an analysis of housing data from 2011-2017
Clean housing data In this notebook, housing data is downloaded from popular datasets on Kaggle, where data is merged, cleaned, filtered, and prepared for statistical analysis
[
(https://mybinder.org/v2/gh/Carreau/ipython-in-
depth/pycon-2019?urlpath=%2Flab)
This repository contain material and instructions to
follow the "IPython and Jupyter in Depth: High
productivity, interactive Python" tutorial during
PyCon 2019.
Please read the following section and install the
required software ahead of time. We may ask you to update versions of the
software more closely to the tutorial date.
Please do not rely on cloud hosting to follow this
tutorial, as the network connection may be unreliable. If possible, come to
the tutorial with a computer where you have administrative privileges.
For this tutorial, we are standardizing on a conda-
based python distribution (miniconda or Anaconda). We may not be able to help
with installation issues if you are using a different python distribution.
- Install either the full anaconda distribution
(very large, includes lots of conda packages by default) or miniconda
(much smaller, with only essential packages by default, but any conda
package can be installed).
- To get the tutorial materials, clone this
repository. **Please plan to update the materials
shortly before the tutorial.**
```
git clone https://github.com/ipython/ipython-
in-depth ```
To update the materials:
```
cd ipython-in-depth
git pull
```
Feel free to open an issue or send a pull
request to update these materials if things are
unclear.
-
Set up your environment.
Create a conda environment:
conda create -n pycon2019 -c conda-forge --yes
python=3.7 pip cookiecutter=1.6 'notebook=5.7'
pandas=0.24 nodejs=9.11 jupyterlab bqplot ipyvolume
pythreejs aiohttp line_profiler matplotlib rpy2
simplegeneric trio cython pillow ```
(You could instead create the environment from
the supplied environment file with `conda env create
-f ipython-in-depth/environment.yml`)
Activate the conda environment:
```
conda activate pycon2019
```
Install extra JupyterLab extensions:
```
jupyter labextension install @jupyter-
widgets/jupyterlab-manager jupyter-threejs ipyvolume
bqplot @jupyterlab/geojson-extension
@jupyterlab/fasta-extension ```
If you open multiple terminal windows make sure to
activate the environment in each of them. Your
terminal prompt should be preceded by the name of
the current environment, for example:
(pycon2019) ~/ipython-in-depth $
Enter the following command in a new terminal window
to start JupyterLab.
$ jupyter lab
You can delete the environment by using the
following in a terminal prompt.
conda env remove --name pycon2019 --yes
This will not delete any data, but only the
conda environment named pycon2019 .
If you experience an out-of-memory error, you can
increase the memory available:
NODE_OPTIONS=--max_old_space_size=4096 jupyter lab
build
or
NODE_OPTIONS=--max_old_space_size=4096 jupyter
labextension install ...
This increases the available memory for the build
process to 4Gb.