Dataset Preview
The full dataset viewer is not available (click to read why). Only showing a preview of the rows.
The dataset generation failed because of a cast error
Error code: DatasetGenerationCastError
Exception: DatasetGenerationCastError
Message: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 64 new columns ({'Largest Property Use Type_Automobile Dealership', 'Community Board', 'Largest Property Use Type_Performing Arts', 'Largest Property Use Type_Adult Education', 'Largest Property Use Type_Residence Hall/Dormitory', 'Largest Property Use Type_Convenience Store without Gas Station', 'Census Tract', 'log_Water Intensity (All Water Sources) (gal/ft²)', 'Largest Property Use Type_Other - Services', 'Largest Property Use Type_Worship Facility', 'Largest Property Use Type_Hotel', 'Largest Property Use Type_Other', 'Largest Property Use Type_Residential Care Facility', 'Largest Property Use Type_Restaurant', 'Largest Property Use Type_Multifamily Housing', 'Longitude', 'Largest Property Use Type_Bank Branch', 'Largest Property Use Type_Senior Care Community', 'Largest Property Use Type_Self-Storage Facility', 'Latitude', 'Largest Property Use Type_Enclosed Mall', 'Largest Property Use Type_Other - Recreation', 'Largest Property Use Type_Pre-school/Daycare', 'Property Id', 'Largest Property Use Type_Other - Entertainment/Public Assembly', 'Largest Property Use Type_Manufacturing/Industrial Plant', 'Largest Property Use Type_Supermarket/Grocery Store', 'Weather Normalized Site Natural Gas Intensity (therms/ft²)', 'Occupancy', 'Largest Property Use Type_Non-Refrigerated Warehouse', 'Largest Property Use Type_Other - Public Services', 'Order', 'Largest Property Use Type_K-12 School', 'Largest Property Use Type_Medical Office', 'Weather Normalized Site Electricity Intensity (kWh/ft²)', 'Largest Property Use Type_Other - Mall', 'Borough_Staten Island', 'Largest Property Use Type_Movie Theater', 'Site EUI (kBtu/ft²)', 'Largest Property Use Type_Repair Services (Vehicle, Shoe, Locksmith, etc.)', 'Largest Property Use Type_Hospital (General Medical & Surgical)', 'Largest Property Use Type_Museum', 'Largest Property Use Type_Financial Office', 'Largest Property Use Type_Mailing Center/Post Office', 'Largest Property Use Type_Distribution Center', 'Largest Property Use Type_Parking', 'Largest Property Use Type_Outpatient Rehabilitation/Physical Therapy', 'Largest Property Use Type_Library', 'Number of Buildings - Self-reported', 'Largest Property Use Type_Courthouse', 'Largest Property Use Type_Retail Store', 'Largest Property Use Type_Strip Mall', 'Largest Property Use Type_Other - Lodging/Residential', 'Largest Property Use Type_Social/Meeting Hall', 'Largest Property Use Type_Urgent Care/Clinic/Other Outpatient', 'Water Intensity (All Water Sources) (gal/ft²)', 'DOF Gross Floor Area', 'Largest Property Use Type_Wholesale Club/Supercenter', 'Largest Property Use Type_Other - Specialty Hospital', 'Largest Property Use Type_Refrigerated Warehouse', 'Largest Property Use Type_Other - Education', 'Year Built', 'log_Direct GHG Emissions (Metric Tons CO2e)', 'Largest Property Use Type_College/University'}) and 12 missing columns ({'Sex', 'Survived', 'Embarked', 'Parch', 'Pclass', 'Age', 'Ticket', 'SibSp', 'Name', 'Fare', 'Cabin', 'PassengerId'}).
