Distribution fitting of Multiple columns

Question

I am trying to get the distribution fitting of my data using scipy.stats. The data contains multiple columns col_1, col_2, col_3 in a single CSV file.

• The problem is distribution fitting only takes a single column to identify a best distribution fittings as I have shown in the below code.

• How to get the distribution fitting of all columns at the same time? e.g distribution fitting ofcol_1, col_2, col_3

   import warnings
   warnings.filterwarnings("ignore")
  
   import pandas as pd
   import numpy as np
   import scipy
   from sklearn.preprocessing import StandardScaler
   import scipy.stats
   import matplotlib.pyplot as plt
  
   # Load data and select first column
  
   from sklearn import datasets
   data_set = datasets.load_breast_cancer()
  
   # Multiple columns of csv
   col_1=data_set.data[:,0]
   col_2=data_set.data[:,1]
   col_3=data_set.data[:,2]
  
   # Create an index array (x) for data
  
   x = np.arange(len(col_1))
   size = len(col_1)
  
   plt.hist(col_1)
   plt.show()
  
  
   sc=StandardScaler() 
   yy = col_1.reshape (-1,1)
   sc.fit(yy)
   y_std =sc.transform(yy)
   y_std = y_std.flatten()
   y_std
   del yy
  
  
   dist_names = ['beta',
                 'expon',
                 'gamma',
                 'lognorm',
                 'norm',
                 'pearson3',
                 'triang',
                 'uniform',
                 'weibull_min', 
                 'weibull_max']
  
   # Set up empty lists to stroe results
   chi_square = []
   p_values = []
  
  
   # Set up 50 bins for chi-square test
   # Observed data will be approximately evenly distrubuted aross all bins
   percentile_bins = np.linspace(0,100,51)
   percentile_cutoffs = np.percentile(y_std, percentile_bins)
   observed_frequency, bins = (np.histogram(y_std, bins=percentile_cutoffs))
   cum_observed_frequency = np.cumsum(observed_frequency)
  
  
  
   # Loop through candidate distributions
  
   for distribution in dist_names:
       # Set up distribution and get fitted distribution parameters
       dist = getattr(scipy.stats, distribution)
       param = dist.fit(y_std)
  
       # Obtain the KS test P statistic, round it to 5 decimal places
       p = scipy.stats.kstest(y_std, distribution, args=param)[1]
       p = np.around(p, 5)
       p_values.append(p)    
  
       # Get expected counts in percentile bins
       # This is based on a 'cumulative distrubution function' (cdf)
       cdf_fitted = dist.cdf(percentile_cutoffs, *param[:-2], loc=param[-2], 
                             scale=param[-1])
       expected_frequency = []
       for bin in range(len(percentile_bins)-1):
           expected_cdf_area = cdf_fitted[bin+1] - cdf_fitted[bin]
           expected_frequency.append(expected_cdf_area)
  
       # calculate chi-squared
       expected_frequency = np.array(expected_frequency) * size
       cum_expected_frequency = np.cumsum(expected_frequency)
       ss = sum (((cum_expected_frequency - cum_observed_frequency) ** 2) / cum_observed_frequency)
       chi_square.append(ss)
  
   # Collate results and sort by goodness of fit (best at top)
  
   results = pd.DataFrame()
   results['Distribution'] = dist_names
   results['chi_square'] = chi_square
   results['p_value'] = p_values
   results.sort_values(['chi_square'], inplace=True)
  
   # Report results
  
   print ('\nDistributions sorted by goodness of fit:')
   print ('----------------------------------------')
   print (results)