update wenhao data

data/clean/f_761_wenhao.py

@@ -25,9 +25,7 @@ def f_761(df, column):
     
     Example:
     >>> df = pd.DataFrame({'Category': ['A', 'B', 'B', 'C', 'A', 'D', 'E', 'E', 'D']})
-    >>> ax = f_761(df, 'Category')
-    # This generates and displays a bar chart showing the distribution of each category within the 'Category' column.
-    
+    >>> ax = f_761(df, 'Category')    
     >>> df = pd.DataFrame({'Type': ['A', 'A', 'C', 'E', 'D', 'E', 'D']})
     >>> ax = f_761(df, 'Type')
     """

data/clean/f_772_wenhao.py

@@ -12,10 +12,11 @@ def f_772(word):
     list: A list of random pairs of adjacent letters from the word. If the word has fewer than 2 letters, returns a list of empty strings based on POSSIBLE_LETTERS length.
     
     Examples:
-    >>> random.seed(0); f_772('abcdef')
-    ['ab', 'bc', 'cd']
-    >>> random.seed(0); f_772('xyz')
-    ['xy', 'xy', 'yz']
+    >>> random.seed(0)
+    >>> f_772('abcdef')
+    ['de', 'de', 'ab']
+    >>> f_772('xyz')
+    ['yz', 'yz', 'yz']
     """
     if not all(char in string.ascii_letters for char in word):
         raise ValueError("Input must only contain letters.")

data/clean/f_832_wenhao.py

@@ -32,8 +32,8 @@ def f_832(length: int, predicates: list, seed: int = None):
     - random
 
     Example:
-    >>> f_832(10, ['has_uppercase', 'has_numbers'], seed=42)
-    ('8czu("@iNc', {'has_uppercase': True, 'has_numbers': True})
+    >>> f_832(10, ['has_uppercase', 'has_numbers'], seed=42)[0]
+    '8czu("@iNc'
     >>> f_832(5, ['has_lowercase'], seed=123)
     ('eiMk[', {'has_lowercase': True})
     """

data/processed/f_331_jenny_w_doc.py

@@ -14,7 +14,7 @@ def f_331(data, column="c"):
     - column (str): Name of column to remove. Defaults to "c".
 
     Returns:
-    - matplotlib.axes._subplots.Axes or None: The Axes object of the heatmap
+    - matplotlib.axes._axes.Axes or None: The Axes object of the heatmap
       or None if the heatmap is not generated.
 
     Requirements:

data/processed/f_367_jenny_wo_doc.py

@@ -29,7 +29,7 @@ def f_367(file_path="data.csv", columns=["A", "B", "C"]):
     0      1.0      2.0      3.0
     1      4.0      5.0      6.0
     >>> ax
-    <matplotlib.axes._subplots.Axes object at 0x7f24b00f4a90>
+    <matplotlib.axes._axes.Axes object at 0x7f24b00f4a90>
     >>> croot
     0    1.0    
     """

data/processed/f_407_jenny_wo_doc.py

@@ -14,7 +14,7 @@ def f_407(data):
                    are not the expected type, this function raises TypeError.
 
     Returns:
-    - matplotlib.axes._subplots.Axes: The generated plot's Axes object.
+    - matplotlib.axes._axes.Axes: The generated plot's Axes object.
 
     Requirements:
     - pandas

data/processed/f_411_jenny_w_doc.py

@@ -10,7 +10,7 @@ def f_411(data):
     data (list): A list of dictionaries. The keys are labels and the values are data points.
 
     Returns:
-    matplotlib.axes._subplots.Axes or None: Axes object of the plot showing 'Data over Time',
+    matplotlib.axes._axes.Axes or None: Axes object of the plot showing 'Data over Time',
                                                    with 'Time' on the x-axis and 'Data Points' on the y-axis.
                                                    If data is empty, return None.
 

data/processed/f_413_jenny_wo_doc.py

@@ -16,7 +16,7 @@ def f_413(input_file):
     Returns:
         - result (dict): each key corresponds to those in the input dictionaries, and the corresponding
           value is another dict with keys 'mean' and 'median', representing the calculated statistics.
-        - plots  (list[matplotlib.axes._subplots.Axes]): A list of bar charts, one for
+        - plots  (list[matplotlib.axes._axes.Axes]): A list of bar charts, one for
           each key in the dictionaries, visualizing the mean and median values.
 
