import pandas as pd
import os
from dotenv import load_dotenv
from lida import Manager, llm
from llmx import TextGenerationConfig 
import matplotlib.pyplot as plt
import gradio as gr
import seaborn as sns
from openai import OpenAI
import io
from PIL import Image

# Load environment variables from .env
load_dotenv()

# LLM + Manager setup
text_gen = llm(provider="openai", api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o-mini")
manager = Manager(text_gen=text_gen)
config = TextGenerationConfig(model="gpt-4o-mini", temperature=0.2)
client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))

# Load the data from both Excel files
# df_regionalliga_batting = pd.read_excel('F50/BCV-Regionalliga-Bayern 2024 Batting Records - Bayerischer Cricket Verband e.V..xlsx')
df_verbandsliga_batting = pd.read_excel('F50/BCV-Verbandsliga-Bayern 2025 Batting Records - Bayerischer Cricket Verband e.V..xlsx')

# Standardize column names
# df_regionalliga_batting.columns = df_regionalliga_batting.columns.str.strip().str.lower()
df_verbandsliga_batting.columns = df_verbandsliga_batting.columns.str.strip().str.lower()

# Ensure both DataFrames have the same columns
# df_regionalliga_batting = df_regionalliga_batting[df_verbandsliga_batting.columns]

# Merge the two DataFrames along rows
df_combined_batting_odi = df_verbandsliga_batting.copy()  # Start with Verbandsliga data

output_file = 'F50/Combined_BCV_Batting_Records_2025.xlsx'
df_combined_batting_odi.to_excel(output_file, index=False)

# Convert columns 'runs' and 'avg' to numeric, forcing errors to NaN (if any)
df_combined_batting_odi['runs'] = pd.to_numeric(df_combined_batting_odi['runs'], errors='coerce')
df_combined_batting_odi['avg'] = pd.to_numeric(df_combined_batting_odi['avg'], errors='coerce')

# Sort by 'runs' (descending)
top_20_by_runs = df_combined_batting_odi.sort_values(by=['runs'], ascending=[False]).head(20)
top_20_by_runs

# Calculate the mean of 'mat' column to use as 'n'
n = df_combined_batting_odi['mat'].mean()
print('Mean of matches across players:', n)
# Filter players who have played at least 'n' matches
filtered_df = df_combined_batting_odi[df_combined_batting_odi['mat'] >= n]

# Sort by 'avg' in descending order to find the top 10 players by average
top_20_by_avg = filtered_df.sort_values(by=['avg'], ascending=[False]).head(20)
top_20_by_avg

Mean of matches across players: 5.685714285714286

# 20 Over Batting
# Load the data from both Excel files
# df_regionalliga_batting = pd.read_excel('T20/T20 1.Verbandsliga - 2024 Batting Records - Bayerischer Cricket Verband e.V..xlsx')
df_verbandsliga_batting = pd.read_excel('T20/T20 2. Verbandsliga - 2025 Batting Records - Bayerischer Cricket Verband e.V..xlsx')

# Standardize column names
# df_regionalliga_batting.columns = df_regionalliga_batting.columns.str.strip().str.lower()
df_verbandsliga_batting.columns = df_verbandsliga_batting.columns.str.strip().str.lower()

# Ensure both DataFrames have the same columns
# df_regionalliga_batting = df_regionalliga_batting[df_verbandsliga_batting.columns]

# Merge the two DataFrames along rows
# df_combined_batting = pd.concat([df_regionalliga_batting, df_verbandsliga_batting], ignore_index=True)
df_combined_batting_t20 = df_verbandsliga_batting.copy()

output_file = 'T20/Combined_BCV_Batting_Records_2025.xlsx'
df_combined_batting_t20.to_excel(output_file, index=False)

# Convert columns 'runs' and 'avg' to numeric, forcing errors to NaN (if any)
df_combined_batting_t20['runs'] = pd.to_numeric(df_combined_batting_t20['runs'], errors='coerce')
df_combined_batting_t20['avg'] = pd.to_numeric(df_combined_batting_t20['avg'], errors='coerce')

# Sort by 'runs' (descending) 
top_20_by_runs = df_combined_batting_t20.sort_values(by=['runs'], ascending=[False]).head(20)
top_20_by_runs

# Calculate the mean of 'mat' column to use as 'n'
n = df_combined_batting_t20['mat'].mean()
print('Mean of matches across players:', n)
# Filter players who have played at least 'n' matches
filtered_df = df_combined_batting_t20[df_combined_batting_t20['mat'] >= n]

