Pandas Dataframe: How to select a row by index, and then get the next few rows

Question

In pandas, I have a Dataframe indexed by timestamp. Looks like the following table:

                 A      B     C     D  
DATE                                                             
2018-01-17        157.52        163.74       157.28         159.84   
2018-01-16        158.25        159.35       155.93         157.40   
2018-01-15        157.15        159.59       156.79         158.86   
2018-01-12        158.25        158.62       157.40         157.52  

And I'm trying to select a row by index, and also select the next few rows. (For example, select two rows start at 2018-01-12). I found both .loc and .iloc are hard to do such task. Is there any other ways to do that?

Answer 1

Solution #1: Using the DataFrame's index, followed by head(2):

df['2018-01-12':].head(2)

Solution #2: Using iloc:

i = df.index.get_loc('2018-01-12')
df.iloc[i:i+2]

Bonus solution #3: It seems like you're analyzing stock data. Maybe you're interested in something that could be more efficiently done using rolling windows? (Moving Averages maybe?) Consider using rolling, e.g. to calculate the rolling mean:

df.rolling(2).mean()

Answer 2

You should be able to do something like:

date_of_wanted_row = "2018-01-12"
iloc_of_wanted_row = df.index.get_loc(date_of_wanted_row)
return df.iloc[iloc_of_wanted_row: iloc_of_wanted_row + 4]

(but with more sensible variable names, and I'm assuming the dates in the index are not really strings)

Answer 3

You can use (to get values between dates):

df.loc['2018-01-12':'2018-01-14']

or, to get all data from January:

df.loc['2018-01']