Question

Python Help:

Using the stock.py data, write a program that does the following:

1. Create a NumPy array from the nasdaq list from stocks.py. Do the same with the other three lists from stocks.py. Thus, at the end of this tasks, you will have four NumPy arrays.
2. Print out the type (that is, the object type, not the element type) of each array in #1 and the data type typecode of the elements in each array in #1. This task is just 8 print statements; there's no looping needed.
3. Print out the shape of each array in #1.
4. Print out whether the size of each of the four arrays in #1 is the same.
5. Write code that creates a single 2-D array where the first column is the nasdaq array, the second column is the sp500 array, and the third column is the djia array.
6. From the 2-D array in #5, extract the 2-D sub-array that consists of trading days 21 to 39 of the sp500column and djia columns. By "trading day 21" and "trading day 39," I mean the elements in the sp500column and djia columns that correspond to the value of trading_days equaling 21 and the value of trading_days equaling 39. I do not mean the twenty-first value of the trading_days list or the thirty-ninth value of the trading_days list. Also, when I say "consists of trading days 21 to 39," I mean, "includes the stock index values corresponding to trading day 21 through trading day 39, including trading day 21 and trading day 39." Print the shape of this extracted sub-array.

#==============================================================================

# Lists of stock index values

# There are three lists defined here:

# * nasdaq: Close of index at the end of each trading day in trading_days.

# * sp500: Close of index at the end of each trading day in trading_days.

# * djia: Close of index at the end of each trading day in trading_days.

# * trading_days: Number of trading days since Jun 1, 2016. Jun 1 is

# trading day 0.

#

# Values downloaded from http://finance.yahoo.com. Values of all lists are

# daily trading day closings from Jun 1-Aug 31, 2016, inclusive.

#==============================================================================

nasdaq = [4952.25,

4971.359863,

4942.52002,

4968.709961,

4961.75,

4974.640137,

4958.620117,

4894.549805,

4848.439941,

4843.549805,

4834.930176,

4844.919922,

4800.339844,

4837.209961,

4843.759766,

4833.319824,

4910.040039,

4707.97998,

4594.439941,

4691.870117,

4779.25,

4842.669922,

4862.569824,

4822.899902,

4859.160156,

4876.810059,

4956.759766,

4988.640137,

5022.819824,

5005.72998,

5034.060059,

5029.589844,

5055.779785,

5036.370117,

5089.930176,

5073.899902,

5100.160156,

5097.629883,

5110.049805,

5139.810059,

5154.97998,

5162.129883,

5184.200195,

5137.72998,

5159.740234,

5166.25,

5221.120117,

5213.140137,

5225.47998,

5204.580078,

5228.399902,

5232.890137,

5262.02002,

5227.109863,

5228.660156,

5240.149902,

5238.379883,

5244.600098,

5260.080078,

5217.689941,

5212.200195,

5218.919922,

5232.330078,

5222.990234,

5213.220215]

sp500 = [2099.330078,

2105.26001,

2099.129883,

2109.409912,

2112.129883,

2119.120117,

2115.47998,

2096.070068,

2079.060059,

2075.320068,

2071.5,

2077.98999,

2071.219971,

2083.25,

2088.899902,

2085.449951,

2113.320068,

2037.410034,

2000.540039,

2036.089966,

2070.77002,

2098.860107,

2102.949951,

2088.550049,

2099.72998,

2097.899902,

2129.899902,

2137.159912,

2152.139893,

2152.429932,

2163.75,

2161.73999,

2166.889893,

2163.780029,

2173.02002,

2165.169922,

2175.030029,

2168.47998,

2169.179932,

2166.580078,

2170.060059,

2173.600098,

2170.840088,

2157.030029,

2163.790039,

2164.25,

2182.870117,

2180.889893,

2181.73999,

2175.48999,

2185.790039,

2184.050049,

2190.149902,

2178.149902,

2182.219971,

2187.02002,

2183.870117,

2182.639893,

2186.899902,

2175.439941,

2172.469971,

2169.040039,

2180.379883,

2176.120117,

2170.949951]

