Create pandas dataframe from series with ordered dict as rows - pandas

Question

I am trying to extract lmfit parameter results as dataframes. I pass 1 column x, 1 column data through a fit_func and parameters pars and the output of the minimize function in lmfit outputs as OrderedDict.
out = minimize(fit_func, pars, method = 'leastsq', args=(x, data))
res = out.params.valuesdict()
res
Output:
OrderedDict([('a1', 12.850309404600393),
('c1', 1346.833513206811),
('s1', 44.22337472274829),
('f1', 1.1275639898142586),
('a2', 77.15732669480884),
('c2', 1580.5712512351947),
('s2', 16.239969775527275),
('f2', 0.8684363668111492)])
The output I want in DataFrames I achieved like this with pd.DataFrame(res,index=[0]) :
I have 3 data columns that I want to quickly fit:
x = d.iloc[:,0]
fit_odict = pd.DataFrame(d.iloc[:,1:4].\
apply(lambda y: minimize(fit_func, pars, method = 'leastsq', args=(x, y))\
.params.valuesdict()),index=[1])
But I get a series of Ordered Dicts as rows in the Dataframe:
How do I get the output I want with the three parameter results as rows ? Is there a better way to apply the function?
UPDATE:
Appended #M Newville in my solution. Might be helpful for those who want to quickly extract lmfit parameter results from multiple data columns d1.iloc[:,1:] :
def fff(cols):
out = minimize(fit_func, pars, method = 'leastsq', args=(x, cols))
return {key: par.value for key, par in out.params.items()}
results = d1.iloc[:,1:].apply(fff,result_type='expand').transpose()
Output:

Answer 1

For a single fit, this would probably be what you are looking for:
out = minimize(fit_func, pars, method = 'leastsq', args=(x, data))
fit_odict = pd.DataFrame({key: [par.value] for key, par in out.params.items()})
I think you probably are looking for something like this:
results = {key: [] for key in pars}
for data in datasets:
out = minimize(fit_func, pars, method='leastsq', args=(x, data))
for par_name, val_list in results.items():
val_list.append(out.params[par_name].value)
results = pd.DataFrame(results)
You could probably stuff that all into a single long line, but I wouldn't recommend it -- someone may want to read that code ;).

Answer 2

This is quick work around that you can do. The code is not efficient but you can optimize it. Note that index start 1 but you are welcome to re-index using pandas library
import pandas as pd
# Your output is a list of tuple
OrderedDict = [('a1', 12.850309404600393),('c1', 1346.833513206811),('s1',
44.22337472274829),('f1', 1.1275639898142586),('a2', 77.15732669480884),('c2',
1580.5712512351947),('s2', 16.239969775527275),('f2', 0.8684363668111492)]
# Create a dataframe from the list of tuple and tanspose
df = pd.DataFrame(OrderedDict).T
# Get the first row for the dataframe columns namea
columns = df.loc[0].values.tolist()
df.columns = columns
output = df.drop(df.index[0])
output
a1 c1 s1 f1 a2 c2 s2 f2
1 12.8503 1346.83 44.2234 1.12756 77.1573 1580.57 16.24 0.868436