# Sort by 'avg' in descending order to find the top 10 players by average
top_20_by_avg = filtered_df.sort_values(by=['avg'], ascending=[False]).head(20)
top_20_by_avg

Mean of matches across players: 7.0

# Calculate the mean of 'mat' column to use as 'n'
n = df_combined_batting_t20['runs'].mean()
print('Mean of runs across players:', n)
# Filter players who have scored atleast n runs
filtered_df = df_combined_batting_t20[df_combined_batting_t20['runs'] >= n]

# Sort by 'avg' in descending order to find the top 10 players by strike rate
top_20_by_strikerate = filtered_df.sort_values(by=['sr'], ascending=[False]).head(20)
top_20_by_strikerate

Mean of runs across players: 61.11764705882353

# Load the data from both Excel files
# df_regionalliga_bowling = pd.read_excel('F50/BCV-Regionalliga-Bayern 2024 Bowling Records - Bayerischer Cricket Verband e.V..xlsx')
df_verbandsliga_bowling = pd.read_excel('F50/BCV-Verbandsliga-Bayern 2025 Bowling Records - Bayerischer Cricket Verband e.V..xlsx')

# Standardize column names
# df_regionalliga_bowling.columns = df_regionalliga_bowling.columns.str.strip().str.lower()
df_verbandsliga_bowling.columns = df_verbandsliga_bowling.columns.str.strip().str.lower()

# Ensure both DataFrames have the same columns
# df_regionalliga_bowling = df_regionalliga_bowling[df_verbandsliga_bowling.columns]

# Merge the two DataFrames along rows
# df_combined_bowling = pd.concat([df_regionalliga_bowling, df_verbandsliga_bowling], ignore_index=True)
df_combined_bowling_odi =df_verbandsliga_bowling.copy()

output_file = 'F50/Combined_BCV_Bowling_Records_2025.xlsx'
df_combined_bowling_odi.to_excel(output_file, index=False)

# Convert columns 'wkts' and 'econ' to numeric, forcing errors to NaN (if any)
df_combined_bowling_odi['wkts'] = pd.to_numeric(df_combined_bowling_odi['wkts'], errors='coerce')
df_combined_bowling_odi['econ'] = pd.to_numeric(df_combined_bowling_odi['econ'], errors='coerce')
df_combined_bowling_odi['overs'] = pd.to_numeric(df_combined_bowling_odi['overs'], errors='coerce')

# Sort by 'wkts' (descending)
top_20_by_wkts = df_combined_bowling_odi.sort_values(by=['wkts'], ascending=[False]).head(20)
top_20_by_wkts

# Calculate the mean of 'overs' column to use as 'n'
n = df_combined_bowling_odi['overs'].mean()
print('Mean of overs across players:', n)
# Filter players who have bowled min n overs
filtered_df = df_combined_bowling_odi[df_combined_bowling_odi['overs'] >= n]

# Sort by 'economy rate' in ascending order to find the top 10 players by economy
top_20_by_economy = filtered_df.sort_values(by=['econ'], ascending=[True]).head(20)
top_20_by_economy

Mean of overs across players: 23.39565217391304

# Load the data from both Excel files
# df_regionalliga_bowling = pd.read_excel('T20/T20 1.Verbandsliga - 2024 Bowling Records - Bayerischer Cricket Verband e.V..xlsx')
df_verbandsliga_bowling = pd.read_excel('T20/T20 2. Verbandsliga - 2025 Bowling Records - Bayerischer Cricket Verband e.V..xlsx')

# Standardize column names
# df_regionalliga_bowling.columns = df_regionalliga_bowling.columns.str.strip().str.lower()
df_verbandsliga_bowling.columns = df_verbandsliga_bowling.columns.str.strip().str.lower()

# Ensure both DataFrames have the same columns
# df_regionalliga_bowling = df_regionalliga_bowling[df_verbandsliga_bowling.columns]

# Merge the two DataFrames along rows
# df_combined_bowling = pd.concat([df_regionalliga_bowling, df_verbandsliga_bowling], ignore_index=True)
df_combined_bowling_t20 = df_verbandsliga_bowling.copy()

output_file = 'T20/Combined_BCV_Bowling_Records_2025.xlsx'
df_combined_bowling_t20.to_excel(output_file, index=False)

# Convert columns 'wkts' and 'econ' to numeric, forcing errors to NaN (if any)
df_combined_bowling_t20['wkts'] = pd.to_numeric(df_combined_bowling_t20['wkts'], errors='coerce')
df_combined_bowling_t20['econ'] = pd.to_numeric(df_combined_bowling_t20['econ'], errors='coerce')
df_combined_bowling_t20['overs'] = pd.to_numeric(df_combined_bowling_t20['overs'], errors='coerce')

