CODEWITHSUNDEEP
pythonpandas
Thomas Strickner
Thomas Strickner

Reputation: 35

Python/Pandas - Best practice how to join 2 arrays with dates in Python/Pandas

I am a complete newbie in Python - for doing a procedural calculation of interests within a mortgage plan i need to calculate with dates. There is a date array for the payments and another date array for summing up the interests.

I am completely stuck with merging the two date arrays - i would need only one merged array with sorted dates. I read through different approaches (DataFrames, Joins, Merges) but couldn't find any solution.

import pandas as pd
date_range_1 = pd.date_range(start='1/1/2019', periods=12,freq='M')
date_range_2 = pd.date_range(start='31/3/2019',periods=4,freq='3M')

Question-1: How to join these 2 arrays
Question-2: How to sort by date

Output should be:
'1/1/2019'
'1/2/2019'
'1/3/2019'
'31/3/2019'
'1/4/2019'
'1/5/2019'
'1/6/2019'
'30/6/2019'
...

Edit: Full input + solution:

import pandas as pd
dates_1= pd.date_range('2018-01-01','2020-01-01' , freq='1M')-pd.offsets.MonthBegin(1)
dates_2= pd.date_range('2018-03-31','2020-03-31' , freq='3M')-pd.offsets.MonthEnd(0)
Union_dates = pd.Series(sorted(dates_1.union(dates_2)))
print(Union_dates)

Upvotes: 2

Views: 922

Answers (2)

Paweł Kowalski
Paweł Kowalski

Reputation: 544

You can append one range to another and then sort_values.

import pandas as pd

date_range_1 = pd.date_range(start='1/1/2019', periods=12, freq='MS')
date_range_2 = pd.date_range(start='31/3/2019', periods=4, freq='3M')

date_range_1.append(date_range_2).sort_values()

DatetimeIndex(['2019-01-01', '2019-02-01', '2019-03-01', '2019-03-31',
               '2019-04-01', '2019-05-01', '2019-06-01', '2019-06-30',
               '2019-07-01', '2019-08-01', '2019-09-01', '2019-09-30',
               '2019-10-01', '2019-11-01', '2019-12-01', '2019-12-31'],
              dtype='datetime64[ns]', freq=None)

Upvotes: 2

anky
anky

Reputation: 75080

IIUC, you need Index.union:

pd.Series(sorted(date_range_1.union(date_range_2)))

0    2019-01-31
1    2019-02-28
2    2019-03-31
3    2019-04-30
4    2019-05-31
5    2019-06-30
6    2019-07-31
7    2019-08-31
8    2019-09-30
9    2019-10-31
10   2019-11-30
11   2019-12-31
dtype: datetime64[ns]

Upvotes: 3

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