Original link: https://www.jianshu.com/p/f773b4b82c66
value_counts () is a quick way to see how many different values in the table has a column, and different values are calculated for each number of duplicate values in the column.
value_counts () method has Series, when DataFrame generally used, it is necessary to specify which column or row, is returned by the function type Series, and different values for the index of the column, is the number of values of different values
1 import pandas as pd 2 import numpy as np 3 filepath='C:\python\data_src\GFSCOFOG_03-05-2018 03-04-36-54_timeSeries\GFSCOFOG_CHA.csv' 4 data = pd.read_csv(filepath,encoding='utf-8')
Sample data is shown below in FIG.
What are the different values View Unit Name in there, and each value calculated how many duplicate values
data['Unit Name'].value_counts()
1 data['Unit Name'].value_counts() 2 #输出 3 Percent of GDP 3561 4 Domestic currency 3561 5 Percent of total expenditure 470 6 Name: Unit Name, dtype: int64
See which different values Sector Name in there, and each value calculated how many duplicate values
data['Sector Name'].value_counts()
1 data['Sector Name'].value_counts() 2 #输出结果 3 Extrabudgetary central government 1020 4 Social security funds 1002 5 Central government (incl. social security funds) 944 6 Budgetary central government 944 7 Local governments 944 8 General government 944 9 Central government (excl. social security funds) 944 10 State governments 850 11 Name: Sector Name, dtype: int64