Mixing
Create pandas dataframe where each cell basis on slope calculation with time series rows from another df
I have a dataframe with about 40 columns and about 100000 rows:
ID MONTH_NUM_
FROM_EVENT F1 F2 F3 F4 etc…
2 1 4.0 133.0 28.0 NaN
2 2 NaN 132.0 29.0 24.0
2 3 NaN 131.0 NaN 29.0
2 4 4.0 130.0 31.0 7.0
2 5 8.0 129.0 26.0 2.0
2 6 8.0 128.0 25.0 3.0
4 1 5.0 139.0 29.0 7.0
4 2 5.0 138.0 NaN 22.0
4 3 5.0 137.0 30.0 28.0
4 4 5.0 136.0 29.0 25.0
4 5 5.0 135.0 NaN 27.0
4 6 5.0 134.0 27.0 29.0
etc…
each columns F is a 6m time series data with NaN for each rows ID client
I want to output new dataframe without monthes like so:
ID F1 F2 F3 F4 etc…
2
4
etc…
where each cell of new data frame is a slope calculation of 6m time series for each F colums with following code example:
x = [6, 5, 4, 3, 2, 1] #its constanta for each calcul, monthes with reverse orders because 1 is last month before event prediction
y = df.F1[df['ID']==2]
xm = np.ma.masked_array(x,mask=np.isnan(y)).compressed() #ignore Nans
ym = np.ma.masked_array(y,mask=np.isnan(y)).compressed() #ignore Nans
from scipy.stats import linregress
linregress(xm, ym).slope
What is the efficient way to looping this calculation and create new df?
Thanx in advance...
python pandas dataframe regression
asked Nov 21 '18 at 14:27
Jungleman JunglemanJungleman Jungleman
112
add a comment |
I have a dataframe with about 40 columns and about 100000 rows:
ID MONTH_NUM_
FROM_EVENT F1 F2 F3 F4 etc…
2 1 4.0 133.0 28.0 NaN
2 2 NaN 132.0 29.0 24.0
2 3 NaN 131.0 NaN 29.0
2 4 4.0 130.0 31.0 7.0
2 5 8.0 129.0 26.0 2.0
2 6 8.0 128.0 25.0 3.0
4 1 5.0 139.0 29.0 7.0
4 2 5.0 138.0 NaN 22.0
4 3 5.0 137.0 30.0 28.0
4 4 5.0 136.0 29.0 25.0
4 5 5.0 135.0 NaN 27.0
4 6 5.0 134.0 27.0 29.0
etc…
each columns F is a 6m time series data with NaN for each rows ID client
I want to output new dataframe without monthes like so:
ID F1 F2 F3 F4 etc…
2
4
etc…
where each cell of new data frame is a slope calculation of 6m time series for each F colums with following code example:
x = [6, 5, 4, 3, 2, 1] #its constanta for each calcul, monthes with reverse orders because 1 is last month before event prediction
y = df.F1[df['ID']==2]
xm = np.ma.masked_array(x,mask=np.isnan(y)).compressed() #ignore Nans
ym = np.ma.masked_array(y,mask=np.isnan(y)).compressed() #ignore Nans
from scipy.stats import linregress
linregress(xm, ym).slope
What is the efficient way to looping this calculation and create new df?
Thanx in advance...
python pandas dataframe regression
asked Nov 21 '18 at 14:27
Jungleman JunglemanJungleman Jungleman
112
add a comment |
I have a dataframe with about 40 columns and about 100000 rows:
ID MONTH_NUM_
FROM_EVENT F1 F2 F3 F4 etc…
2 1 4.0 133.0 28.0 NaN
2 2 NaN 132.0 29.0 24.0
2 3 NaN 131.0 NaN 29.0
2 4 4.0 130.0 31.0 7.0
2 5 8.0 129.0 26.0 2.0
2 6 8.0 128.0 25.0 3.0
4 1 5.0 139.0 29.0 7.0
4 2 5.0 138.0 NaN 22.0
4 3 5.0 137.0 30.0 28.0
4 4 5.0 136.0 29.0 25.0
4 5 5.0 135.0 NaN 27.0
4 6 5.0 134.0 27.0 29.0
etc…
each columns F is a 6m time series data with NaN for each rows ID client
I want to output new dataframe without monthes like so:
ID F1 F2 F3 F4 etc…
2
4
etc…
where each cell of new data frame is a slope calculation of 6m time series for each F colums with following code example:
x = [6, 5, 4, 3, 2, 1] #its constanta for each calcul, monthes with reverse orders because 1 is last month before event prediction
y = df.F1[df['ID']==2]
xm = np.ma.masked_array(x,mask=np.isnan(y)).compressed() #ignore Nans
ym = np.ma.masked_array(y,mask=np.isnan(y)).compressed() #ignore Nans
from scipy.stats import linregress
linregress(xm, ym).slope
What is the efficient way to looping this calculation and create new df?
Thanx in advance...
python pandas dataframe regression
asked Nov 21 '18 at 14:27
Jungleman JunglemanJungleman Jungleman
112
I have a dataframe with about 40 columns and about 100000 rows:
ID MONTH_NUM_
FROM_EVENT F1 F2 F3 F4 etc…
2 1 4.0 133.0 28.0 NaN
2 2 NaN 132.0 29.0 24.0
2 3 NaN 131.0 NaN 29.0
2 4 4.0 130.0 31.0 7.0
2 5 8.0 129.0 26.0 2.0
2 6 8.0 128.0 25.0 3.0
4 1 5.0 139.0 29.0 7.0
4 2 5.0 138.0 NaN 22.0
4 3 5.0 137.0 30.0 28.0
4 4 5.0 136.0 29.0 25.0
4 5 5.0 135.0 NaN 27.0
4 6 5.0 134.0 27.0 29.0
etc…
each columns F is a 6m time series data with NaN for each rows ID client
I want to output new dataframe without monthes like so:
ID F1 F2 F3 F4 etc…
2
4
etc…
where each cell of new data frame is a slope calculation of 6m time series for each F colums with following code example:
x = [6, 5, 4, 3, 2, 1] #its constanta for each calcul, monthes with reverse orders because 1 is last month before event prediction
y = df.F1[df['ID']==2]
xm = np.ma.masked_array(x,mask=np.isnan(y)).compressed() #ignore Nans
ym = np.ma.masked_array(y,mask=np.isnan(y)).compressed() #ignore Nans
from scipy.stats import linregress
linregress(xm, ym).slope
What is the efficient way to looping this calculation and create new df?
Thanx in advance...
python pandas dataframe regression
python pandas dataframe regression
asked Nov 21 '18 at 14:27
Jungleman JunglemanJungleman Jungleman
112
asked Nov 21 '18 at 14:27
Jungleman JunglemanJungleman Jungleman
112
edited Nov 22 '18 at 9:12
Jungleman Jungleman
asked Nov 21 '18 at 14:27
Jungleman JunglemanJungleman Jungleman
112
asked Nov 21 '18 at 14:27
Jungleman JunglemanJungleman Jungleman
112
asked Nov 21 '18 at 14:27
Jungleman JunglemanJungleman Jungleman
112
112
add a comment |
add a comment |
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