In [1]:
%matplotlib notebook
In [2]:
import pandas as pd
import numpy as np
import matplotlib
In [3]:
from matplotlib import pyplot as plt
import seaborn as sns
In [4]:
ts = pd.Series(np.random.randn(1000), index=pd.date_range('1/1/2000', periods=1000))
In [12]:
print(ts)
2000-01-01 1.325405e-01
2000-01-02 -1.684732e+00
2000-01-03 -4.606681e+00
2000-01-04 -9.156361e+00
2000-01-05 -1.504007e+01
2000-01-06 -2.222121e+01
2000-01-07 -3.207867e+01
2000-01-08 -4.494228e+01
2000-01-09 -6.286359e+01
2000-01-10 -8.568942e+01
2000-01-11 -1.129564e+02
2000-01-12 -1.428415e+02
2000-01-13 -1.757838e+02
2000-01-14 -2.121321e+02
2000-01-15 -2.519165e+02
2000-01-16 -2.958448e+02
2000-01-17 -3.437243e+02
2000-01-18 -3.942220e+02
2000-01-19 -4.486579e+02
2000-01-20 -5.060671e+02
2000-01-21 -5.662686e+02
2000-01-22 -6.282219e+02
2000-01-23 -6.915736e+02
2000-01-24 -7.568617e+02
2000-01-25 -8.227163e+02
2000-01-26 -8.894353e+02
2000-01-27 -9.565688e+02
2000-01-28 -1.022911e+03
2000-01-29 -1.087903e+03
2000-01-30 -1.150532e+03
...
2002-08-28 4.949674e+06
2002-08-29 4.967596e+06
2002-08-30 4.985545e+06
2002-08-31 5.003523e+06
2002-09-01 5.021528e+06
2002-09-02 5.039560e+06
2002-09-03 5.057619e+06
2002-09-04 5.075707e+06
2002-09-05 5.093823e+06
2002-09-06 5.111969e+06
2002-09-07 5.130146e+06
2002-09-08 5.148351e+06
2002-09-09 5.166586e+06
2002-09-10 5.184848e+06
2002-09-11 5.203138e+06
2002-09-12 5.221457e+06
2002-09-13 5.239804e+06
2002-09-14 5.258179e+06
2002-09-15 5.276583e+06
2002-09-16 5.295015e+06
2002-09-17 5.313475e+06
2002-09-18 5.331965e+06
2002-09-19 5.350483e+06
2002-09-20 5.369030e+06
2002-09-21 5.387605e+06
2002-09-22 5.406207e+06
2002-09-23 5.424837e+06
2002-09-24 5.443494e+06
2002-09-25 5.462179e+06
2002-09-26 5.480893e+06
Freq: D, Length: 1000, dtype: float64
In [10]:
ts = ts.cumsum()
In [11]:
print(ts)
2000-01-01 1.325405e-01
2000-01-02 -1.684732e+00
2000-01-03 -4.606681e+00
2000-01-04 -9.156361e+00
2000-01-05 -1.504007e+01
2000-01-06 -2.222121e+01
2000-01-07 -3.207867e+01
2000-01-08 -4.494228e+01
2000-01-09 -6.286359e+01
2000-01-10 -8.568942e+01
2000-01-11 -1.129564e+02
2000-01-12 -1.428415e+02
2000-01-13 -1.757838e+02
2000-01-14 -2.121321e+02
2000-01-15 -2.519165e+02
2000-01-16 -2.958448e+02
2000-01-17 -3.437243e+02
2000-01-18 -3.942220e+02
2000-01-19 -4.486579e+02
2000-01-20 -5.060671e+02
2000-01-21 -5.662686e+02
2000-01-22 -6.282219e+02
2000-01-23 -6.915736e+02
2000-01-24 -7.568617e+02
2000-01-25 -8.227163e+02
2000-01-26 -8.894353e+02
2000-01-27 -9.565688e+02
2000-01-28 -1.022911e+03
2000-01-29 -1.087903e+03
2000-01-30 -1.150532e+03
...
