[Pandas] Code to reduce memory

def mem_usage(pandas_obj):
    if isinstance(pandas_obj,pd.DataFrame):
        usage_b = pandas_obj.memory_usage(deep=True).sum()
    else: # we assume if not a df it's a series
        usage_b = pandas_obj.memory_usage(deep=True)
    usage_mb = usage_b / 1024 ** 2 # convert bytes to megabytes
    return "{:03.2f} MB".format(usage_mb)


def type_memory(data) :
    for dtype in ['float','int','object']:
        selected_dtype = data.select_dtypes(include=[dtype])
        mean_usage_b = selected_dtype.memory_usage(deep=True).mean()
        mean_usage_mb = mean_usage_b / 1024 ** 2
        print("Average memory usage for {} columns: {:03.2f} MB".format(dtype,mean_usage_mb))

def int_memory_reduce(data) :
    data_int = data.select_dtypes(include=['int'])
    converted_int = data_int.apply(pd.to_numeric,downcast='unsigned')

    print(mem_usage(data_int))
    print(mem_usage(converted_int))
    return data[converted_int.columns] = converted_int


def float_memory_reduce(data) :
    data_float = data.select_dtypes(include=['float'])
    converted_float = data_float.apply(pd.to_numeric,downcast='float')

    print(mem_usage(data_float))
    print(mem_usage(converted_float))
    return data[converted_float.columns] = converted_float


def object_memory_reduce(data) :
    gl_obj = data.select_dtypes(include=['object']).copy()

    converted_obj = pd.DataFrame()

    for col in gl_obj.columns:
        num_unique_values = len(gl_obj[col].unique())
        num_total_values = len(gl_obj[col])
        if num_unique_values / num_total_values < 0.5:
            converted_obj.loc[:,col] = gl_obj[col].astype('category')
        else:
            converted_obj.loc[:,col] = gl_obj[col]
    print(mem_usage(gl_obj))
    print(mem_usage(converted_obj))
    return data[converted_obj.columns] = converted_obj