Linear Regression in Various Products

Question

I ran a simple regression to a database with a product (Product, Volume, Price). It ran perfectly. But I would like to run the same regression on a basis with more products, but I want to be able to choose the product I want to run the regression, see:

ex.

Produto | Volume | Preço

A

A         

B

B

I want to run the regression only on product B.

• How to do this?

• How to run the regression on all products, however, return separately so that I can analyze them side by side?

Code.

import pandas as pd

Pasta1 = pd.ExcelFile ('Pasta2.xlsx')
Daniel = pd.read_excel (Pasta1, 'Tela')


from scipy.stats import linregress

x= Daniel ['Preço']
y= Daniel ['Volume'] 
m, b, R, p, SEm = linregress (x, y)

pd.DataFrame ([m , b, R, p, SEm] , columns=['Valores'] , index=['declive', 
'ordenada_na_origem', 'coeficiente_de_correlação_(de_Pearson)', 'p-value', 
'erro_padrão'])

Upshot:

Valores

declive: 421.398071 

ordenada_na_origem: 1432.443189 

coeficiente_de_correlação_(de_Pearson): 0.331966 

p-value: 0.000003 

erro_padrão: 86.869651 

Answer 1

Given what seems to be your data, I was able to solve using the attribute .loc of the pandas dataframe.

An example of how I did:

import pandas as pd
import numpy as np

df1 = pd.DataFrame(np.random.randn(6,4),index=list('abadaf'),columns=list('ABCD'))
>>df1
          A         B         C         D
a -0.973031  0.305699  1.330237 -0.799858
b -0.879060  0.238690 -2.729635 -0.457865
a -2.001388  1.058163 -0.328737  0.134416
d  0.994644 -2.305340 -0.714434  0.298462
a -2.242108 -0.331434  0.969981  0.973202
f -0.483833  0.783812  0.925608  0.590251

>>df1.loc['a']
          A         B         C         D
a -0.973031  0.305699  1.330237 -0.799858
a -2.001388  1.058163 -0.328737  0.134416
a -2.242108 -0.331434  0.969981  0.973202

>> df1.loc['a','A']
a   -0.973031
a   -2.001388
a   -2.242108

Here the "product name" is as index. If you want to call the data based on its values (strings or numbers), you can use the .loc along with bollean expressions :

>> df1 = pd.DataFrame([['a',1,2,3],['b',2,3,4],['a',3,4,5],['c',4,5,6]],index=list('defg'),columns=list('higj'))
>> df1
   h  i  g  j
d  a  1  2  3
e  b  2  3  4
f  a  3  4  5
g  c  4  5  6

>> df1.h=='a'
d     True
e    False
f     True
g    False
Name: h, dtype: bool
>> df1.loc[ df1.h=='a',:]
   h  i  g  j
d  a  1  2  3
f  a  3  4  5
>> df1.loc[ df1.h=='a','i']
d    1
f    3

Answer 2

With the help of Guto, I decided as follows:

import pandas as pd
import matplotlib.pyplot as plt

Pasta1 = pd.ExcelFile ('Pasta2.xlsx')
Daniel = pd.read_excel (Pasta1, 'Tela')


from scipy.stats import linregress

x= Daniel.loc [(Daniel ['Preço'] > 0) & (Daniel ['Produto'] == 'A')]
x1= x ['Preço']
y= Daniel.loc [(Daniel ['Volume'] > 0) & (Daniel ['Produto'] == 'A')]
y1= y ['Volume']
Produto_A = linregress (x1, y1)


x2= Daniel.loc [(Daniel ['Preço'] > 0) & (Daniel ['Produto'] == 'B')]
x3= x2 ['Preço']
y2= Daniel.loc [(Daniel ['Volume'] > 0) & (Daniel ['Produto'] == 'B')]
y3= y2 ['Volume']
Produto_B = linregress (x3, y3)


pd.DataFrame ([Produto_A, Produto_B] , index=['Valores', 'Valores2'])

Now I just need to find a way to run with more products, without the need to create a block for each product.