Categorical variables usage in pandas for ANOVA and regression?

Question

To prepare a little toy example:

import pandas as pd
import numpy as np

high, size = 100, 20
df = pd.DataFrame({'perception': np.random.randint(0, high, size),
                   'age': np.random.randint(0, high, size),
                   'outlook': pd.Categorical(np.tile(['positive', 'neutral', 'negative'], size//3+1)[:size]),
                   'smokes': pd.Categorical(np.tile(['lots', 'little', 'not'], size//3+1)[:size]),
                   'outcome': np.random.randint(0, high, size)
                  })
df['age_range'] = pd.Categorical(pd.cut(df.age, range(0, high+5, size//2), right=False,
                             labels=["{0} - {1}".format(i, i + 9) for i in range(0, high, size//2)]))
np.random.shuffle(df['smokes'])

Which will give you something like:

In [2]: df.head(10)
Out[2]:
   perception  age   outlook  smokes  outcome age_range
0          13   65  positive  little       22   60 - 69
1          95   21   neutral    lots       95   20 - 29
2          61   53  negative     not        4   50 - 59
3          27   98  positive     not       42   90 - 99
4          55   99   neutral  little       93   90 - 99
5          28    5  negative     not        4     0 - 9
6          84   83  positive    lots       18   80 - 89
7          66   22   neutral    lots       35   20 - 29
8          13   22  negative    lots       71   20 - 29
9          58   95  positive     not       77   90 - 99

Goal: figure out likelihood of outcome, given {perception, age, outlook, smokes}.

Secondary goal: figure out how important each column is in determining outcome.

Third goal: prove attributes about distribution (here we have randomly generated, so a random distribution should imply the null hypothesis is true?)

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Clearly these are all questions findable with statistical hypothesis testing. What's the right way of answering these questions in pandas?

Answer 1

Finding out likelihood of outcome given columns and Feature importance (1 and 2)

Categorical data

As the dataset contains categorical values, we can use the LabelEncoder() to convert the categorical data into numeric data.

from sklearn.preprocessing import LabelEncoder

enc = LabelEncoder()
df['outlook'] = enc.fit_transform(df['outlook'])
df['smokes'] = enc.fit_transform(df['smokes'])

Result

df.head()

   perception  age  outlook  smokes  outcome age_range
0          67   43        2       1       78     0 - 9
1          77   66        1       1       13     0 - 9
2          33   10        0       1        1     0 - 9
3          74   46        2       1       22     0 - 9
4          14   26        1       2       16     0 - 9

Without creating any model, we can make use of the chi-squared test, p-value and correlation matrix to determine the relation.

Correlation matrix

import matplotlib.pyplot as plt
import seaborn as sns

corr = df.iloc[:, :-1].corr()
sns.heatmap(corr,
            xticklabels=corr.columns,
            yticklabels=corr.columns)
plt.show()

Chi-squared test and p-value

from sklearn.feature_selection import chi2

res = chi2(df.iloc[:, :4], df['outcome'])
features = pd.DataFrame({
    'features': df.columns[:4],
    'chi2': res[0],
    'p-value': res[1]
})

Result

features.head()

     features         chi2        p-value
0  perception  1436.012987  1.022335e-243
1         age  1416.063117  1.221377e-239
2     outlook    61.139303   9.805304e-01
3      smokes    57.147404   9.929925e-01

Randomly generated data, so null hypothesis is true. We can verify this by trying to fit a normal curve to the outcome.

Distribution

import scipy as sp

sns.distplot(df['outcome'], fit=sp.stats.norm, kde=False)
plt.show()

From the plot we can conclude that the data does not fit a normal distribution (as it is randomly generated.)

Note: As the data is all randomly generated, you results can vary, based on the size of the data set.

References

• Hypothesis testing

• Feature selection