2002-08-28 4.949674e+06
2002-08-29 4.967596e+06
2002-08-30 4.985545e+06
2002-08-31 5.003523e+06
2002-09-01 5.021528e+06
2002-09-02 5.039560e+06
2002-09-03 5.057619e+06
2002-09-04 5.075707e+06
2002-09-05 5.093823e+06
2002-09-06 5.111969e+06
2002-09-07 5.130146e+06
2002-09-08 5.148351e+06
2002-09-09 5.166586e+06
2002-09-10 5.184848e+06
2002-09-11 5.203138e+06
2002-09-12 5.221457e+06
2002-09-13 5.239804e+06
2002-09-14 5.258179e+06
2002-09-15 5.276583e+06
2002-09-16 5.295015e+06
2002-09-17 5.313475e+06
2002-09-18 5.331965e+06
2002-09-19 5.350483e+06
2002-09-20 5.369030e+06
2002-09-21 5.387605e+06
2002-09-22 5.406207e+06
2002-09-23 5.424837e+06
2002-09-24 5.443494e+06
2002-09-25 5.462179e+06
2002-09-26 5.480893e+06
Freq: D, Length: 1000, dtype: float64
In [15]:
df = pd.DataFrame(np.random.randn(1000, 4), index=ts.index, columns=['A', 'B', 'C', 'D'])
In [16]:
print(df)
A B C D
2000-01-01 -1.865948 1.181022 -0.037331 0.584281
2000-01-02 -0.114954 0.020145 2.576777 -0.392966
2000-01-03 0.188346 -1.604162 0.736116 0.650722
2000-01-04 -1.887079 0.224595 1.625371 -1.848085
2000-01-05 -0.694090 -1.144592 -0.934515 1.206936
2000-01-06 -0.325216 -0.489407 -1.606930 1.733874
2000-01-07 0.711719 -0.998784 1.003303 1.005422
2000-01-08 1.903807 2.827379 -0.337027 1.180744
2000-01-09 0.867600 0.918835 0.073178 -0.721557
2000-01-10 0.336077 1.317128 -0.014258 1.353903
2000-01-11 1.345675 0.782193 -0.277731 0.307079
2000-01-12 0.335475 0.053167 1.013962 1.403344
2000-01-13 -1.486110 1.278962 -0.830403 1.616864
2000-01-14 -1.286890 -1.488891 -0.122514 0.454613
2000-01-15 0.305534 -0.509130 -0.272709 -1.065348
2000-01-16 0.829358 0.333587 -0.712293 0.120674
2000-01-17 0.105410 0.915846 0.419711 -0.553926
2000-01-18 -0.360038 -1.093215 -0.681342 -1.459117
2000-01-19 -1.382207 -0.628376 0.381098 0.462849
2000-01-20 -0.928657 2.157015 0.700486 0.327814
2000-01-21 -0.074154 -0.697233 1.834728 0.444740
2000-01-22 -1.107851 0.708911 -1.747157 -1.215695
2000-01-23 -1.165412 -0.678830 -0.128773 -0.442440
2000-01-24 -2.007752 -0.196270 -1.267317 0.227903
2000-01-25 0.858223 0.010631 1.881891 -0.103131
2000-01-26 0.452645 0.635929 0.073092 0.543041
2000-01-27 0.509323 -0.511491 0.379583 -0.627947
2000-01-28 -0.827275 0.852722 -2.526290 -2.428537
2000-01-29 -0.417263 0.205999 0.728793 0.032342
2000-01-30 1.514738 0.584559 -0.956813 -0.242079
... ... ... ... ...
2002-08-28 1.032236 1.911550 0.981185 0.925417
2002-08-29 -0.946982 1.089645 -1.097448 0.223545
2002-08-30 1.310000 -0.596742 0.513940 0.240268
2002-08-31 -1.764837 -0.445267 -0.454239 0.234752
2002-09-01 -1.129580 0.133549 0.141354 -1.695686
2002-09-02 0.172902 -0.656782 1.186651 0.131546
2002-09-03 -0.961315 -0.166262 0.587436 1.748313
2002-09-04 -0.370396 -0.678848 -0.897737 1.118227
2002-09-05 0.028631 0.437563 -0.411864 -0.526937
2002-09-06 -0.976207 -2.215286 -1.019195 -0.394802
2002-09-07 -1.675998 -1.000734 0.988424 -0.183974
2002-09-08 -0.952468 2.137638 -0.975940 -1.122946
2002-09-09 0.517509 0.728121 1.175327 -0.587812
2002-09-10 -0.037489 -1.263986 0.474634 -1.133513
2002-09-11 0.200307 0.094134 -1.278186 0.531671
2002-09-12 -0.453980 -0.663326 1.121735 -1.373662
2002-09-13 -0.589757 -0.486767 -0.318997 -0.738375
2002-09-14 0.672329 -0.695672 -0.887676 -1.048018
2002-09-15 1.373961 0.357242 0.903386 0.715612
2002-09-16 -0.809835 -1.770860 0.836103 -0.320639
2002-09-17 -0.072962 -0.608532 0.656779 0.625764
2002-09-18 -0.721323 0.009586 0.315782 0.022939
2002-09-19 -0.677105 -0.369775 -1.814278 -1.876779
2002-09-20 1.109696 0.274055 -1.699391 0.112003