This happened while the csv dataset builder was generating data using
hf://datasets/Beifengdo/Machine-Learning/课件压缩/code/07-机器学习实践/机器学习项目流程/能源预测项目/data/training_features.csv (at revision 5e85e8e035920595656dbb774a1ac19d0ccae54a), ['hf://datasets/Beifengdo/Machine-Learning@5e85e8e035920595656dbb774a1ac19d0ccae54a/智科机器学习课件machine-learning-master/machine-learning-master/data/titanic/train.csv', 'hf://datasets/Beifengdo/Machine-Learning@5e85e8e035920595656dbb774a1ac19d0ccae54a/课件压缩/code/07-机器学习实践/机器学习项目流程/能源预测项目/data/training_features.csv', 'hf://datasets/Beifengdo/Machine-Learning@5e85e8e035920595656dbb774a1ac19d0ccae54a/课件压缩/code/07-机器学习实践/机器学习项目流程/能源预测项目/data/training_labels.csv']
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1848, in _prepare_split_single
writer.write_table(table)
~~~~~~~~~~~~~~~~~~^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 784, in write_table
self._write_table(pa_table, writer_batch_size=writer_batch_size)
~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 795, in _write_table
pa_table = table_cast(pa_table, self._schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
Order: int64
Property Id: int64
DOF Gross Floor Area: double
Year Built: int64
Number of Buildings - Self-reported: int64
Occupancy: int64
Site EUI (kBtu/ft²): double
Weather Normalized Site Electricity Intensity (kWh/ft²): double
Weather Normalized Site Natural Gas Intensity (therms/ft²): double
Water Intensity (All Water Sources) (gal/ft²): double
Latitude: double
Longitude: double
Community Board: double
Census Tract: double
log_Direct GHG Emissions (Metric Tons CO2e): double
log_Water Intensity (All Water Sources) (gal/ft²): double
Borough_Staten Island: int64
Largest Property Use Type_Adult Education: int64
Largest Property Use Type_Automobile Dealership: int64
Largest Property Use Type_Bank Branch: int64
Largest Property Use Type_College/University: int64
Largest Property Use Type_Convenience Store without Gas Station: int64
Largest Property Use Type_Courthouse: int64
Largest Property Use Type_Distribution Center: int64
Largest Property Use Type_Enclosed Mall: int64
Largest Property Use Type_Financial Office: int64
Largest Property Use Type_Hospital (General Medical & Surgical): int64
Largest Property Use Type_Hotel: int64
Largest Property Use Type_K-12 School: int64
Largest Property Use Type_Library: int64
Largest Property Use Type_Mailing Center/Post Office: int64
Largest Property Use Type_Manufacturing/Industrial Plant: int64
Largest Property Use Type_Medical Office: int64
Largest Property Use Type_Movie Theater: int64
Largest Property Use Type_Multifamily Housing: i
...
roperty Use Type_Other - Entertainment/Public Assembly: int64
Largest Property Use Type_Other - Lodging/Residential: int64
Largest Property Use Type_Other - Mall: int64
Largest Property Use Type_Other - Public Services: int64
Largest Property Use Type_Other - Recreation: int64
Largest Property Use Type_Other - Services: int64
Largest Property Use Type_Other - Specialty Hospital: int64
Largest Property Use Type_Outpatient Rehabilitation/Physical Therapy: int64
Largest Property Use Type_Parking: int64
Largest Property Use Type_Performing Arts: int64
Largest Property Use Type_Pre-school/Daycare: int64
Largest Property Use Type_Refrigerated Warehouse: int64
Largest Property Use Type_Repair Services (Vehicle, Shoe, Locksmith, etc.): int64
Largest Property Use Type_Residence Hall/Dormitory: int64
Largest Property Use Type_Residential Care Facility: int64
Largest Property Use Type_Restaurant: int64
Largest Property Use Type_Retail Store: int64
Largest Property Use Type_Self-Storage Facility: int64
Largest Property Use Type_Senior Care Community: int64
Largest Property Use Type_Social/Meeting Hall: int64
Largest Property Use Type_Strip Mall: int64
Largest Property Use Type_Supermarket/Grocery Store: int64
Largest Property Use Type_Urgent Care/Clinic/Other Outpatient: int64
Largest Property Use Type_Wholesale Club/Supercenter: int64
Largest Property Use Type_Worship Facility: int64
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 11736
to
{'PassengerId': Value('int64'), 'Survived': Value('int64'), 'Pclass': Value('int64'), 'Name': Value('string'), 'Sex': Value('string'), 'Age': Value('float64'), 'SibSp': Value('int64'), 'Parch': Value('int64'), 'Ticket': Value('string'), 'Fare': Value('float64'), 'Cabin': Value('string'), 'Embarked': Value('string')}
because column names don't match
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
~~~~~~~~~~~~~~~~~~~~~~~~~^
builder, max_dataset_size_bytes=max_dataset_size_bytes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1694, in _prepare_split
for job_id, done, content in self._prepare_split_single(
~~~~~~~~~~~~~~~~~~~~~~~~~~^
gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
):
^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1850, in _prepare_split_single
raise DatasetGenerationCastError.from_cast_error(
...<4 lines>...
