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.
Copy this repository to your computer
# get this repository git clone https://github.com/alanderex/pydata-pandas-workshop.git cd pydata-pandas-workshop
Make sure to update to the latest vesion just when the training starts:
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)
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
DataSeries & DataFrames / NumPy
- Ode to NumPy
Data selection & Indexing
- Access by label
- Access by label
- Peek into joining data
- Returns a copy / inplace
- Boolean indexing
- plot your data directly into your notebook
Peek Into Statistical Data Analysis & Aggregation
- DateTime Index
Scaling and Optimizing
- Faster file I/O with Parquet
- Scaling and Distributing with Dask