# Sort by 'wkts' (descending)
top_20_by_wkts = df_combined_bowling_t20.sort_values(by=['wkts'], ascending=[False]).head(20)
top_20_by_wkts

# Calculate the mean of 'overs' column to use as 'n'
n = df_combined_bowling_t20['overs'].mean()
print('Mean of overs across players:', n)
# Filter players who have bowled min n overs
filtered_df = df_combined_bowling_t20[df_combined_bowling_t20['overs'] >= n]

# Sort by 'economy rate' in ascending order to find the top 10 players by economy
top_20_by_economy = filtered_df.sort_values(by=['econ'], ascending=[True]).head(20)
top_20_by_economy

Mean of overs across players: 18.413636363636364

# Example: Summarize one dataset
summary = manager.summarize(df_combined_batting_odi, textgen_config=config)
print("Summary:", summary)

# Example: Ask LIDA to generate goals (questions it can answer)
goals = manager.goals(summary=summary, textgen_config=config)
print("Possible goals:", goals)

# Example: Visualize data to answer a specific goal
viz_list = manager.visualize(
    summary=summary,
    goal="Show top 5 run scorers in ODI batting",
    textgen_config=config,
    library="seaborn"
)

viz = viz_list[0]
chart_code = viz.code
# print("Generated code:\n", chart_code)

# Execute the code with your DataFrame as 'data'
exec(chart_code, {"data": df_combined_batting_odi, "plt": plt})

# Show the chart
plt.show()

Summary: {'name': '', 'file_name': '', 'dataset_description': '', 'fields': [{'column': '#', 'properties': {'dtype': 'number', 'std': 10, 'min': 1, 'max': 35, 'samples': [27, 14, 25], 'num_unique_values': 35, 'semantic_type': '', 'description': ''}}, {'column': 'player', 'properties': {'dtype': 'string', 'samples': ['Abhijeesh Maroli', 'Arjun Nadol', 'Nitin Madan'], 'num_unique_values': 35, 'semantic_type': '', 'description': ''}}, {'column': 'group', 'properties': {'dtype': 'category', 'samples': ['South Centre - I', 'South Centre - II'], 'num_unique_values': 2, 'semantic_type': '', 'description': ''}}, {'column': 'team', 'properties': {'dtype': 'category', 'samples': ['BATCC-I', 'BATCC-II'], 'num_unique_values': 2, 'semantic_type': '', 'description': ''}}, {'column': 'mat', 'properties': {'dtype': 'number', 'std': 2, 'min': 1, 'max': 10, 'samples': [1, 6], 'num_unique_values': 10, 'semantic_type': '', 'description': ''}}, {'column': 'inns', 'properties': {'dtype': 'number', 'std': 2, 'min': 1, 'max': 10, 'samples': [1, 6], 'num_unique_values': 10, 'semantic_type': '', 'description': ''}}, {'column': 'no', 'properties': {'dtype': 'number', 'std': 1, 'min': 0, 'max': 4, 'samples': [1, 4], 'num_unique_values': 4, 'semantic_type': '', 'description': ''}}, {'column': 'runs', 'properties': {'dtype': 'number', 'std': 61, 'min': 1, 'max': 243, 'samples': [74, 44], 'num_unique_values': 34, 'semantic_type': '', 'description': ''}}, {'column': "4's", 'properties': {'dtype': 'number', 'std': 5, 'min': 0, 'max': 21, 'samples': [21, 16], 'num_unique_values': 16, 'semantic_type': '', 'description': ''}}, {'column': "6's", 'properties': {'dtype': 'number', 'std': 3, 'min': 0, 'max': 19, 'samples': [7, 3], 'num_unique_values': 8, 'semantic_type': '', 'description': ''}}, {'column': "50's", 'properties': {'dtype': 'number', 'std': 0, 'min': 0, 'max': 2, 'samples': [2, 1], 'num_unique_values': 3, 'semantic_type': '', 'description': ''}}, {'column': "100's", 'properties': {'dtype': 'number', 'std': 0, 'min': 0, 'max': 0, 'samples': [0], 'num_unique_values': 1, 'semantic_type': '', 'description': ''}}, {'column': 'hs', 'properties': {'dtype': 'number', 'std': 20, 'min': 1, 'max': 66, 'samples': [2], 'num_unique_values': 29, 'semantic_type': '', 'description': ''}}, {'column': 'sr', 'properties': {'dtype': 'number', 'std': 27.225843793206867, 'min': 12.5, 'max': 129.95, 'samples': [35.29], 'num_unique_values': 35, 'semantic_type': '', 'description': ''}}, {'column': 'avg', 'properties': {'dtype': 'number', 'std': 8.261177912634594, 'min': 1.0, 'max': 31.67, 'samples': [4.0], 'num_unique_values': 32, 'semantic_type': '', 'description': ''}}, {'column': 'points', 'properties': {'dtype': 'number', 'std': 82, 'min': -5, 'max': 356, 'samples': [-5], 'num_unique_values': 32, 'semantic_type': '', 'description': ''}}], 'field_names': ['#', 'player', 'group', 'team', 'mat', 'inns', 'no', 'runs', "4's", "6's", "50's", "100's", 'hs', 'sr', 'avg', 'points']}
Possible goals: [Goal(question='What is the distribution of runs scored by players?', visualization='histogram of runs', rationale='This tells us about the performance of players in terms of runs scored, allowing us to identify trends, outliers, and the overall scoring distribution among players.', index=0), Goal(question='How do the average scores compare between different teams?', visualization='bar chart of avg grouped by team', rationale='This visualization will help us understand which team has the highest average score, providing insights into team performance and potential areas for improvement.', index=1), Goal(question='What is the relationship between the number of matches played and total runs scored?', visualization='scatter plot of mat vs runs', rationale='This will help us analyze if there is a correlation between the number of matches played and the total runs scored, indicating player consistency and performance over time.', index=2), Goal(question="How many players have scored 50's and 100's?", visualization="bar chart of count of players with 50's and 100's", rationale='This visualization will provide insights into the frequency of significant scoring milestones among players, highlighting standout performances.', index=3), Goal(question='What is the distribution of strike rates across players?', visualization='box plot of sr', rationale="This will help us understand the variability and central tendency of players' strike rates, identifying which players are more aggressive or conservative in their batting approach.", index=4)]