)
datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 64 new columns ({'Largest Property Use Type_Automobile Dealership', 'Community Board', 'Largest Property Use Type_Performing Arts', 'Largest Property Use Type_Adult Education', 'Largest Property Use Type_Residence Hall/Dormitory', 'Largest Property Use Type_Convenience Store without Gas Station', 'Census Tract', 'log_Water Intensity (All Water Sources) (gal/ft²)', 'Largest Property Use Type_Other - Services', 'Largest Property Use Type_Worship Facility', 'Largest Property Use Type_Hotel', 'Largest Property Use Type_Other', 'Largest Property Use Type_Residential Care Facility', 'Largest Property Use Type_Restaurant', 'Largest Property Use Type_Multifamily Housing', 'Longitude', 'Largest Property Use Type_Bank Branch', 'Largest Property Use Type_Senior Care Community', 'Largest Property Use Type_Self-Storage Facility', 'Latitude', 'Largest Property Use Type_Enclosed Mall', 'Largest Property Use Type_Other - Recreation', 'Largest Property Use Type_Pre-school/Daycare', 'Property Id', 'Largest Property Use Type_Other - Entertainment/Public Assembly', 'Largest Property Use Type_Manufacturing/Industrial Plant', 'Largest Property Use Type_Supermarket/Grocery Store', 'Weather Normalized Site Natural Gas Intensity (therms/ft²)', 'Occupancy', 'Largest Property Use Type_Non-Refrigerated Warehouse', 'Largest Property Use Type_Other - Public Services', 'Order', 'Largest Property Use Type_K-12 School', 'Largest Property Use Type_Medical Office', 'Weather Normalized Site Electricity Intensity (kWh/ft²)', 'Largest Property Use Type_Other - Mall', 'Borough_Staten Island', 'Largest Property Use Type_Movie Theater', 'Site EUI (kBtu/ft²)', 'Largest Property Use Type_Repair Services (Vehicle, Shoe, Locksmith, etc.)', 'Largest Property Use Type_Hospital (General Medical & Surgical)', 'Largest Property Use Type_Museum', 'Largest Property Use Type_Financial Office', 'Largest Property Use Type_Mailing Center/Post Office', 'Largest Property Use Type_Distribution Center', 'Largest Property Use Type_Parking', 'Largest Property Use Type_Outpatient Rehabilitation/Physical Therapy', 'Largest Property Use Type_Library', 'Number of Buildings - Self-reported', 'Largest Property Use Type_Courthouse', 'Largest Property Use Type_Retail Store', 'Largest Property Use Type_Strip Mall', 'Largest Property Use Type_Other - Lodging/Residential', 'Largest Property Use Type_Social/Meeting Hall', 'Largest Property Use Type_Urgent Care/Clinic/Other Outpatient', 'Water Intensity (All Water Sources) (gal/ft²)', 'DOF Gross Floor Area', 'Largest Property Use Type_Wholesale Club/Supercenter', 'Largest Property Use Type_Other - Specialty Hospital', 'Largest Property Use Type_Refrigerated Warehouse', 'Largest Property Use Type_Other - Education', 'Year Built', 'log_Direct GHG Emissions (Metric Tons CO2e)', 'Largest Property Use Type_College/University'}) and 12 missing columns ({'Sex', 'Survived', 'Embarked', 'Parch', 'Pclass', 'Age', 'Ticket', 'SibSp', 'Name', 'Fare', 'Cabin', 'PassengerId'}).