djia = [17789.669922,

17838.560547,

17807.060547,

17920.330078,

17938.279297,

18005.050781,

17985.189453,

17865.339844,

17732.480469,

17674.820312,

17640.169922,

17733.099609,

17675.160156,

17804.869141,

17829.730469,

17780.830078,

18011.070312,

17400.75,

17140.240234,

17409.720703,

17694.679688,

17929.990234,

17949.369141,

17840.619141,

17918.619141,

17895.880859,

18146.740234,

18226.929688,

18347.669922,

18372.119141,

18506.410156,

18516.550781,

18533.050781,

18559.009766,

18595.029297,

18517.230469,

18570.849609,

18493.060547,

18473.75,

18472.169922,

18456.349609,

18432.240234,

18404.509766,

18313.769531,

18355,

18352.050781,

18543.529297,

18529.289062,

18533.050781,

18495.660156,

18613.519531,

18576.470703,

18636.050781,

18552.019531,

18573.939453,

18597.699219,

18552.570312,

18529.419922,

18547.300781,

18481.480469,

18448.410156,

18395.400391,

18502.990234,

18454.300781,

18400.880859]

trading_days = list(range(len(nasdaq)))

#- Check lengths of all lists are the same:

if (len(nasdaq) != len(sp500)) or (len(sp500) != len(djia)):

raise ValueError("bad data")

#===== end file =====

Answer 1

HERE IS THE PYTHON PROGRAM,

import numpy
import stock

#===========#1=============#
nasdaq_array = numpy.array(stock.nasdaq)
sp500_array = numpy.array(stock.sp500)
djia_array = numpy.array(stock.djia)
trading_days_array = numpy.array(stock.trading_days)

#===========#2=============#
#Object Types
print("Object type of nasdaq_array: ",type(nasdaq_array))
print("Object type of sp500_array: ",type(sp500_array))
print("Object type of djia_array: ",type(djia_array))
print("Object type of trading_days_array: ",type(trading_days_array))

#Typecode of elements in the arrays
print("Data Type of elements in nasdaq_array: ",nasdaq_array.dtype)
print("Data Type of elements in sp500_array: ",sp500_array.dtype)
print("Data Type of elements in djia_array: ",djia_array.dtype)
print("Data Type of elements in trading_days_array: ",trading_days_array.dtype)

#===========#3=============#
print("Shape of nasdaq_array: ",nasdaq_array.shape)
print("Shape of sp500_array: ",sp500_array.shape)
print("Shape of djia_array: ",djia_array.shape)
print("Shape of trading_days_array: ",trading_days_array.shape)

#===========#4=============#
if(nasdaq_array.size==djia_array.size & sp500_array.size==trading_days_array.size & nasdaq_array.size==sp500_array.size):
   print("The size of all the arrays are same")
else:
   print("The sizes are not same")

#===========#5=============#
two_Darray = numpy.array([nasdaq_array,sp500_array,djia_array])

#===========#6=============#
subArray = numpy.array([two_Darray[1][trading_days_array[20]:trading_days_array[38]],two_Darray[2][trading_days_array[20]:trading_days_array[38]]])

print("Shape of subArray created: ",subArray.shape)

The program is in sequence to what has been asked to do.

#1. Numpy arrays are created using the corresponding lists from the stocks.py file with the method numpy.array().

#2. type() function is used to get the type of the Object the passed argument is. The dtype attribute of the numpy array is used to get the Data type of the elements store in a numpy array.

#3. To print the shapes of the array we simply use the shape attribute of the numpy array.

#4. With a simple IF-ELSE statement we determine whether the size of these arrays are equal or not using the built-in size attribute.

#5. A new 2D array is created using the above created arrays. The syntax is similar to creating a normal array but this time we passed a list with first, second and third element as the nasdaq, sp500 and djia arrays respectively.

#6. Basic array slicing is used to create the Sub Array.Note we have used 20 instead of 21 because the first day starts with 0 in the trading_days array.

Feel free to ask anything. Do uprate if you find this useful. Thanks.