How to get definite answer instead of nan in Python?
I need help to understand the output of this code. Why am I getting Nan instead of float value? Please suggest necessary amendments require:
import matplotlib.pyplot as plt
from scipy import stats
import pandas as pd
import fix_yahoo_finance as fyf
from pandas_datareader import data as pdr
import numpy as np
fyf.pdr_override()
p=pdr.get_data_yahoo('IBM',start ='2009-01-01',end ='2013-01-01')
p.to_csv('YF_IBM_2009_2013.csv')
print(p.head())
ret = (p.Close[1:]-p.Close[:-1])/p.Close[1:]
print ('ticker=','IBM','W-test, and P-value')
print (stats.shapiro(ret))
And output is:
ret = (p.Close[1:]-p.Close[:-1])/p.Close[1:]
print ('ticker=','IBM','W-test, and P-value')
print (stats.shapiro(ret))
ticker= IBM W-test, and P-value
(nan, 1.0)
python pandas financial
edited Nov 16 '18 at 20:35
halfer
14.5k758111
asked Nov 14 '18 at 4:46
Himanshu DoneriaHimanshu Doneria
123
add a comment |
I need help to understand the output of this code. Why am I getting Nan instead of float value? Please suggest necessary amendments require:
import matplotlib.pyplot as plt
from scipy import stats
import pandas as pd
import fix_yahoo_finance as fyf
from pandas_datareader import data as pdr
import numpy as np
fyf.pdr_override()
p=pdr.get_data_yahoo('IBM',start ='2009-01-01',end ='2013-01-01')
p.to_csv('YF_IBM_2009_2013.csv')
print(p.head())
ret = (p.Close[1:]-p.Close[:-1])/p.Close[1:]
print ('ticker=','IBM','W-test, and P-value')
print (stats.shapiro(ret))
And output is:
ret = (p.Close[1:]-p.Close[:-1])/p.Close[1:]
print ('ticker=','IBM','W-test, and P-value')
print (stats.shapiro(ret))
ticker= IBM W-test, and P-value
(nan, 1.0)
python pandas financial
edited Nov 16 '18 at 20:35
halfer
14.5k758111
asked Nov 14 '18 at 4:46
Himanshu DoneriaHimanshu Doneria
123
3
Please reformat as code instead of quotation.
– Mad Physicist
Nov 14 '18 at 4:48
add a comment |
I need help to understand the output of this code. Why am I getting Nan instead of float value? Please suggest necessary amendments require:
import matplotlib.pyplot as plt
from scipy import stats
import pandas as pd
import fix_yahoo_finance as fyf
from pandas_datareader import data as pdr
import numpy as np
fyf.pdr_override()
p=pdr.get_data_yahoo('IBM',start ='2009-01-01',end ='2013-01-01')
p.to_csv('YF_IBM_2009_2013.csv')
print(p.head())
ret = (p.Close[1:]-p.Close[:-1])/p.Close[1:]
print ('ticker=','IBM','W-test, and P-value')
print (stats.shapiro(ret))
And output is:
ret = (p.Close[1:]-p.Close[:-1])/p.Close[1:]
print ('ticker=','IBM','W-test, and P-value')
print (stats.shapiro(ret))
ticker= IBM W-test, and P-value
(nan, 1.0)
python pandas financial
edited Nov 16 '18 at 20:35
halfer
14.5k758111
asked Nov 14 '18 at 4:46
Himanshu DoneriaHimanshu Doneria
123
I need help to understand the output of this code. Why am I getting Nan instead of float value? Please suggest necessary amendments require:
import matplotlib.pyplot as plt
from scipy import stats
import pandas as pd
import fix_yahoo_finance as fyf
from pandas_datareader import data as pdr
import numpy as np
fyf.pdr_override()
p=pdr.get_data_yahoo('IBM',start ='2009-01-01',end ='2013-01-01')
p.to_csv('YF_IBM_2009_2013.csv')
print(p.head())
ret = (p.Close[1:]-p.Close[:-1])/p.Close[1:]
print ('ticker=','IBM','W-test, and P-value')
print (stats.shapiro(ret))
And output is:
ret = (p.Close[1:]-p.Close[:-1])/p.Close[1:]
print ('ticker=','IBM','W-test, and P-value')
print (stats.shapiro(ret))
ticker= IBM W-test, and P-value
(nan, 1.0)
python pandas financial
python pandas financial
edited Nov 16 '18 at 20:35
halfer
14.5k758111
asked Nov 14 '18 at 4:46
Himanshu DoneriaHimanshu Doneria
123
edited Nov 16 '18 at 20:35
halfer
14.5k758111
asked Nov 14 '18 at 4:46
Himanshu DoneriaHimanshu Doneria
123
edited Nov 16 '18 at 20:35
halfer
14.5k758111
edited Nov 16 '18 at 20:35
halfer
14.5k758111
edited Nov 16 '18 at 20:35
halfer
14.5k758111
14.5k758111
asked Nov 14 '18 at 4:46
Himanshu DoneriaHimanshu Doneria
123
asked Nov 14 '18 at 4:46
Himanshu DoneriaHimanshu Doneria
123
asked Nov 14 '18 at 4:46
Himanshu DoneriaHimanshu Doneria
123
123
3
Please reformat as code instead of quotation.
