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from scipy.stats.distributions import binom

data = [
    {'name': 'ISTJ', 'count': 12,  'expected': 0.1160}, 
    {'name': 'ISFJ', 'count': 4,   'expected': 0.1380}, 
    {'name': 'INFJ', 'count': 14,  'expected': 0.0150}, 
    {'name': 'INTJ', 'count': 206, 'expected': 0.0210}, 
    {'name': 'ISTP', 'count': 16,  'expected': 0.0540}, 
    {'name': 'ISFP', 'count': 1,   'expected': 0.0880}, 
    {'name': 'INFP', 'count': 56,  'expected': 0.0430}, 

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#!/usr/bin/python
"""Convert CSV table to MindMap format

Usage: python csv_to_mm.py sometable.csv > mymap.mm

CSV format is rows representing tree leaves, e.g.:
    A1,
    A1,B1
    A1,B1,C1
    A1,B1,C2

datatalking

Andrew Schell Twitter [#datatalking](https://twitter.com/hashtag/datatalking) - Email [info@yourdatatalking.com](mailto:info@yourdatatalking.com)

**Salesforce Admin – Google Analytics – Data Marketing Analyst**

Seeking an analyst or technical big data position at a growing company. I work best with action based teams to accomplish goals vs doing theoretical research. In finance I developed a process that tells me with 84% confidence which customers are likely to churn and I am well versed in business development as well as the Analytics supporting Sales. Current pro bono projects are administration of a Salesforce Org for a nonprofit and my artificial intelligence prototype that helps disabled children

**Subject Matter Expert:**

**Business Intelligence**** Analysis**

datatalking

Python3 Virtualenv Setup

Requirements

• Python 3
• Pip 3

$ brew install python3

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# checkDuplicates.py
# Python 2.7.6

"""
Given a folder, walk through all files within the folder and subfolders
and get list of all files that are duplicates
The md5 checcksum for each file will determine the duplicates
"""

import os

datatalking

#!/usr/bin/python
import os
import sys
import csv
import datetime
import time


def main():
	run_simple_speedtest()

datatalking

Tensorflow 1.8 with CUDA on macOS High Sierra 10.13.4

Largely based on the Tensorflow 1.6 gist, and Tensorflow 1.7 gist for xcode and Tensorflow 1.7 gist for eGPU, this should hopefully simplify things a bit.

Requirements

• NVIDIA Web-Drivers 387.10.10.10.30.106 for 10.13.4 (17E199) (w/o Security Update)
• CUDA-Drivers 387.128

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#!/usr/bin/env python
# -*- coding: utf-8 -*-

"""
Outputs some information on CUDA-enabled devices on your computer,
including current memory usage.

It's a port of https://gist.github.com/f0k/0d6431e3faa60bffc788f8b4daa029b1
from C to Python with ctypes, so it can run without compiling anything. Note
that this is a direct translation with no attempt to make the code Pythonic.

datatalking

datatalking

Git

Global Settings

• Related Setup: https://gist.github.com/hofmannsven/6814278
• Related Pro Tips: https://ochronus.com/git-tips-from-the-trenches/
• Interactive Beginners Tutorial: http://try.github.io/
• Git Cheatsheet by GitHub: https://services.github.com/on-demand/downloads/github-git-cheat-sheet/

Reminder

Press minus + shift + s and return to chop/fold long lines!