# scrapelytix
Welcome to Scrapelytix! This package allows you to scrape football event data and visualize various metrics like pass-maps, progressive passes, shot-maps. We will show you how to scrape whoscored, sofascore and fbref to gather data in the first place.
## Installation
First, install the package using pip:
pip install scrapelytix
## Usage Guide
### Step 1: Prepare the Data
To use Scrapelytix, you need to provide the URL of the match scorecard from the match center you want to scrape and your User-Agent.
### Step 2: Scrape the Data
Here's a step-by-step guide to using Scrapelytix:
Import the Required Modules:
import re
import json
import pandas as pd
import requests
import numpy as np
from Datawiz.extraction import pass_data, player_data
from Datawiz.filter import analyze_passes, analyze_shots
from Datawiz.plot import pass_network, prg_passes, shot_map
Prompt the User for the URL:
url = input("Enter the URL for pass data: ")
url_shots = 'https://api.sofascore.com/api/v1/event/11352376/shotmap'
HEADERS = {
'User-Agent': your user agent,
'Referer': "https://www.whoscored.com/",
'Accept-Language': "en-US,en;q=0.5",
'Accept-Encoding': "gzip, deflate, br",
'Connection': "keep-alive",
'Upgrade-Insecure-Requests': "1",
'TE': "Trailers"
}
Extract Player and Pass Data:
players_df, df_passes = player_data(url, HEADERS)
print(players_df.head())
print(df_passes.head())
Analyze Passes:
# Extracting team ids
team_ids = df_passes['teamId'].unique()
# Defining home and away colors for each team
home_colors = ['#FF0000', '#0000FF'] # Example colors (replace with actual colors)
away_colors = ['#FFFFFF', '#FFFFFF'] # Example colors (replace with actual colors)
# Creating DataFrame for clubs with team IDs and labels
df_clubs = pd.DataFrame({'Team ID': team_ids, 'Team Label': ['Team X', 'Team Y'], 'Team Name': ['Team X', 'Team Y'], 'Home Color': home_colors, 'Away Color': away_colors})
# Assuming there are only two teams, you can assign home and away team IDs accordingly
home_team_id = team_ids[0]
away_team_id = team_ids[1]
pass_between_home, pass_between_away, avg_loc_home, avg_loc_away, passes_home, passes_away, df_prg_home, df_comp_prg_home, df_uncomp_prg_home, df_prg_away, df_comp_prg_away, df_uncomp_prg_away = analyze_passes(df_passes, players_df, home_team_id, away_team_id)
# Example print statements to verify the results
print("Passes Between Home Players:")
print(pass_between_home.head())
print("Average Locations of Home Players:")
print(avg_loc_home.head())
print("Home Team Progressive Passes:")
print(df_prg_home.head())
Visualize the Data:
# Pass Network Visualization
pass_network(pass_between_home, pass_between_away, avg_loc_home, avg_loc_away, home_team_id, away_team_id, df_clubs)
# Progressive Passes Visualization
prg_passes(df_comp_prg_home, df_uncomp_prg_home, df_comp_prg_away, df_uncomp_prg_away, home_team_id, away_team_id, df_clubs)
## Output Samples
To better illustrate the steps, you can include screenshots of the outputs after each significant step. This will help users understand the intermediate results and the final visualizations.
1. *Initial Data Extraction:*
- 
- 
2. *Pass Network Visualization:*
- 
3. *Progressive Passes Visualization:*
- 
### Conclusion
Scrapelytix makes it easy to scrape football match data and visualize important statistics. By following the steps above, you can analyze and visualize data for any match you are interested in.
Feel free to explore the package and provide feedback or contributions on GitHub!
### Contribution
Contributions are welcome! If you find any issues or have suggestions, please open an issue or create a pull request on the [GitHub repository](https://github.com/30debrup/scrapelytix)(https://github.com/gxdfather7/scrapelytix).
### License
This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.