This happened while the csv dataset builder was generating data using
hf://datasets/Beifengdo/Machine-Learning/课件压缩/code/07-机器学习实践/机器学习项目流程/能源预测项目/data/training_features.csv (at revision 5e85e8e035920595656dbb774a1ac19d0ccae54a), ['hf://datasets/Beifengdo/Machine-Learning@5e85e8e035920595656dbb774a1ac19d0ccae54a/智科机器学习课件machine-learning-master/machine-learning-master/data/titanic/train.csv', 'hf://datasets/Beifengdo/Machine-Learning@5e85e8e035920595656dbb774a1ac19d0ccae54a/课件压缩/code/07-机器学习实践/机器学习项目流程/能源预测项目/data/training_features.csv', 'hf://datasets/Beifengdo/Machine-Learning@5e85e8e035920595656dbb774a1ac19d0ccae54a/课件压缩/code/07-机器学习实践/机器学习项目流程/能源预测项目/data/training_labels.csv']
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
PassengerId int64 | Survived int64 | Pclass int64 | Name string | Sex string | Age float64 | SibSp int64 | Parch int64 | Ticket string | Fare float64 | Cabin string | Embarked string |
|---|---|---|---|---|---|---|---|---|---|---|---|
1 | 0 | 3 | Braund, Mr. Owen Harris | male | 22 | 1 | 0 | A/5 21171 | 7.25 | null | S |
2 | 1 | 1 | Cumings, Mrs. John Bradley (Florence Briggs Thayer) | female | 38 | 1 | 0 | PC 17599 | 71.2833 | C85 | C |
3 | 1 | 3 | Heikkinen, Miss. Laina | female | 26 | 0 | 0 | STON/O2. 3101282 | 7.925 | null | S |
4 | 1 | 1 | Futrelle, Mrs. Jacques Heath (Lily May Peel) | female | 35 | 1 | 0 | 113803 | 53.1 | C123 | S |
5 | 0 | 3 | Allen, Mr. William Henry | male | 35 | 0 | 0 | 373450 | 8.05 | null | S |
6 | 0 | 3 | Moran, Mr. James | male | null | 0 | 0 | 330877 | 8.4583 | null | Q |
7 | 0 | 1 | McCarthy, Mr. Timothy J | male | 54 | 0 | 0 | 17463 | 51.8625 | E46 | S |
8 | 0 | 3 | Palsson, Master. Gosta Leonard | male | 2 | 3 | 1 | 349909 | 21.075 | null | S |
9 | 1 | 3 | Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) | female | 27 | 0 | 2 | 347742 | 11.1333 | null | S |
10 | 1 | 2 | Nasser, Mrs. Nicholas (Adele Achem) | female | 14 | 1 | 0 | 237736 | 30.0708 | null | C |
11 | 1 | 3 | Sandstrom, Miss. Marguerite Rut | female | 4 | 1 | 1 | PP 9549 | 16.7 | G6 | S |
12 | 1 | 1 | Bonnell, Miss. Elizabeth | female | 58 | 0 | 0 | 113783 | 26.55 | C103 | S |
13 | 0 | 3 | Saundercock, Mr. William Henry | male | 20 | 0 | 0 | A/5. 2151 | 8.05 | null | S |
14 | 0 | 3 | Andersson, Mr. Anders Johan | male | 39 | 1 | 5 | 347082 | 31.275 | null | S |
15 | 0 | 3 | Vestrom, Miss. Hulda Amanda Adolfina | female | 14 | 0 | 0 | 350406 | 7.8542 | null | S |
16 | 1 | 2 | Hewlett, Mrs. (Mary D Kingcome) | female | 55 | 0 | 0 | 248706 | 16 | null | S |
17 | 0 | 3 | Rice, Master. Eugene | male | 2 | 4 | 1 | 382652 | 29.125 | null | Q |
