Parse_dates in Pandas

Question

The following code can\'t parse my date column into dates from csv file.

data=pd.read_csv(\'c:/data.csv\',parse_dates=True,keep_date_col = True) 


        

Answer 1

You can use the date_parser argument to read_csv

In [62]: from pandas.compat import StringIO

In [63]: s = """date,value 
30MAR1990,140000 
30JUN1990,30000  
30SEP1990,120000  
30DEC1990,34555
"""

In [64]: from pandas.compat import StringIO

In [65]: import datetime

date_parser expects a function that will be called on an array of strings. func calls datetime.datetime.strptime on each string. Check out the datetime module in the python docs for more on the format codes.

In [66]: func = lambda dates: [datetime.datetime.strptime(x, '%d%b%Y') for x in dates]

In [67]: s = """date,value 
30MAR1990,140000 
30JUN1990,30000  
30SEP1990,120000  
30DEC1990,34555
"""

In [68]: pd.read_csv(StringIO(s), parse_dates=['date'], date_parser=func)
Out[68]: 
        date  value 
0 1990-03-30  140000
1 1990-06-30   30000
2 1990-09-30  120000
3 1990-12-30   34555

[4 rows x 2 columns]

Answer 2

data=pd.read_csv('c:/data.csv',parse_dates=True,index_col='Date')

Answer 3

This is a non-standard format, so not caught by the default parser, you can pass your own:

In [11]: import datetime as dt

In [12]: dt.datetime.strptime('30MAR1990', '%d%b%Y')
Out[12]: datetime.datetime(1990, 3, 30, 0, 0)

In [13]: parser = lambda date: pd.datetime.strptime(date, '%d%b%Y')

In [14]: pd.read_csv(StringIO(s), parse_dates=[0], date_parser=parser)
Out[14]:
        date  value
0 1990-03-30  140000
1 1990-06-30   30000
2 1990-09-30  120000
3 1990-12-30   34555

Another option is to use to_datetime after you've read in the strings:

df['date'] = pd.to_datetime(df['date'], format='%d%b%Y')