Installing (I)Python & Jupyter
The easiest and most convenient way to install a working version of IPython with all the required packages and tools is using Continuum's Anaconda Distribution. You can install following the instructions in that website, or if you can just run this script (Mac/Linux). After installing the latest version of Anaconda, add the Anaconda/bin directory to your PATH variable.
In Windows you may need to install some version of Visual C++ (e.g. for Python-2.7 you need Visual C++ 9.0 otherwise choose the most up-to-date version) and Microsoft MPI.
In MACOS download and install XCode (from the Apple store) and command line tools (option within XCode or in a terminal execute xcode-select --install).
Since the basic installation may not have all the packages you need and it is always best to keep an environment for your work, after installation:
A. (Recommended) In a terminal window execute
mamba create --name GeoPython310env -c conda-forge -c r --override-channels python=3.10 georasters geopandas pandas spatialpandas statsmodels xlrd networkx ipykernel ipyparallel ipython ipython_genutils ipywidgets jupyter jupyterlab kiwisolver matplotlib-base matplotlib scikit-image scikit-learn scipy seaborn geoplot geopy geotiff pycountry nb_conda_kernels stata_kernel nltk ipympl ipumspy plotnine
mamba activate GeoPython310env
pip install RISE jupyterlab-rise stargazer rdrobust pyfixest lets-plot isounidecode
or download this file and import it into the Anconda Navigator (see here).
If you want a very complete environment
mamba create --name GeoPython310env -c conda-forge -c r --override-channels python=3.10 asciitree autopep8 babel basemap beautifulsoup4 blas bokeh bzip2 cartopy colorcet curl cython cytoolz dask dask-core datashader datashape descartes dill docutils earthengine-api fiona gensim geographiclib geopandas geoplot geopy geotiff google-api-python-client google-auth google-auth-httplib2 georasters imageio imagesize ipykernel ipyparallel ipython ipython_genutils ipywidgets jupyter jupyterlab kiwisolver matplotlib-base matplotlib markdown networkx nose numba numpy openpyxl pandas pandas-datareader pandoc pandocfilters pandana pelican pycountry pymc3 pysal urbanaccess rasterstats r r-base r-irkernel statsmodels sympy rpy2 rasterio r-tidyr r-tibble r-dplyr seaborn scikit-allel scikit-bio scikit-image scikit-learn scipy seaborn swifter theano unidecode urllib3 wikipedia xlrd rstudio camelot-py html5lib ghostscript plotnine nodejs nb_conda_kernels stata_kernel ipympl r-lazyeval r-ggplot2 r-pacman r-fixest r-tidyverse plotly jupyter_nbextensions_configurator markdown-kernel sos-r sos-python sos-bash pweave python-kaleido graph-tool ipumspy dask-geopandas multiprocess fuzzywuzzy
mamba activate GeoPython310env
pip install linearmodels git+https://github.com/jeetsukumaran/DendroPy.git git+https://github.com/dmsul/econtools ecopy facebook-business facebook-sdk fontawesome-markdown geocoder geonamescache git+https://github.com/ozak/google-drive-downloader googletrans ipystata isodate isounidecode latexcodec ldpred linearmodels lingpy mccabe ordered-set plinkio pyarrow pybtex pycldf pycountry pydocstyle pyface pyflakes pylatex pyparsing pyreadstat pytest-cov python-jsonrpc-server python-language-server pytrends quilt ratelim readme-renderer rfc3986 rope simpledbf simplejson smartypants snowballstemmer snuggs stata-kernel tabulate tex2ipy texsoup toolz traits traitsui twine typogrify uritemplate w3lib webencodings wrapt xlwt yapf modin stargazer covid COVID19Py pandasql mplleaflet scikit-gstat binsreg jupyterlab_imarkdown jupyterlab-markup-expr keplergl RISE jupyterlab-rise stargazer jupyter_contrib_nbextensions rdrobust pyfixest lets-plot jupyter_nbextensions_configurator
pip install --upgrade --user stata_setup
conda update --all -c conda-forge -c r -c mro
python -m stata_kernel.install
mamba install -c conda-forge nodejs -y
jupyter labextension install jupyterlab-stata-highlight
jupyter labextension install verdant-history
jupyter labextension install imarkdown
jupyter labextension install @jupyter-widgets/jupyterlab-manager keplergl-jupyter
ipcluster nbextension enable
jupyter contrib nbextension install --sys-prefix
jupyter nbextension enable codefolding/main
jupyter nbextension enable python-markdown/main
jupyter labextension install verdant-history
jupyter nbextension enable nbTranslate/main
jupyter labextension install @jupyter-widgets/jupyterlab-manager keplergl-jupyter
jupyter labextension install imarkdown
r -e "IRkernel::installspec()"
ipcluster nbextension enable
This should create an environment with most of the packages we need. We can always install others down the road.
Windows users will need to follow additional instructions to have the Stata kernel working.
After Installation is complete
This should create an environment with all the packages you will require for this course (and more!).
To start using one of the environment you will need to exectute the following command
conda activate GeoPython310env
or for Python 2.x
source activate GeoPython2env
I would suggest using GeoPython38env, since it is the latest and most up-to-date version (I'll try to keep adding new versions as time passes).
Parallel Computing
One of the advantages of Jupyter Notebooks is that they allow you to work (very easily) with multiple processors using ipyparallel. Once you have ipyparallel installed (automatically done for you with the scritps above), you will need to execute the following code once
ipcluster nbextension enable
Stata Kernel
We can use Stata within Jupyter Notebooks with the Stata kernel. I recommend you make sure it is installed and you have highlighting. To install (in case you do not use the YAML environment file above), execute
pip install stata_kernel
python -m stata_kernel.install
and then
conda install -c conda-forge nodejs -y
jupyter labextension install jupyterlab-stata-highlight
Windows users will need to follow additional instructions to have the Stata kernel working.
R kernel
We can also use R within Jupyter Notebooks with the R kernel. The best way to install it (in case you do not use the YAML environment file above) is to use conda by executing
conda install -c conda-forge -c r r-irkernel
You can also install R and R packages by using conda. Simply execute
conda install -c conda-forge -c r r
conda install -c conda-forge -c r r-PACKAGE_NAME
For the R-kernel to be available you need to open R and execute
IRkernel::installspec()
Running Stata or R in IPython/Jupyter
We can also use Stata or R directly within Python. You only need to use the %magic for each after installing the required packages. For example for Stata you need ipystata. Install by executing
pip install ipystata
Then in jupyter execute
import ipystata
from ipystata.config import config_stata
config_stata('Path to your Stata executable')
Once configured you can use the %%stata magic. E.g.
In[1]: import ipystata
In[2]: %%stata
display "Hello, I am printed in Stata."
More info in the ipystata website.
Similarly, we can use R using rpy2. Install by executing
conda install -c conda-forge -c r rpy2
Then in jupyter execute
In [1]: %load_ext rpy2.ipython
In [2]: %%R
R.version
More info in the rpy2 website.