PermissionError: [Errno 13] Permission denied:を解決したい。

PermissionError                           Traceback (most recent call last)
Cell In[8], line 122
    118     plot_scatter3d(pca_df)
    121 if __name__ == "__main__":
--> 122     main()

Cell In[8], line 100, in main()
     97 CSV_PATH = 'C.csv'
     99 try:
--> 100     pca_df = pd.read_csv(CSV_PATH)
    101 except FileNotFoundError:
    102     npy_image_list = load_image(LOAD_PATH)

File ~\anaconda3\Lib\site-packages\pandas\io\parsers\readers.py:1026, in read_csv(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, 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, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)
   1013 kwds_defaults = _refine_defaults_read(
   1014     dialect,
   1015     delimiter,
   (...)
   1022     dtype_backend=dtype_backend,
   1023 )
   1024 kwds.update(kwds_defaults)
-> 1026 return _read(filepath_or_buffer, kwds)

File ~\anaconda3\Lib\site-packages\pandas\io\parsers\readers.py:620, in _read(filepath_or_buffer, kwds)
    617 _validate_names(kwds.get("names", None))
    619 # Create the parser.
--> 620 parser = TextFileReader(filepath_or_buffer, **kwds)
    622 if chunksize or iterator:
    623     return parser

File ~\anaconda3\Lib\site-packages\pandas\io\parsers\readers.py:1620, in TextFileReader.__init__(self, f, engine, **kwds)
   1617     self.options["has_index_names"] = kwds["has_index_names"]
   1619 self.handles: IOHandles | None = None
-> 1620 self._engine = self._make_engine(f, self.engine)

File ~\anaconda3\Lib\site-packages\pandas\io\parsers\readers.py:1880, in TextFileReader._make_engine(self, f, engine)
   1878     if "b" not in mode:
   1879         mode += "b"
-> 1880 self.handles = get_handle(
   1881     f,
   1882     mode,
   1883     encoding=self.options.get("encoding", None),
   1884     compression=self.options.get("compression", None),
   1885     memory_map=self.options.get("memory_map", False),
   1886     is_text=is_text,
   1887     errors=self.options.get("encoding_errors", "strict"),
   1888     storage_options=self.options.get("storage_options", None),
   1889 )
   1890 assert self.handles is not None
   1891 f = self.handles.handle

File ~\anaconda3\Lib\site-packages\pandas\io\common.py:873, in get_handle(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)
    868 elif isinstance(handle, str):
    869     # Check whether the filename is to be opened in binary mode.
    870     # Binary mode does not support 'encoding' and 'newline'.
    871     if ioargs.encoding and "b" not in ioargs.mode:
    872         # Encoding
--> 873         handle = open(
    874             handle,
    875             ioargs.mode,
    876             encoding=ioargs.encoding,
    877             errors=errors,
    878             newline="",
    879         )
    880     else:
    881         # Binary mode
    882         handle = open(handle, ioargs.mode)

PermissionError: [Errno 13] Permission denied: 'C.csv'

import glob as gb
import shutil
import cv2
import os
from sklearn.cluster import KMeans
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
import seaborn as sns
from mpl_toolkits.mplot3d import Axes3D


# 画像の読み込み
def load_image(path):
    image_list = []
    npy_image_list = []
    image_path_list = gb.glob(f"{path}/*")  # 画像の保存先次第で変更

    for i, image in enumerate(image_path_list):
        image_path = image.replace(chr(92), '/')  # Windows特有の問題を修正
        if i % 100 == 0:
            print(i)
        
        img_npy = cv2.imread(image_path, cv2.IMREAD_COLOR)
        img_npy = cv2.cvtColor(img_npy, cv2.COLOR_BGR2RGB)
        img_npy = cv2.resize(img_npy, (64, 64))
        img_npy = img_npy.flatten()
        npy_image_list.append(img_npy / 255)

    return npy_image_list


def build_kmeans(df, cluster_num):
    kmeans = KMeans(n_clusters=cluster_num, random_state=2021)
    kmeans.fit(df)
    return kmeans


def build_pca(df):
    pca = PCA()
    pca.fit(df)
    return pca


def plot_contribution_rate(pca):
    fig = plt.figure()
    plt.gca().get_xaxis().set_major_locator(ticker.MaxNLocator(integer=True))
    plt.plot([0] + list(np.cumsum(pca.explained_variance_ratio_)), "-o")
    plt.xlabel("Number of principal components")
    plt.ylabel("Cumulative contribution rate")
    plt.grid()
    plt.show()


def plot_scatter2d(df):
    fig = plt.figure()
    sns.scatterplot(data=df, x='PC1', y='PC2', hue='label', palette='bright', legend='full')
    plt.show()


def plot_scatter3d(df):
    fig = plt.figure()
    ax = Axes3D(fig)
    ax.set_xlabel("PC1")
    ax.set_ylabel("PC2")
    ax.set_zlabel("PC3")

    for label in df['label'].values:
        ax.plot(df.loc[df['label'] == label, 'PC1'],
                df.loc[df['label'] == label, 'PC2'],
                df.loc[df['label'] == label, 'PC3'],
                alpha=0.8, marker=".", linestyle='None')
    plt.show()


def make_cluster_dir(load_path, save_path, kmeans):
    shutil.rmtree(save_path, ignore_errors=True)
    os.makedirs(save_path, exist_ok=True)

    for i in range(kmeans.n_clusters):
        cluster_dir = os.path.join(save_path, f"cluster{i}")
        os.makedirs(cluster_dir, exist_ok=True)

    image_path_list = gb.glob(os.path.join(load_path, '*'))
    for label, path in zip(kmeans.labels_, image_path_list):
        shutil.copyfile(path, os.path.join(save_path, f'cluster{label}', os.path.basename(path)))

    print('クラスタごとにファイル作成完了')


def main():
    LOAD_PATH = 'A'
    SAVE_PATH = 'B'
    CSV_PATH = 'C.csv'
    
    try:
        pca_df = pd.read_csv(CSV_PATH)
    except FileNotFoundError:
        npy_image_list = load_image(LOAD_PATH)
        df = pd.DataFrame(npy_image_list)
        print(df.shape)
        
        pca = build_pca(df)
        pca_df = pd.DataFrame(pca.transform(df), columns=[f"PC{x + 1}" for x in range(len(df.columns))])
        plot_contribution_rate(pca)
        pca_df.to_csv(CSV_PATH, index=False)
    
    train_df = pca_df.iloc[:, :1200]
    cluster_num = int(input('cluster_num > '))
    kmeans = build_kmeans(train_df, cluster_num)
    make_cluster_dir(LOAD_PATH, SAVE_PATH, kmeans)

    pca_df['label'] = kmeans.labels_
    plot_scatter2d(pca_df)
    plot_scatter3d(pca_df)


if __name__ == "__main__":
    main()