Python Dataviz Toolbox

Useful libraries and scripts to perform reading, postprocessing, data conversion, regridding and masking to display georeferenced data in maps.

01 Data Structures

Basic data files operations are explained including:

• JSON to Pandas.
• CSV to Pandas.
• CSV to Parent/Child JSON.
• Pandas Numerical Filtering.
• Pandas Text Filtering.

02 Basic Plots

A quick overview of the data can be done with plots using the Matplotlib library:

• Scatter Plot.
• Scatter Regression.
• Time Series.
• Histogram.

03 GeoData Formats

Model output, dataproducts and observational datasets often come in georeferenced data formats. Conversion of these formats is done through:

• CSV to GeoJSON.
• NetCDF to JSON.
• NetCDF Contours to GeoJSON.
• NetCDF to TIF.
• NetCDF to DAT.
• Open MAT.
• TIF to NetCDF.
• SHP to GeoJSON.

04 Regridding and Masking

Operating or just comparing datasets involves a common meshgrid in all input files, or just clipping certain areas of interest. Here is how:

• Regridding Xarray.
• SHP into Gridpoints.
• Mask NetCDF with SHP.
• Calculate Area of SHP.
• Seed Random dots on SHP.

05 Mapping

Plotting georeferenced files in native Python libraries always helps to understand the data:

• NetCDF and SHP in Matplotlib.
• NetCDF in Cartopy.
• NetCDF Contour in Folium.
• TIF in Cartopy.
• TIF in Matplotlib.
• Multiple SHP in Matplotlib.
• SHP in Folium.

06 Text Analysis

NLTK is a simple yet powerful library to extract basic metrics in text based datasets:

• Keyword Rank and Frequency.