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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 5 new columns ({'US_Avg_Tone', 'US_Crisis_Events', 'US_Avg_Stability', 'US_Total_Mentions', 'US_Event_Count'}) and 1 missing columns ({'IMF_3'}).
This happened while the csv dataset builder was generating data using
hf://datasets/AmritJain/gdelt-india-research-datasets/correlation_analysis/bq-results-20260115-090715-1768468077035.csv (at revision 1add00035a9ed4096360578f1fa5f5cc7fb788fa)
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/python3.12/site-packages/datasets/builder.py", line 1831, in _prepare_split_single
writer.write_table(table)
File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 714, in write_table
pa_table = table_cast(pa_table, self._schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2272, in table_cast
return cast_table_to_schema(table, schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2218, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
Date: int64
US_Avg_Tone: double
US_Avg_Stability: double
US_Total_Mentions: int64
US_Event_Count: int64
US_Crisis_Events: int64
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 1008
to
{'Date': Value('string'), 'IMF_3': Value('float64')}
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 1334, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 911, in stream_convert_to_parquet
builder._prepare_split(
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1702, in _prepare_split
for job_id, done, content in self._prepare_split_single(
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1833, in _prepare_split_single
raise DatasetGenerationCastError.from_cast_error(
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 5 new columns ({'US_Avg_Tone', 'US_Crisis_Events', 'US_Avg_Stability', 'US_Total_Mentions', 'US_Event_Count'}) and 1 missing columns ({'IMF_3'}).
This happened while the csv dataset builder was generating data using
hf://datasets/AmritJain/gdelt-india-research-datasets/correlation_analysis/bq-results-20260115-090715-1768468077035.csv (at revision 1add00035a9ed4096360578f1fa5f5cc7fb788fa)
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.
Date string | IMF_3 float64 |
|---|---|
2024-01-02 | -0.048395 |
2024-01-03 | 0.017301 |
2024-01-04 | 0.060147 |
2024-01-05 | 0.021957 |
2024-01-08 | -0.044739 |
2024-01-09 | -0.048826 |
2024-01-10 | 0.017335 |
2024-01-11 | 0.059468 |
2024-01-12 | 0.017591 |
2024-01-15 | -0.054174 |
2024-01-16 | -0.049834 |
2024-01-17 | 0.024658 |
2024-01-18 | 0.056676 |
2024-01-19 | 0.006605 |
2024-01-22 | -0.04283 |
2024-01-23 | -0.020092 |
2024-01-24 | 0.030407 |
2024-01-25 | 0.025817 |
2024-01-26 | -0.017895 |
2024-01-29 | -0.029105 |
2024-01-30 | 0.009217 |
2024-01-31 | 0.034855 |
2024-02-01 | 0.005251 |
2024-02-02 | -0.035507 |
2024-02-05 | -0.023336 |
2024-02-06 | 0.021514 |
2024-02-07 | 0.031913 |
2024-02-08 | -0.001123 |
2024-02-09 | -0.027018 |
2024-02-12 | -0.014546 |
2024-02-13 | 0.012635 |
2024-02-14 | 0.018737 |
2024-02-15 | -0.000549 |
2024-02-16 | -0.013402 |
2024-02-19 | -0.00348 |
2024-02-20 | 0.007638 |
2024-02-21 | 0.00415 |
2024-02-22 | -0.001884 |
2024-02-23 | -0.008148 |
2024-02-26 | -0.00858 |
2024-02-27 | 0.00067 |
2024-02-28 | 0.010246 |
2024-02-29 | 0.004252 |
2024-03-01 | -0.008878 |
2024-03-04 | -0.008885 |
2024-03-05 | 0.005256 |
2024-03-06 | 0.009708 |
2024-03-07 | -0.000957 |
2024-03-08 | -0.007159 |
2024-03-11 | -0.001399 |
2024-03-12 | 0.000375 |
2024-03-13 | -0.007848 |
2024-03-14 | -0.006773 |
2024-03-15 | 0.012061 |
2024-03-18 | 0.01854 |
2024-03-19 | -0.003076 |
2024-03-20 | -0.031103 |
2024-03-21 | -0.02841 |
2024-03-22 | 0.018633 |
2024-03-25 | 0.049538 |
2024-03-26 | 0.009797 |
2024-03-27 | -0.03844 |
2024-03-28 | -0.024807 |
2024-03-29 | 0.022025 |
2024-04-01 | 0.029998 |
2024-04-02 | -0.007902 |
2024-04-03 | -0.031915 |
2024-04-04 | -0.00294 |
2024-04-05 | 0.032163 |
2024-04-08 | 0.016257 |
