development environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 380 3.3.6 Creating a Windows development environment . . . . . . . . . . . . . . . . . . . . . . . . 381 3.3.7 Making changes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 677 14.2.2 Rolling Windows . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 678 14.2.3 Time-aware vs. Resampling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 683 14.2.6 Centering Windows . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 683 14.2.7 Binary

development environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 378 3.3.6 Creating a Windows development environment . . . . . . . . . . . . . . . . . . . . . . . . 379 3.3.7 Making changes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 675 14.2.2 Rolling Windows . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 676 14.2.3 Time-aware vs. Resampling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 681 14.2.6 Centering Windows . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 681 14.2.7 Binary

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 707 14.2.2 Rolling Windows . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 708 14.2.3 Time-aware vs. Resampling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 713 14.2.6 Centering Windows . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 713 14.2.7 Binary different functions to DataFrame columns . . . . . . . . . . . . . . . . . . . . . . 719 14.4 Expanding Windows . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 720 14.4.1

development environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 330 3.3.6 Creating a Windows development environment . . . . . . . . . . . . . . . . . . . . . . . . 331 3.3.7 Making changes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 607 15.2.2 Rolling Windows . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 608 15.2.3 Time-aware vs. Resampling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 612 15.2.5 Centering Windows . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 613 15.2.6 Binary

development environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 332 3.3.6 Creating a Windows development environment . . . . . . . . . . . . . . . . . . . . . . . . 333 3.3.7 Making changes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 609 15.2.2 Rolling Windows . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 610 15.2.3 Time-aware vs. Resampling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 614 15.2.5 Centering Windows . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 615 15.2.6 Binary

. . . . . . . 679 2.15.4 Expanding windows . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 683 2.15.5 Exponentially weighted windows . . . . . . . . . . . . . . . . . . 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 the installer, the installed in it. To put your self inside this environment run: 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

. . . . . . . 679 2.15.4 Expanding windows . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 683 2.15.5 Exponentially weighted windows . . . . . . . . . . . . . . . . . . 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 the installer, the installed in it. To put your self inside this environment run: 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

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 installer, the installed in it. To put your self inside this environment run: 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 System Conda PyPI Linux Successful Failed(pyarrow==3.0 Successful) macOS Successful Failed Windows Failed Failed 1.4. Tutorials 11 pandas: powerful Python data analysis toolkit, Release 1.3.2 Access

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 installer, the pandas: powerful Python data analysis toolkit, Release 1.3.3 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 System Conda PyPI Linux Successful Failed(pyarrow==3.0 Successful) macOS Successful Failed Windows Failed Failed Access data in the cloud Dependency Minimum Version Notes fsspec 0.7.4 Handling

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 installer, the pandas: powerful Python data analysis toolkit, Release 1.3.4 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 System Conda PyPI Linux Successful Failed(pyarrow==3.0 Successful) macOS Successful Failed Windows Failed Failed Access data in the cloud Dependency Minimum Version Notes fsspec 0.7.4 Handling