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Reputation: 8090

Pandas custom date interval/frequency

I'm trying to convert a Pandas series to a period index. I found several useful time intervals already (quarterly, yearly), but I'd like to expand on this and have 2-yearly and 5-yearly intervals.

Providing a string to the to_period method such as '2Y' or '5Y' raises a ValueError('Only mult == 1 supported').

period_width = 'Q' # 'Y'
df['period'] = df['date'].dt.to_period(period_width)

How can I create custom intervals?

Update

The result should behave the same as to_period().

In [2]:

import datetime
import pandas as pd
​
df = pd.DataFrame({
        'date': [
            datetime.datetime(2010, 2, 2), 
            datetime.datetime(2010, 2, 5), 
            datetime.datetime(2011, 2, 2), 
            datetime.datetime(2012, 3, 5)
        ]
    })
period_width = 'Y'
df['period'] = df['date'].dt.to_period(period_width)
df
Out[2]:
date    period
0   2010-02-02  2010
1   2010-02-05  2010
2   2011-02-02  2011
3   2012-03-05  2012
In [3]:

df.dtypes
Out[3]:
date      datetime64[ns]
period            object
dtype: object

Upvotes: 0

Views: 1586

Answers (1)

Woody Pride
Woody Pride

Reputation: 13975

Well, to create 5 year gaps you might just use the datetime module.

That would look something like this to create 5 year intervals of Jan 1 starting in the year 2000:

import pandas as pd
import datetime as datetime

five_y = pd.Series([datetime.datetime(Year, 1, 1).date() for Year in xrange(2000, 2100, 5)])

In [2]: five_y
Out[2]: 
0     2000-01-01
1     2005-01-01
2     2010-01-01
3     2015-01-01
4     2020-01-01
5     2025-01-01
6     2030-01-01
7     2035-01-01
8     2040-01-01
9     2045-01-01
10    2050-01-01
11    2055-01-01
12    2060-01-01
13    2065-01-01
14    2070-01-01
15    2075-01-01
16    2080-01-01
17    2085-01-01
18    2090-01-01
19    2095-01-01
dtype: object

Upvotes: 1

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