. 379 3.3.5 Creating a development environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 380 3.3.6 Creating a Windows development environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 572 11.3 Setting Startup Options in python/ipython Environment . . . . . . . . . . . . . . . . . . . . . . . . 573 11.4 Frequently Used Options . . . . . . pandas is built on top of NumPy and is intended to integrate well within a scientific computing environment with many other 3rd party libraries. Here are just a few of the things that pandas does well:

. 377 3.3.5 Creating a development environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 378 3.3.6 Creating a Windows development environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 570 11.3 Setting Startup Options in python/ipython Environment . . . . . . . . . . . . . . . . . . . . . . . . 571 11.4 Frequently Used Options . . . . . . pandas is built on top of NumPy and is intended to integrate well within a scientific computing environment with many other 3rd party libraries. Here are just a few of the things that pandas does well:

. . . . . . . . . . . . . . . . . . . . . 396 12.3 Setting Startup Options in python/ipython Environment . . . . . . . . . . . . . . . . . . . . . . . . 397 12.4 Frequently Used Options . . . . . . pandas is built on top of NumPy and is intended to integrate well within a scientific computing environment with many other 3rd party libraries. Here are just a few of the things that pandas does well: DataFrame.to_latex now takes a longtable keyword, which if True will return a table in a longtable environment. (GH6617) • Add option to turn off escaping in DataFrame.to_latex (GH6472) • pd.read_clipboard

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 407 3.3.4 Creating a development environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 407 3.3.4.1 Installing a C Complier . . . . . . . 408 3.3.4.2 Creating a Python Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . 408 3.3.4.3 Creating a Python Environment (pip) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 600 11.3 Setting Startup Options in python/ipython Environment . . . . . . . . . . . . . . . . . . . . . . . . 601 11.4 Frequently Used Options . . . . . .

. 331 3.3.5 Creating a development environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 332 3.3.6 Creating a Windows development environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 512 12.3 Setting Startup Options in python/ipython Environment . . . . . . . . . . . . . . . . . . . . . . . . 513 12.4 Frequently Used Options . . . . . . pandas is built on top of NumPy and is intended to integrate well within a scientific computing environment with many other 3rd party libraries. Here are just a few of the things that pandas does well:

. 329 3.3.5 Creating a development environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 330 3.3.6 Creating a Windows development environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 510 12.3 Setting Startup Options in python/ipython Environment . . . . . . . . . . . . . . . . . . . . . . . . 511 12.4 Frequently Used Options . . . . . . pandas is built on top of NumPy and is intended to integrate well within a scientific computing environment with many other 3rd party libraries. Here are just a few of the things that pandas does well:

this for you. The installer can be found here The next step is to create a new conda environment. A conda environment is like a virtualenv that allows you to specify a specific version of Python and set 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 command building from the git source tree. Further, see creating a development environment if you wish to create a pandas development environment. 2.4 Running the test suite pandas is equipped with an exhaustive

this for you. The installer can be found here The next step is to create a new conda environment. A conda environment is like a virtualenv that allows you to specify a specific version of Python and set 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 command building from the git source tree. Further, see creating a development environment if you wish to create a pandas development environment. 2.3 Running the test suite pandas is equipped with an exhaustive

. . . . . . . . . . . . . . . . . . . . . 326 11.3 Setting Startup Options in python/ipython Environment . . . . . . . . . . . . . . . . . . . . . . . . 327 11.4 Frequently Used Options . . . . . . pandas is built on top of NumPy and is intended to integrate well within a scientific computing environment with many other 3rd party libraries. Here are just a few of the things that pandas does well: DataFrame.to_latex now takes a longtable keyword, which if True will return a table in a longtable environment. (GH6617) • Add option to turn off escaping in DataFrame.to_latex (GH6472) • pd.read_clipboard

. . . . . . . . . . . . . . . . . . . . . 318 11.3 Setting Startup Options in python/ipython Environment . . . . . . . . . . . . . . . . . . . . . . . . 319 11.4 Frequently Used Options . . . . . . pandas is built on top of NumPy and is intended to integrate well within a scientific computing environment with many other 3rd party libraries. Here are just a few of the things that pandas does well: DataFrame.to_latex now takes a longtable keyword, which if True will return a table in a longtable environment. (GH6617) • Add option to turn off escaping in DataFrame.to_latex (GH6472) • pd.read_clipboard