update wenhao data

Author: terryyz

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/open-eval.jsonl

@@ -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_331_jenny_w_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_367_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_407_jenny_wo_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_411_jenny_w_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_413_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: