index modules | next | Scrapy 0.20.2 documentation » Scrapy 0.20 documentation This documentation contains everything you need to know about Scrapy. Getting help Having trouble? We’d like to help! support? Scrapy is supported under Python 2.7 only. Python 2.6 support was dropped starting at Scrapy 0.20. Does Scrapy work with Python 3? No, but there are plans to support Python 3.3+. At the moment,

support? Scrapy is supported under Python 2.7 only. Python 2.6 support was dropped starting at Scrapy 0.20. 5.1.3 Does Scrapy work with Python 3? No, but there are plans to support Python 3.3+. At the moment

improvements of note in each release. 1.1 v0.20.3 (July 7, 2017) This is a minor bug-fix release in the 0.20.x series and includes some small regression fixes and bug fixes. We recommend that all users upgrade downstream packages’ tests suites (GH16680) 1.1.1.1 Conversion • Bug in pickle compat prior to the v0.20.x series, when UTC is a timezone in a Series/DataFrame/Index (GH16608) • Bug in Series construction with categorical data (GH16793) 1.2 v0.20.2 (June 4, 2017) This is a minor bug-fix release in the 0.20.x series and includes some small regression fixes, bug fixes and performance improvements. We recommend

improvements of note in each release. 1.1 v0.20.2 (June 4, 2017) This is a minor bug-fix release in the 0.20.x series and includes some small regression fixes, bug fixes and performance improvements. We recommend grouping objects as the keys. For example, consider the following DataFrame: Note: New in version 0.20. A string passed to groupby may refer to either a column or an index level. If a string matches both doo -1.220674 baz bee -0.608004 foo bop 1.023898 qux bop 0.000895 dtype: float64 New in version 0.20. Index level names may be supplied as keys. In [37]: s.groupby(['first', 'second']).sum() Out[37]:

year indiv 1977 A 1.20 0.60 B 1.50 0.50 C 1.70 0.80 1978 A 0.20 0.06 B 0.70 0.20 C 0.80 0.30 D 0.90 0.50 E 1.40 0.90 1979 C 0.20 0.15 D 0.14 0.05 E 0.50 0.15 F 1.20 0.50 G 3.40 1.90 H 5.40 40 2.70 I 6.40 1.20 In [598]: df.ix[1978] Out[598]: zit xit indiv A 0.2 0.06 B 0.7 0.20 C 0.8 0.30 D 0.9 0.50 E 1.4 0.90 15.2.3 Automatically “sniffing” the delimiter read_csv is capable of inferring zit xit 0 1977 A 1.2 0.60 1 1977 B 1.5 0.50 2 1977 C 1.7 0.80 3 1978 A 0.2 0.06 4 1978 B 0.7 0.20 5 1978 C 0.8 0.30 6 1978 D 0.9 0.50 By specifiying a chunksize to read_csv or read_table, the return

year indiv 1977 A 1.20 0.60 B 1.50 0.50 C 1.70 0.80 1978 A 0.20 0.06 B 0.70 0.20 C 0.80 0.30 D 0.90 0.50 E 1.40 0.90 1979 C 0.20 0.15 D 0.14 0.05 E 0.50 0.15 F 1.20 0.50 G 3.40 1.90 H 5.40 40 2.70 I 6.40 1.20 In [598]: df.ix[1978] Out[598]: zit xit indiv A 0.2 0.06 B 0.7 0.20 C 0.8 0.30 D 0.9 0.50 E 1.4 0.90 15.2.3 Automatically “sniffing” the delimiter read_csv is capable of inferring zit xit 0 1977 A 1.2 0.60 1 1977 B 1.5 0.50 2 1977 C 1.7 0.80 3 1978 A 0.2 0.06 4 1978 B 0.7 0.20 5 1978 C 0.8 0.30 6 1978 D 0.9 0.50 By specifiying a chunksize to read_csv or read_table, the return

year indiv 1977 A 1.20 0.60 B 1.50 0.50 C 1.70 0.80 1978 A 0.20 0.06 B 0.70 0.20 C 0.80 0.30 D 0.90 0.50 E 1.40 0.90 1979 C 0.20 0.15 D 0.14 0.05 E 0.50 0.15 F 1.20 0.50 G 3.40 1.90 H 5.40 \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\Out[174]: ˓→ zit xit indiv A 0.2 0.06 B 0.7 0.20 C 0.8 0.30 D 0.9 0.50 E 1.4 0.90 4.1. IO tools (text, CSV, HDF5, . . . ) 243 pandas: powerful read_excel('path_to_file.xls', 'Sheet1', converters={'MyInts': cfun}) Dtype specifications New in version 0.20. As an alternative to converters, the type for an entire column can be specified using the dtype

year indiv 1977 A 1.20 0.60 B 1.50 0.50 C 1.70 0.80 1978 A 0.20 0.06 B 0.70 0.20 C 0.80 0.30 D 0.90 0.50 E 1.40 0.90 1979 C 0.20 0.15 D 0.14 0.05 E 0.50 0.15 F 1.20 0.50 G 3.40 1.90 H 5.40 40 2.70 I 6.40 1.20 In [614]: df.ix[1978] Out[614]: zit xit indiv A 0.2 0.06 B 0.7 0.20 C 0.8 0.30 D 0.9 0.50 E 1.4 0.90 15.2.4 Automatically “sniffing” the delimiter read_csv is capable of inferring zit xit 0 1977 A 1.2 0.60 1 1977 B 1.5 0.50 2 1977 C 1.7 0.80 3 1978 A 0.2 0.06 4 1978 B 0.7 0.20 5 1978 C 0.8 0.30 6 1978 D 0.9 0.50 By specifiying a chunksize to read_csv or read_table, the return

year indiv 1977 A 1.20 0.60 B 1.50 0.50 C 1.70 0.80 1978 A 0.20 0.06 B 0.70 0.20 C 0.80 0.30 D 0.90 0.50 E 1.40 0.90 1979 C 0.20 0.15 D 0.14 0.05 E 0.50 0.15 F 1.20 0.50 G 3.40 1.90 H 5.40 \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\Out[174]: ˓→ zit xit indiv A 0.2 0.06 B 0.7 0.20 C 0.8 0.30 D 0.9 0.50 E 1.4 0.90 4.1. IO tools (text, CSV, HDF5, . . . ) 243 pandas: powerful read_excel('path_to_file.xls', 'Sheet1', converters={'MyInts': cfun}) Dtype specifications New in version 0.20. As an alternative to converters, the type for an entire column can be specified using the dtype

year indiv 1977 A 1.20 0.60 B 1.50 0.50 C 1.70 0.80 1978 A 0.20 0.06 B 0.70 0.20 C 0.80 0.30 D 0.90 0.50 E 1.40 0.90 1979 C 0.20 0.15 D 0.14 0.05 E 0.50 0.15 F 1.20 0.50 G 3.40 1.90 H 5.40 \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\Out[169]: ˓→ zit xit indiv A 0.2 0.06 B 0.7 0.20 C 0.8 0.30 D 0.9 0.50 E 1.4 0.90 Reading columns with a MultiIndex By specifying list of row read_excel('path_to_file.xls', 'Sheet1', converters={'MyInts': cfun}) dtype Specifications New in version 0.20. As an alternative to converters, the type for an entire column can be specified using the dtype