pandas implementation of filtering data by date range

s_date = ('20050606', '%Y%m%d').date()
e_date = ('20071016', '%Y%m%d').date()

df = df[(df['tra_date'] >= s_date) & (df['tra_date'] <= e_date)]

import pandas as pd
 
#Read the file
df = pd.read_csv('./')
 
# A few ways to get September data
#Method 1 Use row index slicing, ['2019/9/1':'2019/9/30'], with the disadvantage of requiring the dates to be consecutive. For ease of viewing take the first 5, all other methods below take the first 5, the order will vary as they are not sorted
df.set_index('Date',inplace=True)
print(df['2019/9/1':'2019/9/30'].head())  #orprint(['2019/9/1':'2019/9/30',:])
'''
Print:
     Maximum Temperature Minimum Temperature Weather Wind Direction Wind Level Air Quality
Date
2019/9/1 33℃ 19℃ Cloudy~Sunny Southwest Wind Level 2 Good
2019/9/2 34℃ 20℃ Sunny Southeasterly wind Grade 2 Good
2019/9/3 33℃ 20℃ Sunny Southeast wind Grade 2 Good
2019/9/7 34℃ 21℃ Sunny Southwest wind 2 step (of stairs) very much
2019/9/8 35℃ 22℃ clear~cloudy Northeast wind 2 step (of stairs) very much
'''
 
#Method 2 Generate bool list using list generator style and startwith('2019/9'),Disadvantage, more cumbersome.
print([[True if ('2019/9') else False for i in ()],:].head())
'''
Print:
     Maximum Temperature Minimum Temperature Weather Wind Direction Wind Level Air Quality
Date
2019/9/4 32℃ 19℃ Sunny Southeast wind Grade 2 Good
2019/9/5 33℃ 20℃ Sunny Southeast wind Grade 2 Good
2019/9/6 33℃ 20℃ Sunny Southeast wind Grade 1 Good
2019/9/1 33℃ 19℃ Cloudy~sunny Southwest wind 2 step (of stairs) very much
2019/9/2 34℃ 20℃ Sunny Southeasterly wind 2 step (of stairs) very much
'''
 
#Method 3 Utilize str and startswith('2019/9')|contains('2019/9') in pandas.
df1 = pd.read_csv('./')
print(df1[df1['Date'].('2019/9')].head())
'''
Print:
     Date Maximum Temperature Minimum Temperature Weather Wind Direction Wind Level Air Quality
243 2019/9/4 32℃ 19℃ Sunny Southeast Wind Level 2 Good
244 2019/9/5 33℃ 20℃ Sunny Southeast wind Grade 2 Good
245 2019/9/6 33℃ 20℃ Sunny Southeast wind Grade 1 Good
246 2019/9/1 33℃ 19℃ Cloudy~sunny Southwest wind 2 step (of stairs) very much
247 2019/9/2 34℃ 20℃ Sunny Southeasterly wind 2 step (of stairs) very much
'''
 
#Method 4: Convert a date to datetime.
df1['Date'] = pd.to_datetime(df1['Date'])
df1.set_index('Date',inplace=True,drop=True)
#print(df1['2019']) # take 2019 data, or ['2019']
print(df1['2019/09'].head())  
'''
 Fetch the data of month 201909, other morphing writing df['2019-9'] df['2019-09'] df['2019/9'] ['2019-9',:] ['2019-09',:] ['2019/09',:] ['2019/9',:]
Print:
      Maximum Temperature Minimum Temperature Weather Wind Direction Wind Level Air Quality
Date
2019-09-04 32℃ 19℃ Sunny Southeast wind Grade 2 Good
2019-09-05 33℃ 20℃ Sunny Southeast wind Grade 2 Good
2019-09-06 33℃ 20℃ Sunny Southeast Wind Grade 1 Good
2019-09-01 33℃ 19℃ Cloudy~Sunny Southwest wind 2 step (of stairs) very much
2019-09-02 34℃ 20℃ Sunny Southeasterly wind 2 step (of stairs) very much
'''
#Note that if you want to get data for a particular day, you must use slicing, e.g. df['2019/9/1':'2019/9/1']
'''
Getting some time
(after = '2019-9-01') # Returns after previous data
(before = '2019-9-01') # Returns data from before
df1['20190901':'2019/9/10']
'''
 
#Method 5 # When reading a file, convert the date to datetime by parse_dates=['date'], which is equivalent to pd.to_datetime, and use index_col to index that column as a row, which is equivalent to set_index.
df2 = pd.read_csv('./',parse_dates=['Date'])
df2['Year'] = df2['Date'].
df2['Moon'] = df2['Date'].
qstr = "surname Nian=='2019' and moon=='9'"
print((qstr).head())
'''
Print:
      Date Maximum Temperature Minimum Temperature Weather Wind Direction Wind Level Air Quality Year Month
243 2019-09-04 32℃ 19℃ Clear Southeast Wind Level 2 Good 2019 9
244 2019-09-05 33℃ 20℃ Clear Southeast Wind Level 2 Good 2019 9
245 2019-09-06 33℃ 20℃ Sunny Southeast Wind Grade 1 Good 2019 9
246 2019-09-01 33℃ 19℃ Cloudy~sunny Southwest wind 2 step (of stairs) very much 2019 9
247 2019-09-02 34℃ 20℃ Sunny Southeasterly wind Grade 2 Good 2019 9
'''
 
'''
Other common properties and methods of dt are listed below:
df['Date'].  # Date of withdrawal
df['Date']. # Year of extraction
df['Date']. # of hours of extraction
df['Date']. # of minutes of extraction
df['Date']. # Extracted in seconds
df['Date']. # Week of the year
df['Date']. # Returns the day of the week, with 0 being Monday and 6 being Sunday.
df['Date']. # Return to the day of the year
df['Date']. # Get the quarter for each date.
df['Date'].dt.is_month_start # Determine if the date is the first day of the month
df['Date'].dt.is_month_end # Determine if the date is the last day of the month
df['Date'].dt.is_leap_year # Determine if it is a leap year
df['Date'].dt.month_name() # Return the English name of the month
df['Date'].dt.to_period('Q') # M for month, Q for quarter, A for annual, D for daily
df['Date'].dt.weekday_name # return to the day of the week in English Due to pandas version problems, change pandas version in cmd type: pip install --upgrade pandas==0.25.3
() The # function converts the time in the given series of objects to midnight.
'''