Development Team # All rights reserved. # # Distributed under the terms of the BSD Simplified License. # # The full license is in the LICENSE file, distributed with this software. #--------------------- section. For instance, you can copy the following text to the clipboard (CTRL-C on many operating systems): A B C x 1 4 p y 2 5 q z 3 6 r And then import the data directly to a DataFrame by calling: clipdf below functionality. On Windows, doing this is quite an ordeal at the moment, but users on Unix-like systems should find it quite easy. As a general rule, I would recommend using the latest revision of rpy2

Development Team # All rights reserved. # # Distributed under the terms of the BSD Simplified License. # # The full license is in the LICENSE file, distributed with this software. #--------------------- section. For instance, you can copy the following text to the clipboard (CTRL-C on many operating systems): A B C x 1 4 p y 2 5 q z 3 6 r And then import the data directly to a DataFrame by calling: clipdf below functionality. On Windows, doing this is quite an ordeal at the moment, but users on Unix-like systems should find it quite easy. As a general rule, I would recommend using the latest revision of rpy2

Development Team # All rights reserved. # # Distributed under the terms of the BSD Simplified License. # # The full license is in the LICENSE file, distributed with this software. #--------------------- section. For instance, you can copy the following text to the clipboard (CTRL-C on many operating systems): A B C x 1 4 p y 2 5 q z 3 6 r And then import the data directly to a DataFrame by calling: clipdf below functionality. On Windows, doing this is quite an ordeal at the moment, but users on Unix-like systems should find it quite easy. rpy2 evolves in time and the current interface is designed for the 2.2

pandas: powerful Python data analysis toolkit, Release 0.12.0 ======= License ======= pandas is distributed under a 3-clause ("Simplified" or "New") BSD license. Parts of NumPy, SciPy, numpydoc, bottleneck Development Team # All rights reserved. # # Distributed under the terms of the BSD Simplified License. # # The full license is in the LICENSE file, distributed with this software. #--------------------- 5]’], dtype=object) qcut computes sample quantiles. For example, we could slice up some normally distributed data into equal-size 144 Chapter 8. Essential Basic Functionality pandas: powerful Python data

pandas: powerful Python data analysis toolkit, Release 0.13.1 ======= License ======= pandas is distributed under a 3-clause ("Simplified" or "New") BSD license. Parts of NumPy, SciPy, numpydoc, bottleneck Development Team # All rights reserved. # # Distributed under the terms of the BSD Simplified License. # # The full license is in the LICENSE file, distributed with this software. #--------------------- dtype=object) Length: 20 qcut computes sample quantiles. For example, we could slice up some normally distributed data into equal-size quartiles like so: In [100]: arr = np.random.randn(30) In [101]: factor

rolling mean mode (mean=True) so that the calculated weighted means (e.g. ‘triang’, ‘gaussian’) are distributed about the same means as those calculated without weighting (i.e. ‘boxcar’). See the note on normalization pandas: powerful Python data analysis toolkit, Release 0.15.2 ======= License ======= pandas is distributed under a 3-clause ("Simplified" or "New") BSD license. Parts of NumPy, SciPy, numpydoc, bottleneck Development Team # All rights reserved. # # Distributed under the terms of the BSD Simplified License. # # The full license is in the LICENSE file, distributed with this software. #---------------------

rolling mean mode (mean=True) so that the calculated weighted means (e.g. ‘triang’, ‘gaussian’) are distributed about the same means as those calculated without weighting (i.e. ‘boxcar’). See the note on normalization pandas: powerful Python data analysis toolkit, Release 0.15.1 ======= License ======= pandas is distributed under a 3-clause ("Simplified" or "New") BSD license. Parts of NumPy, SciPy, numpydoc, bottleneck Development Team # All rights reserved. # # Distributed under the terms of the BSD Simplified License. # # The full license is in the LICENSE file, distributed with this software. #---------------------

pandas: powerful Python data analysis toolkit, Release 0.14.0 ======= License ======= pandas is distributed under a 3-clause ("Simplified" or "New") BSD license. Parts of NumPy, SciPy, numpydoc, bottleneck Development Team # All rights reserved. # # Distributed under the terms of the BSD Simplified License. # # The full license is in the LICENSE file, distributed with this software. #--------------------- dtype=object) Length: 20 qcut computes sample quantiles. For example, we could slice up some normally distributed data into equal-size quartiles like so: 9.5. Descriptive statistics 215 pandas: powerful Python

(GH11302) • Series.sort_index() now correctly handles the inplace option (GH11402) • Incorrectly distributed .c file in the build on PyPi when reading a csv of floats and passing na_values= would rolling mean mode (mean=True) so that the calculated weighted means (e.g. ‘triang’, ‘gaussian’) are distributed about the same means as those calculated without weighting (i.e. ‘boxcar’). See the note on normalization in contributing, please visit the project website. License ======= License ======= pandas is distributed under a 3-clause ("Simplified" or "New") BSD 348 Chapter 5. Package overview pandas: powerful

(GH11302) • Series.sort_index() now correctly handles the inplace option (GH11402) • Incorrectly distributed .c file in the build on PyPi when reading a csv of floats and passing na_values= would rolling mean mode (mean=True) so that the calculated weighted means (e.g. ‘triang’, ‘gaussian’) are distributed about the same means as those calculated without weighting (i.e. ‘boxcar’). See the note on normalization in contributing, please visit the project website. License ======= License ======= pandas is distributed under a 3-clause ("Simplified" or "New") BSD 350 Chapter 5. Package overview pandas: powerful