datashader-cli

Quick visualization of large datasets using CLI based on datashader.

Installation

Use pip

pip install datashader-cli

Use pip from Github

pip install git+https://github.com/wybert/datashader-cli.git

Quick Start

Visualize 10 million NYC taxi trip data points in Gigabytes.

Tips: You can download the NYC taxi trip data from here or use the command datashader examples to download the datashader examples. The nyc_taxi.csv file will be located in datashader-examples/data/nyc_taxi.csv. You can also find some data in this repository, download these use wget https://raw.githubusercontent.com/wybert/datashader-cli/main/tests/data.csv and wget https://raw.githubusercontent.com/wybert/datashader-cli/main/tests/data.geo.parquet, etc.

Create a shaded scatter plot of points and save it to png file, set background color to black.

datashader_cli points nyc_taxi.parquet --x pickup_x --y pickup_y pickup-scatter.png --background black

Visualize the geospaital data, support Geoparquet, Shapefile, Geojson, Geopackage, etc.

datashader_cli points data.geo.parquet data.png --geo true

Use matplotlib to render the image, matplotlib will enable the colorbar, but it can't use spread function

datashader_cli points data.geo.parquet data.png --geo true --matplotlib true

Usage

datashader_cli --help
# sage: datashader_cli [OPTIONS] COMMAND [ARGS]...# Quick visualization of large datasets using CLI based on datashader.# Supported data format: csv, parquet, hdf, feather, geoparquet, shapefile,# geojson, geopackage, etc.# Options:# --help Show this message and exit.# Commands:# points Visualize points data.

Quick visualization of large point datasets using CLI based on datashader.

datashader_cli points --help
# Usage: datashader_cli points [OPTIONS] DATA_PATH OUTPUT_APTH# Visualize points data.# Options:# --x TEXT Name of the x column, if geo=True, x is optional# --y TEXT Name of the y column, if geo=True, y is optional# --w INTEGER How many pixels wide to make the image# --h INTEGER How many pixels high to make the image# --x_range TEXT Range of the x axis, in the form of "xmin,xmax"# --y_range TEXT Range of the y axis, in the form of "ymin,ymax"# --agg TEXT Aggregation function, e.g. "mean", "count", "sum", see# datashader docs# (https://datashader.org/api.html#reductions) for more# options# --agg_col TEXT Column to aggregate on, e.g. "value"# --by TEXT Column to group by, e.g. "category", see datashader# docs (https://datashader.org/api.html#reductions) for# more options# --spread_px INTEGER How many pixels to spread points by, e.g. 1, see https# ://datashader.org/api.html#datashader.transfer_functio# ns.spread# --how TEXT How to map values to colors, valid strings are# ‘eq_hist’ [default], ‘cbrt’ (cube root), ‘log’# (logarithmic), and ‘linear’. see https://datashader.or# g/api.html#datashader.transfer_functions.set_backgroun# d# --cmap TEXT Name of the colormap, see https://colorcet.holoviz.org# for more options# --geo BOOLEAN Whether the data is geospatial, if True, x and y are# optional, need Geopandas installed to use this option,# supported data format: Geoparquet, shapefile, geojson,# geopackage, etc.# --background TEXT Background color, e.g. "black", "white", "#000000",# "#ffffff"# --matplotlib BOOLEAN Whether to use matplotlib to render the image, if# True, need matplotlib installed to use this option.# Matplotlib will enable the colorbar, but it can't use# spread function# --help Show this message and exit.

Credits

• Free software: MIT license
• Documentation: https://wybert.github.io/datashader-cli

Features

• point data visualization