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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) or
data=pd.read_csv('c:/data.csv',parse_dates=[0]) data is like following
date value 30MAR1990 140000 30JUN1990 30000 30SEP1990 120000 30DEC1990 34555 What did I do wrong? Please help!
Thanks.
398
We can use the parse_dates parameter to convince pandas to turn things into real datetime types. parse_dates takes a list of columns (since you could want to parse multiple columns into datetimes ).
By default, date columns are represented as object when loading data from a CSV file. To read the date column correctly, we can use the argument parse_dates to specify a list of date columns.
parse_dates : boolean or list of ints or names or list of lists or dict, default False. boolean.
If True and parse_dates is enabled, pandas will attempt to infer the format of the datetime strings in the columns, and if it can be inferred, switch to a faster method of parsing them. In some cases this can increase the parsing speed by 5-10x.
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') If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
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