numexpr: for accelerating certain numerical operations. numexpr uses multiple cores as well as smart chunk- ing and caching to achieve large speedups. If installed, must be Version 2.6.2 or higher. • bottleneck: iterator [boolean, default False] Return TextFileReader object for iteration or getting chunks with get_chunk(). chunksize [int, default None] Return TextFileReader object for iteration. See iterating and chunking 2058 try: -> 2059 data = self._reader.read(nrows) 2060 except StopIteration: 2061 if self._first_chunk: ~/sandbox/pandas-release/pandas/pandas/_libs/parsers.pyx in pandas._libs.parsers. �→TextReader

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 (GH3076) 122 Chapter 1. What’s numexpr: for accelerating certain numerical operations. numexpr uses multiple cores as well as smart chunk- ing and caching to achieve large speedups. If installed, must be Version 2.1 or higher. • bottleneck: multiple files, appending to create a single dataframe Reading a csv chunk-by-chunk Reading only certain rows of a csv chunk-by-chunk Reading the first few lines of a frame Reading a file that is compressed

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 (GH3076) 116 Chapter 1. What’s numexpr: for accelerating certain numerical operations. numexpr uses multiple cores as well as smart chunk- ing and caching to achieve large speedups. If installed, must be Version 2.1 or higher. • bottleneck: multiple files, appending to create a single dataframe Reading a csv chunk-by-chunk Reading only certain rows of a csv chunk-by-chunk Reading the first few lines of a frame Reading a file that is compressed

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 (GH3076) • You can now select numexpr: for accelerating certain numerical operations. numexpr uses multiple cores as well as smart chunk- ing and caching to achieve large speedups. If installed, must be Version 2.1 or higher. • bottleneck: multiple files, appending to create a single dataframe Reading a csv chunk-by-chunk Reading only certain rows of a csv chunk-by-chunk Reading the first few lines of a frame 300 Chapter 8. Cookbook pandas:

single DataFrame . . . . . . . . . . . . . . . . . . . . . . 1021 24.1.23 Iterating through files chunk by chunk . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1021 24.1.24 Specifying the parser specifying an index, each chunk used to have an independently generated index from 0 to n-1. They are now given instead a progressive index, starting from 0 for the first chunk, from n for the second, and 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 (GH3076) • You can now select

single DataFrame . . . . . . . . . . . . . . . . . . . . . . 1017 24.1.23 Iterating through files chunk by chunk . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1017 24.1.24 Specifying the parser specifying an index, each chunk used to have an independently generated index from 0 to n-1. They are now given instead a progressive index, starting from 0 for the first chunk, from n for the second, and 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 (GH3076) • You can now select

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 (GH3076) • You can now select numexpr: for accelerating certain numerical operations. numexpr uses multiple cores as well as smart chunk- ing and caching to achieve large speedups. • bottleneck: for accelerating certain types of nan evaluations of SQL vs HDF5 6.9.1 CSV The CSV docs read_csv in action appending to a csv Reading a csv chunk-by-chunk Reading the first few lines of a frame Reading a file that is compressed but not by gzip/bz2

single DataFrame . . . . . . . . . . . . . . . . . . . . . . 1056 24.1.23 Iterating through files chunk by chunk . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1056 24.1.24 Specifying the parser specifying an index, each chunk used to have an independently generated index from 0 to n-1. They are now given instead a progressive index, starting from 0 for the first chunk, from n for the second, and 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 (GH3076) • You can now select

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 (GH3076) • You can now select numexpr: for accelerating certain numerical operations. numexpr uses multiple cores as well as smart chunk- ing and caching to achieve large speedups. • bottleneck: for accelerating certain types of nan evaluations The CSV docs read_csv in action appending to a csv Reading a csv chunk-by-chunk Reading only certain rows of a csv chunk-by-chunk Reading the first few lines of a frame Reading a file that is compressed

. . . . . . . . . . . . . . . . . . . . . . . . . . . . 921 25.1.21 Iterating through files chunk by chunk . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 922 25.1.22 Specifying the parser engine specifying an index, each chunk used to have an independently generated index from 0 to n-1. They are now given instead a progressive index, starting from 0 for the first chunk, from n for the second, and 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 (GH3076) • You can now select