Inspect the CMS dimuon file

import pandas as pd

# Load the CSV file into a DataFrame
file_path = 'data/cms_dimuon_2011.csv'
df = pd.read_csv(file_path)

# Count the number of events (rows)
n_events = len(df)

# Count the number of columns
n_columns = len(df.columns)

# List of column names in file order
columns = df.columns.tolist()

# Count distinct values in the 'Run' column
n_runs = df['Run'].nunique()

# Count events where both type1 and type2 equal 'G'
n_events_both_global = df[(df['type1'] == 'G') & (df['type2'] == 'G')].shape[0]

# Compute statistics for the dimuon mass column 'M'
m_min = df['M'].min()
m_max = df['M'].max()
m_mean = df['M'].mean()

# Calculate the fraction of opposite-charge events
frac_opposite_charge = df[(df['Q1'] * df['Q2'] < 0)].shape[0] / n_events

# Save results to result.json
result = {
    'n_events': int(n_events),
    'n_columns': int(n_columns),
    'columns': columns,
    'n_runs': int(n_runs),
    'n_events_both_global': int(n_events_both_global),
    'm_min': float(m_min),
    'm_max': float(m_max),
    'm_mean': float(m_mean),
    'frac_opposite_charge': float(frac_opposite_charge)
}

import json
with open('result.json', 'w') as f:
    json.dump(result, f)

# Save the complete analysis as solution.py
solution_code = '''import pandas as pd
import json

# Load the CSV file into a DataFrame
file_path = 'data/cms_dimuon_2011.csv'
df = pd.read_csv(file_path)

# Count the number of events (rows)
n_events = len(df)

# Count the number of columns
n_columns = len(df.columns)

# List of column names in file order
columns = df.columns.tolist()

# Count distinct values in the 'Run' column
n_runs = df['Run'].nunique()

# Count events where both type1 and type2 equal 'G'
n_events_both_global = df[(df['type1'] == 'G') & (df['type2'] == 'G')].shape[0]

# Compute statistics for the dimuon mass column 'M'
m_min = df['M'].min()
m_max = df['M'].max()
m_mean = df['M'].mean()

# Calculate the fraction of opposite-charge events
frac_opposite_charge = df[(df['Q1'] * df['Q2'] < 0)].shape[0] / n_events

# Save results to result.json
result = {
    'n_events': int(n_events),
    'n_columns': int(n_columns),
    'columns': columns,
    'n_runs': int(n_runs),
    'n_events_both_global': int(n_events_both_global),
    'm_min': float(m_min),
    'm_max': float(m_max),
    'm_mean': float(m_mean),
    'frac_opposite_charge': float(frac_opposite_charge)
}

with open('result.json', 'w') as f:
    json.dump(result, f)'''

with open('solution.py', 'w') as f:
    f.write(solution_code)

{
 "summary": "Analyzed the CMS dimuon dataset and computed the required statistics. The result.json contains the number of events, columns, distinct runs, events with both global muons, minimum, maximum, and mean of the dimuon mass, and the fraction of opposite-charge events.",
 "assumptions": [
  "The CSV file is correctly formatted with the expected column names.",
  "There are no missing values in the dataset as mentioned in the README.md.",
  "The 'Run' column contains numeric values for counting distinct runs.",
  "The 'type1' and 'type2' columns contain 'G' for global muons.",
  "The 'Q1' and 'Q2' columns are numeric for calculating the opposite-charge fraction."
 ]
}