import pandas as pd
import matplotlib.pyplot as plt

# Load the zipped datasets
with pd.read_csv('/mnt/data/f1db_csv.zip', compression='zip') as zip_file:
    # Load the necessary CSV files
    drivers_df = pd.read_csv(zip_file.open('drivers.csv'))
    driver_standings_df = pd.read_csv(zip_file.open('driver_standings.csv'))
    races_df = pd.read_csv(zip_file.open('races.csv'))

# Filter for Alonso and Hamilton
alonso_hamilton_ids = drivers_df[(drivers_df['surname'] == 'Alonso') | (drivers_df['surname'] == 'Hamilton')]['driverId']

# Filter driver standings for 2023 and the two drivers
driver_standings_2023 = driver_standings_df[driver_standings_df['raceId'].isin(races_df[races_df['year'] == 2023]['raceId'])]
alonso_hamilton_standings = driver_standings_2023[driver_standings_2023['driverId'].isin(alonso_hamilton_ids)]

# Merge with races for round information
alonso_hamilton_standings = alonso_hamilton_standings.merge(races_df[['raceId', 'round']], on='raceId')

# Pivot the data for plotting
plot_data = alonso_hamilton_standings.pivot(index='round', columns='driverId', values='points')

# Get driver names for labeling
driver_names = drivers_df.set_index('driverId').loc[plot_data.columns, 'surname']

# Plotting
plt.figure(figsize=(12, 6))
for driver_id in plot_data.columns:
    plt.plot(plot_data.index, plot_data[driver_id], label=driver_names[driver_id], marker='o')
plt.title('Alonso and Hamilton Driver Standing Points for 2023 Season')
plt.xlabel('Round')
plt.ylabel('Points')
plt.legend()
plt.grid(True)
plt.show()


---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In[1], line 5
      2 import matplotlib.pyplot as plt
      4 # Load the zipped datasets
----> 5 with pd.read_csv('/mnt/data/f1db_csv.zip', compression='zip') as zip_file:
      6     # Load the necessary CSV files
      7     drivers_df = pd.read_csv(zip_file.open('drivers.csv'))
      8     driver_standings_df = pd.read_csv(zip_file.open('driver_standings.csv'))

File ~/.local/lib/python3.8/site-packages/pandas/util/_decorators.py:311, in deprecate_nonkeyword_arguments.<locals>.decorate.<locals>.wrapper(*args, **kwargs)
    305 if len(args) > num_allow_args:
    306     warnings.warn(
    307         msg.format(arguments=arguments),
    308         FutureWarning,
    309         stacklevel=stacklevel,
    310     )
--> 311 return func(*args, **kwargs)

File ~/.local/lib/python3.8/site-packages/pandas/io/parsers/readers.py:586, in read_csv(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, error_bad_lines, warn_bad_lines, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)
    571 kwds_defaults = _refine_defaults_read(
    572     dialect,
    573     delimiter,
   (...)
    582     defaults={"delimiter": ","},
    583 )
    584 kwds.update(kwds_defaults)
--> 586 return _read(filepath_or_buffer, kwds)

File ~/.local/lib/python3.8/site-packages/pandas/io/parsers/readers.py:482, in _read(filepath_or_buffer, kwds)
    479 _validate_names(kwds.get("names", None))
    481 # Create the parser.
--> 482 parser = TextFileReader(filepath_or_buffer, **kwds)
    484 if chunksize or iterator:
    485     return parser

File ~/.local/lib/python3.8/site-packages/pandas/io/parsers/readers.py:811, in TextFileReader.__init__(self, f, engine, **kwds)
    808 if "has_index_names" in kwds:
    809     self.options["has_index_names"] = kwds["has_index_names"]
--> 811 self._engine = self._make_engine(self.engine)

File ~/.local/lib/python3.8/site-packages/pandas/io/parsers/readers.py:1040, in TextFileReader._make_engine(self, engine)
   1036     raise ValueError(
   1037         f"Unknown engine: {engine} (valid options are {mapping.keys()})"
   1038     )
   1039 # error: Too many arguments for "ParserBase"
-> 1040 return mapping[engine](self.f, **self.options)

