Pandas is the Swiss-Multipurpose Knife for Data Analysis in Python. With Pandas dealing with data-analysis is easy and simple but there are some things you need to get your head around first as Data-Frames and Data-Series.

The tutorial provides a compact introduction to Pandas for beginners for I/O, data visualisation, statistical data analysis and aggregation within Jupiter notebooks.

Having Anaconda installed simply create your ENV with

# get this repository
git clone https://github.com/alanderex/pandas-pydata-berlin-2017

cd pandas-pydata-berlin-2017
# install working environment with conda
conda env create -n pandas-pydata-berlin-2017 -f environment.yml

# environment should be activated now
# if not type: source activate pandas-pydata-berlin-2017

# start juypter notebook
jupyter notebook

# paste the url displayed in your browser, looks like:
# http://localhost:8888/?token=fa08a1f56d3d0fbbdf7d07fec0c39cd471e06501f79a782a

If you don't want to use anaconda, you can use python3 and

pip install pandas jupyter barnum numpy matplotlib xlsxwriter seaborn bokeh

(at your own risk)

• CSV

• Excel

• JSON

• Clipboard

• data

  • .info
  • .describe

• Ode to NumPy
• Data-Series
• Data-Frames

• Data-Series:
  • Slicing
  • Access by label
  • Index
• Data-Frames:
  • Slicing
  • Access by label
  • Peek into joining data
• Returns a copy / inplace
• Boolean indexing

• add/substract
• multiply
• mention Index but don't go deep

• plot your data directly into your notebook