P-value normal test for multiple rows
I got the following simple code to calculate normality over an array:
import pandas as pd
df = pd.read_excel("directoryfile.xlsx")
import numpy as np
x=df.iloc[:,1:].values.flatten()
import scipy.stats as stats
from scipy.stats import normaltest
stats.normaltest(x,axis=None)
This gives me nicely a p-value and a statistic.
The only thing I want right now is to:
Add 2 columns in the file with this p value and statistic and if i have multiple rows, do it for all the rows (calculate p value & statistic for each row and add 2 columns with these values in it).
Can someone help?
python-3.x pandas statistics
edited Nov 25 '18 at 19:16
desertnaut
19.8k74178
asked Nov 25 '18 at 18:56
Steven PaulySteven Pauly
528
add a comment |
I got the following simple code to calculate normality over an array:
import pandas as pd
df = pd.read_excel("directoryfile.xlsx")
import numpy as np
x=df.iloc[:,1:].values.flatten()
import scipy.stats as stats
from scipy.stats import normaltest
stats.normaltest(x,axis=None)
This gives me nicely a p-value and a statistic.
The only thing I want right now is to:
Add 2 columns in the file with this p value and statistic and if i have multiple rows, do it for all the rows (calculate p value & statistic for each row and add 2 columns with these values in it).
Can someone help?
python-3.x pandas statistics
edited Nov 25 '18 at 19:16
desertnaut
19.8k74178
asked Nov 25 '18 at 18:56
Steven PaulySteven Pauly
528
If you want to add a column you may do this:df['name_of_new_colum'] = value_to_store
– jalazbe
Nov 25 '18 at 19:12
thanks, but how can I put the p value in one column and the statistic in the other. Seems I can't split them up...
– Steven Pauly
Nov 25 '18 at 19:17
add a comment |
I got the following simple code to calculate normality over an array:
import pandas as pd
df = pd.read_excel("directoryfile.xlsx")
import numpy as np
x=df.iloc[:,1:].values.flatten()
import scipy.stats as stats
from scipy.stats import normaltest
stats.normaltest(x,axis=None)
This gives me nicely a p-value and a statistic.
The only thing I want right now is to:
Add 2 columns in the file with this p value and statistic and if i have multiple rows, do it for all the rows (calculate p value & statistic for each row and add 2 columns with these values in it).
Can someone help?
python-3.x pandas statistics
edited Nov 25 '18 at 19:16
desertnaut
19.8k74178
asked Nov 25 '18 at 18:56
Steven PaulySteven Pauly
528
I got the following simple code to calculate normality over an array:
import pandas as pd
df = pd.read_excel("directoryfile.xlsx")
import numpy as np
x=df.iloc[:,1:].values.flatten()
import scipy.stats as stats
from scipy.stats import normaltest
stats.normaltest(x,axis=None)
This gives me nicely a p-value and a statistic.
The only thing I want right now is to:
Add 2 columns in the file with this p value and statistic and if i have multiple rows, do it for all the rows (calculate p value & statistic for each row and add 2 columns with these values in it).
Can someone help?
python-3.x pandas statistics
python-3.x pandas statistics
edited Nov 25 '18 at 19:16
desertnaut
19.8k74178
asked Nov 25 '18 at 18:56
Steven PaulySteven Pauly
528
edited Nov 25 '18 at 19:16
desertnaut
19.8k74178
asked Nov 25 '18 at 18:56
Steven PaulySteven Pauly
528
edited Nov 25 '18 at 19:16
desertnaut
19.8k74178
edited Nov 25 '18 at 19:16
desertnaut
19.8k74178
edited Nov 25 '18 at 19:16
desertnaut
19.8k74178
19.8k74178
asked Nov 25 '18 at 18:56
Steven PaulySteven Pauly
528
asked Nov 25 '18 at 18:56
Steven PaulySteven Pauly
528
asked Nov 25 '18 at 18:56
Steven PaulySteven Pauly
528
528
If you want to add a column you may do this:df['name_of_new_colum'] = value_to_store
– jalazbe
Nov 25 '18 at 19:12
thanks, but how can I put the p value in one column and the statistic in the other. Seems I can't split them up...
– Steven Pauly
Nov 25 '18 at 19:17
add a comment |
If you want to add a column you may do this:df['name_of_new_colum'] = value_to_store
– jalazbe
Nov 25 '18 at 19:12
thanks, but how can I put the p value in one column and the statistic in the other. Seems I can't split them up...
