CODEWITHSUNDEEP
python-3.xpandasdataframeslice
mk_sch
mk_sch

Reputation: 1070

Slicing specific rows of a column in pandas Dataframe

In the flowing data frame in Pandas, I want to extract columns corresponding dates between '03/01' and '06/01'. I don't want to use the index at all, as my input would be a start and end dates. How could I do so ?

      A       B
0   01/01   56
1   02/01   54
2   03/01   66
3   04/01   77
4   05/01   66
5   06/01   72
6   07/01   132
7   08/01   127

Upvotes: 0

Views: 472

Answers (2)

PdevG
PdevG

Reputation: 3677

First create a list of the dates you need using daterange. I'm adding the year 2000 since you need to supply a year for this to work, im then cutting it off to get the desired strings. In real life you might want to pay attention to the actual year due to things like leap-days.

date_start = '03/01'
date_end = '06/01'
dates = [x.strftime('%m/%d') for x in pd.date_range('2000/{}'.format(date_start), 
                        '2000/{}'.format(date_end), freq='D')]

dates is now equal to:

['03/01',
 '03/02',
 '03/03',
 '03/04',
 .....
 '05/29',
 '05/30',
 '05/31',
 '06/01']

Then simply use the isin argument and you are done

df = df.loc[df.A.isin(dates)]
df

If your columns is a datetime column I guess you can skip the strftime part in th list comprehension to get the right result.

Upvotes: 2

CaptainMeow
CaptainMeow

Reputation: 351

You are welcome to use boolean masking, i.e.:

df[(df.A >= start_date) && (df.A <= end_date)]

Inside the bracket is a boolean array of True and False. Only rows that fulfill your given condition (evaluates to True) will be returned. This is a great tool to have and it works well with pandas and numpy.

Upvotes: 1

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