metadata
dataset_info:
features:
- name: age
dtype: int64
- name: workclass
dtype:
class_label:
names:
'0': '?'
'1': Federal-gov
'2': Local-gov
'3': Never-worked
'4': Private
'5': Self-emp-inc
'6': Self-emp-not-inc
'7': State-gov
'8': Without-pay
- name: fnlwgt
dtype: int64
- name: education
dtype:
class_label:
names:
'0': 10th
'1': 11th
'2': 12th
'3': 1st-4th
'4': 5th-6th
'5': 7th-8th
'6': 9th
'7': Assoc-acdm
'8': Assoc-voc
'9': Bachelors
'10': Doctorate
'11': HS-grad
'12': Masters
'13': Preschool
'14': Prof-school
'15': Some-college
- name: education-num
dtype: int64
- name: marital-status
dtype:
class_label:
names:
'0': Divorced
'1': Married-AF-spouse
'2': Married-civ-spouse
'3': Married-spouse-absent
'4': Never-married
'5': Separated
'6': Widowed
- name: occupation
dtype:
class_label:
names:
'0': '?'
'1': Adm-clerical
'2': Armed-Forces
'3': Craft-repair
'4': Exec-managerial
'5': Farming-fishing
'6': Handlers-cleaners
'7': Machine-op-inspct
'8': Other-service
'9': Priv-house-serv
'10': Prof-specialty
'11': Protective-serv
'12': Sales
'13': Tech-support
'14': Transport-moving
- name: relationship
dtype:
class_label:
names:
'0': Husband
'1': Not-in-family
'2': Other-relative
'3': Own-child
'4': Unmarried
'5': Wife
- name: race
dtype:
class_label:
names:
'0': Amer-Indian-Eskimo
'1': Asian-Pac-Islander
'2': Black
'3': Other
'4': White
- name: sex
dtype:
class_label:
names:
'0': Female
'1': Male
- name: capital-gain
dtype: int64
- name: capital-loss
dtype: int64
- name: hours-per-week
dtype: int64
- name: native-country
dtype:
class_label:
names:
'0': '?'
'1': Cambodia
'2': Canada
'3': China
'4': Columbia
'5': Cuba
'6': Dominican-Republic
'7': Ecuador
'8': El-Salvador
'9': England
'10': France
'11': Germany
'12': Greece
'13': Guatemala
'14': Haiti
'15': Holand-Netherlands
'16': Honduras
'17': Hong
'18': Hungary
'19': India
'20': Iran
'21': Ireland
'22': Italy
'23': Jamaica
'24': Japan
'25': Laos
'26': Mexico
'27': Nicaragua
'28': Outlying-US(Guam-USVI-etc)
'29': Peru
'30': Philippines
'31': Poland
'32': Portugal
'33': Puerto-Rico
'34': Scotland
'35': South
'36': Taiwan
'37': Thailand
'38': Trinadad&Tobago
'39': United-States
'40': Vietnam
'41': Yugoslavia
- name: income
dtype:
class_label:
names:
'0': <=50K
'1': '>50K'
splits:
- name: train
num_bytes: 3907320
num_examples: 32561
- name: test
num_bytes: 1953720
num_examples: 16281
download_size: 800983
dataset_size: 5861040
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
Dataset Card for Census Income (Adult)
This dataset is a precise version of Adult or Census Income. This dataset from UCI somehow happens to occupy two links, but we checked and confirm that they are identical.
We used the following python script to create this Hugging Face dataset.
import pandas as pd
from datasets import Dataset, DatasetDict, Features, Value, ClassLabel
# URLs
url1 = "https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data"
url2 = "https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.test"
# Column names
columns = [
"age", "workclass", "fnlwgt", "education", "education-num", "marital-status",
"occupation", "relationship", "race", "sex", "capital-gain", "capital-loss",
"hours-per-week", "native-country", "income"
]
# Load datasets
df_train = pd.read_csv(url1, names=columns, skipinitialspace=True)
df_test = pd.read_csv(url2, names=columns, skipinitialspace=True, skiprows=1)
# Convert continuous columns to float
continuous_columns = ["age", "fnlwgt", "education-num", "capital-gain", "capital-loss", "hours-per-week"]
for col in continuous_columns:
df_train[col] = pd.to_numeric(df_train[col], errors='coerce')
df_test[col] = pd.to_numeric(df_test[col], errors='coerce')
df_test['income'] = df_test['income'].str.rstrip('.') # This is somewhat critical.
# Define categorical columns
categorical_columns = [
"workclass", "education", "marital-status", "occupation", "relationship",
"race", "sex", "native-country", "income"
]
# Dictionary to store category mappings
category_mappings = {}
for col in categorical_columns:
# Convert train column to category and extract categories
df_train[col] = df_train[col].astype("category")
category_mappings[col] = df_train[col].cat.categories.to_list() # Store category order
# Apply the same category mapping to test
df_test[col] = pd.Categorical(df_test[col], categories=category_mappings[col])
# Convert to integer codes
df_train[col] = df_train[col].cat.codes
df_test[col] = df_test[col].cat.codes
# Define Hugging Face dataset schema
hf_features = Features({
"age": Value("int64"),
"workclass": ClassLabel(names=category_mappings["workclass"]),
"fnlwgt": Value("int64"),
"education": ClassLabel(names=category_mappings["education"]),
"education-num": Value("int64"),
"marital-status": ClassLabel(names=category_mappings["marital-status"]),
"occupation": ClassLabel(names=category_mappings["occupation"]),
"relationship": ClassLabel(names=category_mappings["relationship"]),
"race": ClassLabel(names=category_mappings["race"]),
"sex": ClassLabel(names=category_mappings["sex"]),
"capital-gain": Value("int64"),
"capital-loss": Value("int64"),
"hours-per-week": Value("int64"),
"native-country": ClassLabel(names=category_mappings["native-country"]),
"income": ClassLabel(names=category_mappings["income"])
})
# Convert pandas DataFrame to Hugging Face Dataset
hf_train = Dataset.from_pandas(df_train, features=hf_features)
hf_test = Dataset.from_pandas(df_test, features=hf_features)
# Create a dataset dictionary
hf_dataset = DatasetDict({
"train": hf_train,
"test": hf_test
})
# Print dataset structure
print(hf_dataset)
The printed output could look like
DatasetDict({
train: Dataset({
features: ['age', 'workclass', 'fnlwgt', 'education', 'education-num', 'marital-status', 'occupation', 'relationship', 'race', 'sex', 'capital-gain', 'capital-loss', 'hours-per-week', 'native-country', 'income'],
num_rows: 32561
})
test: Dataset({
features: ['age', 'workclass', 'fnlwgt', 'education', 'education-num', 'marital-status', 'occupation', 'relationship', 'race', 'sex', 'capital-gain', 'capital-loss', 'hours-per-week', 'native-country', 'income'],
num_rows: 16281
})
})