File ~/.local/lib/python3.8/site-packages/pandas/io/parsers/c_parser_wrapper.py:51, in CParserWrapper.__init__(self, src, **kwds)
     48 kwds["usecols"] = self.usecols
     50 # open handles
---> 51 self._open_handles(src, kwds)
     52 assert self.handles is not None
     54 # Have to pass int, would break tests using TextReader directly otherwise :(

File ~/.local/lib/python3.8/site-packages/pandas/io/parsers/base_parser.py:222, in ParserBase._open_handles(self, src, kwds)
    218 def _open_handles(self, src: FilePathOrBuffer, kwds: dict[str, Any]) -> None:
    219     """
    220     Let the readers open IOHandles after they are done with their potential raises.
    221     """
--> 222     self.handles = get_handle(
    223         src,
    224         "r",
    225         encoding=kwds.get("encoding", None),
    226         compression=kwds.get("compression", None),
    227         memory_map=kwds.get("memory_map", False),
    228         storage_options=kwds.get("storage_options", None),
    229         errors=kwds.get("encoding_errors", "strict"),
    230     )

File ~/.local/lib/python3.8/site-packages/pandas/io/common.py:679, in get_handle(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)
    677             raise ValueError(f"Zero files found in ZIP file {path_or_buf}")
    678         else:
--> 679             raise ValueError(
    680                 "Multiple files found in ZIP file. "
    681                 f"Only one file per ZIP: {zip_names}"
    682             )
    684 # XZ Compression
    685 elif compression == "xz":

ValueError: Multiple files found in ZIP file. Only one file per ZIP: ['circuits.csv', 'constructor_results.csv', 'constructor_standings.csv', 'constructors.csv', 'driver_standings.csv', 'drivers.csv', 'lap_times.csv', 'pit_stops.csv', 'qualifying.csv', 'races.csv', 'results.csv', 'seasons.csv', 'sprint_results.csv', 'status.csv']


# It seems there was an error in the previous approach. Let's try a different method to handle the zip file and read the data.

import pandas as pd
import matplotlib.pyplot as plt
import zipfile

# Path to the zip file
zip_file_path = '/mnt/data/f1db_csv.zip'

# Open the zip file
with zipfile.ZipFile(zip_file_path, 'r') as zip_file:
    # Read the necessary CSV files
    with zip_file.open('drivers.csv') as file:
        drivers_df = pd.read_csv(file)

    with zip_file.open('driver_standings.csv') as file:
        driver_standings_df = pd.read_csv(file)

    with zip_file.open('races.csv') as file:
        races_df = pd.read_csv(file)

# Filter for Alonso and Hamilton
alonso_hamilton_ids = drivers_df[(drivers_df['surname'] == 'Alonso') | (drivers_df['surname'] == 'Hamilton')]['driverId']

# Filter driver standings for 2023 and the two drivers
driver_standings_2023 = driver_standings_df[driver_standings_df['raceId'].isin(races_df[races_df['year'] == 2023]['raceId'])]
alonso_hamilton_standings = driver_standings_2023[driver_standings_2023['driverId'].isin(alonso_hamilton_ids)]

# Merge with races for round information
alonso_hamilton_standings = alonso_hamilton_standings.merge(races_df[['raceId', 'round']], on='raceId')

# Pivot the data for plotting
plot_data = alonso_hamilton_standings.pivot(index='round', columns='driverId', values='points')

# Get driver names for labeling
driver_names = drivers_df.set_index('driverId').loc[plot_data.columns, 'surname']

# Plotting
plt.figure(figsize=(12, 6))
for driver_id in plot_data.columns:
    plt.plot(plot_data.index, plot_data[driver_id], label=driver_names[driver_id], marker='o')
plt.title('Alonso and Hamilton Driver Standing Points for 2023 Season')
plt.xlabel('Round')
plt.ylabel('Points')
plt.legend()
plt.grid(True)
plt.show()


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