# Path: impyute/ops/matrix.py
# def nan_indices(data):
# def map_nd(fn, arr):
# def every_nd(fn, arr):


from impyute.ops import matrix
        The data you want to get a description from
    verbose: boolean(optional)
        Decides whether the description is short or long form

    Returns
    -------
    dict
        missingness: list
            Confidence interval of data being MCAR, MAR or MNAR - in that order
        nan_xy: list of tuples
            Indices of all null points
        nan_n: list
            Total number of null values for each column
        pmissing_n: float
            Percentage of missing values in dataset
        nan_rows: list
            Indices of all rows that are completely null
        nan_cols: list
            Indices of all columns that are completely null
        mean_rows: list
            Mean value of each row
        mean_cols: list
            Mean value of each column
        std_dev: list
            std dev for each row/column
        min_max: list
            Finds the minimum and maximum for each row

    """
#    missingness = [0.33, 0.33, 0.33]  # find_missingness(data)

# Path: impyute/ops/matrix.py
# def nan_indices(data):
# def map_nd(fn, arr):
# def every_nd(fn, arr):


# Path: impyute/contrib/describe.py
from impyute.ops import matrix

        The data you want to get a description from
    verbose: boolean(optional)
        Decides whether the description is short or long form

    Returns
    -------
    dict
        missingness: list
            Confidence interval of data being MCAR, MAR or MNAR - in that order
        nan_xy: list of tuples
            Indices of all null points
        nan_n: list
            Total number of null values for each column
        pmissing_n: float
            Percentage of missing values in dataset
        nan_rows: list
            Indices of all rows that are completely null
        nan_cols: list
            Indices of all columns that are completely null
        mean_rows: list
            Mean value of each row
        mean_cols: list
            Mean value of each column
        std_dev: list
            std dev for each row/column
        min_max: list
            Finds the minimum and maximum for each row

    """
#    missingness = [0.33, 0.33, 0.33]  # find_missingness(data)

    nan_xy = matrix.nan_indices(data)