– Steven Pauly
Nov 25 '18 at 19:17
If you want to add a column you may do this:
df['name_of_new_colum'] = value_to_store– jalazbe
Nov 25 '18 at 19:12
If you want to add a column you may do this:
df['name_of_new_colum'] = value_to_store– jalazbe
Nov 25 '18 at 19:12
thanks, but how can I put the p value in one column and the statistic in the other. Seems I can't split them up...
– Steven Pauly
Nov 25 '18 at 19:17
thanks, but how can I put the p value in one column and the statistic in the other. Seems I can't split them up...
– Steven Pauly
Nov 25 '18 at 19:17
add a comment |
1 Answer
1
active
oldest
votes
If you want to calculate row-wise normaltest, you should not flatten your data in x and use axis=1 such as
df = pd.DataFrame(np.random.random(105).reshape(5,21)) # to generate data
# calculate normaltest row-wise without the first column like you
df['stat'] ,df['p'] = stats.normaltest(df.iloc[:,1:],axis=1)
Then df contains two columns 'stat' and 'p' with the values your are looking for IIUC.
Note: to be able to perform normaltest, you need at least 8 values (according to what I experienced) so you need at least 8 columns in df.iloc[:,1:] otherwise it will raise an error. And even, it would be better to have more than 20 values in each row.
answered Nov 25 '18 at 20:15
Ben.TBen.T
6,5052928
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
If you want to calculate row-wise normaltest, you should not flatten your data in x and use axis=1 such as
df = pd.DataFrame(np.random.random(105).reshape(5,21)) # to generate data
# calculate normaltest row-wise without the first column like you
df['stat'] ,df['p'] = stats.normaltest(df.iloc[:,1:],axis=1)
Then df contains two columns 'stat' and 'p' with the values your are looking for IIUC.
Note: to be able to perform normaltest, you need at least 8 values (according to what I experienced) so you need at least 8 columns in df.iloc[:,1:] otherwise it will raise an error. And even, it would be better to have more than 20 values in each row.
answered Nov 25 '18 at 20:15
Ben.TBen.T
6,5052928
add a comment |
If you want to calculate row-wise normaltest, you should not flatten your data in x and use axis=1 such as
df = pd.DataFrame(np.random.random(105).reshape(5,21)) # to generate data
# calculate normaltest row-wise without the first column like you
df['stat'] ,df['p'] = stats.normaltest(df.iloc[:,1:],axis=1)
Then df contains two columns 'stat' and 'p' with the values your are looking for IIUC.
Note: to be able to perform normaltest, you need at least 8 values (according to what I experienced) so you need at least 8 columns in df.iloc[:,1:] otherwise it will raise an error. And even, it would be better to have more than 20 values in each row.
answered Nov 25 '18 at 20:15
Ben.TBen.T
6,5052928
add a comment |
If you want to calculate row-wise normaltest, you should not flatten your data in x and use axis=1 such as
df = pd.DataFrame(np.random.random(105).reshape(5,21)) # to generate data
# calculate normaltest row-wise without the first column like you
df['stat'] ,df['p'] = stats.normaltest(df.iloc[:,1:],axis=1)
Then df contains two columns 'stat' and 'p' with the values your are looking for IIUC.
Note: to be able to perform normaltest, you need at least 8 values (according to what I experienced) so you need at least 8 columns in df.iloc[:,1:] otherwise it will raise an error. And even, it would be better to have more than 20 values in each row.
answered Nov 25 '18 at 20:15
Ben.TBen.T
6,5052928
If you want to calculate row-wise normaltest, you should not flatten your data in x and use axis=1 such as
df = pd.DataFrame(np.random.random(105).reshape(5,21)) # to generate data
# calculate normaltest row-wise without the first column like you
df['stat'] ,df['p'] = stats.normaltest(df.iloc[:,1:],axis=1)
Then df contains two columns 'stat' and 'p' with the values your are looking for IIUC.
Note: to be able to perform normaltest, you need at least 8 values (according to what I experienced) so you need at least 8 columns in df.iloc[:,1:] otherwise it will raise an error. And even, it would be better to have more than 20 values in each row.
answered Nov 25 '18 at 20:15
Ben.TBen.T
6,5052928
answered Nov 25 '18 at 20:15
Ben.TBen.T
6,5052928
answered Nov 25 '18 at 20:15
Ben.TBen.T
6,5052928
answered Nov 25 '18 at 20:15
Ben.TBen.T
6,5052928
6,5052928
add a comment |
add a comment |
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If you want to add a column you may do this:
df['name_of_new_colum'] = value_to_store– jalazbe
Nov 25 '18 at 19:12
thanks, but how can I put the p value in one column and the statistic in the other. Seems I can't split them up...
– Steven Pauly
Nov 25 '18 at 19:17