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"description": "# scrapelytix\nWelcome to Scrapelytix! This package allows you to scrape football event data and visualize various metrics like pass-maps, progressive passes, shot-maps. We will show you how to scrape whoscored, sofascore and fbref to gather data in the first place.\n\n## Installation\nFirst, install the package using pip:\npip install scrapelytix\n\n## Usage Guide\n### Step 1: Prepare the Data\nTo use Scrapelytix, you need to provide the URL of the match scorecard from the match center you want to scrape and your User-Agent.\n\n### Step 2: Scrape the Data\nHere's a step-by-step guide to using Scrapelytix:\n\nImport the Required Modules:\n\nimport re\nimport json\nimport pandas as pd\nimport requests\nimport numpy as np\nfrom Datawiz.extraction import pass_data, player_data\nfrom Datawiz.filter import analyze_passes, analyze_shots\nfrom Datawiz.plot import pass_network, prg_passes, shot_map\n\nPrompt the User for the URL:\n\nurl = input(\"Enter the URL for pass data: \")\nurl_shots = 'https://api.sofascore.com/api/v1/event/11352376/shotmap'\nHEADERS = {\n 'User-Agent': your user agent,\n 'Referer': \"https://www.whoscored.com/\",\n 'Accept-Language': \"en-US,en;q=0.5\",\n 'Accept-Encoding': \"gzip, deflate, br\",\n 'Connection': \"keep-alive\",\n 'Upgrade-Insecure-Requests': \"1\",\n 'TE': \"Trailers\"\n}\n\nExtract Player and Pass Data:\n\nplayers_df, df_passes = player_data(url, HEADERS)\nprint(players_df.head())\nprint(df_passes.head())\n\nAnalyze Passes:\n\n# Extracting team ids\nteam_ids = df_passes['teamId'].unique()\n\n# Defining home and away colors for each team\nhome_colors = ['#FF0000', '#0000FF'] # Example colors (replace with actual colors)\naway_colors = ['#FFFFFF', '#FFFFFF'] # Example colors (replace with actual colors)\n\n# Creating DataFrame for clubs with team IDs and labels\ndf_clubs = pd.DataFrame({'Team ID': team_ids, 'Team Label': ['Team X', 'Team Y'], 'Team Name': ['Team X', 'Team Y'], 'Home Color': home_colors, 'Away Color': away_colors})\n\n# Assuming there are only two teams, you can assign home and away team IDs accordingly\nhome_team_id = team_ids[0]\naway_team_id = team_ids[1]\n\npass_between_home, pass_between_away, avg_loc_home, avg_loc_away, passes_home, passes_away, df_prg_home, df_comp_prg_home, df_uncomp_prg_home, df_prg_away, df_comp_prg_away, df_uncomp_prg_away = analyze_passes(df_passes, players_df, home_team_id, away_team_id)\n\n# Example print statements to verify the results\nprint(\"Passes Between Home Players:\")\nprint(pass_between_home.head())\n\nprint(\"Average Locations of Home Players:\")\nprint(avg_loc_home.head())\n\nprint(\"Home Team Progressive Passes:\")\nprint(df_prg_home.head())\n\nVisualize the Data:\n\n# Pass Network Visualization\npass_network(pass_between_home, pass_between_away, avg_loc_home, avg_loc_away, home_team_id, away_team_id, df_clubs)\n\n# Progressive Passes Visualization\nprg_passes(df_comp_prg_home, df_uncomp_prg_home, df_comp_prg_away, df_uncomp_prg_away, home_team_id, away_team_id, df_clubs)\n\n\n## Output Samples\n\nTo better illustrate the steps, you can include screenshots of the outputs after each significant step. This will help users understand the intermediate results and the final visualizations.\n\n1. *Initial Data Extraction:*\n - \n - \n\n2. *Pass Network Visualization:*\n - \n\n3. *Progressive Passes Visualization:*\n - \n\n### Conclusion\n\nScrapelytix makes it easy to scrape football match data and visualize important statistics. By following the steps above, you can analyze and visualize data for any match you are interested in.\n\nFeel free to explore the package and provide feedback or contributions on GitHub!\n\n### Contribution\n\nContributions are welcome! If you find any issues or have suggestions, please open an issue or create a pull request on the [GitHub repository](https://github.com/30debrup/scrapelytix)(https://github.com/gxdfather7/scrapelytix).\n\n### License\n\nThis project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.\n",
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