<string>:14: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

<string>:17: UserWarning: No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
<string>:14: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

<string>:17: UserWarning: No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.

# Datasets
datasets = {
    "Batting ODI": df_combined_batting_odi,
    "Batting T20": df_combined_batting_t20,
    "Bowling ODI": df_combined_bowling_odi,
    "Bowling T20": df_combined_bowling_t20
}

# Helper function to convert matplotlib figure to image
def plot_to_image(fig):
    buf = io.BytesIO()
    fig.savefig(buf, format="png", bbox_inches="tight")
    buf.seek(0)
    img = Image.open(buf)
    return img

# Helper function to generate text answer using OpenAI
def get_text_answer(df, question):
    # Convert DataFrame to CSV string for context (limit size if needed)
    csv_str = df.to_csv(index=False)
    prompt = f"Here is cricket data:\n{csv_str}\n\nQuestion: {question}\nAnswer in concise text."
    
    response = client.chat.completions.create(
        model="gpt-4",
        messages=[
            {"role": "system", "content": "You are a cricket stats analyst."},
            {"role": "user", "content": prompt}
        ],
        temperature=0
        )
    answer = response.choices[0].message.content
    # print("Answer:", answer)
    return answer

# Gradio function
def ask(dataset_name, question, return_type):
    df = datasets[dataset_name]
    
    if return_type == "Text Answer":
        answer = get_text_answer(df, question)
        return answer, None  # second output (chart) is None
    
    elif return_type == "Chart":
        summary = manager.summarize(df, textgen_config=config)
        viz_list = manager.visualize(summary=summary, goal=question, textgen_config=config, library="seaborn")
        viz = viz_list[0]
        chart_code = viz.code
        
        plt.figure(figsize=(10,6))
        exec(chart_code, {"data": df, "plt": plt, "sns": sns})
        fig = plt.gcf()
        plt.close(fig)
        
        return "", plot_to_image(fig)  # first output empty text, second output chart image


# Gradio interface
with gr.Blocks(css="""
#input-row {background-color: #ffffff; padding: 20px; border-radius: 10px; box-shadow: 0 4px 8px rgba(0,0,0,0.1);}
#dataset-dropdown, #query-textbox, #return-type {background-color: #ffffff; border-radius: 8px; border: 1px solid #ccc;}
#ask-button {background-color: #1f77b4; color: #ffffff; border-radius: 8px;}
#text-output {background-color: #ffffff; border-radius: 8px; border: 1px solid #ccc;}
#chart-output {border: 2px solid #1f77b4; border-radius: 8px;}
""") as iface:
    
