 non-null      object
18. dtypes: float64(1), int64(2), object(8)
19. memory usage: 656.0+ bytes
20. None
21.    Unnamed: 0 Name Sex  Age  Age02  ...  Education Company  StockMarket Score Others
22. 0           0   马云   男   56   56.0  ...          1    阿里巴巴           美股     3    150
23. 1           1  马化腾   男   49   49.0  ...          1      腾讯           港股     2    200
24. 2           2  李彦宏   男   51   51.0  ...          2      百度           美股    -3     50
25. 3           3  刘强东   男   47   47.0  ...          1      京东           美股    -8      0
26. 4           4  董明珠   女   66   66.0  ...          2      格力           A股    -2    300
27. 
28. [5 rows x 12 columns]
29. T1、统计某一【类别型】列StockMarket中各个值出现的次数： 
30.  美股     3
31. A股     1
32. 未上市    1
33. 港股     1
34. Name: StockMarket, dtype: int64
35. T2、统计某一【类别型】列StockMarket中各个值出现的次数： 
36.  美股    2
37. 港股    1
38. Name: StockMarket, dtype: int64

1. #DS之信息挖掘：利用pandas库统计某一列col中各个值出现的次数(降序输出)
2. 
3. import pandas as pd
4. from NDataScience.Makedata import data2csv
5. data_frame=pd.read_csv('F:\File_Python\Resources\data_csv_xls\demo_dataset\data_test01.csv')
6. print(data_frame.head())
7. 
8. 
9. CatColumn_name='StockMarket'
10. print('统计某一【类别型】列%s中各个值出现的次数：'%CatColumn_name,'\n',data_frame[CatColumn_name].value_counts()[:4]) 
11. print('统计某一【类别型】列%s中各个值出现的次数：'%CatColumn_name,'\n',data_frame[CatColumn_name].head(3).value_counts()) 
12. 
13. 
14. 
15. data_frame[CatColumn_name].value_counts().plot(kind='bar')
16. plt.xlabel(CatColumn_name)
17. plt.xticks(rotation=0)
18. plt.title('Distribution of category type columns')        
19. plt.show()