not affect the existing column order) and the new column names will be the concatenation of the component column names: In [97]: print(open('tmp.csv').read()) KORD,19990127, 19:00:00, 18:56:00, 0.8100 -0.59 5 1999-01-27 23:00:00 1999-01-27 22:56:00 KORD -0.59 By default the parser removes the component date columns, but you can choose to retain them via the keep_date_col keyword: In [100]: df = pd DateOffsetNone None DateOffset For time series data, it’s conventional to represent the time component in the index of a Series or DataFrame so manipulations can be performed with respect to the time

not affect the existing column order) and the new column names will be the concatenation of the component column names: In [97]: print(open('tmp.csv').read()) KORD,19990127, 19:00:00, 18:56:00, 0.8100 -0.59 5 1999-01-27 23:00:00 1999-01-27 22:56:00 KORD -0.59 By default the parser removes the component date columns, but you can choose to retain them via the keep_date_col keyword: In [100]: df = pd DateOffsetNone None DateOffset For time series data, it’s conventional to represent the time component in the index of a Series or DataFrame so manipulations can be performed with respect to the time

not affect the existing column order) and the new column names will be the concatenation of the component column names: In [97]: print(open('tmp.csv').read()) KORD,19990127, 19:00:00, 18:56:00, 0.8100 -0.59 5 1999-01-27 23:00:00 1999-01-27 22:56:00 KORD -0.59 By default the parser removes the component date columns, but you can choose to retain them via the keep_date_col keyword: In [100]: df = pd DateOffsetNone None DateOffset For time series data, it’s conventional to represent the time component in the index of a Series or DataFrame so manipulations can be performed with respect to the time

not affect the existing column order) and the new column names will be the concatenation of the component column names: 236 Chapter 3. User Guide pandas: powerful Python data analysis toolkit, Release -0.59 5 1999-01-27 23:00:00 1999-01-27 22:56:00 KORD -0.59 By default the parser removes the component date columns, but you can choose to retain them via the keep_date_col keyword: In [103]: df = pd DateOffsetNone None DateOffset For time series data, it’s conventional to represent the time component in the index of a Series or DataFrame so manipulations can be performed with respect to the time

not affect the existing column order) and the new column names will be the concatenation of the component column names: 242 Chapter 2. User Guide pandas: powerful Python data analysis toolkit, Release -0.59 5 1999-01-27 23:00:00 1999-01-27 22:56:00 KORD -0.59 By default the parser removes the component date columns, but you can choose to retain them via the keep_date_col keyword: In [103]: df = pd DateOffsetNone None DateOffset For time series data, it’s conventional to represent the time component in the index of a Series or DataFrame so manipulations can be performed with respect to the time

not affect the existing column order) and the new column names will be the concatenation of the component column names: 242 Chapter 2. User Guide pandas: powerful Python data analysis toolkit, Release -0.59 5 1999-01-27 23:00:00 1999-01-27 22:56:00 KORD -0.59 By default the parser removes the component date columns, but you can choose to retain them via the keep_date_col keyword: In [103]: df = pd DateOffsetNone None DateOffset For time series data, it’s conventional to represent the time component in the index of a Series or DataFrame so manipulations can be performed with respect to the time

not affect the existing column order) and the new column names will be the concatenation of the component column names: 2.4. IO tools (text, CSV, HDF5, . . . ) 247 pandas: powerful Python data analysis -0.59 5 1999-01-27 23:00:00 1999-01-27 22:56:00 KORD -0.59 By default the parser removes the component date columns, but you can choose to retain them via the keep_date_col keyword: In [103]: df = pd DateOffsetNone None DateOffset For time series data, it’s conventional to represent the time component in the index of a Series or DataFrame so manipulations can be performed with respect to the time

not affect the existing column order) and the new column names will be the concatenation of the component column names: 2.4. IO tools (text, CSV, HDF5, . . . ) 247 pandas: powerful Python data analysis -0.59 5 1999-01-27 23:00:00 1999-01-27 22:56:00 KORD -0.59 By default the parser removes the component date columns, but you can choose to retain them via the keep_date_col keyword: In [103]: df = pd DateOffsetNone None DateOffset For time series data, it’s conventional to represent the time component in the index of a Series or DataFrame so manipulations can be performed with respect to the time

not affect the existing column order) and the new column names will be the concatenation of the component column names: 244 Chapter 3. User Guide pandas: powerful Python data analysis toolkit, Release -0.59 5 1999-01-27 23:00:00 1999-01-27 22:56:00 KORD -0.59 By default the parser removes the component date columns, but you can choose to retain them via the keep_date_col keyword: In [103]: df = pd DateOffsetNone None DateOffset For time series data, it’s conventional to represent the time component in the index of a Series or DataFrame so manipulations can be performed with respect to the time

not affect the existing column order) and the new column names will be the concatenation of the component column names: In [100]: print(open("tmp.csv").read()) KORD,19990127, 19:00:00, 18:56:00, 0.8100 -0.59 5 1999-01-27 23:00:00 1999-01-27 22:56:00 KORD -0.59 By default the parser removes the component date columns, but you can choose to retain them via the keep_date_col keyword: 246 Chapter 2. User DateOffsetNone None DateOffset For time series data, it’s conventional to represent the time component in the index of a Series or DataFrame so manipulations can be performed with respect to the time