    # Page background
    gr.HTML("<style>body {background-color: #f0f4f8;}</style>")

    # Top section: inputs and button inside a styled column (like a card)
    with gr.Row(elem_id="input-row", variant="panel"):
        dataset_dropdown = gr.Dropdown(
            choices=list(datasets.keys()), label="Select Dataset", elem_id="dataset-dropdown"
        )
        query_textbox = gr.Textbox(
            label="Your Question", lines=2, elem_id="query-textbox"
        )
        return_type = gr.Radio(
            choices=["Text Answer", "Chart"], label="Return Type", elem_id="return-type"
        )
        submit_btn = gr.Button("Ask", elem_id="ask-button")

    # Text answer
    text_output = gr.Textbox(label="Answer", lines=5, elem_id="text-output")

    # Chart at bottom
    chart_output = gr.Image(label="Chart", elem_id="chart-output")

    # Connect function
    submit_btn.click(
        ask,
        inputs=[dataset_dropdown, query_textbox, return_type],
        outputs=[text_output, chart_output]
    )

# Launch interface without css parameter
iface.launch(share=True)

* Running on local URL:  http://127.0.0.1:7861
* Running on public URL: https://1c7f4610628da651b2.gradio.live

This share link expires in 1 week. For free permanent hosting and GPU upgrades, run `gradio deploy` from the terminal in the working directory to deploy to Hugging Face Spaces (https://huggingface.co/spaces)

# ---- CREATE TOTAL RUNS PER PLAYER ----
df_batting_all = pd.concat(
    [df_combined_batting_odi, df_combined_batting_t20],
    ignore_index=True
)

df_batting_totals = (
    df_batting_all.groupby("player", as_index=False)["runs"]
    .sum()
    .rename(columns={"runs": "Total Runs"})
)


# ---- CREATE TOTAL WICKETS PER PLAYER ----
df_bowling_all = pd.concat(
    [df_combined_bowling_odi, df_combined_bowling_t20],
    ignore_index=True
)

df_bowling_totals = (
    df_bowling_all.groupby("player", as_index=False)["wkts"]
    .sum()
    .rename(columns={"wkts": "Total Wickets"})
)

import matplotlib.pyplot as plt

# Sort and take top 10
top10 = df_bowling_totals.sort_values(
    by="Total Wickets", ascending=False
).head(10)

plt.figure(figsize=(10, 6))
plt.bar(top10["player"], top10["Total Wickets"])
plt.xticks(rotation=45, ha='right')
plt.xlabel("Player")
plt.ylabel("Total Wickets")
plt.title("Top 10 Players by Total Wickets (Combined ODI + T20)")
plt.tight_layout()
plt.show()

top10_batting = df_batting_totals.sort_values(
    by="Total Runs", ascending=False
).head(10)

plt.figure(figsize=(10, 6))
plt.bar(top10_batting["player"], top10_batting["Total Runs"])
plt.xticks(rotation=45, ha='right')
plt.xlabel("Player")
plt.ylabel("Total Runs")
plt.title("Top 10 Players by Total Runs (Combined ODI + T20)")
plt.tight_layout()
plt.show()