18 | 1 | 2 | Williams, Mr. Charles Eugene | male | null | 0 | 0 | 244373 | 13 | null | S |
19 | 0 | 3 | Vander Planke, Mrs. Julius (Emelia Maria Vandemoortele) | female | 31 | 1 | 0 | 345763 | 18 | null | S |
20 | 1 | 3 | Masselmani, Mrs. Fatima | female | null | 0 | 0 | 2649 | 7.225 | null | C |
21 | 0 | 2 | Fynney, Mr. Joseph J | male | 35 | 0 | 0 | 239865 | 26 | null | S |
22 | 1 | 2 | Beesley, Mr. Lawrence | male | 34 | 0 | 0 | 248698 | 13 | D56 | S |
23 | 1 | 3 | McGowan, Miss. Anna "Annie" | female | 15 | 0 | 0 | 330923 | 8.0292 | null | Q |
24 | 1 | 1 | Sloper, Mr. William Thompson | male | 28 | 0 | 0 | 113788 | 35.5 | A6 | S |
25 | 0 | 3 | Palsson, Miss. Torborg Danira | female | 8 | 3 | 1 | 349909 | 21.075 | null | S |
26 | 1 | 3 | Asplund, Mrs. Carl Oscar (Selma Augusta Emilia Johansson) | female | 38 | 1 | 5 | 347077 | 31.3875 | null | S |
27 | 0 | 3 | Emir, Mr. Farred Chehab | male | null | 0 | 0 | 2631 | 7.225 | null | C |
28 | 0 | 1 | Fortune, Mr. Charles Alexander | male | 19 | 3 | 2 | 19950 | 263 | C23 C25 C27 | S |
29 | 1 | 3 | O'Dwyer, Miss. Ellen "Nellie" | female | null | 0 | 0 | 330959 | 7.8792 | null | Q |
30 | 0 | 3 | Todoroff, Mr. Lalio | male | null | 0 | 0 | 349216 | 7.8958 | null | S |
31 | 0 | 1 | Uruchurtu, Don. Manuel E | male | 40 | 0 | 0 | PC 17601 | 27.7208 | null | C |
32 | 1 | 1 | Spencer, Mrs. William Augustus (Marie Eugenie) | female | null | 1 | 0 | PC 17569 | 146.5208 | B78 | C |
33 | 1 | 3 | Glynn, Miss. Mary Agatha | female | null | 0 | 0 | 335677 | 7.75 | null | Q |
34 | 0 | 2 | Wheadon, Mr. Edward H | male | 66 | 0 | 0 | C.A. 24579 | 10.5 | null | S |
35 | 0 | 1 | Meyer, Mr. Edgar Joseph | male | 28 | 1 | 0 | PC 17604 | 82.1708 | null | C |
36 | 0 | 1 | Holverson, Mr. Alexander Oskar | male | 42 | 1 | 0 | 113789 | 52 | null | S |
37 | 1 | 3 | Mamee, Mr. Hanna | male | null | 0 | 0 | 2677 | 7.2292 | null | C |
38 | 0 | 3 | Cann, Mr. Ernest Charles | male | 21 | 0 | 0 | A./5. 2152 | 8.05 | null | S |
39 | 0 | 3 | Vander Planke, Miss. Augusta Maria | female | 18 | 2 | 0 | 345764 | 18 | null | S |
40 | 1 | 3 | Nicola-Yarred, Miss. Jamila | female | 14 | 1 | 0 | 2651 | 11.2417 | null | C |
41 | 0 | 3 | Ahlin, Mrs. Johan (Johanna Persdotter Larsson) | female | 40 | 1 | 0 | 7546 | 9.475 | null | S |
42 | 0 | 2 | Turpin, Mrs. William John Robert (Dorothy Ann Wonnacott) | female | 27 | 1 | 0 | 11668 | 21 | null | S |
43 | 0 | 3 | Kraeff, Mr. Theodor | male | null | 0 | 0 | 349253 | 7.8958 | null | C |
44 | 1 | 2 | Laroche, Miss. Simonne Marie Anne Andree | female | 3 | 1 | 2 | SC/Paris 2123 | 41.5792 | null | C |
45 | 1 | 3 | Devaney, Miss. Margaret Delia | female | 19 | 0 | 0 | 330958 | 7.8792 | null | Q |
