update dwenhao data

data/clean/f_746_wenhao.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.AxesSubplot'>
+    <class 'matplotlib.axes._subplots.Axes'>
 
     >>> ax = f_746(data, keys=['x', 'y'])
     >>> type(ax)
-    <class 'matplotlib.axes._subplots.AxesSubplot'>
+    <class 'matplotlib.axes._subplots.Axes'>
     """
     # Convert the list of dictionaries to a DataFrame
     df = pd.DataFrame(d)

data/clean/f_752_wenhao.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.AxesSubplot'>
+    <class 'matplotlib.axes._subplots.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/clean/f_761_wenhao.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.AxesSubplot: The Axes object for the generated plot.
+    - matplotlib.axes._subplots.Axes: The Axes object for the generated plot.
     
     Requirements:
     - pandas
@@ -30,7 +30,6 @@ def f_761(df, column):
     
     >>> df = pd.DataFrame({'Type': ['A', 'A', 'C', 'E', 'D', 'E', 'D']})
     >>> ax = f_761(df, 'Type')
-    # This generates and displays a bar chart showing the distribution of each category within the 'Type' column, including categories with zero occurrences.
     """
     # Define the categories
     CATEGORIES = ['A', 'B', 'C', 'D', 'E']

data/clean/f_762_wenhao.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.AxesSubplot: The matplotlib Axes object representing the heatmap.
+    matplotlib.axes._subplots.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.AxesSubplot'>
+    <class 'matplotlib.axes._subplots.Axes'>
 
     """
     correlation_matrix = df.corr()

data/clean/f_765_wenhao.py

@@ -24,10 +24,11 @@ def f_765(person_names, email_domains, num_records=5):
     - ValueError: If the number of names provided is less than the number of records requested or if no email domains are provided.
     
     Example:
+    >>> random.seed(0)  # Initialize random seed
     >>> f_765(['John Doe', 'Jane Smith'], ['gmail.com', 'yahoo.com'], 2)
              Name              Email
-    0    John Doe  john[at]yahoo.com
-    1  Jane Smith  jane[at]gmail.com
+    0  Jane Smith  jane[at]gmail.com
+    1    John Doe  john[at]yahoo.com
     >>> f_765(['Alice'], ['outlook.com'], 1)
         Name                 Email
     0  Alice  alice[at]outlook.com

data/clean/f_770_wenhao.py

@@ -26,8 +26,8 @@ def f_770(word: str) -> dict:
     - The function uses the `string` library to get a string of lowercase alphabets.
 
     Example:
-    >>> f_770('abcdef')
-    {'ab': 1, 'ac': 0, 'ad': 0, ..., 'yx': 0, 'yz': 0, 'za': 0, ..., 'zx': 0, 'zy': 0}
+    >>> list(f_770('abcdef').items())[:5]
+    [('ab', 1), ('ac', 0), ('ad', 0), ('ae', 0), ('af', 0)]
     """
     ALPHABETS = string.ascii_lowercase
     # Generate all two-letter combinations of alphabets

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.AxesSubplot or None: The Axes object of the heatmap
+    - matplotlib.axes._subplots.Axes or None: The Axes object of the heatmap
       or None if the heatmap is not generated.
 
     Requirements:
@@ -23,7 +23,7 @@ def f_331(data, column="c"):
 
     Example:
     >>> f_331({'a': [1, 2, 3], 'b': [4, 5, 6], 'c': [7, 8, 9]})
-    <Axes: >
+    <Axes:>
     >>> f_331(pd.DataFrame({'a': ["foo", "bar"]}))
     """
     df = pd.DataFrame(data)

data/processed/f_336_jenny_w_doc.py

@@ -14,7 +14,7 @@ def f_336(df1, df2):
     Returns:
     - tuple: A tuple containing:
         - list: A list of the selected features.
-        - AxesSubplot: A heatmap showing the correlation between the selected features.
+        - Axes: A heatmap showing the correlation between the selected features.
 