	#	player	group	team	mat	inns	overs	runs	wkts	bbf	...	dots	econ	avg	sr	4w	5w	wides	nb	points
0	1	Abdul Nasir Kunari	South Centre - I	BATCC-I	8	8	50.0	116	18	9/ 4	...	244	2.32	6.44	16.7	1	0	22	0	570
1	2	Muhammed Minhaj Madathodi	South Centre - II	BATCC-II	8	8	54.5	269	13	27/ 3	...	238	4.91	20.69	25.3	0	0	62	9	388
2	3	Amit Rajendra Desai	South Centre - I	BATCC-I	7	7	37.2	138	12	28/ 3	...	166	3.70	11.50	18.7	0	0	33	4	383
3	4	Mohammed Shakir	South Centre - II	BATCC-II	5	5	36.0	155	11	22/ 5	...	145	4.31	14.09	19.6	0	1	32	1	351
4	5	Haris Ali	South Centre - I	BATCC-I	8	8	38.1	154	10	7/ 4	...	175	4.03	15.40	22.9	1	0	45	4	332
5	6	Mohammad Essa Haqbin	South Centre - I	BATCC-I	9	5	21.5	58	8	4/ 3	...	108	2.66	7.25	16.4	0	0	10	2	258
6	7	Rajesh Kumar Srivastava	South Centre - I	BATCC-I	10	8	30.2	142	8	51/ 3	...	128	4.68	17.75	22.8	0	0	29	3	225
7	8	Paras Jain	South Centre - II	BATCC-II	3	3	15.0	83	8	5/ 5	...	65	5.53	10.38	11.2	0	1	17	1	240
8	9	Bhavin Kantilal Solanki	South Centre - II	BATCC-II	7	7	34.5	194	8	27/ 3	...	137	5.57	24.25	26.1	0	0	44	2	221
9	10	Vineep Bhat	South Centre - I	BATCC-I	8	6	30.0	67	7	16/ 4	...	137	2.23	9.57	25.7	1	0	5	0	280
10	11	Nitin Madan	South Centre - I	BATCC-I	5	4	23.3	83	7	28/ 5	...	103	3.53	11.86	20.1	0	1	5	1	224
11	12	Rohit Verma	South Centre - I	BATCC-I	5	5	16.3	58	6	23/ 3	...	69	3.52	9.67	16.5	0	0	6	3	167
12	13	Satya Prakash Sahoo	South Centre - I	BATCC-I	5	5	27.0	63	5	10/ 2	...	134	2.33	12.60	32.4	0	0	8	0	231
13	14	Venkat Vinay Ayyagari	South Centre - II	BATCC-II	7	7	41.1	194	5	45/ 2	...	156	4.71	38.80	49.4	0	0	17	3	145
14	15	Ankit Gupta	South Centre - II	BATCC-II	8	6	19.0	114	5	6/ 2	...	68	6.00	22.80	22.8	0	0	11	2	140
15	16	Moynul Hasan	South Centre - I	BATCC-I	4	3	11.0	62	4	23/ 2	...	48	5.64	15.50	16.5	0	0	11	1	106
16	17	Muhammad Omer Farooq	South Centre - II	BATCC-II	5	2	6.2	37	3	34/ 2	...	22	5.84	12.33	12.7	0	0	5	0	75
17	18	Shahroz Ismail	South Centre - II	BATCC-II	1	1	7.0	44	3	44/ 3	...	27	6.29	14.67	14.0	0	0	7	0	76
18	19	Avi Patel	South Centre - I	BATCC-I	8	2	4.2	11	2	8/ 2	...	19	2.54	5.50	13.0	0	0	2	0	62
19	20	Abhijeesh Maroli	South Centre - II	BATCC-II	3	2	9.0	83	2	33/ 1	...	26	9.22	41.50	27.0	0	0	11	2	40

	#	player	group	team	mat	inns	overs	runs	wkts	bbf	...	dots	econ	avg	sr	4w	5w	wides	nb	points
9	10	Vineep Bhat	South Centre - I	BATCC-I	8	6	30.0	67	7	16/ 4	...	137	2.23	9.57	25.7	1	0	5	0	280
0	1	Abdul Nasir Kunari	South Centre - I	BATCC-I	8	8	50.0	116	18	9/ 4	...	244	2.32	6.44	16.7	1	0	22	0	570
12	13	Satya Prakash Sahoo	South Centre - I	BATCC-I	5	5	27.0	63	5	10/ 2	...	134	2.33	12.60	32.4	0	0	8	0	231
2	3	Amit Rajendra Desai	South Centre - I	BATCC-I	7	7	37.2	138	12	28/ 3	...	166	3.70	11.50	18.7	0	0	33	4	383
4	5	Haris Ali	South Centre - I	BATCC-I	8	8	38.1	154	10	7/ 4	...	175	4.03	15.40	22.9	1	0	45	4	332
3	4	Mohammed Shakir	South Centre - II	BATCC-II	5	5	36.0	155	11	22/ 5	...	145	4.31	14.09	19.6	0	1	32	1	351
6	7	Rajesh Kumar Srivastava	South Centre - I	BATCC-I	10	8	30.2	142	8	51/ 3	...	128	4.68	17.75	22.8	0	0	29	3	225
13	14	Venkat Vinay Ayyagari	South Centre - II	BATCC-II	7	7	41.1	194	5	45/ 2	...	156	4.71	38.80	49.4	0	0	17	3	145
1	2	Muhammed Minhaj Madathodi	South Centre - II	BATCC-II	8	8	54.5	269	13	27/ 3	...	238	4.91	20.69	25.3	0	0	62	9	388
8	9	Bhavin Kantilal Solanki	South Centre - II	BATCC-II	7	7	34.5	194	8	27/ 3	...	137	5.57	24.25	26.1	0	0	44	2	221

