This borrows from https://jordanthomasg.medium.com/python-development-on-macos-with-pyenv-2509c694a808.
Install Homebrew if necessary.
/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
Before installing pyenv do the following:
After spending many hours trying to get FreeTDS and unixodbc to run on a Mac OS X 10.8 system with the python module, pyodbc, I eventually came to this recipe, which is remarkably simple thanks to homebrew. I also found unixodbc was unnecessary and I couldn't get it to play well with FreeTDS, so this install does not include unixodbc. See also http://www.acloudtree.com/how-to-install-freetds-and-unixodbc-on-osx-using-homebrew-for-use-with-ruby-php-and-perl/ and http://www.cerebralmastication.com/2013/01/installing-debugging-odbc-on-mac-os-x/.
Prerequisites: Be sure you have XCode and the Commandline Tools for XCode installed from Apple. Also install homebrew followed with brew update and brew doctor.
Install FreeTDS:
brew install freetds
Test your install:
From an open Jupyter Notebook homework assignment, select "Coursera" to take you to the home page. Make a new notebook and fill it with the following and excute the cell with:
%%bash
tar cvfz hw.tar.gz .
This may take a little while to run depending on the packages. Select "Coursera" again to take you to the Home directory. Check the hw.tar.gz file and then Download. After the file is downloaded, delete it.
| import contextlib | |
| from functools import partial | |
| from sklearn.pipeline import Pipeline | |
| @contextlib.contextmanager | |
| def intermediate_transforms(pipe: Pipeline, keys: list=[], bypass_list: list=[]): | |
| """Allows for the retrieval of all or parts of the transformations in a | |
| sklearn Pipeline, as well as the ability to dynamically bypass parts of | |
| the pipeline. |
| import logging | |
| from collections.abc import Iterable | |
| def is_container(obj): | |
| return isinstance(obj, Iterable) and not isinstance(obj, (str, bytes, bytearray)) | |
| # https://stackoverflow.com/a/54000999/394430 | |
| def walk_collection(obj, callback=None, _path: list=[], **kwargs): | |
| """Walk an arbitrarily nested structure of lists and/or dicts such as would be made when | |
| reading JSON as an object. Walking is performed in a depth-first search manner. |
This is a recipe for making box plots overlaying histograms in Tableau version 9.3. It largely borrows from http://vizpainter.com/some-tableau-tips-options-for-box-and-whisker/ and http://vizdiff.blogspot.com/2015/11/overlaying-histogram-with-box-and.html.
Create a fixed continuous variable for number of objects per dimension. For example, the number of unique assignments per user:
[Assignments Per User] = {FIXED [Userid] : COUNTD([Assignmentid])}
Set the variable's Default Aggregation to COUNT.
Drag the variable from Measures to the columns shelf.
Set it to "Dimension" instead of CNT().