Reputation: 2093
My data sets looks like:
Date Value
1/1/1988 0.62
1/2/1988 0.64
1/3/1988 0.65
1/4/1988 0.66
1/5/1988 0.67
1/6/1988 0.66
1/7/1988 0.64
1/8/1988 0.66
1/9/1988 0.65
1/10/1988 0.65
1/11/1988 0.64
1/12/1988 0.66
1/13/1988 0.67
1/14/1988 0.66
1/15/1988 0.65
1/16/1988 0.64
1/17/1988 0.62
1/18/1988 0.64
1/19/1988 0.62
1/20/1988 0.62
1/21/1988 0.64
1/22/1988 0.62
1/23/1988 0.60
I used this code to read this data:
df.set_index(df['Date'], drop=False, append=False, inplace=False, verify_integrity=False).drop('Date', 1)
But the problem is the index is not in date format. So the question is how to set this column as date index?
Upvotes: 49
Views: 118148
Reputation: 41327
If you're loading data from a file, use parse_dates
and index_col
at load time, e.g.:
df = pd.read_csv('data.csv', parse_dates=['Date'], index_col=['Date'])
# Value
# Date
# 1988-01-01 0.62
# 1988-01-02 0.64
# ...
# 1988-01-23 0.60
df.index
# DatetimeIndex(['1988-01-01', '1988-01-02', ..., '1988-01-23'],
# dtype='datetime64[ns]', name='Date', freq=None)
parse_dates
is supported by most of the read_*
methods:
Upvotes: 6
Reputation: 394041
Your question lacked a proper explanation, but you can do the following:
In [75]:
# convert to datetime
df['Date'] = pd.to_datetime(df['Date'])
df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 23 entries, 0 to 22
Data columns (total 2 columns):
Date 23 non-null datetime64[ns]
Value 23 non-null float64
dtypes: datetime64[ns](1), float64(1)
memory usage: 448.0 bytes
In [76]:
# set the index
df.set_index('Date', inplace=True)
df.info()
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 23 entries, 1988-01-01 to 1988-01-23
Data columns (total 1 columns):
Value 23 non-null float64
dtypes: float64(1)
memory usage: 368.0 bytes
So here to_datetime
will convert date strings to datetime
dtype, set_index
with param inplace=True
is all you need,
Upvotes: 86