	#	player	group	team	mat	inns	overs	runs	wkts	bbf	...	dots	econ	avg	sr	4w	5w	wides	nb	points
0	1	Vineep Bhat	South Centre - I	BATCC-I	10	10	37.5	196	18	18/ 4	...	121	5.18	10.89	12.6	1	0	23	0	456
1	2	Muhammed Minhaj Madathodi	South Centre - II	BATCC-II	12	12	44.0	253	17	16/ 3	...	166	5.75	14.88	15.5	0	0	42	2	401
2	3	Abdul Nasir Kunari	South Centre - I	BATCC-I	10	10	36.0	169	14	10/ 4	...	132	4.69	12.07	15.4	2	0	21	2	358
3	4	Satya Prakash Sahoo	South Centre - I	BATCC-I	8	8	30.0	133	13	15/ 3	...	101	4.43	10.23	13.8	0	0	6	2	330
4	5	Amit Rajendra Desai	South Centre - I	BATCC-I	9	9	31.5	177	13	16/ 3	...	116	5.56	13.62	14.7	0	0	17	1	325
5	6	Rajesh Kumar Srivastava	South Centre - II	BATCC-II	12	10	34.0	212	11	20/ 3	...	125	6.24	19.27	18.5	0	0	30	0	293
6	7	Mohammed Shakir	South Centre - II	BATCC-II	10	10	35.0	191	9	11/ 2	...	126	5.46	21.22	23.3	0	0	16	2	275
7	8	Bhavin Kantilal Solanki	South Centre - II	BATCC-II	9	8	27.4	158	8	20/ 3	...	110	5.71	19.75	20.8	0	0	36	1	222
8	9	Venkat Vinay Ayyagari	South Centre - II	BATCC-II	12	11	34.0	196	8	15/ 2	...	110	5.76	24.50	25.5	0	0	27	1	198
9	10	Nitin Madan	South Centre - I	BATCC-I	4	4	9.1	73	7	27/ 5	...	26	7.96	10.43	7.9	0	1	3	0	156
10	11	Paras Jain	South Centre - II	BATCC-II	4	3	9.2	63	4	20/ 2	...	34	6.75	15.75	14.0	0	0	8	0	86
13	14	Avi Patel	South Centre - I	BATCC-I	6	3	9.0	64	3	20/ 2	...	29	7.11	21.33	18.0	0	0	3	0	73
11	12	Deepak Kamal	South Centre - II	BATCC-II	11	3	7.0	43	3	6/ 1	...	19	6.14	14.33	14.0	0	0	2	0	67
12	13	Mohammad Essa Haqbin	South Centre - I	BATCC-I	10	7	14.4	96	3	9/ 1	...	39	6.55	32.00	29.3	0	0	10	1	70
14	15	Haris Ali	South Centre - I	BATCC-I	4	4	10.0	52	2	12/ 1	...	37	5.20	26.00	30.0	0	0	11	2	53
15	16	Abhijeesh Marol	South Centre - II	BATCC-II	7	4	11.0	66	2	12/ 1	...	32	6.00	33.00	33.0	0	0	13	1	48
16	17	Deep Lamba	South Centre - II	BATCC-II	2	2	3.0	25	2	9/ 1	...	9	8.33	12.50	9.0	0	0	3	1	39
17	18	Fahad Munawwar	South Centre - I	BATCC-I	2	2	6.0	51	2	38/ 2	...	15	8.50	25.50	18.0	0	0	2	3	44
18	19	Moynul Hasan	South Centre - I	BATCC-I	1	1	2.0	4	1	4/ 1	...	8	2.00	4.00	12.0	0	0	0	0	27
19	20	Ankit Gupta	South Centre - II	BATCC-II	10	4	10.0	64	1	26/ 1	...	31	6.40	64.00	60.0	0	0	10	1	30