     Requirements:
@@ -28,7 +28,7 @@ def f_413(input_file):
     Example:
     >>> results, plots = f_413("sample_data.json")
     >>> type(plots[0])
-    <class 'matplotlib.axes._subplots.Axes'>
+    <class 'matplotlib.axes._axes.Axes'>
     >>> results
     {'a': {'mean': 3.0, 'median': 3.0}, 'b': {'mean': 6.0, 'median': 6.0}}
     """

data/processed/f_423_jenny_wo_doc.py

@@ -13,7 +13,7 @@ def f_423(db_name="test.db", table_name="People"):
     table_name (str, optional): The name of the table to plot from. Defaults to 'People'.
 
     Returns:
-    matplotlib.axes._subplots.Axes: Axes object representing the age distribution plot,
+    matplotlib.axes._axes.Axes: Axes object representing the age distribution plot,
                                            with x-axis showing age and a default of bins=30, kde=True.
 
     Requirements:

data/processed/f_746_wenhao_w_doc.py

@@ -20,11 +20,11 @@ def f_746(d, keys=['x', 'y', 'z']):
     >>> data = [{'x': 1, 'y': 10, 'z': 5}, {'x': 3, 'y': 15, 'z': 6}, {'x': 2, 'y': 1, 'z': 7}]
     >>> ax = f_746(data)
     >>> type(ax)
-    <class 'matplotlib.axes._subplots.Axes'>
+    <class 'matplotlib.axes._axes.Axes'>
 
     >>> ax = f_746(data, keys=['x', 'y'])
     >>> type(ax)
-    <class 'matplotlib.axes._subplots.Axes'>
+    <class 'matplotlib.axes._axes.Axes'>
     """
     # Convert the list of dictionaries to a DataFrame
     df = pd.DataFrame(d)

data/processed/f_750_wenhao_wo_doc.py

@@ -17,7 +17,7 @@ def f_750(directory: str, pattern: str) -> list:
     - pattern (str): The regular expression pattern to match the filenames.
 
     Returns:
-    - A list of matplotlib.axes._subplots.Axes objects, each representing a plot of sales data from a matched CSV file.
+    - A list of matplotlib.axes._axes.Axes objects, each representing a plot of sales data from a matched CSV file.
 
     Example usage:
     >>> axes = f_750('/path/to/data/', r'^sales_data_\d{4}.csv')

data/processed/f_752_wenhao_w_doc.py

@@ -31,7 +31,7 @@ def f_752(letters, repetitions, colors):
     Example:
     >>> ax = f_752(['A', 'B', 'C'], [3, 5, 2], ['red', 'green', 'blue'])
     >>> type(ax)
-    <class 'matplotlib.axes._subplots.Axes'>
+    <class 'matplotlib.axes._axes.Axes'>
     """
     if len(letters) != len(repetitions) or len(letters) != len(colors) or len(letters) == 0:
         raise ValueError("All lists must be the same length and non-empty.")

data/processed/f_757_wenhao_w_doc.py

@@ -14,7 +14,7 @@ def f_757(df, z_threshold=2):
     Returns:
     tuple: A tuple containing the following elements:
         - pandas.DataFrame: A DataFrame containing the outliers in the 'closing_price' column.
-        - matplotlib.axes._subplots.Axes: The plot object displaying the outliers.
+        - matplotlib.axes._axes.Axes: The plot object displaying the outliers.
     
     Requirements:
     - pandas

data/processed/f_758_wenhao_w_doc.py

@@ -21,7 +21,7 @@ def f_758(df: pd.DataFrame) -> tuple:
                     with stock closing prices.
 
     Returns:
-    tuple: A tuple containing two matplotlib.axes._subplots.Axes objects: the first for the boxplot
+    tuple: A tuple containing two matplotlib.axes._axes.Axes objects: the first for the boxplot
            and the second for the histogram.
 
     Example:

data/processed/f_761_wenhao_w_doc.py

@@ -16,7 +16,7 @@ def f_761(df, column):
     - column (str): The name of the column in the DataFrame that contains the categories.
     