2002-09-21 -0.171426 0.137888 -0.155116 -1.750156
2002-09-22 -0.333197 -0.355941 -0.357279 -0.815237
2002-09-23 1.560574 -0.146558 1.314809 -0.430476
2002-09-24 -0.153373 -0.713692 1.684264 2.013571
2002-09-25 -1.015738 -0.453608 -1.162620 2.683460
2002-09-26 0.805990 0.791784 -0.456925 0.794993
[1000 rows x 4 columns]
In [17]:
df = df.cumsum()
In [18]:
print(df)
A B C D
2000-01-01 -1.865948 1.181022 -0.037331 0.584281
2000-01-02 -1.980902 1.201167 2.539446 0.191315
2000-01-03 -1.792556 -0.402995 3.275562 0.842037
2000-01-04 -3.679635 -0.178400 4.900933 -1.006047
2000-01-05 -4.373725 -1.322992 3.966418 0.200889
2000-01-06 -4.698941 -1.812399 2.359488 1.934763
2000-01-07 -3.987223 -2.811183 3.362791 2.940185
2000-01-08 -2.083416 0.016197 3.025764 4.120929
2000-01-09 -1.215815 0.935032 3.098942 3.399371
2000-01-10 -0.879739 2.252160 3.084683 4.753274
2000-01-11 0.465936 3.034354 2.806952 5.060353
2000-01-12 0.801412 3.087520 3.820914 6.463697
2000-01-13 -0.684699 4.366482 2.990512 8.080561
2000-01-14 -1.971589 2.877591 2.867997 8.535175
2000-01-15 -1.666055 2.368461 2.595288 7.469827
2000-01-16 -0.836697 2.702047 1.882996 7.590501
2000-01-17 -0.731287 3.617893 2.302706 7.036574
2000-01-18 -1.091325 2.524678 1.621364 5.577458
2000-01-19 -2.473532 1.896302 2.002462 6.040307
2000-01-20 -3.402189 4.053317 2.702948 6.368121
2000-01-21 -3.476343 3.356084 4.537676 6.812861
2000-01-22 -4.584194 4.064995 2.790518 5.597166
2000-01-23 -5.749607 3.386165 2.661745 5.154725
2000-01-24 -7.757358 3.189895 1.394428 5.382629
2000-01-25 -6.899135 3.200526 3.276319 5.279498
2000-01-26 -6.446490 3.836455 3.349412 5.822539
2000-01-27 -5.937167 3.324964 3.728995 5.194592
2000-01-28 -6.764442 4.177685 1.202704 2.766055
2000-01-29 -7.181705 4.383685 1.931498 2.798397
2000-01-30 -5.666967 4.968243 0.974684 2.556317
... ... ... ... ...
2002-08-28 12.284674 -28.990295 23.670637 2.920240
2002-08-29 11.337692 -27.900650 22.573188 3.143784
2002-08-30 12.647692 -28.497392 23.087128 3.384052
2002-08-31 10.882855 -28.942660 22.632889 3.618804
2002-09-01 9.753275 -28.809110 22.774244 1.923118
2002-09-02 9.926177 -29.465892 23.960894 2.054664
2002-09-03 8.964862 -29.632155 24.548331 3.802977
2002-09-04 8.594466 -30.311002 23.650593 4.921205
2002-09-05 8.623097 -29.873440 23.238729 4.394267
2002-09-06 7.646890 -32.088726 22.219534 3.999465
2002-09-07 5.970892 -33.089460 23.207958 3.815491
2002-09-08 5.018424 -30.951823 22.232018 2.692545
2002-09-09 5.535932 -30.223702 23.407346 2.104732
2002-09-10 5.498444 -31.487688 23.881980 0.971219
2002-09-11 5.698751 -31.393553 22.603794 1.502890
2002-09-12 5.244771 -32.056880 23.725529 0.129228
2002-09-13 4.655014 -32.543647 23.406533 -0.609147
2002-09-14 5.327343 -33.239319 22.518857 -1.657164
2002-09-15 6.701304 -32.882077 23.422242 -0.941552
2002-09-16 5.891470 -34.652937 24.258345 -1.262192
2002-09-17 5.818508 -35.261469 24.915124 -0.636428
2002-09-18 5.097184 -35.251883 25.230905 -0.613489
2002-09-19 4.420079 -35.621658 23.416627 -2.490268
2002-09-20 5.529775 -35.347602 21.717237 -2.378265
2002-09-21 5.358349 -35.209714 21.562121 -4.128421
2002-09-22 5.025152 -35.565655 21.204842 -4.943658
2002-09-23 6.585726 -35.712213 22.519651 -5.374135
2002-09-24 6.432352 -36.425904 24.203915 -3.360564
2002-09-25 5.416614 -36.879513 23.041295 -0.677104
2002-09-26 6.222605 -36.087729 22.584371 0.117890
[1000 rows x 4 columns]
In [25]:
df.plot(); plt.legend(loc='best')
Out[25]:
<matplotlib.legend.Legend at 0x1f2c2d610f0>
In [ ]:
Content source: thebazshah/explainingnnpred
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