I'm using OLS with respect to two dataframes:
gab = ols(formula= 'only_volume ~ all_but_volume', data=data_p ).fit()
where,
only_volume = data_p.iloc[:,0] #Only first colum
all_but_volume = data_p.iloc[:, 1:data_p.shape[1]] #All but first column
When I try to extract something, say parameter or pvals, I get something like this:
In [3]: gab.params
Out[3]:
Intercept 2.687598e+06
all_but_volume[0] 5.500544e+01
all_but_volume[1] 2.696902e+02
all_but_volume[2] 3.389568e+04
all_but_volume[3] -2.385838e+04
all_but_volume[4] 5.419860e+02
all_but_volume[5] 3.815161e+02
all_but_volume[6] -2.281344e+04
all_but_volume[7] 1.794128e+04
...
all_but_volume[22] 1.374321e+00
Since gab.params provides with 23 values in LHS and all_but_volume has 23 columns, I was hoping if there was a way to get a list/zip of params with column names, instead of params with all_but_volume[i]
Like,
TMC 9.801195e+01
TAC 2.214464e+02
...
What I've tried:
removing all_but_volume and simply using data_p.iloc[:, 1:data_p.shape[1]]
Didn't work:
...
data_p.iloc[:, 1:data_p.shape[1]][21] 2.918531e+04
data_p.iloc[:, 1:data_p.shape[1]][22] 1.395342e+00
Edit: Sample Data:
data_p.iloc[1:5,:]
Out[31]:
Volume A B C\
1 569886.171878 759.089217 272.446022 4.163908
2 561695.886128 701.165406 330.301260 4.136530
3 627221.486089 377.746089 656.838394 4.130720
4 625181.750625 361.489041 670.575110 4.134467
D E F G H I \
1 1.000842 12993.06 3371.28 236.90 4.92 6.13
2 0.981514 13005.44 3378.69 236.94 4.92 6.13
3 0.836920 13017.22 3384.47 236.98 4.93 6.13
4 0.810541 13028.56 3388.85 237.01 4.94 6.13
J K L M N \
1 ... 0 0 0 0 0
2 ... 0 0 0 0 0
3 ... 0 0 0 0 0
4 ... 0 0 0 0 0
O P Q R S
1 0 0 0 1 9202.171648
2 0 0 0 0 4381.373520
3 0 0 0 0 -13982.443554
4 0 0 0 0 -22878.843149
only_volume is the first column 'volume'
all_but_volume is all columns except 'volume'
You can use DataFrame constructor or rename, because gab.params is Series:
Sample:
np.random.seed(2018)
import statsmodels.formula.api as sm
data_p = pd.DataFrame(np.random.rand(10, 5), columns=['Volume','A','B','C','D'])
print (data_p)
Volume A B C D
0 0.882349 0.104328 0.907009 0.306399 0.446409
1 0.589985 0.837111 0.697801 0.802803 0.107215
2 0.757093 0.999671 0.725931 0.141448 0.356721
3 0.942704 0.610162 0.227577 0.668732 0.692905
4 0.416863 0.171810 0.976891 0.330224 0.629044
5 0.160611 0.089953 0.970822 0.816578 0.571366
6 0.345853 0.403744 0.137383 0.900934 0.933936
7 0.047377 0.671507 0.034832 0.252691 0.557125
8 0.525823 0.352968 0.092983 0.304509 0.862430
9 0.716937 0.964071 0.539702 0.950540 0.667982
only_volume = data_p.iloc[:,0] #Only first colum
all_but_volume = data_p.iloc[:, 1:data_p.shape[1]] #All but first column
gab = sm.ols(formula= 'only_volume ~ all_but_volume', data=data_p ).fit()
print (gab.params)
Intercept 0.077570
all_but_volume[0] 0.395072
all_but_volume[1] 0.313150
all_but_volume[2] -0.100752
all_but_volume[3] 0.247532
dtype: float64
print (type(gab.params))
<class 'pandas.core.series.Series'>
df = pd.DataFrame({'cols':data_p.columns[1:], 'par': gab.params.values[1:]})
print (df)
cols par
0 A 0.395072
1 B 0.313150
2 C -0.100752
3 D 0.247532
If want return Series:
s = gab.params.rename(dict(zip(gab.params.index, data_p.columns)))
print (s)
Volume 0.077570
A 0.395072
B 0.313150
C -0.100752
D 0.247532
dtype: float64
Series without first value:
s = gab.params.iloc[1:].rename(dict(zip(gab.params.index, data_p.columns)))
print (s)
A 0.395072
B 0.313150
C -0.100752
D 0.247532
dtype: float64