pythonwhat/checks/check_object.py
def check_df(
    state, index, missing_msg=None, not_instance_msg=None, expand_msg=None
):
    """Check whether a DataFrame was defined and it is the right type
    
    ``check_df()`` is a combo of ``check_object()`` and ``is_instance()`` that checks whether the specified object exists
    and whether the specified object is pandas DataFrame.

    You can continue checking the data frame with ``check_keys()`` function to 'zoom in' on a particular column in the pandas DataFrame:

    Args:
        index (str): Name of the data frame to zoom in on.
        missing_msg (str): See ``check_object()``.
        not_instance_msg (str): See ``is_instance()``.
        expand_msg (str): If specified, this overrides any messages that are prepended by previous SCT chains.

    :Example:

        Suppose you want the student to create a DataFrame ``my_df`` with two columns.
        The column ``a`` should contain the numbers 1 to 3,
        while the contents of column ``b`` can be anything: ::

            import pandas as pd
            my_df = pd.DataFrame({"a": [1, 2, 3], "b": ["a", "n", "y"]})

        The following SCT would robustly check that: ::

            Ex().check_df("my_df").multi(
                check_keys("a").has_equal_value(),
                check_keys("b")
            )

        - ``check_df()`` checks if ``my_df`` exists (``check_object()`` behind the scenes) and is a DataFrame (``is_instance()``)
        - ``check_keys("a")`` zooms in on the column ``a`` of the data frame, and ``has_equal_value()`` checks if the columns correspond between student and solution process.
        - ``check_keys("b")`` zooms in on hte column ``b`` of the data frame, but there's no 'equality checking' happening
        
        The following submissions would pass the SCT above: ::
            
            my_df = pd.DataFrame({"a": [1, 1 + 1, 3], "b": ["a", "l", "l"]})
            my_df = pd.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6], "c": [7, 8, 9]})

    """
    child = check_object(
        state,
        index,
        missing_msg=missing_msg,
        expand_msg=expand_msg,
        typestr="pandas DataFrame",
    )
    is_instance(child, pd.DataFrame, not_instance_msg=not_instance_msg)
    return child
def check_df(
    state, index, missing_msg=None, not_instance_msg=None, expand_msg=None
):
    """Check whether a DataFrame was defined and it is the right type
    
    ``check_df()`` is a combo of ``check_object()`` and ``is_instance()`` that checks whether the specified object exists
    and whether the specified object is pandas DataFrame.

    You can continue checking the data frame with ``check_keys()`` function to 'zoom in' on a particular column in the pandas DataFrame:

    Args:
        index (str): Name of the data frame to zoom in on.
        missing_msg (str): See ``check_object()``.
        not_instance_msg (str): See ``is_instance()``.
        expand_msg (str): If specified, this overrides any messages that are prepended by previous SCT chains.

    :Example:

        Suppose you want the student to create a DataFrame ``my_df`` with two columns.
        The column ``a`` should contain the numbers 1 to 3,
        while the contents of column ``b`` can be anything: ::

            import pandas as pd
            my_df = pd.DataFrame({"a": [1, 2, 3], "b": ["a", "n", "y"]})

        The following SCT would robustly check that: ::

            Ex().check_df("my_df").multi(
                check_keys("a").has_equal_value(),
                check_keys("b")
            )

        - ``check_df()`` checks if ``my_df`` exists (``check_object()`` behind the scenes) and is a DataFrame (``is_instance()``)
        - ``check_keys("a")`` zooms in on the column ``a`` of the data frame, and ``has_equal_value()`` checks if the columns correspond between student and solution process.
        - ``check_keys("b")`` zooms in on hte column ``b`` of the data frame, but there's no 'equality checking' happening
        
        The following submissions would pass the SCT above: ::
            
            my_df = pd.DataFrame({"a": [1, 1 + 1, 3], "b": ["a", "l", "l"]})
            my_df = pd.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6], "c": [7, 8, 9]})

    """
    child = check_object(
        state,
        index,
        missing_msg=missing_msg,
        expand_msg=expand_msg,
        typestr="pandas DataFrame",
    )
    is_instance(child, pd.DataFrame, not_instance_msg=not_instance_msg)
    return child
def check_df ( state , index , missing_msg = None , not_instance_msg = None , expand_msg = None ) : child = check_object ( state , index , missing_msg = missing_msg , expand_msg = expand_msg , typestr = "pandas DataFrame" , ) is_instance ( child , pd . DataFrame , not_instance_msg = not_instance_msg ) return child
Check whether a DataFrame was defined and it is the right type
    
    ``check_df()`` is a combo of ``check_object()`` and ``is_instance()`` that checks whether the specified object exists
    and whether the specified object is pandas DataFrame.

    You can continue checking the data frame with ``check_keys()`` function to 'zoom in' on a particular column in the pandas DataFrame:

    Args:
        index (str): Name of the data frame to zoom in on.
        missing_msg (str): See ``check_object()``.
        not_instance_msg (str): See ``is_instance()``.
        expand_msg (str): If specified, this overrides any messages that are prepended by previous SCT chains.

    :Example:

        Suppose you want the student to create a DataFrame ``my_df`` with two columns.
        The column ``a`` should contain the numbers 1 to 3,
        while the contents of column ``b`` can be anything: ::

            import pandas as pd
            my_df = pd.DataFrame({"a": [1, 2, 3], "b": ["a", "n", "y"]})

        The following SCT would robustly check that: ::

            Ex().check_df("my_df").multi(
                check_keys("a").has_equal_value(),
                check_keys("b")
            )

        - ``check_df()`` checks if ``my_df`` exists (``check_object()`` behind the scenes) and is a DataFrame (``is_instance()``)
        - ``check_keys("a")`` zooms in on the column ``a`` of the data frame, and ``has_equal_value()`` checks if the columns correspond between student and solution process.
        - ``check_keys("b")`` zooms in on hte column ``b`` of the data frame, but there's no 'equality checking' happening
        
        The following submissions would pass the SCT above: ::
            
            my_df = pd.DataFrame({"a": [1, 1 + 1, 3], "b": ["a", "l", "l"]})
            my_df = pd.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6], "c": [7, 8, 9]})
Check whether a DataFrame was defined and it is the right type check_df () is a combo of check_object () and is_instance () that checks whether the specified object exists and whether the specified object is pandas DataFrame .