pandas: powerful Python data analysis toolkit - 0.17.0. . . . . . . . . . . . . . . . . . . . . . . . . . 246 6 10 Minutes to pandas 249 6.1 Object Creation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 249 . . . . . . . . . . . . . . . . . . . . . . . . . . . 648 22 Categorical Data 649 22.1 Object Creation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 649 duration of the timedelta in seconds. See here • Period and PeriodIndex can handle multiplied freq like 3D, which corresponding to 3 days span. See here • Development installed versions of pandas will now0 码力 | 1787 页 | 10.76 MB | 1 年前3
pandas: powerful Python data analysis toolkit - 0.19.1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 325 2.2.7 Running the test suite . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 325 2.3 Dependencies . . . . . . . . 337 Running the test suite . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 338 Running the performance test suite . . . . . . . . . . . . . . . . . . . Tests . . . . . . . . . . . . . . . . . . . . . . . . . . . 339 Running the vbench performance test suite (phasing out) . . . . . . . . . . . . . . . . . . . . 340 3.5.3 Documenting your code . . . . .0 码力 | 1943 页 | 12.06 MB | 1 年前3
pandas: powerful Python data analysis toolkit - 0.19.0. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 323 2.2.7 Running the test suite . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 323 2.3 Dependencies . . . . . . . . 335 Running the test suite . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 336 Running the performance test suite . . . . . . . . . . . . . . . . . . . Tests . . . . . . . . . . . . . . . . . . . . . . . . . . . 337 Running the vbench performance test suite (phasing out) . . . . . . . . . . . . . . . . . . . . 338 3.5.3 Documenting your code . . . . .0 码力 | 1937 页 | 12.03 MB | 1 年前3
pandas: powerful Python data analysis toolkit - 0.20.2. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 371 2.2.6 Running the test suite . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 371 2.3 Dependencies . . . . 386 3.5.4 Running the test suite . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 387 3.5.5 Running the performance test suite . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 394 5 10 Minutes to pandas 397 5.1 Object Creation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3970 码力 | 1907 页 | 7.83 MB | 1 年前3
pandas: powerful Python data analysis toolkit - 0.20.3. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 373 2.2.6 Running the test suite . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 373 2.3 Dependencies . . 388 3.5.4 Running the test suite . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 389 viii 3.5.5 Running the performance test suite . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 396 5 10 Minutes to pandas 399 5.1 Object Creation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3990 码力 | 2045 页 | 9.18 MB | 1 年前3
pandas: powerful Python data analysis toolkit - 0.21.1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 401 2.2.6 Running the test suite . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 401 2.3 Dependencies . . . . 416 3.5.4 Running the test suite . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 417 3.5.5 Running the performance test suite . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 425 5 10 Minutes to pandas 427 5.1 Object Creation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4270 码力 | 2207 页 | 8.59 MB | 1 年前3
pandas: powerful Python data analysis toolkit - 0.12. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 2.7 Running the test suite . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 3 Frequently . . . . . . . . . . . . . . . . . . . . . . . . . . . 78 5 10 Minutes to Pandas 81 5.1 Object Creation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 0.12.0 dtype(’O’) 1.2.7 API changes • Added to_series() method to indicies, to facilitate the creation of indexers (GH3275) • HDFStore – added the method select_column to select a single column from0 码力 | 657 页 | 3.58 MB | 1 年前3
pandas: powerful Python data analysis toolkit - 0.24.0Improved performance of Period constructor, additionally benefitting PeriodArray and PeriodIndex creation (GH24084, GH24118) • Improved performance of tz-aware DatetimeArray binary operations (GH24491) change, where it now correctly works per group (GH21200, GH21235). • Bug preventing hash table creation with very large number (2^32) of rows (GH22805) • Bug in groupby when grouping on categorical causes development environment if you wish to create a pandas development environment. 2.4 Running the test suite pandas is equipped with an exhaustive set of unit tests, covering about 97% of the code base as of0 码力 | 2973 页 | 9.90 MB | 1 年前3
pandas: powerful Python data analysis toolkit - 0.13.1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100 2.7 Running the test suite . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100 3 Frequently . . . . . . . . . . . . . . . . . . . . . . . . . . 110 5 10 Minutes to Pandas 113 5.1 Object Creation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113 index type, Float64Index. This will be automatically created when passing floating values in index creation. This enables a pure label-based slicing paradigm that makes [],ix,loc for scalar indexing and slicing0 码力 | 1219 页 | 4.81 MB | 1 年前3
pandas: powerful Python data analysis toolkit - 0.14.0. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128 2.7 Running the test suite . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128 3 Frequently . . . . . . . . . . . . . . . . . . . . . . . . . . 138 5 10 Minutes to Pandas 141 5.1 Object Creation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141 251 250 D1 253 252 255 254 [24 rows x 4 columns] You can use a pd.IndexSlice to shortcut the creation of these slices In [55]: idx = pd.IndexSlice In [56]: df.loc[idx[:,:,[’C1’,’C3’]],idx[:,’foo’]]0 码力 | 1349 页 | 7.67 MB | 1 年前3
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