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bigdata-demo's Introduction

Guide to Python Data Viz Demonstration

Process

  1. Live code parts of 01-data-cleanse-example
  2. Live code sections of 02-plot-crimes
  3. Display complete code for 03-plot-calls-for-service
    • Only output PNG files are included in this 03 folder

Objectives

  • Understand conversion of CSV file to a dataframe
  • Understand use of conditionals and looping
  • Examine methods for data cleansing and discuss importance of doing so
  • Discuss value of normalizing data sets
  • Review Python libraries such as matplotlib, pandas, and seaborn for visualizing data
  • Be able to run .ipynb files from Jupyter Notebook
  • Be able to run .py files from Gitbash, Command Prompt or Teminal

Requirements

Necessary if attendees want to run programs

  • Python 3.6.x 64-bit from Anaconda

  • Jupyter Notebook (typically installed with Anaconda)

  • Optionally Git

  • Download or clone the repository

    • With SSH key git clone [email protected]:CityOfLosAngeles/data-batcave-trainings.git
    • no SSH key? try git clone https://github.com/CityOfLosAngeles/data-batcave-trainings.git

Matplotlib Install Reference

  • From within virtual enivornment enter command: python -mpip install --upgrade matplotlib
    • This will update your matplotlib version so it works better with the code
    • Optionally users can run command from local machine with command: pip install --upgrade matplotlib
    • confirm version print(mpl.__version__)

Resources

This is a short list of Python Resources. Signing up for MOTW-3 is highly recommended

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