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 so on, so that, when concatenated argument that is passed to the underlying parser library. You can use this to read non-ascii encoded web pages (GH7323). • read_excel now supports reading from URLs in the same way that read_csv does. (GH6809) builds, which only process portions you have changed, will be faster. Open the following file in a web browser to see the full documentation you just built: pandas/docs/build/html/index.html And you’ll

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 so on, so that, when concatenated argument that is passed to the underlying parser library. You can use this to read non-ascii encoded web pages (GH7323). • read_excel now supports reading from URLs in the same way that read_csv does. (GH6809) builds, which only process portions you have changed, will be faster. Open the following file in a web browser to see the full documentation you just built: pandas/docs/build/html/index.html And you’ll

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 so on, so that, when concatenated argument that is passed to the underlying parser library. You can use this to read non-ascii encoded web pages (GH7323). • read_excel now supports reading from URLs in the same way that read_csv does. (GH6809) builds, which only process portions you have changed, will be faster. Open the following file in a web browser to see the full documentation you just built: pandas/docs/build/html/index.html And you’ll

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 so on, so that, when concatenated argument that is passed to the underlying parser library. You can use this to read non-ascii encoded web pages (GH7323). • read_excel now supports reading from URLs in the same way that read_csv does. (GH6809) builds, which only process portions you have changed, will be faster. Open the following file in a web browser to see the full documentation you just built: pandas/docs/build/html/index.html And you’ll

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 so on, so that, when concatenated argument that is passed to the underlying parser library. You can use this to read non-ascii encoded web pages (GH7323). • read_excel now supports reading from URLs in the same way that read_csv does. (GH6809) builds, which only process portions you have changed, will be faster. Open the following file in a web browser to see the full documentation you just built: 3.4. Contributing to the documentation 411

tables on the web are not big enough for the parsing algorithm runtime to matter. It is more likely that the bottleneck will be in the process of reading the raw text from the URL over the web, i.e., IO (input-output) in rdr: do_something(chunk) The specification for the xport file format is available from the SAS web site. No official documentation is available for the SAS7BDAT format. 4.1. IO tools (text, CSV, HDF5 DataFrame({'host': ['other', 'other', 'that', 'this', 'this'], .....: 'service': ['mail', 'web', 'mail', 'mail', 'web'], .....: 'no': [1, 2, 1, 2, 1]}).set_index(['host', 'service']) .....: In [140]: mask

tables on the web are not big enough for the parsing algorithm runtime to matter. It is more likely that the bottleneck will be in the process of reading the raw text from the URL over the web, i.e., IO (input-output) in rdr: do_something(chunk) The specification for the xport file format is available from the SAS web site. No official documentation is available for the SAS7BDAT format. 4.1. IO tools (text, CSV, HDF5 DataFrame({'host': ['other', 'other', 'that', 'this', 'this'], .....: 'service': ['mail', 'web', 'mail', 'mail', 'web'], .....: 'no': [1, 2, 1, 2, 1]}).set_index(['host', 'service']) .....: In [140]: mask

tables on the web are not big enough for the parsing algorithm runtime to matter. It is more likely that the bottleneck will be in the process of reading the raw text from the URL over the web, i.e., IO (input-output) in rdr: do_something(chunk) The specification for the xport file format is available from the SAS web site. No official documentation is available for the SAS7BDAT format. 4.1.15 Other file formats DataFrame({'host': ['other', 'other', 'that', 'this', 'this'], .....: 'service': ['mail', 'web', 'mail', 'mail', 'web'], .....: 'no': [1, 2, 1, 2, 1]}).set_index(['host', 'service']) .....: In [150]: mask

tables on the web are not big enough for the parsing algorithm runtime to matter. It is more likely that the bottleneck will be in the process of reading the raw text from the URL over the web, i.e., IO (input-output) Rowling .....: 2005 .....: 29.99 .....: .....: .....: .....: Erik T. Ray .....: 2003 Everyday Italian Giada De Laurentiis 2005 30.00 1 children Harry Potter J K. Rowling 2005 29.99 2 web Learning XML Erik T. Ray 2003 39.95 Read a URL with no options: 322 Chapter 2. User Guide pandas:

tables on the web are not big enough for the parsing algorithm runtime to matter. It is more likely that the bottleneck will be in the process of reading the raw text from the URL over the web, i.e., IO (input-output) Rowling .....: 2005 .....: 29.99 .....: .....: .....: .....: Erik T. Ray .....: 2003 Everyday Italian Giada De Laurentiis 2005 30.00 1 children Harry Potter J K. Rowling 2005 29.99 2 web Learning XML Erik T. Ray 2003 39.95 Read a URL with no options: In [321]: df = pd.read_xml("https://www