Python Elif Optimization Problems

How to optimize this code?

How to optimize this code as short as possible. I am a beginner please help me

out.CONDITIONS:

• arr must be a valid 2D array with valid numbers only.
• data can be : 'column' or 'row'
• value can be : 'min' or 'max' or 'mean' or 'median'
• array must be a valid 2D Array with Integer / Float ONLY

def get_math_value(array, data, value):
    if data == 'row' and value == 'min':
        arr = array
        min_row = list(map(min, arr))
        print(min_row)
    elif data == 'row' and value == 'max':
        arr = array
        max_row = list(map(min, arr))
        print(max_row)
    elif data == 'row' and value == 'mean':
        arr = array
        mean_row = np.mean(arr, axis=1)
        print(mean_row)
    elif data == 'row' and value == 'median':
        arr = array
        median_row = np.median(arr, axis=1)
        print(median_row)
    elif data == 'column' and value == 'min':
        arr = array
        min_column = list(map(min, zip(*arr)))
        print(min_column)
    elif data == 'column' and value == 'max':
        arr = array
        max_column = list(map(max, zip(*arr)))
        print(max_column)
    elif data == 'column' and value == 'mean':
        arr = array
        mean_column = np.mean(arr, axis=0)
        print(mean_column)
    elif data == 'column' and value == 'median':
        arr = array
        median_column = np.median(arr, axis=0)
        print(median_column)
    else:
        print('[]')

The first thing to know is that numpy has min and max functions, so you don't need the list(map(min, arr)) calls, you can use e.g. np.min(arr, axis=1) instead.

With that in mind you could do something like

def get_math_value(array, data, value):        
    axis = {'column': 0, 'row': 1}.get(data)
    func = {'mean': np.mean, 
            'median': np.median,
            'min': np.min, 
            'max': np.max}.get(value)
    if axis is None or func is None:
        print('[]')
    else:
        print(func(array, axis=axis))