toolkit, Release 0.25.0 test([extra_args]) pandas.test pandas.test(extra_args=None) {{ header }} 6.3 Series 6.3.1 Constructor Series([data, index, dtype, name, copy, ...]) One-dimensional ndarray with contains boxed values. at Access a single value for a row/column label pair. Continued on next page 6.3. Series 971 pandas: powerful Python data analysis toolkit, Release 0.25.0 Table 26 – continued from pd.Series(pd.Categorical(['a', 'b', 'a'])) >>> ser.array [a, b, a] Categories (2, object): [a, b] 6.3. Series 973 pandas: powerful Python data analysis toolkit, Release 0.25.0 pandas.Series.asobject

toolkit, Release 0.25.1 test([extra_args]) pandas.test pandas.test(extra_args=None) {{ header }} 6.3 Series 6.3.1 Constructor Series([data, index, dtype, name, copy, ...]) One-dimensional ndarray with contains boxed values. at Access a single value for a row/column label pair. Continued on next page 6.3. Series 971 pandas: powerful Python data analysis toolkit, Release 0.25.1 Table 26 – continued from pd.Series(pd.Categorical(['a', 'b', 'a'])) >>> ser.array [a, b, a] Categories (2, object): [a, b] 6.3. Series 973 pandas: powerful Python data analysis toolkit, Release 0.25.1 pandas.Series.asobject

toolkit, Release 0.24.0 6.2.8 Testing test([extra_args]) pandas.test pandas.test(extra_args=None) 6.3 Series 6.3.1 Constructor Series([data, index, dtype, name, copy, ...]) One-dimensional ndarray with depending on the dtype. pandas.Series.T Series.T Return the transpose, which is by definition self. 6.3. Series 995 pandas: powerful Python data analysis toolkit, Release 0.24.0 pandas.Series.array Series >>> df.at[4, 'B'] = 10 >>> df.at[4, 'B'] 10 Get value within a Series >>> df.loc[5].at['B'] 4 6.3. Series 997 pandas: powerful Python data analysis toolkit, Release 0.24.0 pandas.Series.axes Series

mohanraj + • tadeja + • tamuhey + • thatneat • topper-123 • willweil + • yehia67 + • yhaque1213 + 6.3 Version 0.24 6.3.1 Whats new in 0.24.2 (March 12, 2019) Warning: The 0.24.x series of releases will regression in creating a period-dtype array from a read-only NumPy array of period objects. (GH25403) 6.3. Version 0.24 2413 pandas: powerful Python data analysis toolkit, Release 1.0.0 • Fixed regression • Tao He + • Thomas A Caswell • Tom Augspurger • Vibhu Agarwal + • William Ayd • Zach Angell 6.3. Version 0.24 2415 pandas: powerful Python data analysis toolkit, Release 1.0.0 6.3.2 Whats new in

and the raw ndarray(s) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 6.3 Flexible binary operations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . object dtype. If there are only floats and integers, the resulting array will be of float dtype. 6.3 Flexible binary operations With binary operations between pandas data structures, there are two key 666027 -0.146436 -1.019284 d NaN 0.577285 -0.109474 In [19]: df.sub(column, axis=’index’) Out[19]: 6.3. Flexible binary operations 45 pandas: powerful Python data analysis toolkit, Release 0.7.1 one three

and the raw ndarray(s) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 6.3 Flexible binary operations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . object dtype. If there are only floats and integers, the resulting array will be of float dtype. 6.3 Flexible binary operations With binary operations between pandas data structures, there are two key 796433 0.145898 2.047038 d NaN 1.043293 -0.496764 In [19]: df.sub(column, axis=’index’) Out[19]: 6.3. Flexible binary operations 45 pandas: powerful Python data analysis toolkit, Release 0.7.2 one three

and the raw ndarray(s) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 6.3 Flexible binary operations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . object dtype. If there are only floats and integers, the resulting array will be of float dtype. 6.3 Flexible binary operations With binary operations between pandas data structures, there are two key 050451 -3.231782 0.182734 d NaN -2.074872 -0.669178 In [19]: df.sub(column, axis=’index’) Out[19]: 6.3. Flexible binary operations 51 pandas: powerful Python data analysis toolkit, Release 0.7.3 one three

What’s new in 0.25.0 (July 18, 2019) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2422 6.3 Version 0.24 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . mohanraj + • tadeja + • tamuhey + • thatneat • topper-123 • willweil + • yehia67 + • yhaque1213 + 6.3 Version 0.24 6.3.1 Whats new in 0.24.2 (March 12, 2019) Warning: The 0.24.x series of releases will returning the size (GH25580) • Bug in resampling raising for nullable integer-dtype columns (GH25580) 6.3. Version 0.24 2465 pandas: powerful Python data analysis toolkit, Release 1.0.3 Contributors A total

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 6.3 MultiIndexing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . to take the complement of a boolean array, see here Efficiently creating columns using applymap 6.3 MultiIndexing The multindexing docs. Creating a multi-index from a labeled frame 101 pandas: powerful

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 251 6.3 Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2013-01-06 -0.673690 0.113648 -1.478427 0.524988 2013-01-05 -0.424972 0.567020 0.276232 -1.087401 6.3 Selection Note: While standard Python / Numpy expressions for selecting and setting are intuitive included In [28]: df.loc['20130102':'20130104',['A','B']] Out[28]: A B 2013-01-02 1.212112 -0.173215 6.3. Selection 253 pandas: powerful Python data analysis toolkit, Release 0.17.0 2013-01-03 -0.861849