parse_dates doesn't work with default datetime format

Question

I am exporting data in datetime format to csv. When I import it back, I need to be able to read the data in as dates without any column name or column number references.

It looks like Pandas read_csv has options to automatically parse dates into datetime format but it doesn't seem to be working here.

# Create date data
df_list = [['2014-01-01','2014-02-01'],['2015-01-01','2015-02-01']]
df = pd.DataFrame(df_list,columns=['date1','date2'])

# Convert to datetime format
df['date1'] = pd.to_datetime(df['date1']) 

# Export to csv
df.to_csv('_csv_file.csv',index=False)

# Read in the data and parse dates
in_df = pd.read_csv('_csv_file.csv',parse_dates=True,infer_datetime_format=True)

# Dates are not of correct type
print df.dtypes
print 
print in_df.dtypes

Out [1]:

date1    datetime64[ns]
date2            object
dtype: object

date1    object
date2    object
dtype: object

Is there any way to automatically parse the date columns on import without identifying the column names or locations explicitly?

Answer 1

I think you can change True to ['date1'] in parameter parse_dates of read_csv, because True means parsing index and ['date1'] parsing column date1:

# Read in the data and parse dates
in_df = pd.read_csv('_csv_file.csv', parse_dates=['date1'], infer_datetime_format=True )

#second solution
#instead column name - number of column
#in_df = pd.read_csv('_csv_file.csv',parse_dates=[0], infer_datetime_format=True )

# Dates are not of correct type
print df.dtypes
print 
print in_df.dtypes

date1    datetime64[ns]
date2            object
dtype: object

date1    datetime64[ns]
date2            object
dtype: object

Docs:

parse_dates : boolean, list of ints or names, list of lists, or dict, default False

If True -> try parsing the index. If [1, 2, 3] -> try parsing columns 1, 2, 3 each as a separate date column. If [[1, 3]] -> combine columns 1 and 3 and parse as a single date column. {‘foo’ : [1, 3]} -> parse columns 1, 3 as date and call result ‘foo’ A fast-path exists for iso8601-formatted dates.

infer_datetime_format : boolean, default False

If True and parse_dates is enabled for a column, attempt to infer the datetime format to speed up the processing

It works, if you set column date1 as index:

# Read in the data and parse dates
in_df = pd.read_csv('_csv_file.csv', parse_dates=True, infer_datetime_format=True, 
                    index_col='date1' )

# Dates are not of correct type

print 
print in_df.dtypes
print in_df.index

date2    object
dtype: object
DatetimeIndex(['2014-01-01', '2015-01-01'], dtype='datetime64[ns]', name=u'date1', freq=None)

EDIT:

If you want parse all columns to datetime, you can specify all columns by numbers of columns to parameter parse_dates:

in_df = pd.read_csv('_csv_file.csv', parse_dates=[0, 1, 2, 3])

But there can be errors - some integers can be parsed as datetimes e.g.:

print df
print df.dtypes

       date1       date2  int1      int2
0 2014-01-01  2014-02-01  2000  20111230
1 2015-01-01  2015-02-01  2014  20151230
date1    datetime64[ns]
date2            object
int1              int64
int2              int64
dtype: object

print 
print in_df
print in_df.dtypes

       date1      date2       int1       int2
0 2014-01-01 2014-02-01 2000-01-01 2011-12-30
1 2015-01-01 2015-02-01 2014-01-01 2015-12-30
date1    datetime64[ns]
date2    datetime64[ns]
int1     datetime64[ns]
int2     datetime64[ns]
dtype: object