will cause data to be overwritten if there are duplicate names in the columns. General parsing configuration dtype [Type name or dict of column -> type, default None] Data type for data or columns. E.g 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

will cause data to be overwritten if there are duplicate names in the columns. General parsing configuration dtype [Type name or dict of column -> type, default None] Data type for data or columns. E.g 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

The returned dtype of unique() now matches the input dtype. (GH27874) • Changed the default configuration value for options.matplotlib.register_converters from True to "auto" (GH18720). Now, pandas custom will cause data to be overwritten if there are duplicate names in the columns. General parsing configuration dtype [Type name or dict of column -> type, default None] Data type for data or columns. E.g 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

average gratuity by size of the party and sex of the server. In Excel, we use the following configuration for the PivotTable: The equivalent in pandas: In [66]: pd.pivot_table( ....: tips, values="tip" will cause data to be overwritten if there are duplicate names in the columns. General parsing configuration dtype [Type name or dict of column -> type, default None] Data type for data or columns. E.g 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

average gratuity by size of the party and sex of the server. In Excel, we use the following configuration for the PivotTable: The equivalent in pandas: In [66]: pd.pivot_table( ....: tips, values="tip" will cause data to be overwritten if there are duplicate names in the columns. General parsing configuration dtype [Type name or dict of column -> type, default None] Data type for data or columns. E.g 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

will cause data to be overwritten if there are duplicate names in the columns. General parsing configuration dtype [Type name or dict of column -> type, default None] Data type for data or columns. E.g 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

will cause data to be overwritten if there are duplicate names in the columns. General parsing configuration dtype [Type name or dict of column -> type, default None] Data type for data or columns. E.g 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

average gratuity by size of the party and sex of the server. In Excel, we use the following configuration for the PivotTable: The equivalent in pandas: In [66]: pd.pivot_table( ....: tips, values="tip" User Guide pandas: powerful Python data analysis toolkit, Release 1.5.0rc0 General parsing configuration dtype [Type name or dict of column -> type, default None] Data type for data or columns. E.g not affect the existing column order) and the new column names will be the concatenation of the component column names: In [106]: data = ( .....: "KORD,19990127, 19:00:00, 18:56:00, 0.8100\n" .....:

average gratuity by size of the party and sex of the server. In Excel, we use the following configuration for the PivotTable: The equivalent in pandas: In [66]: pd.pivot_table( ....: tips, values="tip" will cause data to be overwritten if there are duplicate names in the columns. General parsing configuration dtype [Type name or dict of column -> type, default None] Data type for data or columns. E.g not affect the existing column order) and the new column names will be the concatenation of the component column names: In [104]: print(open("tmp.csv").read()) KORD,19990127, 19:00:00, 18:56:00, 0.8100

average gratuity by size of the party and sex of the server. In Excel, we use the following configuration for the PivotTable: The equivalent in pandas: In [66]: pd.pivot_table( ....: tips, values="tip" will cause data to be overwritten if there are duplicate names in the columns. General parsing configuration dtype [Type name or dict of column -> type, default None] Data type for data or columns. E.g not affect the existing column order) and the new column names will be the concatenation of the component column names: In [107]: data = ( .....: "KORD,19990127, 19:00:00, 18:56:00, 0.8100\n" .....: