Material for my PyData Jupyter & Pandas Workshops, I'm also available for personal in-house trainings on request
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Data Wrangling & Visualisation with Pandas & Jupyter

Follow-Along tutorial presented at PyData conferences all over Europe.


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.


Copy this repository to your computer

# get this repository
git clone
cd pydata-pandas-workshop

Having Anaconda installed simply create your ENV with

# install working environment with conda
conda env create -n pydata-pandas-workshop -f environment.yml

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

# start juypter lab
jupyter lab

# paste the url displayed in your browser, if it doesn't open anyway:
# http://localhost:8888/lab

Alternatively you can also create a python virtual enviroment and

pip install -r requirements.txt

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

pip install pandas jupyter barnum numpy matplotlib xlsxwriter seaborn bokeh jupyter parquet dask

(at your own risk)

A Practical Start: Reading and Writing Data Across Multiple Formats

  • CSV

  • Excel

  • JSON

  • Clipboard

  • data

    • .info
    • .describe

DataSeries & DataFrames / NumPy

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

Data selection & Indexing

  • 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

Data Visualisation

  • plot your data directly into your notebook

Peek Into Statistical Data Analysis & Aggregation

  • Merging
  • Multi-Index
  • DateTime Index
  • Resampling
  • Pivoting

Scaling and Optimizing

  • Faster file I/O with Parquet
  • Scaling and Distributing with Dask