Initial commit
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2b2861ffd3
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.gitignore
vendored
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163
.gitignore
vendored
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# Byte-compiled / optimized / DLL files
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__pycache__/
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*.py[cod]
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*$py.class
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# C extensions
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*.so
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# Distribution / packaging
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.Python
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build/
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develop-eggs/
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dist/
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downloads/
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eggs/
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.eggs/
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lib/
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lib64/
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parts/
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sdist/
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var/
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wheels/
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share/python-wheels/
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*.egg-info/
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.installed.cfg
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*.egg
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MANIFEST
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# PyInstaller
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# Usually these files are written by a python script from a template
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# before PyInstaller builds the exe, so as to inject date/other infos into it.
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*.manifest
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*.spec
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# Installer logs
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pip-log.txt
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pip-delete-this-directory.txt
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# Unit test / coverage reports
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htmlcov/
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.tox/
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.nox/
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.coverage
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.coverage.*
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.cache
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nosetests.xml
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coverage.xml
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*.cover
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*.py,cover
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.hypothesis/
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.pytest_cache/
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cover/
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# Translations
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*.mo
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*.pot
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# Django stuff:
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*.log
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local_settings.py
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db.sqlite3
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db.sqlite3-journal
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# Flask stuff:
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instance/
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.webassets-cache
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# Scrapy stuff:
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.scrapy
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# Sphinx documentation
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docs/_build/
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# PyBuilder
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.pybuilder/
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target/
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# Jupyter Notebook
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.ipynb_checkpoints
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# IPython
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profile_default/
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ipython_config.py
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# pyenv
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# For a library or package, you might want to ignore these files since the code is
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# intended to run in multiple environments; otherwise, check them in:
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# .python-version
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# pipenv
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# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
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# However, in case of collaboration, if having platform-specific dependencies or dependencies
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# having no cross-platform support, pipenv may install dependencies that don't work, or not
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# install all needed dependencies.
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#Pipfile.lock
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# poetry
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# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
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# This is especially recommended for binary packages to ensure reproducibility, and is more
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# commonly ignored for libraries.
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# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
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#poetry.lock
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# pdm
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# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
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#pdm.lock
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# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
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# in version control.
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# https://pdm.fming.dev/#use-with-ide
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.pdm.toml
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# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
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__pypackages__/
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# Celery stuff
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celerybeat-schedule
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celerybeat.pid
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# SageMath parsed files
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*.sage.py
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# Environments
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.env
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.venv
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env/
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venv/
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ENV/
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env.bak/
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venv.bak/
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# Spyder project settings
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.spyderproject
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.spyproject
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# Rope project settings
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.ropeproject
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# mkdocs documentation
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/site
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# mypy
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.mypy_cache/
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.dmypy.json
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dmypy.json
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# Pyre type checker
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.pyre/
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# pytype static type analyzer
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.pytype/
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# Cython debug symbols
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cython_debug/
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# PyCharm
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# JetBrains specific template is maintained in a separate JetBrains.gitignore that can
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# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
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# and can be added to the global gitignore or merged into this file. For a more nuclear
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# option (not recommended) you can uncomment the following to ignore the entire idea folder.
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#.idea/
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backup
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25
Makefile
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25
Makefile
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2023: .ipl-data/.c-install 2023/ipl-data-2023.pdf
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.ipl-data/bin/python parse.py 2023/ipl-data-2023.pdf 2023/ipl-data-2023.json
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2023/ipl-data-2023.pdf:
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mkdir -p 2023
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curl https://documents.bcci.tv/bcci/documents/1676632383158_TATA%20IPL%202023%20-%20Match%20Schedule.pdf > 2023/ipl-data-2023.pdf
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.ipl-data/bin/activate:
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python -m venv .ipl-data
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.ipl-data/bin/pip-compile: .ipl-data/bin/activate
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.ipl-data/bin/python -m pip install pip-tools
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requirements.txt: requirements.in .ipl-data/bin/pip-compile
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.ipl-data/bin/pip-compile --output-file=- > requirements.txt
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.ipl-data/.c-install: requirements.txt
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.ipl-data/bin/pip install -r requirements.txt
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touch .ipl-data/.c-install
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clean:
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rm -rf .ipl-data
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rm -rf __pycache__
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rm -rf requirements.txt
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rm -rf 2023/
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13
README.md
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13
README.md
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# IPL Fixtures dataset
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## Requirements
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- Python 3
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- Some version of Linux
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## Run
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To get the fixture data for 2023, run
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`make 2023`
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Output is present in `2023/ipl-data-2023.json`
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55
parse.py
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55
parse.py
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import tabula
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import json
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import sys
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class Match:
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day_n: int
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match_n: int
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day: str
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date: str
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time: str
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home: str
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away: str
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venue: str
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def main():
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input_file = sys.argv[1]
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output_file = sys.argv[2]
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print ("Reading from " + input_file)
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dataframes = tabula.read_pdf(input_file, pages = [1, 2])
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matches = []
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teams = ['Sunrisers Hyderabad', 'Gujarat Titans', 'Mumbai Indians', 'Chennai Super Kings',
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'Lucknow Super Giants', 'Delhi Capitals', 'Kolkata Knight Riders', 'Rajasthan Royals',
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'Royal Challengers Bangalore', 'Punjab Kings', 'Qualifier 1 - Team 1 vs Team 2',
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'Eliminator - Team 3 vs Team 4', 'Qualifier 2 - Winner of Eliminator vs Loser of Qualifier 1',
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'TATA IPL 2023 Final - Winner of Qualifier 1 vs. Winner of Qualifier 2']
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for dataframe in dataframes:
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for i in range (2,len(dataframe["MATCH"])):
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x = Match();
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x.day_n = dataframe["MATCH"][i]
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x.match_n = dataframe["MATCH.1"][i]
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x.day = dataframe["Unnamed: 0"][i]
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x.date = dataframe["Unnamed: 1"][i]
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x.time = dataframe["Unnamed: 2"][i]
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homeaway = dataframe["Unnamed: 3"][i]
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home = ""
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away = ""
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for j in teams:
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if homeaway.startswith(j):
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home = j
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if homeaway.endswith(j):
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away = j
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x.home = home
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x.away = away
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x.venue = dataframe["Unnamed: 5"][i]
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teams.append(x.home)
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matches.append(x)
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print ("Writing to " + output_file)
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f = open(output_file, 'w')
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json.dump(matches, f, indent = 4, default = lambda x: x.__dict__)
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f.close()
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if __name__ == "__main__":
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main()
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1
requirements.in
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1
requirements.in
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@ -0,0 +1 @@
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tabula-py
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