fix: minor data update

data/processed/0_w_doc.py

@@ -21,7 +21,6 @@ def task_func(numbers=list(range(1, 3))):
     >>> isinstance(result, float)
     True
     """
-
     permutations = list(itertools.permutations(numbers))
     sum_diffs = 0
     for perm in permutations:

data/processed/1000_wo_doc.py

@@ -31,7 +31,6 @@ def task_func(url):
     0  Alice   25       New York
     1    Bob   30  San Francisco
     """
-
     urllib.request.urlretrieve(url, TARGET_JSON_FILE)
     with open(TARGET_JSON_FILE, "r") as f:
         data = json.load(f)

data/processed/1001_wo_doc.py

@@ -27,7 +27,6 @@ def task_func(csv_file_path: str):
     >>> ax.get_title()
     "          Plot Title :  Normalized Column 1"
     """
-
     df = pd.read_csv(csv_file_path)
     mean = df["column1"].mean()
     std = df["column1"].std()

data/processed/1002_w_doc.py

@@ -33,7 +33,6 @@ def task_func(data, column_name="target_column"):
     0       1     10
     1       2     15
     """
-
     df = pd.DataFrame(data)
     if column_name not in df.columns:
         raise ValueError(f"Column '{column_name}' not found in the DataFrame.")

data/processed/1003_wo_doc.py

@@ -39,7 +39,6 @@ def task_func(url):
     >>> df = task_func('http://example.com/invalid_structure.xml')
     ValueError: XML structure does not match expected format.
     """
-
     try:
         with urllib.request.urlopen(url) as response:
             xml_data = response.read()

data/processed/1004_wo_doc.py

@@ -35,7 +35,6 @@ def task_func(url):
     - matplotlib
     
     """
-
     with urllib.request.urlopen(url) as response:
         text = response.read().decode()
         words = re.findall(r"\b\w+\b", text)

data/processed/1005_wo_doc.py

@@ -39,7 +39,6 @@ def task_func(
 
 
     """
-
     try:
         if os.path.exists(save_path):
             os.remove(save_path)

data/processed/1006_wo_doc.py

@@ -38,7 +38,6 @@ def task_func(url, download_path="mnt/data/downloads/"):
     >>> task_func('https://example.com/file.zip')
        'mnt/data/downloads/file'
     """
-
     if not os.path.exists(download_path):
         os.makedirs(download_path)
     try:

data/processed/1007_wo_doc.py

@@ -37,7 +37,6 @@ def task_func(url: str) -> pd.DataFrame:
     - It checks the HTTP response status and raises an HTTPError for unsuccessful status codes.
     - Directly converts the HTTP response to JSON and then to a DataFrame, without intermediate processing.
     """
-
     try:
         response = requests.get(url, timeout=5)
         response.raise_for_status()  # Raises an HTTPError if the HTTP request returned an unsuccessful status code

data/processed/1008_wo_doc.py

@@ -56,7 +56,6 @@ def task_func(url, table_id):
     Columns: []
     Index: []
     """
-
     try:
         response = requests.get(url, timeout=5)
         response.raise_for_status()  # Raises an HTTPError if the HTTP request returned an unsuccessful status code

data/processed/1009_wo_doc.py

@@ -41,7 +41,6 @@ def task_func(xml_content, output_csv_path):
     - The output CSV path should be a valid file path where the user has write
       permissions, to prevent IOError.
     """
-
     try:
         root = ET.fromstring(xml_content)
         data = [[elem.tag, elem.text] for elem in root.iter()]

data/processed/100_wo_doc.py

@@ -36,7 +36,6 @@ def task_func(seed=42):
         >>> ax.get_ylabel()
         'Value'
     """
-
     try:
         plt.rc('font', family='Arial')
         random.seed(seed)

data/processed/1010_wo_doc.py

@@ -38,7 +38,6 @@ def task_func(url):
     - The function will not handle redirections or authentication scenarios. It
       expects a direct link to an image resource.
     """
-
     try:
         response = requests.get(url, timeout=5)
         response.raise_for_status()

data/processed/1011_wo_doc.py

@@ -43,7 +43,6 @@ def task_func(csv_file_path, col1_name="column1", col2_name="column2"):
     - The function does not handle missing data. Ensure that the CSV file has clean and complete data for accurate results.
     - The bar plot is customizable using matplotlib's functionality after the function returns the Axes object.
     """
-
     df = pd.read_csv(csv_file_path)
     groupby_data = df.groupby(col1_name)[col2_name].mean()
     _, ax = plt.subplots(figsize=(10, 6))

