Converting statsmodels summary object to Pandas Dataframe

Store your model fit as a variable results, like so:

import statsmodels.api as sm
model = sm.OLS(y,x)
results = model.fit()

Then create a a function like below:

def results_summary_to_dataframe(results):
    '''take the result of an statsmodel results table and transforms it into a dataframe'''
    pvals = results.pvalues
    coeff = results.params
    conf_lower = results.conf_int()[0]
    conf_higher = results.conf_int()[1]

    results_df = pd.DataFrame({"pvals":pvals,
                               "coeff":coeff,
                               "conf_lower":conf_lower,
                               "conf_higher":conf_higher
                                })

    #Reordering...
    results_df = results_df[["coeff","pvals","conf_lower","conf_higher"]]
    return results_df

You can further explore all the attributes of the results object by using dir() to print, then add them to the function and df accordingly.

An easy solution is just one line of code:

LRresult = (result.summary2().tables[1])

As ZaxR mentioned in the following comment, Summary2 is not yet considered stable, while it works well with Summary too. So this could be correct answer:

LRresult = (result.summary().tables[1])

This will give you a dataframe object:

type(LRresult)

pandas.core.frame.DataFrame

To get the significant variables and run the test again:

newlist = list(LRresult[LRresult['P>|z|']<=0.05].index)[1:]
myform1 = 'binary_Target' + ' ~ ' + ' + '.join(newlist)

M1_test2 = smf.logit(formula=myform1,data=myM1_1)

result2 = M1_test2.fit(maxiter=200)
LRresult2 = (result2.summary2().tables[1])
LRresult2

You may write as below.It will be a easy fix and work almost appropriate every time.

lr.summary2()

If you want the surrounding information, try the following:

import pandas as pd
dfs = {}
fs = fa_model.summary()
for item in fs.tables[0].data:
    dfs[item[0].strip()] = item[1].strip()
    dfs[item[2].strip()] = item[3].strip()
for item in fs.tables[2].data:
    dfs[item[0].strip()] = item[1].strip()
    dfs[item[2].strip()] = item[3].strip()
dfs = pd.Series(dfs)

The code below puts all the metrics into a dictionary accessible by key. The intermediate result is actually a DataFrame you can use, I did not make the coefficients into a dictionary, but you can apply a similar method but then two levels deep dict[var][metric].

In order to make the keys easy to type, I converted some of the metric names into more easily typed versions. E.g. “Prob(Omnibus):” becomes prob_omnibus such that you can access the value by
res_dict[‘prob_omnibus’].

import pandas as pd

res = sm.OLS(y, X).fit()
model_results_df = []
coefficient_df = None
for i, tab in enumerate(res.summary().tables):
    header, index_col = None, None
    if i == 1:
        coefficient_df = pd.read_html(tab.as_html(), header=0, index_col=0)[0]
    else:
        df = pd.read_html(tab.as_html())[0]
        model_results_df += [df.iloc[:,0:2], df.iloc[:,2:4]]

model_results_df = pd.DataFrame(np.concatenate(model_results_df), columns=['metric', 'value'])
model_results_df.dropna(inplace=True, axis=0)
model_results_df.metric = model_results_df.metric.apply(lambda x : x.lower().replace(' (', '_')
                                                        .replace('.', '').replace('(', '_')
                                                        .replace(')', '').replace('-', '_')
                                                       .replace(':', '').replace(' ', '_'))

res_dict = dict(zip(model_results_df.metric.values, model_results_df.value.values))
res_dict['f_statistic']

I still don’t think there is a clean answer that captures the query in its totality. Here is one way to capture everything in two dataframes (one for the middle table, one for the metrics on the top and bottom).

def reform_df(dft):
    # quick and dirty stacking of cols 2,3 on 0,1
    dfl = dft[[0,1]]
    dfr = dft[[2,3]]
    dfr.columns = 0,1
    dfout = pd.concat([dfl,dfr])
    dfout.columns=['Parameter','Value']
    return dfout

def model_summary_to_dataframe(model):
    # first the middle table      
    results_df = pd.DataFrame(model.summary().tables[1])
    results_df = results_df.set_index(0)
    results_df.columns = results_df.iloc[0]
    results_df = results_df.iloc[1:]
    results_df.index.name='Parameter'

    # now for the surrounding information
    metrics_top = reform_df(pd.DataFrame(model.summary().tables[0]))
    metrics_bot = reform_df(pd.DataFrame(model.summary().tables[2]))
    metrics_df = pd.concat([metrics_top,metrics_bot])

    return pd.DataFrame(results_df),metrics_df

It works but I found a small bug in item[3]
This is the fix:

import pandas as pd
dfs = {}
fs = stepwise_fit.summary()
for item in fs.tables[0].data:
    #print("item " + str(item))
    dfs[item[0].strip()] = item[1].strip()
    dfs[item[2].strip()] = str(item[3]).strip()
for item in fs.tables[2].data:
    dfs[item[0].strip()] = item[1].strip()
    dfs[item[2].strip()] = str(item[3]).strip()
dfs = pd.Series(dfs)
print(type(dfs))

This worked for me:

st, data, ss2 = summary_table(result, alpha=0.05)

df = pd.DataFrame( data=data, columns=ss2 )