5

I have a Series, called 'scores', with a datetime index.

I wish to subset it by quarter and year
pseudocode: series.loc['q2 of 2013']

Attempts so far:
s.dt.quarter

AttributeError: Can only use .dt accessor with datetimelike values

s.index.dt.quarter

AttributeError: 'DatetimeIndex' object has no attribute 'dt'

This works (inspired by this answer), but I can't believe it is the right way to do this in Pandas:

d = pd.DataFrame(s)
d['date'] = pd.to_datetime(d.index)
d.loc[(d['date'].dt.quarter == 2) & (d['date'].dt.year == 2013)]['scores']

I expect there is a way to do this without transforming into a dataset, forcing the index into datetime, and then getting a Series from it.

What am I missing, and what is the elegant way to do this on a Pandas series?

Community
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sapo_cosmico
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4 Answers4

3
import numpy as np
import pandas as pd

index = pd.date_range('2013-01-01', freq='M', periods=12)
s = pd.Series(np.random.rand(12), index=index)
print(s)

# 2013-01-31    0.820672
# 2013-02-28    0.994890
# 2013-03-31    0.928376
# 2013-04-30    0.848532
# 2013-05-31    0.122263
# 2013-06-30    0.305741
# 2013-07-31    0.088432
# 2013-08-31    0.647288
# 2013-09-30    0.640308
# 2013-10-31    0.737139
# 2013-11-30    0.233656
# 2013-12-31    0.245214
# Freq: M, dtype: float64

d = pd.Series(s.index, index=s.index)
quarter = d.dt.quarter.astype(str) + 'Q' + d.dt.year.astype(str)
print(quarter)

# 2013-01-31    1Q2013
# 2013-02-28    1Q2013
# 2013-03-31    1Q2013
# 2013-04-30    2Q2013
# 2013-05-31    2Q2013
# 2013-06-30    2Q2013
# 2013-07-31    3Q2013
# 2013-08-31    3Q2013
# 2013-09-30    3Q2013
# 2013-10-31    4Q2013
# 2013-11-30    4Q2013
# 2013-12-31    4Q2013
# Freq: M, dtype: object

print(s[quarter == '1Q2013'])

# 2013-01-31    0.124398
# 2013-02-28    0.052828
# 2013-03-31    0.126374
# Freq: M, dtype: float64

If you don't want to create a new Series that holds a label for each quarter (e.g., if you are subsetting just once), you could even do

print(s[(s.index.quarter == 1) & (s.index.year == 2013)])

# 2013-01-31    0.124398
# 2013-02-28    0.052828
# 2013-03-31    0.126374
# Freq: M, dtype: float64
Alicia Garcia-Raboso
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3

Suppose you have a dataframe like this:

sa
Out[28]: 
             0
1970-01-31   1
1970-02-28   2
1970-03-31   3
1970-04-30   4
1970-05-31   5
1970-06-30   6
1970-07-31   7
1970-08-31   8
1970-09-30   9
1970-10-31  10
1970-11-30  11
1970-12-31  12

If the index is datetime then you can get the quarter as sa.index.quarter:

sa.index.quarter
Out[30]: array([1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4])
shivsn
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0

If you know the the year and quarter, say Q2 2013, then you can do this:

s['2013-04':'2013-06']

Wrap it up into a function:

qmap = pd.DataFrame([
        ('01', '03'), ('04', '06'), ('07', '09'), ('10', '12')
    ], list('1234'), list('se')).T

def get_quarter(df, year, quarter):
    s, e = qmap[str(quarter)]
    y = str(year)
    s = y + '-' + s
    e = y + '-' + e
    return df[s:e]

and call it:

get_quarter(s, 2013, 2)

suppose s is:

s = pd.Series(range(32), pd.date_range('2011-01-01', periods=32, freq='Q'))

Then I get:

2013-03-31    8
Freq: Q-DEC, dtype: int64
piRSquared
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0

For future-comers, just skip the .dt with a DateTimeIndex and use s.quarter instead of s.dt.quarter. The other answers are way too long for this.

Shadi
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