     Requirements:
     - pandas

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.AxesSubplot object at 0x7f24b00f4a90>
+    <matplotlib.axes._subplots.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.AxesSubplot: The generated plot's Axes object.
+    - matplotlib.axes._subplots.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.AxesSubplot or None: Axes object of the plot showing 'Data over Time',
+    matplotlib.axes._subplots.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.AxesSubplot]): A list of bar charts, one for
+        - plots  (list[matplotlib.axes._subplots.Axes]): A list of bar charts, one for
           each key in the dictionaries, visualizing the mean and median values.
 
     Requirements:

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.AxesSubplot: Axes object representing the age distribution plot,
+    matplotlib.axes._subplots.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.AxesSubplot'>
+    <class 'matplotlib.axes._subplots.Axes'>
 
     >>> ax = f_746(data, keys=['x', 'y'])
     >>> type(ax)
-    <class 'matplotlib.axes._subplots.AxesSubplot'>
+    <class 'matplotlib.axes._subplots.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.AxesSubplot objects, each representing a plot of sales data from a matched CSV file.
+    - A list of matplotlib.axes._subplots.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.AxesSubplot'>
+    <class 'matplotlib.axes._subplots.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.AxesSubplot: The plot object displaying the outliers.
+        - matplotlib.axes._subplots.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.AxesSubplot objects: the first for the boxplot
+    tuple: A tuple containing two matplotlib.axes._subplots.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.AxesSubplot: The Axes object for the generated plot.
+    - matplotlib.axes._subplots.Axes: The Axes object for the generated plot.
     
     Requirements:
     - pandas
@@ -30,7 +30,6 @@ def f_761(df, column):
     
     >>> df = pd.DataFrame({'Type': ['A', 'A', 'C', 'E', 'D', 'E', 'D']})
     >>> ax = f_761(df, 'Type')
-    # This generates and displays a bar chart showing the distribution of each category within the 'Type' column, including categories with zero occurrences.
     """
     # Define the categories
     CATEGORIES = ['A', 'B', 'C', 'D', 'E']

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.AxesSubplot: The matplotlib Axes object representing the heatmap.
+    matplotlib.axes._subplots.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.AxesSubplot'>
+    <class 'matplotlib.axes._subplots.Axes'>
 
     """
     correlation_matrix = df.corr()

data/processed/f_765_wenhao_w_doc.py

@@ -24,10 +24,11 @@ def f_765(person_names, email_domains, num_records=5):
     - ValueError: If the number of names provided is less than the number of records requested or if no email domains are provided.
     
     Example:
+    >>> random.seed(0)  # Initialize random seed
     >>> f_765(['John Doe', 'Jane Smith'], ['gmail.com', 'yahoo.com'], 2)
              Name              Email
-    0    John Doe  john[at]yahoo.com
-    1  Jane Smith  jane[at]gmail.com
+    0  Jane Smith  jane[at]gmail.com
+    1    John Doe  john[at]yahoo.com
     >>> f_765(['Alice'], ['outlook.com'], 1)
         Name                 Email
     0  Alice  alice[at]outlook.com

data/processed/f_770_wenhao_w_doc.py

@@ -26,8 +26,8 @@ def f_770(word: str) -> dict:
     - The function uses the `string` library to get a string of lowercase alphabets.
 
     Example:
-    >>> f_770('abcdef')
-    {'ab': 1, 'ac': 0, 'ad': 0, ..., 'yx': 0, 'yz': 0, 'za': 0, ..., 'zx': 0, 'zy': 0}
+    >>> list(f_770('abcdef').items())[:5]
+    [('ab', 1), ('ac', 0), ('ad', 0), ('ae', 0), ('af', 0)]
     """
     ALPHABETS = string.ascii_lowercase
     # Generate all two-letter combinations of alphabets

data/processed/f_778_wenhao_w_doc.py

@@ -15,7 +15,7 @@ def f_778(word):
                 Should contain only lowercase alphabetic characters.
                 
