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在statsols类中使用分类变量

statsmodels pandas python

Todd Shannon · 技术社区 · 6 年前

statsmodels

import statsmodels.api as sm
import pandas as pd
import numpy as np

dict = {'industry': ['mining', 'transportation', 'hospitality', 'finance', 'entertainment'],
  'debt_ratio':np.random.randn(5), 'cash_flow':np.random.randn(5) + 90} 

df = pd.DataFrame.from_dict(dict)

x = data[['debt_ratio', 'industry']]
y = data['cash_flow']

def reg_sm(x, y):
    x = np.array(x).T
    x = sm.add_constant(x)
    results = sm.OLS(endog = y, exog = x).fit()
    return results

reg_sm(x, y)

我得到以下错误:

TypeError: '>=' not supported between instances of 'float' and 'str'

我试过把 industry 变量,但我仍然得到一个错误。我别无选择。

1 回复 | 直到 6 年前

Brad Solomon 6 年前

转换为分类数据类型的方法是正确的。但是,一旦将DataFrame转换为NumPy数组,就会得到一个 object dtype(NumPy数组作为一个整体是一个统一的类型)。这意味着个人价值观仍然是潜在的 str 回归肯定不会喜欢。

dummify 此功能。而不是 factorizing 它将有效地将变量视为连续变量,您希望保持某种分类的外观:

>>> import statsmodels.api as sm
>>> import pandas as pd
>>> import numpy as np
>>> np.random.seed(444)
>>> data = {
...     'industry': ['mining', 'transportation', 'hospitality', 'finance', 'entertainment'],
...    'debt_ratio':np.random.randn(5),
...    'cash_flow':np.random.randn(5) + 90
... }
>>> data = pd.DataFrame.from_dict(data)
>>> data = pd.concat((
...     data,
...     pd.get_dummies(data['industry'], drop_first=True)), axis=1)
>>> # You could also use data.drop('industry', axis=1)
>>> # in the call to pd.concat()
>>> data
         industry  debt_ratio  cash_flow  finance  hospitality  mining  transportation
0          mining    0.357440  88.856850        0            0       1               0
1  transportation    0.377538  89.457560        0            0       0               1
2     hospitality    1.382338  89.451292        0            1       0               0
3         finance    1.175549  90.208520        1            0       0               0
4   entertainment   -0.939276  90.212690        0            0       0               0

drop_first 是为了避免 dummy trap :

>>> y = data['cash_flow']
>>> x = data.drop(['cash_flow', 'industry'], axis=1)
>>> sm.OLS(y, x).fit()
<statsmodels.regression.linear_model.RegressionResultsWrapper object at 0x115b87cf8>

最后,只需要一个小指针:它有助于避免使用隐藏了内置对象类型的名称来命名引用,例如 dict

Dogemore 4 年前

我也有这个问题,有很多列需要被视为绝对的,这使它相当恼人的处理 dummify . 并转换为 string 不适合我。

R接口提供了一种很好的方法:

import statsmodels.formula.api as smf
import pandas as pd
import numpy as np

dict = {'industry': ['mining', 'transportation', 'hospitality', 'finance', 'entertainment'],
  'debt_ratio':np.random.randn(5), 'cash_flow':np.random.randn(5) + 90} 

df = pd.DataFrame.from_dict(dict)

x = df[['debt_ratio', 'industry']]
y = df['cash_flow']

# NB. unlike sm.OLS, there is "intercept" term is included here
smf.ols(formula="cash_flow ~ debt_ratio + C(industry)", data=df).fit()

参考文献: https://www.statsmodels.org/stable/example_formulas.html#categorical-variables