Get range of values in pandas object.Dataframe

2

I have a pandas.Dataframe object with a date field of python. How do I get a recordset according to date range?

An example of the data set (last 10 records):

         datpre codneg     nomres modref  preabe  premax  premin  preult  \
153  2017-08-14  PETR4  PETROBRAS     R$   12.98   13.22   12.91   13.08   
154  2017-08-15  PETR4  PETROBRAS     R$   13.08   13.24   13.05   13.15   
155  2017-08-16  PETR4  PETROBRAS     R$   13.30   13.38   13.10   13.13   
156  2017-08-17  PETR4  PETROBRAS     R$   13.12   13.26   13.02   13.05   
157  2017-08-18  PETR4  PETROBRAS     R$   13.16   13.60   13.16   13.60   
158  2017-08-21  PETR4  PETROBRAS     R$   13.64   13.66   13.28   13.34   
159  2017-08-22  PETR4  PETROBRAS     R$   13.70   13.87   13.64   13.79   
160  2017-08-23  PETR4  PETROBRAS     R$   13.78   13.93   13.65   13.76   
161  2017-08-24  PETR4  PETROBRAS     R$   13.78   13.89   13.68   13.80   
162  2017-08-25  PETR4  PETROBRAS     R$   13.87   13.94   13.81   13.88   
       quatot        codisi       data    porvar  prevar  
153  31607400  BRPETRACNPR6 2017-08-14  1.003861    0.13  
154  24737300  BRPETRACNPR6 2017-08-15  0.535168    0.07  
155  44946600  BRPETRACNPR6 2017-08-16 -0.152091   -0.02  
156  23503300  BRPETRACNPR6 2017-08-17 -0.609292   -0.08  
157  66263500  BRPETRACNPR6 2017-08-18  4.214559    0.55  
158  40485900  BRPETRACNPR6 2017-08-21 -1.911765   -0.26  
159  55240700  BRPETRACNPR6 2017-08-22  3.373313    0.45  
160  47679700  BRPETRACNPR6 2017-08-23 -0.217549   -0.03  
161  32300600  BRPETRACNPR6 2017-08-24  0.290698    0.04  
162  24185600  BRPETRACNPR6 2017-08-25  0.579710    0.08 

The date column contains dates in the date format. I needed to get the dataset within a date range.

    
asked by anonymous 28.09.2017 / 14:49

1 answer

0
Hello, you can use the pandas iloc () function that follows the same notations as the lib Numpy. dataframe.iloc[20:50,2] ie index 20 through index 49 and only index column 2

    
06.03.2018 / 03:38