>= 4.0.2 and Hypothesis >= 3.58, then run: >>> pd.test() running: pytest --skip-slow --skip-network C:\Users\TP\Anaconda3\envs\py36\lib\site- �→packages\pandas ============================= test session ============================= platform win32 -- Python 3.6.2, pytest-3.6.0, py-1.4.34, pluggy-0.4.0 rootdir: C:\Users\TP\Documents\Python\pandasdev\pandas, inifile: setup.cfg collected 12145 items / 3 skipped orientation of the data set when writing functions; axes are considered more or less equivalent (except when C- or Fortran-contiguousness matters for performance). In pandas, the axes are intended to lend more semantic

standards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 385 3.5.1.1 C (cpplint) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 385 3.5.1.2 . . . . . . . . . . . . . . . . . . . . 1105 26 Enhancing Performance 1107 26.1 Cython (Writing C extensions for pandas) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1107 26.1.1 Pure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1146 31.2.1 Slicing with R’s c . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1146 31.2.2 aggregate

standards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 383 3.5.1.1 C (cpplint) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 383 3.5.1.2 . . . . . . . . . . . . . . . . . . . . 1103 26 Enhancing Performance 1105 26.1 Cython (Writing C extensions for pandas) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1105 26.1.1 Pure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1144 31.2.1 Slicing with R’s c . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1144 31.2.2 aggregate

. . . . . . . . . . . . . . . . . . . . 1008 27 Enhancing Performance 1009 27.1 Cython (Writing C extensions for pandas) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1009 27.1.1 Pure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1054 32.2.1 Slicing with R’s c . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1054 32.2.2 aggregate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1090 34.7.2 Data Interop . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1090 xxi

. . . . . . . . . . . . . . . . . . . . 1006 27 Enhancing Performance 1007 27.1 Cython (Writing C extensions for pandas) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1007 27.1.1 Pure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1052 32.2.1 Slicing with R’s c . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1052 32.2.2 aggregate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1088 34.7.2 Data Interop . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1088 35 API

development environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 407 3.3.4.1 Installing a C Complier . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 408 3.3.4.2 Creating a Python standards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 412 3.5.1.1 C (cpplint) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 412 ix 3.5 . . . . . . . . . . . . . . . . . 1141 xxii 26 Enhancing Performance 1143 26.1 Cython (Writing C extensions for pandas) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1143 26.1.1 Pure

. . . . . . . . . . . . . . . . . . . . . 858 26 Enhancing Performance 861 26.1 Cython (Writing C extensions for pandas) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 861 26.2 Using : date_range('20130101',periods=3,tz='US/Eastern'), ...: 'C' : date_range('20130101',periods=3,tz='CET')}) ...: In [2]: df Out[2]: A B C 0 2013-01-01 2013-01-01 00:00:00-05:00 2013-01-01 00:00:00+01:00 2013-01-03 00:00:00+01:00 In [3]: df.dtypes Out[3]: A datetime64[ns] B datetime64[ns, US/Eastern] C datetime64[ns, CET] dtype: object In [4]: df.B Out[4]: 0 2013-01-01 00:00:00-05:00 1 2013-01-02 00:00:00-05:00

column. In [12]: df = pd.DataFrame([{'var1': 'a,b,c', 'var2': 1}, ....: {'var1': 'd,e,f', 'var2': 2}]) ....: In [13]: df Out[13]: var1 var2 0 a,b,c 1 1 d,e,f 2 [2 rows x 2 columns] Creating a long In [14]: df.assign(var1=df.var1.str.split(',')).explode('var1') Out[14]: var1 var2 0 a 1 0 b 1 0 c 1 1 d 2 1 e 2 1 f 2 [6 rows x 2 columns] 1.1.7 Other enhancements • DataFrame.plot() keywords index. In [46]: s = pd.Series(list('abc'), index=ii) In [47]: s Out[47]: (0, 4] a (1, 5] b (5, 8] c Length: 3, dtype: object Selecting from a Series or DataFrame using [] (__getitem__) or loc now only

----------------------- RuntimeError Traceback (most recent call last) c97> in ----> 1 mi.levels[0].name = "new name" /pandas/pandas/core/indexes/base.py in name(self (GH17304) pandas 0.25.x >>> df = pd.DataFrame({"int_col": [1, 2, 3], ... "text_col": ["a", "b", "c"], ... "float_col": [0.0, 0.1, 0.2]}) >>> df.info(verbose=True) bytes pandas 1.0.0 In [34]: df = pd.DataFrame({"int_col": [1, 2, 3], ....: "text_col": ["a", "b", "c"], ....: "float_col": [0.0, 0.1, 0.2]}) ....: In [35]: df.info(verbose=True)

0 码力 | 3015 页 | 10.78 MB | 1 年前

3

column. In [12]: df = pd.DataFrame([{'var1': 'a,b,c', 'var2': 1}, ....: {'var1': 'd,e,f', 'var2': 2}]) ....: In [13]: df Out[13]: var1 var2 0 a,b,c 1 1 d,e,f 2 [2 rows x 2 columns] Creating a long In [14]: df.assign(var1=df.var1.str.split(',')).explode('var1') Out[14]: var1 var2 0 a 1 0 b 1 0 c 1 1 d 2 1 e 2 1 f 2 [6 rows x 2 columns] 1.1.7 Other enhancements • DataFrame.plot() keywords index. In [46]: s = pd.Series(list('abc'), index=ii) In [47]: s Out[47]: (0, 4] a (1, 5] b (5, 8] c Length: 3, dtype: object Selecting from a Series or DataFrame using [] (__getitem__) or loc now only