rfe = rfe.fit(y.astype(float), df.astype(float))

ValueError: Expected 2D array, got 1D array instead:
array=[0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0
 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0
 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0
 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0
 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0
 1 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0
 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 1 0 1 1 0 0 0 1 0 0 0 0 1 1 0 0 0 1
 1 0 0 1 0 1 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
 0 0 0 0 1 0 0 0 0 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 1 0 1 1 0 1 0 0 0 0 0 0
 0 0 0 0 0 0 1 0 1 0 1 0 1 0 0 0 0 0].
Reshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.

import pandas as pd
     from sklearn.feature_selection import RFE
     import statsmodels.api as sm
         def runLogit():

                df = pd.read_excel('InputFile.xlsx', sheetname='InputToCode')

                field1    = df['field1']
                field2    = df['field2']
                field3    = df['field3']
                field4    = df['field4']
                field5      = df['field5']
                field6     = df['field6']
                field7    = df['field7']
                field8    = df['field8']
                field9    = df['field9']

                field10    = df['field10']
                field11e  = df['field11']
                field12  = df['field12']
                field13 = df['field13']

                df = pd.DataFrame(
                                   {
                                    'field1': field1,
                                    'field2': field2,
                                    'field3': field3,
                                    'field4': field4,
                                    'field5': field5,
                                    'field6': field6,
                                    'field7': feild7,
                                    'field8': field8,
                                    'field9': field9,
                                    'field10': field10,
                                    'field11': field11,
                                    'field12': field12,
                                    'field13':   field13
                                    }
                                 )



 # Field1 is an Actual list of 1's and 0's in the input data set (which we are trying to predict through the Logit)
            y = df['field1'].values

             #y = np.arange(1, 611)
    print (len(y))
    print (df.shape)

   #To select the best predictor variables
   #Feature selection

    logistic = LogisticRegression()
    rfe = RFE(logistic, 7)

    #Fails on this next line:

    rfe = rfe.fit(y.astype(float), df.astype(float))

    rfe = rfe.fit(y, df)
    print(rfe.support_)
    print(rfe.ranking_)

    logit_model = sm.Logit(y.astype(float), df.astype(float))
    result = logit_model.fit()
    print (result.summary())




            #==============================================================================
            # Initial call
            #==============================================================================

            runLogit()