Merge Dev

.gitignore

@@ -207,6 +207,9 @@ docs/_build/
 .pybuilder/
 target/
 
+# Jupyter Lab
+.virtual_documents
+
 # Jupyter Notebook
 .ipynb_checkpoints
 
@@ -269,3 +272,8 @@ dmypy.json
 # Cython debug symbols
 cython_debug/
 
+# MacOS dirs
+__MACOSX/
+
+# PDFs
+*.pdf

DEVELOPING.md

@@ -0,0 +1,38 @@
+# Further TODO
+
+
+### Coding
+
+- [ ] (?) Move helper-functions from the `.ipynb` to python-script
+- [ ] (?) Try to define auto-linting and MyPy checking
+- [ ] (?) Try to add pytest for the helper-functions
+- [ ] Try to add Dockerfile
+
+### Python Versions
+
+- [ ] Check python3.9 support
+- [ ] Check python3.8 support
+- [ ] Check python3.7 support
+- [ ] Check python3.6 support
+
+### Docs and readability
+
+- [ ] Export `README.md - Notions-data`  to the `Markdown` and add to the repository directly
+- [ ] Translate RU -> ENG
+- [ ] Add support/link to run the `.ipynb` in Google Colab
+- [ ] Add Table of Contents to the Markdown-files
+- [ ] Add Table of Contents to the `.ipyng`
+
+
+# PS Try DataSpell IDE
+
+> *PS I Tried DataSpell IDE for Data Science - really cool and strong IDE with good-old features of JutBrains' PyCharm,
+  for example. It was pretty-easy to migrate from Jupyter Notebook / Jupyter Lab to the DataSpell because of the
+  familiar interface, tools, short-keys, I have used for while practicing with PyCharm. Try it!*
+
+> *One of the best ways to receive longer educational-license, if you already are not a student, is to try some
+  Stepik Courses (like [Golang Programming \[RU\]](https://stepik.org/course/54403/), solve several tasks and get 
+  licence-key for all JetBrains pack for a few mounts.* 
+
+> *Don't worry, if license will be close to end - you can receive it again, ..., by solving several tasks again :) 
+  So you will have enough time to get to know with this IDE.*

INSTALL.md

@@ -0,0 +1,85 @@
+## Makefile usage
+
+Main entry-point to install all you need and to run `.ipynb` by jupyter-notebook:
+
+```shell
+make
+# the same as `make run`
+```
+
+Only builds the environment by the `pipenv`:
+
+```shell
+make build
+```
+
+Adds the projects' `pipenv` environment as a kernel to jupyter-like (notebook/jupyter-lab):
+```shell
+make build_kernel
+```
+
+Installs jupyter notebook to global environment of the **current** user:
+```shell
+make build-notebook
+```
+
+Installs jupyter-lab to global environment of the **current** user:
+```shell
+make build-lab
+```
+
+Installs all you need and starts jupyter-notebook with the [data_analytics_task.ipynb](data_analytics_task.ipynb) opened:
+```shell
+make run
+```
+
+Installs all you need and starts jupyter-lab with the [data_analytics_task.ipynb](data_analytics_task.ipynb) opened:
+```shell
+make run-lab
+```
+
+Updated `pip` and `pipenv` dependencies:
+```shell
+update
+```
+
+💡NOTE 1: maybe you also need to install pipenv-dev packages, then you should manually run the:
+```shell
+pipenv install --dev
+```
+
+💡NOTE 2: starting/working with jupyter-lab maybe harder: you may some additional extension etc.
+
+⚠️NOTE 3: all the commands here should be run under the repository dir.
+
+
+## How to install new python-packages
+
+> All installations should happen with `pipenv` command.
+
+Note that some packages are needed only for developing, for example, `pytest` should be installed like:
+```shell
+pipenv install --dev pytest
+```
+
+Common packages, that will be needed in production code are installed as:
+```shell
+pipenv install tqdm
+```
+
+### How to activate the environment?
+
+```shell
+pipenv shell
+```
+
+Also you may (and possibly even should) add this pipenv interpreter to the PyCharm or eny other IDE.
+
+For example, the current pipenv with the name has Pipfile and Pipfile.lock files in the repo, but actually the 
+environment with needed packages will be installed in your user-path.  
+
+In my case _(Windows 11, amd64)_: `C:/Users/{my_name}/.virtualenvs/pipenv-data_analytics_test/`, 
+so for the PyCharm I specify the python interpreter as 
+`C:/Users/{my_name}/.virtualenvs/pipenv-data_analytics_test/Scripts/python.exe`
+
+For more info - see pipenv tutorial [Pipenv: Python Dev Workflow for Humans](https://pipenv.pypa.io/en/latest/).

Makefile

@@ -0,0 +1,32 @@
+.DEFAULT_GOAL := run
+D_KERNEL_ENV_NAME = "pipenv-data_analytics_test"
+D_NOTEBOOK = "data_analytics_task.ipynb"
+
+build_kernel : build
+	echo "Starting of 'build_kernel'"
+	pipenv run python -m ipykernel install --user --name=$(D_KERNEL_ENV_NAME)
+
+build :
+	echo "Starting of 'build'"
+	pip install --upgrade pip --user
+	pipenv install
+
+build-notebook :
+	echo "Starting of 'build-notebook'"
+	pip install notebook --user  # installing globally for the user
+
+build-lab :
+	echo "Starting of 'build-lab'"
+	pip install jupyterlab --user # installing globally for the user
+
+run : build-notebook build_kernel
+	echo "Starting of 'run'"
+	jupyter notebook $(D_NOTEBOOK)
+
+run-lab : build-lab build_kernel
+	echo "Starting of `run-lab`"
+	jupyter lab $(D_NOTEBOOK)
+
+update:
+	pip install --upgrade pip --user
+	pipenv update

Pipfile

@@ -0,0 +1,17 @@
+[[source]]
+url = "https://pypi.org/simple"
+verify_ssl = true
+name = "pypi"
+
+[packages]
+ipykernel = "*"
+numpy = "*"
+pandas = "*"
+plotly = "*"
+
+[dev-packages]
+pyppeteer = "*"
+nbconvert = "*"
+jupyter-contrib-nbextensions = "*"
+
+[requires]