analysis toolkit, Release 0.25.3 4.1.1 CSV & text files The workhorse function for reading text files (a.k.a. flat files) is read_csv(). See the cookbook for some advanced strategies. Parsing options read_csv() bar Name: string, dtype: object Selecting coordinates Sometimes you want to get the coordinates (a.k.a the index locations) of your query. This returns an Int64Index of the resulting locations. These coordinates = csv.writer(s_buf) writer.writerows(data_iter) s_buf.seek(0) columns = ', '.join('"{}"'.format(k) for k in keys) if table.schema: table_name = '{}.{}'.format(table.schema, table.name) else: table_name

+ • Paul Lee + • Paul Siegel + • Petr Baev + • Pietro Battiston • Prakhar Pandey + • Puneeth K + • Raghav + • Rajat + • Rajhans Jadhao + • Rajiv Bharadwaj + • Roei.r • Rohit Sanjay + • Ronan analysis toolkit, Release 1.0.0 3.1.1 CSV & text files The workhorse function for reading text files (a.k.a. flat files) is read_csv(). See the cookbook for some advanced strategies. Parsing options read_csv() bar Name: string, dtype: object Selecting coordinates Sometimes you want to get the coordinates (a.k.a the index locations) of your query. This returns an Int64Index of the resulting locations. These coordinates

analysis toolkit, Release 0.25.0 4.1.1 CSV & text files The workhorse function for reading text files (a.k.a. flat files) is read_csv(). See the cookbook for some advanced strategies. Parsing options read_csv() bar Name: string, dtype: object Selecting coordinates Sometimes you want to get the coordinates (a.k.a the index locations) of your query. This returns an Int64Index of the resulting locations. These coordinates = csv.writer(s_buf) writer.writerows(data_iter) s_buf.seek(0) columns = ', '.join('"{}"'.format(k) for k in keys) if table.schema: table_name = '{}.{}'.format(table.schema, table.name) else: table_name

analysis toolkit, Release 0.25.1 4.1.1 CSV & text files The workhorse function for reading text files (a.k.a. flat files) is read_csv(). See the cookbook for some advanced strategies. Parsing options read_csv() bar Name: string, dtype: object Selecting coordinates Sometimes you want to get the coordinates (a.k.a the index locations) of your query. This returns an Int64Index of the resulting locations. These coordinates = csv.writer(s_buf) writer.writerows(data_iter) s_buf.seek(0) columns = ', '.join('"{}"'.format(k) for k in keys) if table.schema: table_name = '{}.{}'.format(table.schema, table.name) else: table_name

(GH11881, GH12572). For further details see here • HDFStore is now iterable: for k in store is equivalent to for k in store.keys() (GH12221). • Add missing methods/fields to .dt for Period (GH8848) = pd.MultiIndex.from_product([['j'], ['l', 'k']], ....: names = ['i1', 'i2'])) ....: In [32]: df Out[32]: col1 foo bar col2 a b a b i1 i2 j l 1 2 3 4 k 5 6 7 8 In [33]: df.to_excel('test.xlsx') (October 9, 2015) 151 pandas: powerful Python data analysis toolkit, Release 0.20.3 i1 i2 j l 1 2 3 4 k 5 6 7 8 Previously, it was necessary to specify the has_index_names argument in read_excel, if the

(GH11881, GH12572). For further details see here • HDFStore is now iterable: for k in store is equivalent to for k in store.keys() (GH12221). • Add missing methods/fields to .dt for Period (GH8848) = pd.MultiIndex.from_product([['j'], ['l', 'k']], ....: names = ['i1', 'i2'])) ....: In [32]: df Out[32]: col1 foo bar col2 a b a b i1 i2 j l 1 2 3 4 k 5 6 7 8 In [33]: df.to_excel('test.xlsx') header=[0,1], index_col=[0,1]) In [35]: df Out[35]: col1 foo bar col2 a b a b i1 i2 j l 1 2 3 4 k 5 6 7 8 Previously, it was necessary to specify the has_index_names argument in read_excel, if the

(GH11881, GH12572). For further details see here • HDFStore is now iterable: for k in store is equivalent to for k in store.keys() (GH12221). • Add missing methods/fields to .dt for Period (GH8848) = pd.MultiIndex.from_product([['j'], ['l', 'k']], ....: names = ['i1', 'i2'])) ....: In [32]: df Out[32]: col1 foo bar col2 a b a b i1 i2 j l 1 2 3 4 k 5 6 7 8 In [33]: df.to_excel('test.xlsx') Chapter 1. What’s New pandas: powerful Python data analysis toolkit, Release 0.21.1 i1 i2 j l 1 2 3 4 k 5 6 7 8 Previously, it was necessary to specify the has_index_names argument in read_excel, if the

(GH11881, GH12572). For further details see here • HDFStore is now iterable: for k in store is equivalent to for k in store.keys() (GH12221). • Add missing methods/fields to .dt for Period (GH8848) = pd.MultiIndex.from_product([['j'], ['l', 'k']], ....: names = ['i1', 'i2'])) ....: In [32]: df Out[32]: col1 foo bar col2 a b a b i1 i2 j l 1 2 3 4 k 5 6 7 8 In [33]: df.to_excel('test.xlsx') header=[0,1], index_col=[0,1]) In [35]: df Out[35]: col1 foo bar col2 a b a b i1 i2 j l 1 2 3 4 k 5 6 7 8 Previously, it was necessary to specify the has_index_names argument in read_excel, if the

(GH11881, GH12572). For further details see here • HDFStore is now iterable: for k in store is equivalent to for k in store.keys() (GH12221). • Add missing methods/fields to .dt for Period (GH8848) = pd.MultiIndex.from_product([['j'], ['l', 'k']], ....: names = ['i1', 'i2'])) ....: In [32]: df Out[32]: col1 foo bar col2 a b a b i1 i2 j l 1 2 3 4 k 5 6 7 8 In [33]: df.to_excel('test.xlsx') header=[0,1], index_col=[0,1]) In [35]: df Out[35]: col1 foo bar col2 a b a b i1 i2 j l 1 2 3 4 k 5 6 7 8 Previously, it was necessary to specify the has_index_names argument in read_excel, if the

analysis toolkit, Release 1.0.5 2.1.1 CSV & text files The workhorse function for reading text files (a.k.a. flat files) is read_csv(). See the cookbook for some advanced strategies. Parsing options read_csv() bar Name: string, dtype: object Selecting coordinates Sometimes you want to get the coordinates (a.k.a the index locations) of your query. This returns an Int64Index of the resulting locations. These coordinates = csv.writer(s_buf) writer.writerows(data_iter) s_buf.seek(0) columns = ', '.join('"{}"'.format(k) for k in keys) if table.schema: table_name = '{}.{}'.format(table.schema, table.name) else: table_name