为什么 Python 会产生这种奇怪的广播错误？

Question

I have an expression A = B/C, where A, B and C are all 1 dimentional numpy arrays with 1258 elements each. Yet Python claims that it could not broadcast input array from shape (1259) into shape (1258). But the input array has not shape 1259. I have printed the dimensions of all three arrays before the division operation, which shows that they are of all length 1258. So why is Python behaving like this?

Here below is the Error Message, (Note the print out of all three dimensions at the top):

print the dimensions of the arrays:
len(close[1:]) 1258
len(close[0:len(close)-1]) 1258
len(close_changes[1:]) 1258
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-6-f1157b8312cc> in <module>
      7 indicator_values_all_stocks = np.zeros(shape=(nr_of_stocks, nr_indicator_values_per_stock))
      8 for i, stock in enumerate(df_open.columns):
----> 9     indicator_values_all_stocks[i] = indicator.change_variance_ratio(df_close[stock], shortLength, longLength)
     10 print(time.time() - start)

~\Desktop\Python Projects Organized\Finance\Indicator Statistics\B.31. Change Variance Ratio\indicator.py in change_variance_ratio(close, shortLength, longLength)
    796     print("len(close_changes[1:])", len(close_changes[1:]))
    797 
--> 798     close_changes[1:] = close[1:]/close[0:len(close)-1]
    799     close_changes[0] = np.NaN
    800 

ValueError: could not broadcast input array from shape (1259) into shape (1258)

The code:

shortLength = 5   
longLength = 50   
nr_of_stocks = len(df_open.columns) 
nr_indicator_values_per_stock = len(df_open)
indicator_values_all_stocks = np.zeros(shape=(nr_of_stocks, nr_indicator_values_per_stock))
for i, stock in enumerate(df_open.columns):
    indicator_values_all_stocks[i] = indicator.change_variance_ratio(df_close[stock], shortLength, longLength)

The function change_variance_ratio:

def change_variance_ratio(close, shortLength, longLength):

    close_changes = np.zeros(len(close))
    
    print("print the dimensions of the arrays:")
    print("len(close[1:])", len(close[1:]))
    print("len(close[0:len(close)-1])", len(close[0:len(close)-1]))
    print("len(close_changes[1:])", len(close_changes[1:]))

    close_changes[1:] = close[1:]/close[0:len(close)-1]   #This line causes the error
    close_changes[0] = np.NaN

    change_variance_ratio = np.zeros(len(close))
    change_variance_ratio[0:longLength] = np.NaN

    for i in range(longLength, len(close)):
        short_val = np.var(np.log(close_changes[i-shortLength:i]))
        long_val = np.var(np.log(close_changes[i-longLength:i]))
        change_variance_ratio[i] = (short_val/long_val)
    
    return change_variance_ratio

Answer 1

The problem come from the division of close that is a pandas Series .

Try to modify change_variance_ratio using the to_numpy() method as follow:

def change_variance_ratio(close, shortLength, longLength):
    close_array = close.to_numpy()
    close_changes = np.zeros(len(close_array))
    
    print("print the dimensions of the arrays:")
    print("len(close_array[1:])", len(close_array[1:]))
    print("len(close_array[0:len(close_array)-1])", len(close[0:len(close_array)-1]))
    print("len(close_changes[1:])", len(close_changes[1:]))

    close_changes[1:] = close_array[1:]/close_array[0:len(close_array)-1]   #This line causes the error
    close_changes[0] = np.NaN

    change_variance_ratio = np.zeros(len(close_array))
    change_variance_ratio[0:longLength] = np.NaN

    for i in range(longLength, len(close_array)):
        short_val = np.var(np.log(close_changes[i-shortLength:i]))
        long_val = np.var(np.log(close_changes[i-longLength:i]))
        change_variance_ratio[i] = (short_val/long_val)
    
    return change_variance_ratio