. . . . . . . . . . . . . . . . . . . . . 396 12.3 Setting Startup Options in python/ipython Environment . . . . . . . . . . . . . . . . . . . . . . . . 397 12.4 Frequently Used Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 585 19.7 Computing indicator / dummy variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 585 more. 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:

. . . . . . . . . . . . . . . . . . . . . 326 11.3 Setting Startup Options in python/ipython Environment . . . . . . . . . . . . . . . . . . . . . . . . 327 11.4 Frequently Used Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 497 18.7 Computing indicator / dummy variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 497 more. 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:

. . . . . . . . . . . . . . . . . . . . . 318 11.3 Setting Startup Options in python/ipython Environment . . . . . . . . . . . . . . . . . . . . . . . . 319 11.4 Frequently Used Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 487 18.7 Computing indicator / dummy variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 487 more. 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:

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 410 15.7 Computing indicator / dummy variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 410 more. 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: tool. • pandas is a dependency of statsmodels, making it an important part of the statistical computing ecosystem in Python. • pandas has been used extensively in production in financial applications

. 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 . . . . . . Window Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 684 14.2.8 Computing rolling pairwise covariances and correlations . . . . . . . . . . . . . . . . . . . 685 14.3 Aggregation

. 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 . . . . . . Window Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 682 14.2.8 Computing rolling pairwise covariances and correlations . . . . . . . . . . . . . . . . . . . 683 14.3 Aggregation

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 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 . . . . . . Window Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 615 15.2.7 Computing rolling pairwise covariances and correlations . . . . . . . . . . . . . . . . . . . 616 15.3 Aggregation

. 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 . . . . . . Window Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 613 15.2.7 Computing rolling pairwise covariances and correlations . . . . . . . . . . . . . . . . . . . 614 15.3 Aggregation

part of the Anaconda distribution, a cross platform distribution for data analysis and scientific computing. This is the recommended installation method for most users. Instructions for installing from source cross-platform (Linux, Mac OS X, Windows) Python distribution for data analytics and scientific computing. After running the installer, the user will have access to pandas and the rest of the SciPy stack 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