data/processed/1012_wo_doc.py

@@ -32,7 +32,6 @@ def task_func(url, filename):
     >>> task_func('http://example.com/myfile.zip', 'myfile.zip')
     ('Download and extraction successful', ['file1.txt', 'file2.txt'])
     """
-
     try:
         response = requests.get(url, stream=True, timeout=5)
         if response.status_code == 200:

data/processed/1013_wo_doc.py

@@ -32,7 +32,6 @@ def task_func(
     >>> task_func('/anotherpage', base_url='https://www.different.com', csv_file='other_links.csv')
     8
     """
-
     full_url = urljoin(base_url, url)
     response = requests.get(full_url)
     soup = BeautifulSoup(response.text, "html.parser")

data/processed/1014_wo_doc.py

@@ -31,7 +31,6 @@ def task_func(api_url):
     >>> if plot:
     >>>     plot.show()
     """
-
     if not isinstance(api_url, str):
         raise TypeError("api_url must be a string")
     response = requests.get(api_url, timeout=5)

data/processed/1015_wo_doc.py

@@ -38,7 +38,6 @@ def task_func(webpage_url: str, database_name: str = "my_database.db") -> int:
     >>> print(f"Number of rows parsed: {num_rows}")
     Number of rows parsed: 5
     """
-
     try:
         if webpage_url.startswith("file://"):
             with open(webpage_url[7:], "r", encoding="utf-8") as file:

data/processed/1016_wo_doc.py

@@ -29,7 +29,6 @@ def task_func(url: str) -> "matplotlib.axes._axes.Axes":
     >>> type(ax)
     <class 'matplotlib.axes._axes.Axes'>
     """
-
     response = None  # Initialize response to None
     if not isinstance(url, str) or not url:
         raise ValueError("Invalid URL provided.")

data/processed/1017_wo_doc.py

@@ -39,7 +39,6 @@ class 1        0.89       0.87       0.88          48
         The CSV file must have a column with the name specified by 'target_column', and it should be in a
         format readable by pandas.read_csv().
     """
-
     df = pd.read_csv(csv_file_path)
     if target_column not in df.columns:
         raise ValueError(f"'{target_column}' column not found in the CSV file.")

data/processed/1018_wo_doc.py

@@ -37,7 +37,6 @@ def task_func(url=URL, from_encoding="cp1251", use_lxml=False):
       In such cases, the function returns None, indicating that the webpage could not be successfully retrieved or was not available.
       
     """
-
     if not url:
         return None
     try:

data/processed/1019_wo_doc.py

@@ -37,7 +37,6 @@ def task_func(filename=IMAGE_PATH, from_encoding="cp1251", to_encoding="utf8"):
     >>> print(text)
     'Привет мир'  # This output is the utf-8 encoded version of the extracted text.
     """
-
     with Image.open(filename) as image:
         try:
             extracted_text = pytesseract.image_to_string(image)

data/processed/101_wo_doc.py

@@ -29,7 +29,6 @@ def task_func(data_url="http://lib.stat.cmu.edu/datasets/boston", seed=42):
         >>> type(ax)
         <class 'matplotlib.axes._axes.Axes'>
     """
-
     try:
         font = {'sans-serif': 'Arial', 'family': 'sans-serif'}
         plt.rc('font', **font)

data/processed/1020_wo_doc.py

@@ -42,7 +42,6 @@ def task_func(url=API_URL, from_encoding=None, to_encoding="utf8"):
     - It handles cases with empty content and undetectable encoding by returning an empty dictionary or raising an exception, respectively.
     - The decoding and re-encoding steps ensure compatibility with various data formats and the final JSON parsing.
     """
-
     response = requests.get(url, timeout=5)
     content = response.content
     if from_encoding is None:

data/processed/1021_w_doc.py

@@ -28,7 +28,6 @@ def task_func(input_string, verify_hash=None):
     >>> task_func("Hello, World!", "dffd6021bb2bd5b0af676290809ec3a53191dd81c7f70a4b28688a362182986f")
     True
     """
-
     if not isinstance(input_string, str):
         raise TypeError("Input must be a string")
     if verify_hash is not None and not isinstance(verify_hash, str):

