Python - Apply SciPy Beta Distribution to all rows of Pandas DataFrame

Question

In SciPy one can implement a beta distribution as follows:

x=640495496
alpha=1.5017096
beta=628.110247
A=0
B=148000000000 
p = scipy.stats.beta.cdf(x, alpha, beta, loc=A, scale=B-A)

Now, suppose I have a Pandas dataframe with the columns x,alpha,beta,A,B. How do I apply the beta distribution to each row, appending the result as a new column?

Answer 1

Given that I suspect that pandas apply is just looping over all rows, and the scipy.stats distributions have quite a bit of overhead in each call, I would use a vectorized version:

>>> from scipy import stats
>>> df['p'] = stats.beta.cdf(df['x'], df['alpha'], df['beta'], loc=df['A'], scale=df['B']-df['A'])
>>> df
   A             B     alpha        beta          x         p
0  0  148000000000  1.501710  628.110247  640495496  0.858060
1  0  148000000000  1.501704  620.110000  640495440  0.853758

Answer 2

Need apply with function scipy.stats.beta.cdf and axis=1:

df['p'] = df.apply(lambda x:  scipy.stats.beta.cdf(x['x'], 
                                                   x['alpha'], 
                                                   x['beta'], 
                                                   loc=x['A'], 
                                                   scale=x['B']-x['A']), axis=1)

Sample:

import scipy.stats

df = pd.DataFrame({'x':[640495496, 640495440],
                   'alpha':[1.5017096,1.5017045],
                   'beta':[628.110247, 620.110],
                   'A':[0,0],
                   'B':[148000000000,148000000000]})
print (df)
   A             B     alpha        beta          x
0  0  148000000000  1.501710  628.110247  640495496
1  0  148000000000  1.501704  620.110000  640495440

df['p'] = df.apply(lambda x:  scipy.stats.beta.cdf(x['x'], 
                                                   x['alpha'], 
                                                   x['beta'], 
                                                   loc=x['A'], 
                                                   scale=x['B']-x['A']), axis=1)
print (df)
   A             B     alpha        beta          x         p
0  0  148000000000  1.501710  628.110247  640495496  0.858060
1  0  148000000000  1.501704  620.110000  640495440  0.853758