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Analytics with Pandas and Jupyterlab

Follow-Along tutorial to get you started.


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.


Run Jupyterlab in the cloud, requires internet access.



Local Installation

Copy this repository to your computer

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

Make sure to update to the latest vesion just when the training starts:

git pull

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

In case the installation via file fails, simply:

conda env create -n pydata-pandas-workshop python=3.6
source activate pydata-pandas-workshop
conda install pandas jupyterlab xlrd xlsxwriter dask seaborn -y

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 jupyterlab parquet dask

(at your own risk)

Start Juypterlab

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

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


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







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