data/processed/1022_wo_doc.py

@@ -41,7 +41,6 @@ def task_func(csv_file_path, column_name, date_format="%Y-%m-%d"):
     0   2023-12-10  100
     1   2023-12-11  150
     """
-
     if not os.path.isfile(csv_file_path):
         raise FileNotFoundError(f"The file {csv_file_path} does not exist.")
     try:

data/processed/1023_w_doc.py

@@ -33,7 +33,6 @@ def task_func(dataframe):
     >>> print(ax)
     Axes(0.125,0.11;0.775x0.77)
     """
-
     if dataframe.empty:
         raise ValueError("DataFrame is empty.")
     if not all(dataframe.dtypes.apply(lambda x: np.issubdtype(x, np.number))):

data/processed/1024_w_doc.py

@@ -47,7 +47,6 @@ def task_func(data_dict):
     >>> plot.get_title() if plot is not None else 'No plot generated'
     'Value Distribution'
     """
-
     df = pd.DataFrame(data_dict).dropna()
     if df.empty or df.nunique().min() < 2:
         return df, None

data/processed/1025_w_doc.py

@@ -33,7 +33,6 @@ def task_func(data_dict):
     >>> plot_ax.get_title()
     'Scaled Values'
     """
-
     df = pd.DataFrame(data_dict).dropna()
     if df.empty:
         ax = plt.gca()

data/processed/1026_w_doc.py

@@ -43,7 +43,6 @@ def task_func(kwargs):
     >>> results['significant']
     True
     """
-
     alpha = 0.05  # Define the significance level
     group1 = np.array(kwargs.get("group1", []))
     group2 = np.array(kwargs.get("group2", []))

data/processed/1027_wo_doc.py

@@ -24,7 +24,6 @@ def task_func(url):
     >>> task_func('https://www.example.com?q=4a4b4c')
     'JKL'
     """
-
     try:
         parsed_url = urllib.parse.urlparse(url)
         query = urllib.parse.parse_qs(parsed_url.query).get("q", [None])[0]

data/processed/1028_wo_doc.py

@@ -38,7 +38,6 @@ def task_func(interval, duration):
     >>> task_func(5, 60)
     'logfile.log'
     """
-
     if interval <= 0 or duration <= 0:
         raise ValueError("Interval and duration must be greater than zero.")
     start_time = time.time()

data/processed/1029_w_doc.py

@@ -39,7 +39,6 @@ def task_func(rows=100, columns=3):
     x    1
     Name: count, dtype: int64
     """
-
     column_names = [
         chr(97 + i) for i in range(columns)
     ]  # generate column names based on the number of columns

data/processed/102_w_doc.py

@@ -29,7 +29,6 @@ def task_func():
     >>> type(fig).__name__
     'Figure'
     """
-
     font = {'family': 'Arial'}
     plt.rc('font', **font)  # Set the global font to Arial.
     DIABETES = load_diabetes()

data/processed/1030_w_doc.py

@@ -28,7 +28,6 @@ def task_func():
     3        a        a        d
     4        a        a        e
     """
-
     LETTERS = list(string.ascii_lowercase)
     combinations = list(itertools.product(LETTERS, repeat=3))
     df = pd.DataFrame(combinations, columns=["Letter 1", "Letter 2", "Letter 3"])

data/processed/1031_w_doc.py

@@ -30,7 +30,6 @@ def task_func(n_rows=1000):
     >>> ax.get_title()
     'Top 30 Frequencies of Random 3-Letter Strings'
     """
-
     if n_rows <= 0:
         raise ValueError("Number of rows must be greater than 0")
     data = ["".join(random.choices(string.ascii_lowercase, k=3)) for _ in range(n_rows)]

data/processed/1032_w_doc.py

@@ -52,7 +52,6 @@ def task_func(rows=1000, string_length=3):
     >>> ax.get_xlim()
     (0.0, 26.0)
     """
-
     data = ["".join(random.choices(LETTERS, k=string_length)) for _ in range(rows)]
     df = pd.DataFrame({"String": data})
     if df.empty:

data/processed/1033_w_doc.py

@@ -35,7 +35,6 @@ def task_func():
     3  a  a  d
     4  a  a  e
     """
-
     LETTERS = list(string.ascii_lowercase)
     combinations = list(itertools.product(LETTERS, repeat=3))
     df = pd.DataFrame(combinations, columns=["a", "b", "c"])

