In [41]: dfq.to_hdf(path,’dfq’,format=’table’,data_columns=True) Use boolean expressions, with in-line function evaluation. In [42]: read_hdf(path,’dfq’, ....: where="index>Timestamp(’20130104’) & columns=[’A’ 341265 1.844536 [5 rows x 16 columns] The width of each line can be changed via ‘line_width’ (80 by default): In [39]: pd.set_option(’line_width’, 40) In [40]: wide_frame Out[40]: 0 1 2 \ 0 2.520045 pandas Linux Debian stable official Debian repository sudo apt-get install python-pandas Linux Debian & Ubuntu unstable (latest packages) NeuroDebian sudo apt-get install python-pandas Linux Ubuntu

pivot() from long to wide format. With aggregations built-in, a pivot table is created with a single command. To introduction tutorial To user guide Straight to tutorial... Multiple tables can be concatenated installation method for most users. Instructions for installing from source, PyPI, ActivePython, various Linux distributions, or a development version are also provided. Python version support Officially Python that make up the SciPy stack (IPython, NumPy, Matplotlib, ...) is with Anaconda, a cross-platform (Linux, macOS, Windows) Python distribution for data analytics and scientific computing. After running the

pivot() from long to wide format. With aggregations built-in, a pivot table is created with a single command. To introduction tutorial To user guide Straight to tutorial... Multiple tables can be concatenated installation method for most users. Instructions for installing from source, PyPI, ActivePython, various Linux distributions, or a development version are also provided. Python version support Officially Python that make up the SciPy stack (IPython, NumPy, Matplotlib, ...) is with Anaconda, a cross-platform (Linux, macOS, Windows) Python distribution for data analytics and scientific computing. After running the

pivot() from long to wide format. With aggregations built-in, a pivot table is created with a single command. To introduction tutorial To user guide Straight to tutorial... Multiple tables can be concatenated installation method for most users. Instructions for installing from source, PyPI, ActivePython, various Linux distributions, or a development version are also provided. Python version support Officially Python that make up the SciPy stack (IPython, NumPy, Matplotlib, ...) is with Anaconda, a cross-platform (Linux, macOS, Windows) Python distribution for data analytics and scientific computing. After running the

installation method for most users. Instructions for installing from source, PyPI, ActivePython, various Linux distributions, or a development version are also provided. 2.1 Python version support Officially packages that make up the SciPy stack (IPython, NumPy, Matplotlib, ) is with Anaconda, a cross-platform (Linux, Mac OS X, Windows) Python distribution for data analytics and scientific computing. After running Miniconda allows you to create a minimal self contained Python installation, and then use the Conda command to install additional packages. First you will need Conda to be installed and downloading and running

pivot() from long to wide format. With aggregations built-in, a pivot table is created with a single command. To introduction tutorial To user guide Straight to tutorial... Multiple tables can be concatenated installation method for most users. Instructions for installing from source, PyPI, ActivePython, various Linux distributions, or a development version are also provided. Python version support Officially Python that make up the SciPy stack (IPython, NumPy, Matplotlib, ...) is with Anaconda, a cross-platform (Linux, macOS, Windows) Python distribution for data analytics and scientific computing. After running the

pivot() from long to wide format. With aggregations built-in, a pivot table is created with a single command. To introduction tutorial To user guide Straight to tutorial... Multiple tables can be concatenated installation method for most users. Instructions for installing from source, PyPI, ActivePython, various Linux distributions, or a development version are also provided. Python version support Officially Python that make up the SciPy stack (IPython, NumPy, Matplotlib, ...) is with Anaconda, a cross-platform (Linux, macOS, Windows) Python distribution for data analytics and scientific computing. After running the

plot(legend=’reverse’) will now reverse the order of legend labels for most plot kinds. (GH6014) • Line plot and area plot can be stacked by stacked=True (GH6656) • Following keywords are now acceptable values (0.0, 1.0, 2.0 ...). This is intended to make bar plot be located on the same coodinates as line plot. However, bar plot may differs unexpectedly when you manually adjust the bar location or drawing None in colspec like regular python slices. It now reads from the beginning or until the end of the line when colspec contains a None (previously raised a TypeError) • Bug in cache coherence with chained

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 372 2.2.4 Installing using your Linux distribution’s package manager. . . . . . . . . . . . . . . . . . . 372 2.2.5 Installing from source Lines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1004 24.1.6.1 Ignoring line comments and empty lines . . . . . . . . . . . . . . . . . . . . . . . 1004 24.1.6.2 Comments . Normalization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1033 24.2.4 Line delimited json . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1033

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 370 2.2.4 Installing using your Linux distribution’s package manager. . . . . . . . . . . . . . . . . . . 370 2.2.5 Installing from source Lines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1000 24.1.6.1 Ignoring line comments and empty lines . . . . . . . . . . . . . . . . . . . . . . . 1000 24.1.6.2 Comments . Normalization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1028 24.2.4 Line delimited json . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1029