# Helpful function to rip through a data frame and look at what is going on with it's columns
def eda_helper(df):
    dict_list = []
    for col in df.columns:
        data = df[col]
        dict_ = {}
        # The null count for a column. Columns with no nulls are generally more interesting
        dict_.update({"null_count" : data.isnull().sum()})
        # Counting the unique values in a column
        # This is useful for seeing how interesting the column might be as a feature
        dict_.update({"unique_count" : len(data.unique())})
        # Finding the types of data in the column
        # This is useful for finding out potential problems with a column having strings and ints
        dict_.update({"data_type" : set([type(d).__name__ for d in data])})
        #dict_.update({"score" : match[1]})
        dict_list.append(dict_)
    eda_df = pd.DataFrame(dict_list)
    eda_df.index = df.columns

    return eda_df