index modules | next | Scrapy 0.16.5 documentation » Scrapy 0.16 documentation This documentation contains everything you need to know about Scrapy. Getting help Having trouble? We’d like to help! (commit bdf61c4 [https://github.com/scrapy/scrapy/commit/bdf61c4]) Merge branch ‘0.16’ of github.com:scrapy/scrapy into 0.16 (commit d5087b0 [https://github.com/scrapy/scrapy/commit/d5087b0]) Fixed docs 1 (released 2012-10-26) fixed LogStats extension, which got broken after a wrong merge before the 0.16 release (commit 8c780fd [https://github.com/scrapy/scrapy/commit/8c780fd]) better backwards compatibility

• doc: removed broken proxyhub link from FAQ (commit bdf61c4) • Merge branch ‘0.16’ of github.com:scrapy/scrapy into 0.16 (commit d5087b0) • Fixed docs typo in SpiderOpenCloseLogging example (commit 7184094) (released 2012-10-26) • fixed LogStats extension, which got broken after a wrong merge before the 0.16 release (commit 8c780fd) • better backwards compatibility for scrapy.conf.settings (commit 3403089)

5.7 -0.54 3 5.91 -0.33 4 6.4 0.16 5 6.8 0.56 6 7.05 0.81 7 8.9 -0.01 8 8.7 -0.21 9 9 0.09 10 9.05 0.14 GBDT算法 23 ?3 ? = x<3.5 0.15 -0.22 ?4 ? = x<4.5 -0.16 0.11 ?5 ? = x<6.5 0.07 -0.11

locations specified are combined into a single MultiDict [https://werkzeug.palletsprojects.com/en/0.16.x/datastructures/#werkzeug.datastructures.MultiDict]. The last location listed takes precedence in errors=errors) Note: Custom Exceptions must have HTTPException [https://werkzeug.palletsprojects.com/en/0.16.x/exceptions/#werkzeug.exceptions.HTTPException] as the base Exception. Intermediate Usage This

Type Info K8s Cluster Capacity 12 nodes in 3 zones, 16 vCPU * 64 Gi MEM Knative Version Knative 0.16, 0.17, 0.18 Istio Version 1.5, 1.6, 1.7 Istio scalability optimization during Knative Service provisioning

(writing and reading back in with to_parquet() / read_parquet()) is supported starting with pyarrow >= 0.16 (GH20612). • to_parquet() now appropriately handles the schema argument for user defined schemas is_datetimetz (GH23917) • Ability to read pickles containing Categorical instances created with pre-0.16 version of pandas has been removed (GH27538) • Removed pandas.tseries.plotting.tsplot (GH18627) aggregated data In [120]: df = pd.DataFrame({'code': ['foo', 'bar', 'baz'] * 2, .....: 'data': [0.16, -0.21, 0.33, 0.45, -0.59, 0.62], .....: 'flag': [False, True] * 3}) .....: In [121]: code_groups

2015) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2808 5.11 Version 0.16 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2838 aggregated data In [120]: df = pd.DataFrame({'code': ['foo', 'bar', 'baz'] * 2, .....: 'data': [0.16, -0.21, 0.33, 0.45, -0.59, 0.62], .....: 'flag': [False, True] * 3}) .....: In [121]: code_groups index] In [124]: sorted_df Out[124]: code data flag 1 bar -0.21 True 4 bar -0.59 False 0 foo 0.16 False 3 foo 0.45 True 2 baz 0.33 False 5 baz 0.62 True Create multiple aggregated columns In

2015) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2803 5.11 Version 0.16 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2833 aggregated data In [120]: df = pd.DataFrame({'code': ['foo', 'bar', 'baz'] * 2, .....: 'data': [0.16, -0.21, 0.33, 0.45, -0.59, 0.62], .....: 'flag': [False, True] * 3}) .....: In [121]: code_groups index] In [124]: sorted_df Out[124]: code data flag 1 bar -0.21 True 4 bar -0.59 False 0 foo 0.16 False 3 foo 0.45 True 2 baz 0.33 False 5 baz 0.62 True Create multiple aggregated columns In

(October 9, 2015) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2946 5.12 Version 0.16 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2976 aggregated data In [120]: df = pd.DataFrame({'code': ['foo', 'bar', 'baz'] * 2, .....: 'data': [0.16, -0.21, 0.33, 0.45, -0.59, 0.62], .....: 'flag': [False, True] * 3}) .....: In [121]: code_groups index] In [124]: sorted_df Out[124]: code data flag 1 bar -0.21 True 4 bar -0.59 False 0 foo 0.16 False 3 foo 0.45 True 2 baz 0.33 False 5 baz 0.62 True Create multiple aggregated columns In

(October 9, 2015) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2944 5.12 Version 0.16 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2974 aggregated data In [120]: df = pd.DataFrame({'code': ['foo', 'bar', 'baz'] * 2, .....: 'data': [0.16, -0.21, 0.33, 0.45, -0.59, 0.62], .....: 'flag': [False, True] * 3}) .....: In [121]: code_groups index] In [124]: sorted_df Out[124]: code data flag 1 bar -0.21 True 4 bar -0.59 False 0 foo 0.16 False 3 foo 0.45 True 2 baz 0.33 False 5 baz 0.62 True Create multiple aggregated columns In