Enhancements • HDFStore now can read native PyTables table format tables • You can pass nan_rep = 'my_nan_rep' to append, to change the default nan representation on disk (which converts to/from np.nan) conda create -n name_of_my_env python This will create a minimal environment with only Python installed in it. To put your self inside this environment run: source activate name_of_my_env On Windows the the command is: activate name_of_my_env The final step required is to install pandas. This can be done with the following command: conda install pandas To install a specific pandas version: conda install

Enhancements • HDFStore now can read native PyTables table format tables • You can pass nan_rep = 'my_nan_rep' to append, to change the default nan representation on disk (which converts to/from np.nan) conda create -n name_of_my_env python This will create a minimal environment with only Python installed in it. To put your self inside this environment run: source activate name_of_my_env On Windows the the command is: activate name_of_my_env The final step required is to install pandas. This can be done with the following command: conda install pandas To install a specific pandas version: conda install

Enhancements • HDFStore now can read native PyTables table format tables • You can pass nan_rep = ’my_nan_rep’ to append, to change the default nan representation on disk (which converts to/from np.nan) conda create -n name_of_my_env python This will create a minimal environment with only Python installed in it. To put your self inside this environment run: source activate name_of_my_env On Windows the the command is: activate name_of_my_env The final step required is to install pandas. This can be done with the following command: conda install pandas To install a specific pandas version: conda install

Enhancements • HDFStore now can read native PyTables table format tables • You can pass nan_rep = 'my_nan_rep' to append, to change the default nan representation on disk (which converts to/from np.nan) conda create -n name_of_my_env python This will create a minimal environment with only Python installed in it. To put your self inside this environment run: source activate name_of_my_env On Windows the the command is: activate name_of_my_env The final step required is to install pandas. This can be done with the following command: conda install pandas To install a specific pandas version: conda install

Enhancements • HDFStore now can read native PyTables table format tables • You can pass nan_rep = 'my_nan_rep' to append, to change the default nan representation on disk (which converts to/from np.nan) conda create -n name_of_my_env python This will create a minimal environment with only Python installed in it. To put your self inside this environment run: source activate name_of_my_env On Windows the the command is: activate name_of_my_env The final step required is to install pandas. This can be done with the following command: conda install pandas To install a specific pandas version: conda install

Python and set of libraries. Run the following commands from a terminal window: conda create -n name_of_my_env python This will create a minimal environment with only Python installed in it. To put your self Python data analysis toolkit, Release 1.5.0rc0 source activate name_of_my_env On Windows the command is: activate name_of_my_env The final step required is to install pandas. This can be done with not forget to use parentheses (). I’m interested in some basic statistics of the numerical data of my data table In [9]: df.describe() Out[9]: Age count 3.000000 mean 38.333333 std 18.230012 min

Enhancements • HDFStore now can read native PyTables table format tables • You can pass nan_rep = 'my_nan_rep' to append, to change the default nan representation on disk (which converts to/from np.nan) conda create -n name_of_my_env python This will create a minimal environment with only Python installed in it. To put your self inside this environment run: source activate name_of_my_env On Windows the the command is: activate name_of_my_env The final step required is to install pandas. This can be done with the following command: conda install pandas To install a specific pandas version: conda install

Python and set of libraries. Run the following commands from a terminal window: conda create -n name_of_my_env python This will create a minimal environment with only Python installed in it. To put your self powerful Python data analysis toolkit, Release 1.4.2 source activate name_of_my_env On Windows the command is: activate name_of_my_env The final step required is to install pandas. This can be done with not forget to use parentheses (). I’m interested in some basic statistics of the numerical data of my data table 1.4. Tutorials 17 pandas: powerful Python data analysis toolkit, Release 1.4.2 In [9]:

Python and set of libraries. Run the following commands from a terminal window: conda create -n name_of_my_env python This will create a minimal environment with only Python installed in it. To put your self powerful Python data analysis toolkit, Release 1.4.4 source activate name_of_my_env On Windows the command is: activate name_of_my_env The final step required is to install pandas. This can be done with not forget to use parentheses (). I’m interested in some basic statistics of the numerical data of my data table 1.4. Tutorials 17 pandas: powerful Python data analysis toolkit, Release 1.4.4 In [9]:

conda create -n name_of_my_env python This will create a minimal environment with only Python installed in it. To put your self inside this environment run: source activate name_of_my_env On Windows the the command is: activate name_of_my_env The final step required is to install pandas. This can be done with the following command: conda install pandas To install a specific pandas version: conda install plots the general look that you want. Setting the style is as easy as calling matplotlib. style.use(my_plot_style) before creating your plot. For example you could write matplotlib.style. use('ggplot')