Reading CSV File In Pandas With Historical Dates

I'm trying to read a file in with dates in the (UK) format 13/01/1800, however some of the dates are before 1667, which cannot be represented by the nanosecond timestamp (see http:

Solution 1:

you can try to do it this way:

fn = r'D:\temp\.data\36987699.csv'

def dt_parse(s):
    d,m,y = s.split('/')
    return pd.Period(year=int(y), month=int(m), day=int(d), freq='D')


df = pd.read_csv(fn, parse_dates=[0], date_parser=dt_parse)

Input file:

Date,col1
13/01/1800,aaa
25/12/1001,bbb
01/03/1267,ccc

Test:

In [16]: df
Out[16]:
        Date col1
0 1800-01-13  aaa
1 1001-12-25  bbb
2 1267-03-01  ccc

In [17]: df.dtypes
Out[17]:
Date    object
col1    object
dtype: object

In [18]: df['Date'].dt.year
Out[18]:
0    1800
1    1001
2    1267
Name: Date, dtype: int64

PS you may want to add try ... catch block in the dt_parse() function for catching ValueError: exceptions - result of int()...