https://youtu.be/Iqjy9UqKKuo?list=PLQVvvaa0QuDc-3szzjeP6N6b0aDrrKyL-
In [14]:
import pandas as pd;
import datetime;
import pandas_datareader.data as web;
import matplotlib.pyplot as plot;
from matplotlib import style;
style.use("ggplot");
In [21]:
start = datetime.datetime(2010, 1, 1);
end = datetime.datetime(2015, 1, 1);
df = web.DataReader("XOM", "yahoo", start, end);
In [22]:
print(df);
Open High Low Close Volume Adj Close
Date
2010-01-04 68.720001 69.260002 68.190002 69.150002 27809100 56.700952
2010-01-05 69.190002 69.449997 68.800003 69.419998 30174700 56.922342
2010-01-06 69.449997 70.599998 69.339996 70.019997 35044700 57.414323
2010-01-07 69.900002 70.059998 69.419998 69.800003 27192100 57.233934
2010-01-08 69.690002 69.750000 69.220001 69.519997 24891800 57.004337
2010-01-11 69.940002 70.519997 69.650002 70.300003 30685000 57.643919
2010-01-12 69.720001 69.989998 69.519997 69.949997 31496700 57.356925
2010-01-13 69.959999 70.040001 69.260002 69.669998 24884400 57.127334
2010-01-14 69.540001 69.739998 69.349998 69.680000 18630800 57.135536
2010-01-15 69.650002 69.690002 68.650002 69.110001 29411900 56.668153
2010-01-19 68.739998 69.349998 68.419998 69.269997 26081900 56.799345
2010-01-20 68.559998 68.660004 67.930000 68.029999 34629500 55.782583
2010-01-21 68.120003 68.150002 66.500000 66.699997 39114000 54.692021
2010-01-22 66.519997 67.139999 66.000000 66.099998 39085500 54.200040
2010-01-25 66.550003 66.760002 65.690002 65.849998 29305100 53.995048
2010-01-26 65.639999 66.739998 65.500000 65.919998 34083300 54.052445
2010-01-27 65.660004 66.059998 65.000000 65.540001 35723500 53.740859
2010-01-28 65.849998 65.849998 64.570000 64.959999 37349800 53.265274
2010-01-29 65.150002 65.820000 64.019997 64.430000 40880500 52.830691
2010-02-01 65.769997 66.410004 65.349998 66.180000 37703000 54.265639
2010-02-02 66.730003 67.120003 66.470001 66.959999 34057900 54.905215
2010-02-03 66.879997 67.230003 66.559998 66.599998 24024700 54.610025
2010-02-04 66.269997 66.349998 64.680000 64.720001 33858200 53.068483
2010-02-05 64.699997 64.900002 63.560001 64.800003 42297500 53.134082
2010-02-08 64.910004 65.489998 64.339996 64.349998 30519400 53.109322
2010-02-09 65.110001 65.709999 64.559998 65.199997 36243300 53.810842
2010-02-10 65.080002 65.220001 64.160004 64.849998 21699100 53.521982
2010-02-11 64.690002 65.480003 64.410004 65.239998 23555200 53.843856
2010-02-12 64.620003 65.139999 64.279999 64.800003 30636200 53.480720
2010-02-16 65.440002 66.379997 65.080002 66.279999 30514900 54.702189
... ... ... ... ... ... ...
2014-11-18 95.080002 95.750000 94.529999 94.870003 9263200 88.594283
2014-11-19 95.080002 95.739998 94.129997 95.610001 8333200 89.285330
2014-11-20 95.370003 95.900002 95.260002 95.820000 8422400 89.481437
2014-11-21 97.199997 97.199997 96.099998 96.809998 12374900 90.405946
2014-11-24 96.339996 96.800003 95.250000 95.720001 10172400 89.388054
2014-11-25 96.010002 96.029999 94.370003 94.779999 13747400 88.510233
2014-11-26 94.639999 95.129997 94.360001 94.480003 9822100 88.230083
2014-11-28 91.500000 91.849998 90.110001 90.540001 19556700 84.550714
2014-12-01 90.360001 92.870003 90.279999 92.349998 27584200 86.240979
2014-12-02 92.320000 94.669998 92.010002 94.190002 20885300 87.959266
2014-12-03 94.669998 95.330002 94.000000 94.949997 16220400 88.668986
2014-12-04 94.129997 94.610001 93.070000 94.370003 12868600 88.127359
2014-12-05 93.949997 94.589996 93.349998 93.820000 11831900 87.613739
2014-12-08 92.900002 93.129997 91.370003 91.699997 15735900 85.633976
2014-12-09 91.230003 92.059998 90.760002 91.379997 15883800 85.335144
2014-12-10 90.720001 90.730003 88.199997 88.669998 22279400 82.804414
2014-12-11 88.860001 91.540001 88.849998 89.199997 21522200 83.299352
2014-12-12 88.709999 89.000000 86.599998 86.599998 24568300 80.871346
2014-12-15 87.529999 88.599998 86.500000 86.900002 21373600 81.151503
2014-12-16 86.379997 89.199997 86.190002 86.410004 26372400 80.693919
2014-12-17 87.239998 89.900002 87.150002 89.019997 23323000 83.131259
2014-12-18 90.370003 91.160004 88.400002 91.160004 22882700 85.129703
2014-12-19 90.209999 93.639999 89.610001 93.639999 38469000 87.445646
2014-12-22 92.959999 93.669998 92.449997 93.330002 17871800 87.156155
2014-12-23 93.519997 95.180000 93.050003 94.589996 13706500 88.332800
2014-12-24 94.220001 94.250000 92.980003 93.779999 6875700 87.576384
2014-12-26 93.989998 94.430000 92.620003 93.209999 10585000 87.044090
2014-12-29 93.330002 93.860001 92.889999 93.070000 9892100 86.913352
2014-12-30 92.769997 93.389999 92.510002 93.019997 8743400 86.866656
2014-12-31 92.419998 93.129997 92.059998 92.449997 11337200 86.334363
[1258 rows x 6 columns]
In [23]:
df['Adj Close'].plot();
plot.show();
In [ ]:
Content source: shevkunov/workout
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