     Output:
-    - matplotlib.axes._subplots.Axes: The Axes object for the generated plot.
+    - matplotlib.axes._axes.Axes: The Axes object for the generated plot.
     
     Requirements:
     - pandas
@@ -25,9 +25,7 @@ def f_761(df, column):
     
     Example:
     >>> df = pd.DataFrame({'Category': ['A', 'B', 'B', 'C', 'A', 'D', 'E', 'E', 'D']})
-    >>> ax = f_761(df, 'Category')
-    # This generates and displays a bar chart showing the distribution of each category within the 'Category' column.
-    
+    >>> ax = f_761(df, 'Category')    
     >>> df = pd.DataFrame({'Type': ['A', 'A', 'C', 'E', 'D', 'E', 'D']})
     >>> ax = f_761(df, 'Type')
     """

data/processed/f_762_wenhao_w_doc.py

@@ -11,7 +11,7 @@ def f_762(df):
     df (pandas.DataFrame): The DataFrame containing numerical columns to be used for correlation.
 
     Returns:
-    matplotlib.axes._subplots.Axes: The matplotlib Axes object representing the heatmap.
+    matplotlib.axes._axes.Axes: The matplotlib Axes object representing the heatmap.
 
     Requirements:
     - pandas
@@ -22,7 +22,7 @@ def f_762(df):
     >>> df = pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6], 'c': [7, 8, 9]})
     >>> ax = f_762(df)
     >>> type(ax)
-    <class 'matplotlib.axes._subplots.Axes'>
+    <class 'matplotlib.axes._axes.Axes'>
 
     """
     correlation_matrix = df.corr()

data/processed/f_772_wenhao_w_doc.py

@@ -12,10 +12,11 @@ def f_772(word):
     list: A list of random pairs of adjacent letters from the word. If the word has fewer than 2 letters, returns a list of empty strings based on POSSIBLE_LETTERS length.
     
     Examples:
-    >>> random.seed(0); f_772('abcdef')
-    ['ab', 'bc', 'cd']
-    >>> random.seed(0); f_772('xyz')
-    ['xy', 'xy', 'yz']
+    >>> random.seed(0)
+    >>> f_772('abcdef')
+    ['de', 'de', 'ab']
+    >>> f_772('xyz')
+    ['yz', 'yz', 'yz']
     """
     if not all(char in string.ascii_letters for char in word):
         raise ValueError("Input must only contain letters.")

data/processed/f_778_wenhao_w_doc.py

@@ -15,7 +15,7 @@ def f_778(word):
                 Should contain only lowercase alphabetic characters.
                 
     Returns:
-    Axes: A matplotlib.axes._subplots.Axes object representing the generated plot.
+    Axes: A matplotlib.axes._axes.Axes object representing the generated plot.
     
     Requirements:
     - numpy

data/processed/f_798_wenhao_wo_doc.py

@@ -32,7 +32,7 @@ def f_798(mystrings, text):
     Examples:
     >>> ax = f_798(['Lorem ipsum', 'consectetur adipiscing'], 'Lorem ipsum dolor sit amet lorem Ipsum')
     >>> type(ax)
-    <class 'matplotlib.axes._subplots.Axes'>
+    <class 'matplotlib.axes._axes.Axes'>
     """
 
     if not text:

data/processed/f_810_wenhao_w_doc.py

@@ -27,7 +27,7 @@ def f_810(func, x_range=(-2, 2), num_points=1000):
     Example:
     >>> ax = f_810(np.sin)
     >>> type(ax)
-    <class 'matplotlib.axes._subplots.Axes'>
+    <class 'matplotlib.axes._axes.Axes'>
     >>> ax.get_legend_handles_labels()[-1]
     ['sin(x)', 'Integral of sin(x)']
     """

data/processed/f_811_wenhao_w_doc.py

@@ -11,7 +11,7 @@ def f_811(df):
     - df (pandas.DataFrame): A DataFrame with numerical values.
 
     Returns:
-    - matplotlib.axes._subplots.Axes: The Axes object of the Seaborn heatmap.
+    - matplotlib.axes._axes.Axes: The Axes object of the Seaborn heatmap.
 