     Returns:
-    AxesSubplot: A matplotlib.axes._subplots.AxesSubplot object representing the generated plot.
+    Axes: A matplotlib.axes._subplots.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.AxesSubplot'>
+    <class 'matplotlib.axes._subplots.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.AxesSubplot'>
+    <class 'matplotlib.axes._subplots.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.AxesSubplot: The AxesSubplot object of the Seaborn heatmap.
+    - matplotlib.axes._subplots.Axes: The Axes object of the Seaborn heatmap.
 
     Raises:
     - ValueError: If the DataFrame is empty or if no numeric columns are present.
@@ -55,7 +55,7 @@ def test_non_numeric_columns_ignored(self):
         df = pd.DataFrame({"A": [1, 2, 3], "B": ["one", "two", "three"]})
         ax = f_811(df)
         self.assertIsInstance(
-            ax, plt.Axes, "The result should be a matplotlib AxesSubplot object"
+            ax, plt.Axes, "The result should be a matplotlib Axes object"
         )
         self.assertEqual(
             len(ax.get_xticklabels()), 1, "Non-numeric columns should be ignored"
@@ -64,25 +64,25 @@ def test_with_positive_numbers(self):
         df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})
         result = f_811(df)
         self.assertIsInstance(
-            result, plt.Axes, "The result should be a matplotlib AxesSubplot object"
+            result, plt.Axes, "The result should be a matplotlib Axes object"
         )
     def test_with_negative_numbers(self):
         df = pd.DataFrame({"A": [-1, -2, -3], "B": [-4, -5, -6]})
         result = f_811(df)
         self.assertIsInstance(
-            result, plt.Axes, "The result should be a matplotlib AxesSubplot object"
+            result, plt.Axes, "The result should be a matplotlib Axes object"
         )
     def test_with_mixed_numbers(self):
         df = pd.DataFrame({"A": [1, -2, 3], "B": [-4, 5, -6]})
         result = f_811(df)
         self.assertIsInstance(
-            result, plt.Axes, "The result should be a matplotlib AxesSubplot object"
+            result, plt.Axes, "The result should be a matplotlib Axes object"
         )
     def test_with_zeroes(self):
         df = pd.DataFrame({"A": [0, 0, 0], "B": [0, 0, 0]})
         result = f_811(df)
         self.assertIsInstance(
-            result, plt.Axes, "The result should be a matplotlib AxesSubplot object"
+            result, plt.Axes, "The result should be a matplotlib Axes object"
         )
     def test_with_empty_dataframe(self):
         df = pd.DataFrame({"A": [], "B": []})

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.AxesSubplot'>
+    <class 'matplotlib.axes._subplots.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.AxesSubplot: The Axes object containing the scatter plot and the linear regression line.
+    matplotlib.axes._subplots.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.AxesSubplot'>
+    <class 'matplotlib.axes._subplots.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.AxesSubplot'>)
+    (<class 'pandas.core.series.Series'>, <class 'pandas.core.series.Series'>, <class 'matplotlib.axes._subplots.Axes'>)
     """
     data = json.loads(json_data)
     try:

data/processed/f_836_chien_w_doc.py

@@ -15,7 +15,7 @@ def f_836(text):
                 of characters and punctuation.
 
     Returns:
-    matplotlib.axes._subplots.AxesSubplot: An Axes object showing the histogram and optionally the KDE 
+    matplotlib.axes._subplots.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_857_chien_wo_doc.py

@@ -15,7 +15,7 @@ def f_857(api_url):
 
     Returns:
     - DataFrame: A pandas DataFrame with the parsed data from the API.
-    - AxesSubplot or None: A matplotlib AxesSubplot object representing the plot of the data, or None if the data is empty.
+    - Axes or None: A matplotlib Axes object representing the plot of the data, or None if the data is empty.
 
     Raises:
     - HTTPError: If the API request fails due to issues like network problems, invalid response, etc.