Why a date assignments in a dataframe is not a date type?
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3
The df_vol DataFrame is created as follows
df_vol = df.loc[:, 1].map(fd.retrieve_symbol_datetime).to_frame('maturity')
df_vol['date'] = df_vol.index.date
df_vol.head()
maturity date
2018-11-01 11:31:53.023 2022-04-01 2018-11-01
2018-11-01 16:30:15.287 2022-04-01 2018-11-01
2018-11-01 10:23:06.779 2022-10-01 2018-11-01
2018-11-01 16:30:15.291 2022-10-01 2018-11-01
2018-11-01 11:30:56.251 2018-12-01 2018-11-01
A further inspection of df_vol shows
df_vol.dtypes
maturity category
date object
dtype: object
I would expect that maturity column is of a date type as it is filled by the content of the fd.retrieve_symbol_datetime(), a function that returns pandas.datetime().
Also, the date column is an object type though it takes the values from index.date.
I'm interested in having types of datetime since I eventually I want to do the difference
pd.eval("(df_vol.maturity - df_vol.date)")
retrieve_symbol_datetime()
def retrieve_symbol_datetime(future: str):
"""
Retrieves the maturity date of a future whose format is of the form AAAMYY.
Params
-------
future : string, of form 'AAAMYY'
This format is for futures where 'AAA' is the string that identifies
the symbol, 'M' is the character that identifies the month, and 'YY' is
a two-digit number that identifies the year.
Returns : pandas.datetime
Returns the date of maturiry of the future's symbol.
Example
-------
If future = 'DI1Z20', then it returnts a pandas.datetime(2020, 12, 01).
"""
year = 2000 + int(future[4: 6])
month = convert_letter_symbol_month(future[3: 4])
return pd.datetime(year, month, 1).date()
python pandas date
asked Nov 24 '18 at 16:50
user3889486user3889486
3121314
add a comment |
3
The df_vol DataFrame is created as follows
df_vol = df.loc[:, 1].map(fd.retrieve_symbol_datetime).to_frame('maturity')
df_vol['date'] = df_vol.index.date
df_vol.head()
maturity date
2018-11-01 11:31:53.023 2022-04-01 2018-11-01
2018-11-01 16:30:15.287 2022-04-01 2018-11-01
2018-11-01 10:23:06.779 2022-10-01 2018-11-01
2018-11-01 16:30:15.291 2022-10-01 2018-11-01
2018-11-01 11:30:56.251 2018-12-01 2018-11-01
A further inspection of df_vol shows
df_vol.dtypes
maturity category
date object
dtype: object
I would expect that maturity column is of a date type as it is filled by the content of the fd.retrieve_symbol_datetime(), a function that returns pandas.datetime().
Also, the date column is an object type though it takes the values from index.date.
I'm interested in having types of datetime since I eventually I want to do the difference
pd.eval("(df_vol.maturity - df_vol.date)")
retrieve_symbol_datetime()
def retrieve_symbol_datetime(future: str):
"""
Retrieves the maturity date of a future whose format is of the form AAAMYY.
Params
-------
future : string, of form 'AAAMYY'
This format is for futures where 'AAA' is the string that identifies
the symbol, 'M' is the character that identifies the month, and 'YY' is
a two-digit number that identifies the year.
Returns : pandas.datetime
Returns the date of maturiry of the future's symbol.
Example
-------
If future = 'DI1Z20', then it returnts a pandas.datetime(2020, 12, 01).
"""
year = 2000 + int(future[4: 6])
month = convert_letter_symbol_month(future[3: 4])
return pd.datetime(year, month, 1).date()
python pandas date
asked Nov 24 '18 at 16:50
user3889486user3889486
3121314
1
It would be helpful to know what package "fd" corresponds to. Please include the relevant import statements. It also appears that you are doing df.head() which is displaying the contents of the dataframe df, but you are calculating the dataframe df_vol. Did you want to do df_vol.head() instead?
– Tom Johnson
Nov 24 '18 at 17:11
add a comment |
3
3
3
The df_vol DataFrame is created as follows
df_vol = df.loc[:, 1].map(fd.retrieve_symbol_datetime).to_frame('maturity')
df_vol['date'] = df_vol.index.date
df_vol.head()
maturity date
2018-11-01 11:31:53.023 2022-04-01 2018-11-01
2018-11-01 16:30:15.287 2022-04-01 2018-11-01
2018-11-01 10:23:06.779 2022-10-01 2018-11-01
2018-11-01 16:30:15.291 2022-10-01 2018-11-01
2018-11-01 11:30:56.251 2018-12-01 2018-11-01
A further inspection of df_vol shows
df_vol.dtypes
maturity category
date object
dtype: object
I would expect that maturity column is of a date type as it is filled by the content of the fd.retrieve_symbol_datetime(), a function that returns pandas.datetime().
