110977 Freq: D, dtype: float64] You can pass iterator=True to iterator over the unpacked results In [124]: for o in pd.read_msgpack(’foo.msg’,iterator=True): .....: print o .....: A B 0 0.251082 3.0.0) on Python3 • Iterator support via read_hdf that automatically opens and closes the store when iteration is finished. This is only for tables In [25]: path = ’store_iterator.h5’ In [26]: DataFrame(randn(10 object In [53]: s.dropna().values.item() == ’w’ Out[53]: True The last element yielded by the iterator will be a Series containing the last element of the longest string in the Series with all other

856265 0.058945 0.145447 C 0.058945 0.335350 0.390637 • Series.iteritems() is now lazy (returns an iterator rather than a list). This was the documented behavior 6 Chapter 1. What’s New pandas: powerful powerful Python data analysis toolkit, Release 0.14.0 • Raise a TypeError when DataFrame is passed an iterator as the data argument (GH5357) 1.1.2 Display Changes • The default way of printing large DataFrames 110977 Freq: D, dtype: float64] You can pass iterator=True to iterator over the unpacked results In [124]: for o in pd.read_msgpack(’foo.msg’,iterator=True): .....: print o .....: A B 0 0.251082

Query via Data Columns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1072 24.8.6.5 Iterator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1074 24.8.6.6 is_named_tuple . . . . . . . . . . . . . . . . . . . . . . . . . . . 1861 34.16.4.29pandas.api.types.is_iterator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1861 34.16.4.30pandas.api.types.is_bool 'is_hashable', 'is_int64_dtype', 'is_integer', 'is_integer_dtype', 'is_interval', 'is_interval_dtype', 'is_iterator', 'is_list_like', 'is_named_tuple', 'is_number', 'is_numeric_dtype', 'is_object_dtype', 'is_period'

Query via Data Columns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1107 24.8.6.5 Iterator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1109 24.8.6.6 is_named_tuple . . . . . . . . . . . . . . . . . . . . . . . . . . . 2008 34.19.4.29pandas.api.types.is_iterator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2008 34.19.4.30pandas.api.types.is_bool chunksize parameter that can be used when lines=True. If chunksize is passed, read_json now returns an iterator which reads in chunksize lines with each iteration. (GH17048) • read_json() and to_json() now accept

Query via Data Columns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1068 24.8.6.5 Iterator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1070 24.8.6.6 is_named_tuple . . . . . . . . . . . . . . . . . . . . . . . . . . . 1728 34.16.4.29pandas.api.types.is_iterator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1728 34.16.4.30pandas.api.types.is_bool 'is_hashable', 'is_int64_dtype', 'is_integer', 'is_integer_dtype', 'is_interval', 'is_interval_dtype', 'is_iterator', 'is_list_like', 'is_named_tuple', 'is_number', 'is_numeric_dtype', 'is_object_dtype', 'is_period'

3.0.0) on Python3 • Iterator support via read_hdf that automatically opens and closes the store when iteration is finished. This is only for tables In [25]: path = ’store_iterator.h5’ In [26]: DataFrame(randn(10 w dtype: object In [53]: s.dropna().values.item() == ’w’ True The last element yielded by the iterator will be a Series containing the last element of the longest string in the Series with all other provide dotted attribute access to get from stores, e.g. store.df == store[’df’] – new keywords iterator=boolean, and chunksize=number_in_a_chunk are provided to sup- port iteration on select and select_as_multiple

support for a chunksize parameter to read_sql function. Specifying this argument will return an iterator through chunks of the query result (GH2908). • Added support for writing datetime.date and datetime 856265 0.058945 0.145447 C 0.058945 0.335350 0.390637 • Series.iteritems() is now lazy (returns an iterator rather than a list). This was the documented behavior prior to 0.14. (GH6760) • Added nunique and TypeError for invalid types passed to concat (GH6583) • Raise a TypeError when DataFrame is passed an iterator as the data argument (GH5357) 1.5.2 Display Changes • The default way of printing large DataFrames

support for a chunksize parameter to read_sql function. Specifying this argument will return an iterator through chunks of the query result (GH2908). • Added support for writing datetime.date and datetime 856265 0.058945 0.145447 C 0.058945 0.335350 0.390637 • Series.iteritems() is now lazy (returns an iterator rather than a list). This was the documented behavior 44 Chapter 1. What’s New pandas: powerful powerful Python data analysis toolkit, Release 0.15.1 • Raise a TypeError when DataFrame is passed an iterator as the data argument (GH5357) 1.4.2 Display Changes • The default way of printing large DataFrames

XPORT format files. (GH4052). df = pd.read_sas('sas_xport.xpt') It is also possible to obtain an iterator and read an XPORT file incrementally. for df in pd.read_sas('sas_xport.xpt', chunksize=10000) do_something(df) read_excel('path_to_file.xls',sheetname=['Sheet1',3]) • Allow Stata files to be read incrementally with an iterator; support for long strings in Stata files. See the docs here (GH9493:). • Paths beginning with support for a chunksize parameter to read_sql function. Specifying this argument will return an iterator through chunks of the query result (GH2908). • Added support for writing datetime.date and datetime

distinctions present in a string (GH25405) • DataFrame.set_index() now works for instances of abc.Iterator, provided their output is of the same length as the calling frame (GH22484, GH24984) • DatetimeIndex are iterating over. This is not guaranteed to work in all cases. Depending on the data types, the iterator returns a copy and not a view, and writing to it will have no effect! For example, in the following iterrows() allows you to iterate through the rows of a DataFrame as Series objects. It returns an iterator yielding each index value along with a Series containing the data in each row: In [251]: for row_index