46 | 0 | 3 | Rogers, Mr. William John | male | null | 0 | 0 | S.C./A.4. 23567 | 8.05 | null | S |
47 | 0 | 3 | Lennon, Mr. Denis | male | null | 1 | 0 | 370371 | 15.5 | null | Q |
48 | 1 | 3 | O'Driscoll, Miss. Bridget | female | null | 0 | 0 | 14311 | 7.75 | null | Q |
49 | 0 | 3 | Samaan, Mr. Youssef | male | null | 2 | 0 | 2662 | 21.6792 | null | C |
50 | 0 | 3 | Arnold-Franchi, Mrs. Josef (Josefine Franchi) | female | 18 | 1 | 0 | 349237 | 17.8 | null | S |
51 | 0 | 3 | Panula, Master. Juha Niilo | male | 7 | 4 | 1 | 3101295 | 39.6875 | null | S |
52 | 0 | 3 | Nosworthy, Mr. Richard Cater | male | 21 | 0 | 0 | A/4. 39886 | 7.8 | null | S |
53 | 1 | 1 | Harper, Mrs. Henry Sleeper (Myna Haxtun) | female | 49 | 1 | 0 | PC 17572 | 76.7292 | D33 | C |
54 | 1 | 2 | Faunthorpe, Mrs. Lizzie (Elizabeth Anne Wilkinson) | female | 29 | 1 | 0 | 2926 | 26 | null | S |
55 | 0 | 1 | Ostby, Mr. Engelhart Cornelius | male | 65 | 0 | 1 | 113509 | 61.9792 | B30 | C |
56 | 1 | 1 | Woolner, Mr. Hugh | male | null | 0 | 0 | 19947 | 35.5 | C52 | S |
57 | 1 | 2 | Rugg, Miss. Emily | female | 21 | 0 | 0 | C.A. 31026 | 10.5 | null | S |
58 | 0 | 3 | Novel, Mr. Mansouer | male | 28.5 | 0 | 0 | 2697 | 7.2292 | null | C |
59 | 1 | 2 | West, Miss. Constance Mirium | female | 5 | 1 | 2 | C.A. 34651 | 27.75 | null | S |
60 | 0 | 3 | Goodwin, Master. William Frederick | male | 11 | 5 | 2 | CA 2144 | 46.9 | null | S |
61 | 0 | 3 | Sirayanian, Mr. Orsen | male | 22 | 0 | 0 | 2669 | 7.2292 | null | C |
62 | 1 | 1 | Icard, Miss. Amelie | female | 38 | 0 | 0 | 113572 | 80 | B28 | null |
63 | 0 | 1 | Harris, Mr. Henry Birkhardt | male | 45 | 1 | 0 | 36973 | 83.475 | C83 | S |
64 | 0 | 3 | Skoog, Master. Harald | male | 4 | 3 | 2 | 347088 | 27.9 | null | S |
65 | 0 | 1 | Stewart, Mr. Albert A | male | null | 0 | 0 | PC 17605 | 27.7208 | null | C |
66 | 1 | 3 | Moubarek, Master. Gerios | male | null | 1 | 1 | 2661 | 15.2458 | null | C |
67 | 1 | 2 | Nye, Mrs. (Elizabeth Ramell) | female | 29 | 0 | 0 | C.A. 29395 | 10.5 | F33 | S |
68 | 0 | 3 | Crease, Mr. Ernest James | male | 19 | 0 | 0 | S.P. 3464 | 8.1583 | null | S |
69 | 1 | 3 | Andersson, Miss. Erna Alexandra | female | 17 | 4 | 2 | 3101281 | 7.925 | null | S |
70 | 0 | 3 | Kink, Mr. Vincenz | male | 26 | 2 | 0 | 315151 | 8.6625 | null | S |
71 | 0 | 2 | Jenkin, Mr. Stephen Curnow | male | 32 | 0 | 0 | C.A. 33111 | 10.5 | null | S |
72 | 0 | 3 | Goodwin, Miss. Lillian Amy | female | 16 | 5 | 2 | CA 2144 | 46.9 | null | S |
73 | 0 | 2 | Hood, Mr. Ambrose Jr | male | 21 | 0 | 0 | S.O.C. 14879 | 73.5 | null | S |