     Raises:
     - ValueError: If the DataFrame is empty or if no numeric columns are present.

data/processed/f_820_wenhao_w_doc.py

@@ -36,7 +36,7 @@ def f_820(array, features=None, seed=None):
     >>> array = np.random.rand(2, 5)
     >>> ax = f_820(array, features=['A', 'B', 'C', 'D', 'E'], seed=1)
     >>> type(ax)
-    <class 'matplotlib.axes._subplots.Axes'>
+    <class 'matplotlib.axes._axes.Axes'>
     >>> ax.collections[0].get_array().data.flatten()
     array([0.60276338, 0.71518937, 0.4236548 , 0.5488135 , 0.54488318,
            0.891773  , 0.43758721, 0.38344152, 0.64589411, 0.96366276])

data/processed/f_827_wenhao_w_doc.py

@@ -13,7 +13,7 @@ def f_827(df, x_column, y_column):
     y_column (str): The column name for the y-axis. Data contained in column must be numeric.
 
     Returns:
-    matplotlib.axes._subplots.Axes: The Axes object containing the scatter plot and the linear regression line.
+    matplotlib.axes._axes.Axes: The Axes object containing the scatter plot and the linear regression line.
 
     Requirements:
     - pandas
@@ -28,7 +28,7 @@ def f_827(df, x_column, y_column):
     >>> df = pd.DataFrame({'A': [1, 2, 3], 'B': [2, 3, 4]})
     >>> ax = f_827(df, 'A', 'B')
     >>> type(ax)
-    <class 'matplotlib.axes._subplots.Axes'>
+    <class 'matplotlib.axes._axes.Axes'>
     """
     X = df[x_column].values.reshape(-1, 1)
     Y = df[y_column].values

data/processed/f_830_wenhao_w_doc.py

@@ -38,7 +38,7 @@ def f_830(json_data: str, data_key: str):
     >>> json_str = '{"data": {"values": [5, 10, 15, 20, 25]}}'
     >>> original_data, normalized_data, ax = f_830(json_str, 'data.values')
     >>> type(original_data), type(normalized_data), type(ax)
-    (<class 'pandas.core.series.Series'>, <class 'pandas.core.series.Series'>, <class 'matplotlib.axes._subplots.Axes'>)
+    (<class 'pandas.core.series.Series'>, <class 'pandas.core.series.Series'>, <class 'matplotlib.axes._axes.Axes'>)
     """
     data = json.loads(json_data)
     try:

data/processed/f_832_wenhao_w_doc.py

@@ -32,8 +32,8 @@ def f_832(length: int, predicates: list, seed: int = None):
     - random
 
     Example:
-    >>> f_832(10, ['has_uppercase', 'has_numbers'], seed=42)
-    ('8czu("@iNc', {'has_uppercase': True, 'has_numbers': True})
+    >>> f_832(10, ['has_uppercase', 'has_numbers'], seed=42)[0]
+    '8czu("@iNc'
     >>> f_832(5, ['has_lowercase'], seed=123)
     ('eiMk[', {'has_lowercase': True})
     """

data/processed/f_836_chien_w_doc.py

@@ -15,7 +15,7 @@ def f_836(text):
                 of characters and punctuation.
 
     Returns:
-    matplotlib.axes._subplots.Axes: An Axes object showing the histogram and optionally the KDE 
+    matplotlib.axes._axes.Axes: An Axes object showing the histogram and optionally the KDE 
                                            plot of word lengths. This visual representation helps in 
                                            understanding the distribution of word lengths in the given text.
 

data/processed/f_875_chien_w_doc.py

@@ -30,7 +30,7 @@ def f_875(rows=1000, string_length=3):
       Default is 3. A value of 0 results in the generation of empty strings.
 
     Returns:
-    - matplotlib.axes._subplots.Axes or None: A seaborn heatmap plot object if 
+    - matplotlib.axes._axes.Axes or None: A seaborn heatmap plot object if 
       data is generated; otherwise, None.
 
     Requirements:

data/processed/f_887_chien_w_doc.py

@@ -15,7 +15,7 @@ def f_887(data_list):
     - data_list (list): A list containing category labels (strings).
 
     Returns:
-    - Axes object (matplotlib.axes._subplots.Axes): The histogram displaying the distribution of categories.
+    - Axes object (matplotlib.axes._axes.Axes): The histogram displaying the distribution of categories.
 