Also, the date column is an object type though it takes the values from index.date.
I'm interested in having types of datetime since I eventually I want to do the difference
pd.eval("(df_vol.maturity - df_vol.date)")
retrieve_symbol_datetime()
def retrieve_symbol_datetime(future: str):
"""
Retrieves the maturity date of a future whose format is of the form AAAMYY.
Params
-------
future : string, of form 'AAAMYY'
This format is for futures where 'AAA' is the string that identifies
the symbol, 'M' is the character that identifies the month, and 'YY' is
a two-digit number that identifies the year.
Returns : pandas.datetime
Returns the date of maturiry of the future's symbol.
Example
-------
If future = 'DI1Z20', then it returnts a pandas.datetime(2020, 12, 01).
"""
year = 2000 + int(future[4: 6])
month = convert_letter_symbol_month(future[3: 4])
return pd.datetime(year, month, 1).date()
python pandas date
asked Nov 24 '18 at 16:50
user3889486user3889486
3121314
The df_vol DataFrame is created as follows
df_vol = df.loc[:, 1].map(fd.retrieve_symbol_datetime).to_frame('maturity')
df_vol['date'] = df_vol.index.date
df_vol.head()
maturity date
2018-11-01 11:31:53.023 2022-04-01 2018-11-01
2018-11-01 16:30:15.287 2022-04-01 2018-11-01
2018-11-01 10:23:06.779 2022-10-01 2018-11-01
2018-11-01 16:30:15.291 2022-10-01 2018-11-01
2018-11-01 11:30:56.251 2018-12-01 2018-11-01
A further inspection of df_vol shows
df_vol.dtypes
maturity category
date object
dtype: object
I would expect that maturity column is of a date type as it is filled by the content of the fd.retrieve_symbol_datetime(), a function that returns pandas.datetime().
Also, the date column is an object type though it takes the values from index.date.
I'm interested in having types of datetime since I eventually I want to do the difference
pd.eval("(df_vol.maturity - df_vol.date)")
retrieve_symbol_datetime()
def retrieve_symbol_datetime(future: str):
"""
Retrieves the maturity date of a future whose format is of the form AAAMYY.
Params
-------
future : string, of form 'AAAMYY'
This format is for futures where 'AAA' is the string that identifies
the symbol, 'M' is the character that identifies the month, and 'YY' is
a two-digit number that identifies the year.
Returns : pandas.datetime
Returns the date of maturiry of the future's symbol.
Example
-------
If future = 'DI1Z20', then it returnts a pandas.datetime(2020, 12, 01).
"""
year = 2000 + int(future[4: 6])
month = convert_letter_symbol_month(future[3: 4])
return pd.datetime(year, month, 1).date()
python pandas date
python pandas date
asked Nov 24 '18 at 16:50
user3889486user3889486
3121314
asked Nov 24 '18 at 16:50
user3889486user3889486
3121314
edited Nov 24 '18 at 17:35
user3889486
asked Nov 24 '18 at 16:50
user3889486user3889486
3121314
asked Nov 24 '18 at 16:50
user3889486user3889486
3121314
asked Nov 24 '18 at 16:50
user3889486user3889486
3121314
3121314
1
It would be helpful to know what package "fd" corresponds to. Please include the relevant import statements. It also appears that you are doing df.head() which is displaying the contents of the dataframe df, but you are calculating the dataframe df_vol. Did you want to do df_vol.head() instead?
– Tom Johnson
Nov 24 '18 at 17:11
add a comment |
1
It would be helpful to know what package "fd" corresponds to. Please include the relevant import statements. It also appears that you are doing df.head() which is displaying the contents of the dataframe df, but you are calculating the dataframe df_vol. Did you want to do df_vol.head() instead?
– Tom Johnson
Nov 24 '18 at 17:11
1
1
It would be helpful to know what package "fd" corresponds to. Please include the relevant import statements. It also appears that you are doing df.head() which is displaying the contents of the dataframe df, but you are calculating the dataframe df_vol. Did you want to do df_vol.head() instead?
– Tom Johnson
Nov 24 '18 at 17:11
It would be helpful to know what package "fd" corresponds to. Please include the relevant import statements. It also appears that you are doing df.head() which is displaying the contents of the dataframe df, but you are calculating the dataframe df_vol. Did you want to do df_vol.head() instead?