	#	player	group	team	mat	inns	overs	runs	wkts	bbf	...	dots	econ	avg	sr	4w	wides	nb	points
3	4	Satya Prakash Sahoo	South Centre - I	BATCC-I	8	8	30.0	133	13	15/ 3	...	101	4.43	10.23	13.8	0	6	2	330
2	3	Abdul Nasir Kunari	South Centre - I	BATCC-I	10	10	36.0	169	14	10/ 4	...	132	4.69	12.07	15.4	2	21	2	358
0	1	Vineep Bhat	South Centre - I	BATCC-I	10	10	37.5	196	18	18/ 4	...	121	5.18	10.89	12.6	1	23	0	456
6	7	Mohammed Shakir	South Centre - II	BATCC-II	10	10	35.0	191	9	11/ 2	...	126	5.46	21.22	23.3	0	16	2	275
4	5	Amit Rajendra Desai	South Centre - I	BATCC-I	9	9	31.5	177	13	16/ 3	...	116	5.56	13.62	14.7	0	17	1	325
7	8	Bhavin Kantilal Solanki	South Centre - II	BATCC-II	9	8	27.4	158	8	20/ 3	...	110	5.71	19.75	20.8	0	36	1	222
1	2	Muhammed Minhaj Madathodi	South Centre - II	BATCC-II	12	12	44.0	253	17	16/ 3	...	166	5.75	14.88	15.5	0	42	2	401
8	9	Venkat Vinay Ayyagari	South Centre - II	BATCC-II	12	11	34.0	196	8	15/ 2	...	110	5.76	24.50	25.5	0	27	1	198
5	6	Rajesh Kumar Srivastava	South Centre - II	BATCC-II	12	10	34.0	212	11	20/ 3	...	125	6.24	19.27	18.5	0	30	0	293

50 Over Batting¶

Top 20 by runs¶

Top 20 by average. Only those considered who have played more matches than the team mean.¶

T20 Batting¶

Top 20 by runs¶

Top 20 by average. Only those conisdered who have played more matches than the team mean.¶

Top 20 by strike rate. Only those conisdered who have scored more than the team mean.¶

50 Over Bowling¶

Top 20 wicket takers¶

Top 20 by economy rate. Only those conisdered who have bowled more than the team mean.¶

T20 Bowling¶

Top 20 wicket takers¶

Top 20 by economy rate. Only those conisdered who have bowled more than the team mean.¶

Natual Language Statistics¶

Gradio Interface¶

Calculate Best Allrounder¶

	#	player	group	team	mat	inns	no	runs	4's	6's	50's	hs	sr	avg	points
0	1	Ashish Joshi	South Centre - II	BATCC-II	8	8	0	243	21	19	2	66	129.95	30.38	356
1	2	Avi Patel	South Centre - I	BATCC-I	8	8	0	176	16	7	2	65	92.63	22.00	233
2	3	Ankit Gupta	South Centre - II	BATCC-II	8	8	0	172	13	0	1	64	51.19	21.50	186
3	4	Sanjeev Sivaraman	South Centre - I	BATCC-I	6	6	0	167	18	0	1	59	61.17	27.83	198
4	5	Muhammad Talha	South Centre - I	BATCC-I	10	10	1	164	16	4	1	62	67.49	18.22	188
5	6	Mohammad Essa Haqbin	South Centre - I	BATCC-I	9	9	0	145	12	1	1	54	69.38	16.11	167
6	7	Sasi Kiran Alur	South Centre - I	BATCC-I	5	5	0	126	12	0	1	66	67.02	25.20	145
7	8	Rajesh Kumar Srivastava	South Centre - I	BATCC-I	10	10	0	122	6	3	0	41	61.00	12.20	124
8	9	Vineep Bhat	South Centre - I	BATCC-I	8	8	0	120	6	0	0	45	61.86	15.00	122
9	10	Bhavin Kantilal Solanki	South Centre - II	BATCC-II	7	7	0	113	8	4	0	27	58.25	16.14	128
10	11	Venkat Vinay Ayyagari	South Centre - II	BATCC-II	7	7	0	100	13	2	0	38	89.29	14.29	127
11	12	Zeeshan Sami	South Centre - I	BATCC-I	4	4	1	95	9	0	0	35	63.33	31.67	106
12	13	Haris Ali	South Centre - I	BATCC-I	8	8	3	89	8	1	0	22	65.44	17.80	98
13	14	Arjun Nadol	South Centre - II	BATCC-II	5	5	0	85	5	8	0	34	108.97	17.00	120
14	15	Rohit Verma	South Centre - I	BATCC-I	5	5	0	77	11	2	0	42	79.38	15.40	102
15	16	Satya Prakash Sahoo	South Centre - I	BATCC-I	5	5	0	74	3	2	0	32	54.81	14.80	83
16	17	Muhammad Omer Farooq	South Centre - II	BATCC-II	5	5	1	61	3	0	0	48	36.75	15.25	52
17	18	Deepak Kamal	South Centre - II	BATCC-II	7	7	0	59	6	0	0	39	46.09	8.43	57
18	19	Paras Jain	South Centre - II	BATCC-II	3	3	0	53	6	2	0	25	86.89	17.67	67
19	20	Sam Antony Vathezhath Judy	South Centre - II	BATCC-II	7	7	0	44	6	0	0	15	45.36	6.29	34