data/processed/1034_w_doc.py

@@ -34,7 +34,6 @@ def task_func(s1, s2):
     >>> edit_distance
     387.5590277622236
     """
-
     high_sales_categories = s1.index[(s1 > 200) & (s2 > 200)]
     if high_sales_categories.empty:
         return None, 0.0

data/processed/1035_w_doc.py

@@ -34,7 +34,6 @@ def task_func(feature: pd.Series, target: pd.Series) -> (np.ndarray, plt.Axes):
     >>> ax.get_title()
     'Confusion Matrix'
     """
-
     df = pd.DataFrame({"Feature": feature, "Target": target})
     X_train, X_test, y_train, y_test = train_test_split(
         df["Feature"], df["Target"], test_size=0.2, random_state=42

data/processed/1036_w_doc.py

@@ -31,7 +31,6 @@ def task_func(s1, s2):
     >>> ax.get_title()
     'Overlap Between Series1 and Series2'
     """
-
     intersection = set(s1).intersection(set(s2))
     df1 = pd.DataFrame({s1.name: s1, "Type": "Series1"})
     df2 = pd.DataFrame({s2.name: s2, "Type": "Series2"})

data/processed/1037_w_doc.py

@@ -42,7 +42,6 @@ def task_func(s1, s2, n_clusters=3):
 
    
     """
-
     if not isinstance(s1, pd.Series) or not isinstance(s2, pd.Series):
         raise ValueError("s1 and s2 must be pandas Series")
     if len(s1) != len(s2):

data/processed/1038_wo_doc.py

@@ -32,7 +32,6 @@ def task_func(client_socket):
     ... finally:
     ...     server_socket.close()
     """
-
     response_data = {"message": "Hello", "time": str(datetime.now())}
     response = json.dumps(response_data) + "\n"
     client_socket.send(response.encode("utf-8"))

data/processed/1039_wo_doc.py

@@ -41,7 +41,6 @@ def task_func(client_socket, cert_file, key_file, buffer_size=1024):
         >>> print("Sent file hash:", file_hash)
         >>> server_socket.close()
     """
-
     context = ssl.SSLContext(ssl.PROTOCOL_TLS_SERVER)
     context.load_cert_chain(certfile=cert_file, keyfile=key_file)
     secure_socket = None

data/processed/103_wo_doc.py

@@ -31,7 +31,6 @@ def task_func(temperatures):
         >>> type(ax)
         <class 'matplotlib.axes._axes.Axes'>
     """
-
     try:
         if temperatures.empty or not isinstance(temperatures, pd.DataFrame):
             raise ValueError("Input temperatures must be a non-empty pandas DataFrame.")

data/processed/1040_wo_doc.py

@@ -29,7 +29,6 @@ def task_func(
     >>> print(task_func())
     'Server started on localhost:12345. Ran for 5 seconds.'
     """
-
     server = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
     server.setblocking(0)
     server.bind((server_address, server_port))

data/processed/1041_wo_doc.py

@@ -31,7 +31,6 @@ def task_func(request):
     >>> task_func("GET /restricted.txt HTTP/1.1") # Assuming an I/O error occurs
     "HTTP/1.1 500 INTERNAL SERVER ERROR\r\n\r\nInternal Server Error"
     """
-
     match = re.match(r"^GET /([\w\.\-]+) HTTP/1\.1$", request)
     if match:
         file_name = match.group(1)

data/processed/1042_wo_doc.py

@@ -37,7 +37,6 @@ def task_func(client_socket):
     >>> client_socket, addr = server_socket.accept()
     >>> task_func(client_socket)
     """
-
     request = client_socket.recv(BUFFER_SIZE).decode("utf-8")
     print(f"Received: {request}")
     email = EmailMessage()

data/processed/1043_w_doc.py

@@ -43,7 +43,6 @@ def task_func(data_list):
     >>> ax.get_xticks()
     array([0., 1., 2., 3., 4., 5., 6.])
     """
-
     if not data_list:
         raise ValueError("The data list is empty.")
     data_series = pd.Series(data_list)

data/processed/1044_wo_doc.py

@@ -42,7 +42,6 @@ def task_func(date_str, booking_data):
     3  Room4     Not Listed
     4  Room5     Not Listed
     """
-
     try:
         date = datetime.strptime(date_str, "%Y-%m-%d")
         if date < datetime.now():