– Mad Physicist
Nov 14 '18 at 4:48
add a comment |
3
Please reformat as code instead of quotation.
– Mad Physicist
Nov 14 '18 at 4:48
3
3
Please reformat as code instead of quotation.
– Mad Physicist
Nov 14 '18 at 4:48
Please reformat as code instead of quotation.
– Mad Physicist
Nov 14 '18 at 4:48
add a comment |
1 Answer
1
active
oldest
votes
There is a small issue with your code. When you directly subtract two pandas series, the index comes along. Below is the output for
p.Close[1:]
Having index along with values is the reason you're getting nan values. To select only the values from a pandas series, you have to do
p.Close[1:].values
so the ret = line now is
ret = ((p.Close[1:].values-p.Close[:-1].values)/(p.Close[1:].values))
This should do what you're looking for. Comment if anything else is needed.
answered Nov 14 '18 at 5:57
gauravtolanigauravtolani
564
Thanks, It's suffice the purpose.
– Himanshu Doneria
Nov 14 '18 at 7:43
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
There is a small issue with your code. When you directly subtract two pandas series, the index comes along. Below is the output for
p.Close[1:]
Having index along with values is the reason you're getting nan values. To select only the values from a pandas series, you have to do
p.Close[1:].values
so the ret = line now is
ret = ((p.Close[1:].values-p.Close[:-1].values)/(p.Close[1:].values))
This should do what you're looking for. Comment if anything else is needed.
answered Nov 14 '18 at 5:57
gauravtolanigauravtolani
564
Thanks, It's suffice the purpose.
– Himanshu Doneria
Nov 14 '18 at 7:43
add a comment |
There is a small issue with your code. When you directly subtract two pandas series, the index comes along. Below is the output for
p.Close[1:]
Having index along with values is the reason you're getting nan values. To select only the values from a pandas series, you have to do
p.Close[1:].values
so the ret = line now is
ret = ((p.Close[1:].values-p.Close[:-1].values)/(p.Close[1:].values))
This should do what you're looking for. Comment if anything else is needed.
answered Nov 14 '18 at 5:57
gauravtolanigauravtolani
564
Thanks, It's suffice the purpose.
– Himanshu Doneria
Nov 14 '18 at 7:43
add a comment |
There is a small issue with your code. When you directly subtract two pandas series, the index comes along. Below is the output for
p.Close[1:]
Having index along with values is the reason you're getting nan values. To select only the values from a pandas series, you have to do
p.Close[1:].values
so the ret = line now is
ret = ((p.Close[1:].values-p.Close[:-1].values)/(p.Close[1:].values))
This should do what you're looking for. Comment if anything else is needed.
answered Nov 14 '18 at 5:57
gauravtolanigauravtolani
564
There is a small issue with your code. When you directly subtract two pandas series, the index comes along. Below is the output for
p.Close[1:]
Having index along with values is the reason you're getting nan values. To select only the values from a pandas series, you have to do
p.Close[1:].values
so the ret = line now is
ret = ((p.Close[1:].values-p.Close[:-1].values)/(p.Close[1:].values))
This should do what you're looking for. Comment if anything else is needed.
answered Nov 14 '18 at 5:57
gauravtolanigauravtolani
564
edited Nov 14 '18 at 6:44
answered Nov 14 '18 at 5:57
gauravtolanigauravtolani
564
answered Nov 14 '18 at 5:57
gauravtolanigauravtolani
564
answered Nov 14 '18 at 5:57
gauravtolanigauravtolani
564
564
Thanks, It's suffice the purpose.
– Himanshu Doneria
Nov 14 '18 at 7:43
add a comment |
Thanks, It's suffice the purpose.
– Himanshu Doneria
Nov 14 '18 at 7:43
Thanks, It's suffice the purpose.
– Himanshu Doneria
Nov 14 '18 at 7:43
Thanks, It's suffice the purpose.
– Himanshu Doneria
Nov 14 '18 at 7:43
add a comment |
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3
Please reformat as code instead of quotation.
– Mad Physicist
Nov 14 '18 at 4:48