74 | 0 | 3 | Chronopoulos, Mr. Apostolos | male | 26 | 1 | 0 | 2680 | 14.4542 | null | C |
75 | 1 | 3 | Bing, Mr. Lee | male | 32 | 0 | 0 | 1601 | 56.4958 | null | S |
76 | 0 | 3 | Moen, Mr. Sigurd Hansen | male | 25 | 0 | 0 | 348123 | 7.65 | F G73 | S |
77 | 0 | 3 | Staneff, Mr. Ivan | male | null | 0 | 0 | 349208 | 7.8958 | null | S |
78 | 0 | 3 | Moutal, Mr. Rahamin Haim | male | null | 0 | 0 | 374746 | 8.05 | null | S |
79 | 1 | 2 | Caldwell, Master. Alden Gates | male | 0.83 | 0 | 2 | 248738 | 29 | null | S |
80 | 1 | 3 | Dowdell, Miss. Elizabeth | female | 30 | 0 | 0 | 364516 | 12.475 | null | S |
81 | 0 | 3 | Waelens, Mr. Achille | male | 22 | 0 | 0 | 345767 | 9 | null | S |
82 | 1 | 3 | Sheerlinck, Mr. Jan Baptist | male | 29 | 0 | 0 | 345779 | 9.5 | null | S |
83 | 1 | 3 | McDermott, Miss. Brigdet Delia | female | null | 0 | 0 | 330932 | 7.7875 | null | Q |
84 | 0 | 1 | Carrau, Mr. Francisco M | male | 28 | 0 | 0 | 113059 | 47.1 | null | S |
85 | 1 | 2 | Ilett, Miss. Bertha | female | 17 | 0 | 0 | SO/C 14885 | 10.5 | null | S |
86 | 1 | 3 | Backstrom, Mrs. Karl Alfred (Maria Mathilda Gustafsson) | female | 33 | 3 | 0 | 3101278 | 15.85 | null | S |
87 | 0 | 3 | Ford, Mr. William Neal | male | 16 | 1 | 3 | W./C. 6608 | 34.375 | null | S |
88 | 0 | 3 | Slocovski, Mr. Selman Francis | male | null | 0 | 0 | SOTON/OQ 392086 | 8.05 | null | S |
89 | 1 | 1 | Fortune, Miss. Mabel Helen | female | 23 | 3 | 2 | 19950 | 263 | C23 C25 C27 | S |
90 | 0 | 3 | Celotti, Mr. Francesco | male | 24 | 0 | 0 | 343275 | 8.05 | null | S |
91 | 0 | 3 | Christmann, Mr. Emil | male | 29 | 0 | 0 | 343276 | 8.05 | null | S |
92 | 0 | 3 | Andreasson, Mr. Paul Edvin | male | 20 | 0 | 0 | 347466 | 7.8542 | null | S |
93 | 0 | 1 | Chaffee, Mr. Herbert Fuller | male | 46 | 1 | 0 | W.E.P. 5734 | 61.175 | E31 | S |
94 | 0 | 3 | Dean, Mr. Bertram Frank | male | 26 | 1 | 2 | C.A. 2315 | 20.575 | null | S |
95 | 0 | 3 | Coxon, Mr. Daniel | male | 59 | 0 | 0 | 364500 | 7.25 | null | S |
96 | 0 | 3 | Shorney, Mr. Charles Joseph | male | null | 0 | 0 | 374910 | 8.05 | null | S |
97 | 0 | 1 | Goldschmidt, Mr. George B | male | 71 | 0 | 0 | PC 17754 | 34.6542 | A5 | C |
98 | 1 | 1 | Greenfield, Mr. William Bertram | male | 23 | 0 | 1 | PC 17759 | 63.3583 | D10 D12 | C |
99 | 1 | 2 | Doling, Mrs. John T (Ada Julia Bone) | female | 34 | 0 | 1 | 231919 | 23 | null | S |
100 | 0 | 2 | Kantor, Mr. Sinai | male | 34 | 1 | 0 | 244367 | 26 | null | S |
End of preview.
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
version https://git-lfs.github.com/spec/v1 oid sha256:616840c1ae0070cadba2dde6cde8eed9153f4d092bf1f818e7bd32d0210e5485 size 425
- Downloads last month
- 108