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Update pages/1player_information.py
Browse files- pages/1player_information.py +56 -0
pages/1player_information.py
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import streamlit as st
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import pandas as pd
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import matplotlib.pyplot as plt
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import seaborn as sns
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# Load data
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file_path = "South_Africa_Final2.csv"
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df = pd.read_csv(file_path)
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st.title("South Africa Cricket Players - Career Visualizations")
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# Enter Player Name
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player_input = st.text_input("Enter Player Name:")
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if player_input:
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selected_player = player_input.strip()
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if selected_player in df["Player"].values:
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player_data = df[df["Player"] == selected_player].iloc[0]
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# Pie Chart - Matches Played Across Formats
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matches = [
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player_data["Matches_Test"],
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player_data["Matches_ODI"],
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player_data["Matches_T20"],
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player_data["Matches_IPL"]
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]
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labels = ["Test", "ODI", "T20", "IPL"]
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fig, ax = plt.subplots()
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ax.pie(matches, labels=labels, autopct="%1.1f%%", startangle=90, colors=["blue", "green", "red", "purple"])
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ax.set_title(f"Matches Played by {selected_player}")
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st.pyplot(fig)
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# Bar Chart - Runs Scored in Different Formats
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batting_runs = [
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player_data["batting_Runs_Test"],
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player_data["batting_Runs_ODI"],
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player_data["batting_Runs_T20"],
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player_data["batting_Runs_IPL"]
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]
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fig, ax = plt.subplots()
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ax.bar(labels, batting_runs, color=["blue", "green", "red", "purple"])
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ax.set_ylabel("Runs Scored")
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ax.set_title(f"Runs Scored by {selected_player}")
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st.pyplot(fig)
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# Scatter Plot - Matches vs Runs
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fig, ax = plt.subplots()
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sns.scatterplot(x=df["Matches_ODI"], y=df["batting_Runs_ODI"], ax=ax)
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ax.set_xlabel("Matches Played")
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ax.set_ylabel("Runs Scored")
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ax.set_title("Matches vs Runs in ODIs (All Players)")
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st.pyplot(fig)
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else:
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st.error("Player not found! Please enter a valid player name.")
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st.write("Enter a player's name to explore their statistics!")
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