Pandas uses dataframe to analyze data. The way to advance the competition (2): date data processing: filtering, displaying and statistical data by date

qinjianhuang 2020-11-13 05:22:56
pandas uses dataframe analyze data.


First , The data format of the table is as follows :

 Picture description here

1、 Get the data of a year and a month

data_train = pd.read_csv('data/train.csv')
# Convert data type to date type 
data_train['date'] = pd.to_datetime(data_train['date'])
# take date Set to index
df = data_train.set_index('date')
# Get data for a year 
print(df['2010'].head())
# Get data for a month 
print(df['2013-11'].head())

Output results :

 id questions answers
date
2010-10-01 1 742 1561
2010-10-02 2 400 783
2010-10-03 3 388 771
2010-10-04 4 762 1474
2010-10-05 5 821 1639
id questions answers
date
2013-11-01 1128 3401 6858
2013-11-02 1129 2626 5467
2013-11-03 1130 2703 5557
2013-11-04 1131 3602 6941
2013-11-05 1132 3741 7312

2、 Get data before or after a period of time

# Get data before or after a period of time 
# obtain 2014 Years later 
print(df.truncate(before='2014').head())
# obtain 2013-11 Previous data 
print(df.truncate(after='2013-11').head())
# obtain 2016-02 Years later 
print(df.truncate(before='2016-02').head())
# obtain 2016-02-2 Years later 
print(df.truncate(before='2016-02-2').head())

Output results :

 id questions answers
date
2014-01-01 1189 2586 5576
2014-01-02 1190 3541 7175
2014-01-03 1191 3655 7395
2014-01-04 1192 2947 6099
2014-01-05 1193 2847 5935
id questions answers
date
2010-10-01 1 742 1561
2010-10-02 2 400 783
2010-10-03 3 388 771
2010-10-04 4 762 1474
2010-10-05 5 821 1639
id questions answers
date
2016-02-01 1950 5434 10398
2016-02-02 1951 5650 10795
2016-02-03 1952 5744 10879
2016-02-04 1953 5666 10886
2016-02-05 1954 5371 10508
id questions answers
date
2016-02-02 1951 5650 10795
2016-02-03 1952 5744 10879
2016-02-04 1953 5666 10886
2016-02-05 1954 5371 10508
2016-02-06 1955 4296 8800

3、 According to a certain indicator , But no statistics

# Monthly display , But no statistics 
df_period_M = df.to_period('M').head()
print(df_period_M)
# Show by quarter , But no statistics 
df_period_Q = df.to_period('Q').head()
print(df_period_Q)
# Show by year , But no statistics 
df_period_A = df.to_period('A').head()
print(df_period_A)

Output results :

 id questions answers
date
2010-10 1 742 1561
2010-10 2 400 783
2010-10 3 388 771
2010-10 4 762 1474
2010-10 5 821 1639
id questions answers
date
2010Q4 1 742 1561
2010Q4 2 400 783
2010Q4 3 388 771
2010Q4 4 762 1474
2010Q4 5 821 1639
id questions answers
date
2010 1 742 1561
2010 2 400 783
2010 3 388 771
2010 4 762 1474
2010 5 821 1639

4、 According to a certain indicator , And Statistics

# Statistics by year and show
print(df.resample('AS').sum().to_period('A'))
# Statistics by quarter and show
print(df.resample('Q').sum().to_period('Q').head())
# Statistics by month and display
print(df.resample('M').sum().to_period('M').head())
# Statistics by month and display
print(df.resample('W').sum().to_period('W').head())

Output results :

 id questions answers
date
2010 4278 74363 153006
2011 100375 535290 1091651
2012 234423 862831 1718434
2013 367190 1179155 2320421
2014 500415 1487677 2876611
2015 633640 1734023 3368264
2016 698810 1808649 3476335
id questions answers
date
2010Q4 4278 74363 153006
2011Q1 12375 105858 217767
2011Q2 20748 127873 260836
2011Q3 29394 144424 293853
2011Q4 37858 157135 319195
id questions answers
date
2010-10 496 22218 44882
2010-11 1395 25418 52841
2010-12 2387 26727 55283
2011-01 3348 31502 65477
2011-02 3850 33240 67627
id questions answers
date
2010-09-27/2010-10-03 6 1530 3115
2010-10-04/2010-10-10 49 4869 9636
2010-10-11/2010-10-17 98 5079 10344
2010-10-18/2010-10-24 147 5361 10847
2010-10-25/2010-10-31 196 5379 10940

appendix : Screenshot of date type

 Picture description here

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