Pandas has statistical data by week, month, year, and year

The first step

Converts the date to a time format and sets it to an index

import pandas as pd
data=pd.read_excel('5\TB201812.xls',usecols=['Order Creation time'.'Total amount'])
print(data)
data['Order Creation time']=pd.to_datetime(data['Order Creation time'])
data=data.set_index('Order Creation time')
print(data)
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Statistical data by week, month, quarter and year

import pandas as pd
data=pd.read_excel('5\TB201812.xls',usecols=['Order Creation time'.'Total amount'])
data['Order Creation time']=pd.to_datetime(data['Order Creation time'])
data=data.set_index('Order Creation time')
print(data.resample('w').sum())
print(data.resample('m').sum())
print(data.resample('Q').sum())
print(data.resample('AS').sum())
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Optimize using the to_period() method

Show data by month, quarter and year (no statistics)

import pandas as pd
data=pd.read_excel('5\TB201812.xls',usecols=['Order Creation time'.'Total amount'])
data['Order Creation time']=pd.to_datetime(data['Order Creation time'])
data=data.set_index('Order Creation time')
print(data.resample('w').sum().to_period('w'))
print(data.resample('m').sum().to_period('m'))
print(data.resample('q').sum().to_period('q'))
print(data.resample('as').sum().to_period('a'))
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The date display has changed from before