     Requirements:
     - pandas

data/processed/f_891_chien_wo_doc.py

@@ -22,7 +22,7 @@ def f_891(date_str):
     Example:
     >>> ax = f_891('2023-06-15')
     >>> type(ax)
-    <class 'matplotlib.axes._subplots.Axes'>
+    <class 'matplotlib.axes._axes.Axes'>
     """
     date = datetime.strptime(date_str, "%Y-%m-%d")
     num_of_values = date.day

data/processed/f_895_chien_w_doc.py

@@ -13,7 +13,7 @@ def f_895(data_dict):
 
     Returns:
     - tuple: A tuple containing:
-        - matplotlib.axes._subplots.Axes: The axes object of the histogram.
+        - matplotlib.axes._axes.Axes: The axes object of the histogram.
         - str: A message indicating whether the distribution is uniform ("The distribution is uniform.")
                or not ("The distribution is not uniform.").
 

data/processed/f_898_chien_wo_doc.py

@@ -16,12 +16,12 @@ def f_898(file_path):
                      a single numeric value representing an individual in the population.
 
     Returns:
-    - Tuple (float, float, matplotlib.axes._subplots.Axes): The function returns a tuple containing
+    - Tuple (float, float, matplotlib.axes._axes.Axes): The function returns a tuple containing
     three elements:
         - Sample mean (float): The mean of the sample.
         - Sample standard deviation (float): The standard deviation of the sample, calculated with a
            degrees of freedom (ddof) of 1.
-        - Matplotlib subplot (matplotlib.axes._subplots.Axes): An object representing the
+        - Matplotlib subplot (matplotlib.axes._axes.Axes): An object representing the
            generated histogram plot with the normal distribution curve.
 
     Requirements:

data/processed/f_902_chien_w_doc.py

@@ -43,7 +43,7 @@ def f_902(num_pairs=10):
                        possible unique combinations (100), it is adjusted to the valid range (1 to 100).
 
     Returns:
-    - ax (matplotlib.axes._subplots.Axes): The Axes object of the countplot, which can be used for
+    - ax (matplotlib.axes._axes.Axes): The Axes object of the countplot, which can be used for
                                                   further customizations or to retrieve information about the plot.
 
     Requirements:

data/processed/f_906_chien_w_doc.py

@@ -14,7 +14,7 @@ def f_906(arr):
     arr (numpy.ndarray): A 2D numpy array.
 
     Returns:
-    matplotlib.axes._subplots.Axes: A plot representing the time series of row sums.
+    matplotlib.axes._axes.Axes: A plot representing the time series of row sums.
 
     Requirements:
     - pandas

data/processed/f_910_chien_w_doc.py

@@ -28,7 +28,7 @@ def f_910(num_samples=NUM_SAMPLES, num_outliers=NUM_OUTLIERS):
       the artificially introduced outliers.
     - outliers_detected (numpy array): The outliers detected using the IQR method. This 
       detection is based solely on the normally distributed portion of the data.
-    - ax (matplotlib.axes._subplots.Axes): The Axes object for the histogram 
+    - ax (matplotlib.axes._axes.Axes): The Axes object for the histogram 
       plot of the combined dataset.
 
     Requirements:

data/processed/f_915_chien_w_doc.py

@@ -70,7 +70,7 @@ def test_return_types(self):
         self.assertIsInstance(
             ax,
             Axes,
-            "The second return value should be an instance of matplotlib.axes._subplots.Axes.",
+            "The second return value should be an instance of matplotlib.axes._axes.Axes.",
         )
     def test_number_of_lines(self):
         """Check that the correct number of lines are plotted."""

data/processed/f_917_chien_wo_doc.py

@@ -13,7 +13,7 @@ def f_917(time_strings, time_format="%d/%m/%Y %H:%M:%S.%f"):
       The default format is '%d/%m/%Y %H:%M:%S.%f', representing day/month/year hours:minutes:seconds.microseconds.
 
     Returns:
-    - ax (matplotlib.axes._subplots.Axes or None): An Axes object with the histogram plotted if
+    - ax (matplotlib.axes._axes.Axes or None): An Axes object with the histogram plotted if
       parsing is successful. Returns None if a parsing error occurs.
 
     Requirements: