Conditional loop for pandas dataframe rows

Question

I have a dataframe I've parsed out of a larger frame that looks like:

Contract Date
2012.0
2011.0
2011.0
2010.0
1312.0
1235.0
1235.0
1235.0
1230.0
.
.
111.0
111.0
110.0
110.0

I've converted the entire thing to strings so that I can slice the values because these dates are in a very strange format.

Basically the 2012.0 - 2010.0 are December of each year (2012 - 2010) but the dates like 1235.0 and 110.0 are in MMYY format - 1235.0 is Dec 2035 and 110.0 is Jan 2010.

What I want is to make a simple loop to iterate over these dates and translate them into months and year based off the conditions that I described above. I tried obtaining the month first using a poorly made attempt at using iterrows:

for index, row in contract.iterrows():
    if len(row) > 4:
        contract['Month'] = contract['Contract_Exp_Date'].str[:2]
    else:
        contract['Month'] = contract['Contract_Exp_Date'].str[:1]

But this did not work and loops for an exaggerated amount of time - probably because I need to be appending to a new column and not creating a new column Month every iteration.

How can I loop through each row and scrape both the month and the year based off the conditions I mentioned above correctly?

U13-Forward · Accepted Answer · 2018-12-11 02:06:17Z

1

No need condition, just use logic of everything but last three characters, so logic written:

2018.0
   ^^^
   removed
111.0
  ^^^
  removed

So use:

df['Contract']=df['Contract'].str[:-3]

And now:

print(df['Contract'])

Is:

0     201
1     201
2     201
3     201
4     131
5     123
6     123
7     123
8     123
9      11
10     11
11     11
12     11
Name: Contract, dtype: object

answered Dec 11, 2018 at 2:06

U13-Forward

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