Preparation 

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

myData = pd.read_csv("../../scikit-learn-mooc/datasets/adult-census.csv")

myData.head()
age workclass education education-num marital-status occupation relationship race sex capital-gain capital-loss hours-per-week native-country class
0 25 Private 11th 7 Never-married Machine-op-inspct Own-child Black Male 0 0 40 United-States <=50K
1 38 Private HS-grad 9 Married-civ-spouse Farming-fishing Husband White Male 0 0 50 United-States <=50K
2 28 Local-gov Assoc-acdm 12 Married-civ-spouse Protective-serv Husband White Male 0 0 40 United-States >50K
3 44 Private Some-college 10 Married-civ-spouse Machine-op-inspct Husband Black Male 7688 0 40 United-States >50K
4 18 ? Some-college 10 Never-married ? Own-child White Female 0 0 30 United-States <=50K 
print(f"The dataset data contains {myData.shape[0]} samples and {myData.shape[1]} features")

The dataset data contains 48842 samples and 14 features

myData["class"].value_counts()

 <=50K    37155
 >50K     11687
Name: class, dtype: int64
myData.dtypes

age                int64
workclass         object
education         object
education-num      int64
marital-status    object
occupation        object
relationship      object
race              object
sex               object
capital-gain       int64
capital-loss       int64
hours-per-week     int64
native-country    object
class             object
dtype: object
from sklearn.compose import make_column_selector as selector
#
numerical_columns = selector(dtype_include="int64")(myData)
categorical_columns = selector(dtype_include="object")(myData)
all_columns = numerical_columns + categorical_columns
myData = myData[all_columns]

print(f"The dataset data contains {myData.shape[0]} samples and {myData.shape[1]} features")

The dataset data contains 48842 samples and 14 features

data_numerical = myData[numerical_columns]
data_categorical = myData[categorical_columns]

Visualization 

_ = myData.hist(figsize=(20, 14))

_ = sns.pairplot(myData)

_ = sns.pairplot(myData, hue="class")

_ = sns.pairplot(myData, vars=myData)

Education and education-num seem to be linked and they are :

pd.crosstab(index=myData["education"], columns=myData["education-num"])
education-num 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
education
10th 0 0 0 0 0 1389 0 0 0 0 0 0 0 0 0 0
11th 0 0 0 0 0 0 1812 0 0 0 0 0 0 0 0 0
12th 0 0 0 0 0 0 0 657 0 0 0 0 0 0 0 0
1st-4th 0 247 0 0 0 0 0 0 0 0 0 0 0 0 0 0
5th-6th 0 0 509 0 0 0 0 0 0 0 0 0 0 0 0 0
7th-8th 0 0 0 955 0 0 0 0 0 0 0 0 0 0 0 0
9th 0 0 0 0 756 0 0 0 0 0 0 0 0 0 0 0
Assoc-acdm 0 0 0 0 0 0 0 0 0 0 0 1601 0 0 0 0
Assoc-voc 0 0 0 0 0 0 0 0 0 0 2061 0 0 0 0 0
Bachelors 0 0 0 0 0 0 0 0 0 0 0 0 8025 0 0 0
Doctorate 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 594
HS-grad 0 0 0 0 0 0 0 0 15784 0 0 0 0 0 0 0
Masters 0 0 0 0 0 0 0 0 0 0 0 0 0 2657 0 0
Preschool 83 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Prof-school 0 0 0 0 0 0 0 0 0 0 0 0 0 0 834 0
Some-college 0 0 0 0 0 0 0 0 0 10878 0 0 0 0 0 0