# You can also parse the data into dataframes using data_df = demo_parser. # The following keys exist dataĭata # From this value, you can extract player events via: data, etc. # Parse the demofile, output results to dictionary with df name as key data = demo_parser. demo_parser = DemoParser( demofile = "m", demo_id = "og-vs-natus-vincere", parse_rate = 128) 128 indicates a frame per second on professional game demos.
Larger numbers result in fewer frames recorded. It indicates the spacing between parsed ticks. parser import DemoParser # Set parse_rate to a power of 2 between 2^0 and 2^7. Use the example below to get started.įrom csgo. Just choose a demofile and have output in a JSON or Pandas DataFrame in a few seconds. Using the csgo package is straightforward. Check out how to setup csgo Python library in Google Colab. Colab Notebookĭo your work in Colab? No problem, the csgo Python library runs there, too. For more help, you can visit the installation channel in our Discord. Then, change directories to the newly cloned repository, and install the library by running python setup.py install. To install csgo, clone the repository by running git clone. Python acts as a wrapper for the Go code which parses demofiles. Table of ContentsĬsgo requires Python >= 3.7 and Golang >= 1.16. Please join our Discord for discussion around the library, along with other resources for esports analytics. In this repository, you will find the source code, issue tracker and other useful information pertaining to the csgo package.
The csgo package provides data parsing, analytics and visualization capabilities for Counter-Strike: Global Offensive (CSGO) data. Analyzing Counter-Strike: Global Offensive Data
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