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