– Tom Johnson
Nov 24 '18 at 17:11
add a comment |
1 Answer
1
active
oldest
votes
1
There is problem categorical column, one possible solution is decategorical it and for date use floor for remove times:
df_vol['maturity'] = pd.to_datetime(df_vol['maturity'].astype(str))
df_vol['date'] = df_vol.index.floor('d')
df_vol['diff'] = (df_vol['maturity'] - df_vol['date']).dt.days
print (df_vol)
maturity date diff
2018-11-01 11:31:53.023 2022-04-01 2018-11-01 1247
2018-11-01 16:30:15.287 2022-04-01 2018-11-01 1247
2018-11-01 10:23:06.779 2022-10-01 2018-11-01 1430
2018-11-01 16:30:15.291 2022-10-01 2018-11-01 1430
2018-11-01 11:30:56.251 2018-12-01 2018-11-01 30
answered Nov 24 '18 at 17:14
jezraeljezrael
361k26327407
add a comment |
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1 Answer
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1 Answer
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1
There is problem categorical column, one possible solution is decategorical it and for date use floor for remove times:
df_vol['maturity'] = pd.to_datetime(df_vol['maturity'].astype(str))
df_vol['date'] = df_vol.index.floor('d')
df_vol['diff'] = (df_vol['maturity'] - df_vol['date']).dt.days
print (df_vol)
maturity date diff
2018-11-01 11:31:53.023 2022-04-01 2018-11-01 1247
2018-11-01 16:30:15.287 2022-04-01 2018-11-01 1247
2018-11-01 10:23:06.779 2022-10-01 2018-11-01 1430
2018-11-01 16:30:15.291 2022-10-01 2018-11-01 1430
2018-11-01 11:30:56.251 2018-12-01 2018-11-01 30
answered Nov 24 '18 at 17:14
jezraeljezrael
361k26327407
add a comment |
1
There is problem categorical column, one possible solution is decategorical it and for date use floor for remove times:
df_vol['maturity'] = pd.to_datetime(df_vol['maturity'].astype(str))
df_vol['date'] = df_vol.index.floor('d')
df_vol['diff'] = (df_vol['maturity'] - df_vol['date']).dt.days
print (df_vol)
maturity date diff
2018-11-01 11:31:53.023 2022-04-01 2018-11-01 1247
2018-11-01 16:30:15.287 2022-04-01 2018-11-01 1247
2018-11-01 10:23:06.779 2022-10-01 2018-11-01 1430
2018-11-01 16:30:15.291 2022-10-01 2018-11-01 1430
2018-11-01 11:30:56.251 2018-12-01 2018-11-01 30
answered Nov 24 '18 at 17:14
jezraeljezrael
361k26327407
add a comment |
1
1
1
There is problem categorical column, one possible solution is decategorical it and for date use floor for remove times:
df_vol['maturity'] = pd.to_datetime(df_vol['maturity'].astype(str))
df_vol['date'] = df_vol.index.floor('d')
df_vol['diff'] = (df_vol['maturity'] - df_vol['date']).dt.days
print (df_vol)
maturity date diff
2018-11-01 11:31:53.023 2022-04-01 2018-11-01 1247
2018-11-01 16:30:15.287 2022-04-01 2018-11-01 1247
2018-11-01 10:23:06.779 2022-10-01 2018-11-01 1430
2018-11-01 16:30:15.291 2022-10-01 2018-11-01 1430
2018-11-01 11:30:56.251 2018-12-01 2018-11-01 30
answered Nov 24 '18 at 17:14
jezraeljezrael
361k26327407
There is problem categorical column, one possible solution is decategorical it and for date use floor for remove times:
df_vol['maturity'] = pd.to_datetime(df_vol['maturity'].astype(str))
df_vol['date'] = df_vol.index.floor('d')
df_vol['diff'] = (df_vol['maturity'] - df_vol['date']).dt.days
print (df_vol)
maturity date diff
2018-11-01 11:31:53.023 2022-04-01 2018-11-01 1247
2018-11-01 16:30:15.287 2022-04-01 2018-11-01 1247
2018-11-01 10:23:06.779 2022-10-01 2018-11-01 1430
2018-11-01 16:30:15.291 2022-10-01 2018-11-01 1430
2018-11-01 11:30:56.251 2018-12-01 2018-11-01 30
answered Nov 24 '18 at 17:14
jezraeljezrael
361k26327407
answered Nov 24 '18 at 17:14
jezraeljezrael
361k26327407
answered Nov 24 '18 at 17:14
jezraeljezrael
361k26327407
answered Nov 24 '18 at 17:14
jezraeljezrael
361k26327407
361k26327407
add a comment |
add a comment |
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1
It would be helpful to know what package "fd" corresponds to. Please include the relevant import statements. It also appears that you are doing df.head() which is displaying the contents of the dataframe df, but you are calculating the dataframe df_vol. Did you want to do df_vol.head() instead?
– Tom Johnson
Nov 24 '18 at 17:11