Strange result when plotting data using "matplotlib.pyplot()"

Question

This is the array real_stock_price_volumes:

array([[1.32600000e+03, 3.19064498e+08],
       [1.32800000e+03, 9.90153760e+07],
       [1.33300000e+03, 1.34459291e+08],
       [1.32400000e+03, 9.76078850e+07],
       [1.32500000e+03, 1.25713918e+08],
       [1.30800000e+03, 9.14767100e+07],
       [1.31400000e+03, 1.16316712e+08],
       [1.29500000e+03, 9.86506690e+07],
       [1.25100000e+03, 1.00724864e+08],
       [1.19200000e+03, 9.36400500e+07],
       [1.22300000e+03, 7.32284280e+07],
       [1.23500000e+03, 3.02962310e+07],
       [1.22200000e+03, 4.39081200e+07],
       [1.20000000e+03, 1.33755011e+08],
       [1.16300000e+03, 5.22119140e+07],
       [1.15400000e+03, 3.74436890e+07],
       [1.13600000e+03, 3.67476700e+07],
       [1.11900000e+03, 3.74358580e+07],
       [1.09100000e+03, 4.77026620e+07],
       [1.08900000e+03, 4.50759280e+07],
       [1.07500000e+03, 7.15362200e+07],
       [1.07000000e+03, 3.64443230e+07],
       [1.06800000e+03, 3.88530380e+07],
       [1.06600000e+03, 5.20391440e+07],
       [1.06700000e+03, 3.48435300e+07],
       [1.06200000e+03, 3.50862750e+07],
       [1.05700000e+03, 3.11573250e+07],
       [1.07500000e+03, 5.02451850e+07],
       [1.07400000e+03, 4.20791170e+07],
       [1.06700000e+03, 4.64726370e+07]])

And this is the predicted_stock_price_volume :

array([[1.1192834e+03, 1.8556324e+07],
       [1.1616068e+03, 1.8931450e+07],
       [1.2031355e+03, 1.9183112e+07],
       [1.2409023e+03, 1.9258652e+07],
       [1.2728779e+03, 1.9135412e+07],
       [1.2981487e+03, 1.8822046e+07],
       [1.3164802e+03, 1.8347750e+07],
       [1.3283572e+03, 1.7757704e+07],
       [1.3345322e+03, 1.7099750e+07],
       [1.3357021e+03, 1.6413705e+07],
       [1.3322739e+03, 1.5725957e+07],
       [1.3248695e+03, 1.5064580e+07],
       [1.3142756e+03, 1.4456509e+07],
       [1.3013789e+03, 1.3922793e+07],
       [1.2871940e+03, 1.3477512e+07],
       [1.2722299e+03, 1.3122378e+07],
       [1.2567640e+03, 1.2853235e+07],
       [1.2409580e+03, 1.2661774e+07],
       [1.2249242e+03, 1.2538099e+07],
       [1.2087153e+03, 1.2471031e+07],
       [1.1924427e+03, 1.2452524e+07],
       [1.1762994e+03, 1.2476716e+07],
       [1.1604342e+03, 1.2539352e+07],
       [1.1450332e+03, 1.2637726e+07],
       [1.1303459e+03, 1.2769922e+07],
       [1.1166018e+03, 1.2934125e+07],
       [1.1039835e+03, 1.3127256e+07],
       [1.0926007e+03, 1.3344688e+07],
       [1.0826613e+03, 1.3583579e+07],
       [1.0743169e+03, 1.3840295e+07]], dtype=float32)

And I use this code to plotting them:

p1 = real_stock_price_volume[:,0]
v1 = real_stock_price_volume[:,1]
p2 = predicted_stock_price_volume[:,0]
v2 = predicted_stock_price_volume[:,1]
plt.plot(p1, color = 'red', label = 'p1')
plt.plot(v1, color = 'brown', label = 'v1')
plt.plot(p2, color = 'blue', label = 'p2')
plt.plot(v2, color = 'green', label = 'v2')
plt.title('Stock Price Prediction')
plt.xlabel('Time')
plt.ylabel('Stock Price')
plt.legend()
plt.show()

But what do I get is this strange plot:

I say strange because my data are similar(p1,p2 and v1,v2) but the graphs are very different! What is the problem?

EDIT: I also like to know how can plot p1 & p2 in one plot and v1 & v2 in other plot?

"What is the problem?" We can only guess. You haven't described what you were expecting. Your graph looks fine to me. (Due to differences in orders of magnitude and scale, +03 vs +07 vs +08). Perhaps your data isn't as similar as you think it is. :-) — TrebledJ
– TrebledJ, Commented Feb 25, 2019 at 10:54
@TrebuchetMS: As I said in last line, p1 and p2 are similar, but blue and red lines are very different. And the same problem exist for v1 and v2 — lovecode
– lovecode, Commented Feb 25, 2019 at 10:58
p1 and p2 are superimposed. That's why you see a purple line wayyyy near the bottom (they're both relatively close to 0 compared to v1,v2). v1 is one order less than v2. v1 is at +07 while v2 alternates between +08 and +07. No problem for me. Your graph looks all healthy and fine. — TrebledJ
– TrebledJ, Commented Feb 25, 2019 at 11:01
The red line (p2) is hidden under the blue line. It's v1 you're seeing. — Viktoriya Malyasova
– Viktoriya Malyasova, Commented Feb 25, 2019 at 11:03
My bad, The red line (p1) is hidden under the blue line (p2). It's v1 you see as red (it's brown actually). — Viktoriya Malyasova
– Viktoriya Malyasova, Commented Feb 25, 2019 at 11:16

Viktoriya Malyasova · Accepted Answer · 2019-02-25 13:09:59Z

2

Your data are not all that similar: the greatest value of v1 is about 30 times greater than the greatest value of v2.

As for p1 and p2, they are merged together on the graph. The red line (p1) is hidden under the blue line (p2). To see them apart, you can remove the two other plots:

plt.plot(p1, color = 'red', label = 'p1')
plt.plot(p2, color = 'blue', label = 'p2')
plt.show()

Similarly, to plot v1 and v2 in a separate plot, run:

plt.plot(v1, color = 'brown', label = 'v1')
plt.plot(v2, color = 'green', label = 'v2')
plt.show()

edited Feb 25, 2019 at 13:09

answered Feb 25, 2019 at 11:07

Viktoriya Malyasova

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2 Comments

lovecode Over a year ago

I confused because it seems the color is changed when I try to plot 4 graphs. Blue should be the p2 when I plot 4 graphs together.

lovecode Over a year ago

Please add your comment to your answer and also answer the second question if you can. Thanks. I will accept your answer.

hodisr · Accepted Answer · 2019-02-25 10:56:26Z

1

Your data is not at all similar in the volume column.

For instance:

v2[0] = 1.8556324e+07 is 18556324

and

v1[0] = 3.19064498e+08 is 319064498.0

The difference is:

3.19064498e+08 - 1.8556324e+07 = 300508174.0

answered Feb 25, 2019 at 10:56

hodisr

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2 Comments

lovecode Over a year ago

But what about the p columns?

lovecode Over a year ago

I confused because it seems the color is changed when I try to plot 4 graphs. Blue should be the p2 when I plot 4 graphs together.

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