2024-04-09 | -0.021607 |
2024-04-10 | -0.025699 |
2024-04-11 | 0.003141 |
2024-04-12 | 0.017566 |
2024-04-15 | 0.006034 |
2024-04-16 | -0.008807 |
2024-04-17 | 0.001292 |
2024-04-18 | 0.014354 |
2024-04-19 | 0.006811 |
2024-04-22 | -0.008223 |
2024-04-23 | -0.006429 |
2024-04-24 | 0.003779 |
2024-04-25 | 0.002385 |
2024-04-26 | -0.010102 |
2024-04-29 | -0.008326 |
2024-04-30 | 0.010211 |
2024-05-01 | 0.018617 |
2024-05-02 | 0.001918 |
2024-05-03 | -0.016876 |
2024-05-06 | -0.013167 |
2024-05-07 | 0.006854 |
2024-05-08 | 0.014711 |
2024-05-09 | 0.003381 |
2024-05-10 | -0.009522 |
2024-05-13 | -0.010614 |
2024-05-14 | -0.001959 |
2024-05-15 | 0.010491 |
2024-05-16 | 0.016018 |
2024-05-17 | 0.004436 |
2024-05-20 | -0.021204 |
End of preview.
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Check out the documentation for more information.
GDELT India Research Datasets
This dataset collection contains comprehensive data for analyzing the relationship between news sentiment, political events, and economic indicators for India-US relations.
Dataset Categories
1. Exchange Rates
- usd_inr_exchange_rates_1year.csv: Daily USD-INR exchange rates for one year
- exchange_rate_goldstein_merged.csv: Exchange rates merged with Goldstein scale scores
- combined_goldstein_exchange_rates.csv: Combined analysis of Goldstein scores and exchange rate movements
2. GDELT News Data
- combined-gdelt.csv: Combined GDELT news events data
- india_news_combined_sorted.csv: Processed India-related news articles
- india_news_gz_combined_sorted.csv: Compressed India news dataset
- usa_news_combined_sorted.csv: USA news articles
- india_financial_political_news_filtered.csv: Filtered financial and political news
- india_daily_goldstein_averages.csv: Daily average Goldstein scores for India
3. Master Dataset
- Super_Master_Dataset.csv: Comprehensive merged dataset with all variables
4. Correlation Analysis
- goldstein_exchange_correlations.csv: Correlation metrics between Goldstein scale and exchange rates
- political_news_exchange_merged.csv: Political news sentiment merged with exchange rate data
- bq-results-*.csv: BigQuery analysis results
- IMF_3.csv: IMF economic indicators
5. Trade Data (India-USA)
- Multiple CSV files containing bilateral trade data from 2010-2025
- Trade balance analysis
- Commodity shift analysis across multiple years
- Seasonality analysis for 2023-2024
6. Monte Carlo Forecasts
- monte_carlo_forecast.csv: Exchange rate forecasts using Monte Carlo simulation
- monte_carlo_statistics.csv: Statistical summary of simulations
- weekly_forecast_summary.csv: Weekly rolling forecasts
- week_*_detailed_results.csv: Detailed results for each forecast week
7. GARCH Model Outputs
- predictions_price.csv: GARCH model price predictions
- feature_importance.csv: Feature importance analysis
- garch_comparison.csv: Comparison of different GARCH model variants
Research Context
This dataset supports research on:
- Impact of political news sentiment on exchange rates
- GDELT event analysis for India-US relations
- Time series forecasting of exchange rates
- Volatility modeling using GARCH and Monte Carlo methods
- Trade balance and its correlation with economic indicators
Data Sources
- GDELT Project: Global news event database
- Exchange Rate Data: Official currency exchange sources
- Trade Data: US Census Bureau and UN Comtrade
- IMF: International Monetary Fund economic indicators
Citation
If you use this dataset, please cite:
GDELT India Research Datasets (2026)
Compiled from GDELT Project, IMF, and US Census Bureau
Available on Hugging Face Hub
License
Please ensure compliance with individual data source licenses:
- GDELT data is available under their terms of use
- IMF data is publicly available
- Trade data from official government sources
File Formats
All datasets are provided in CSV format for easy processing with:
- Python (pandas, numpy)
- R (data.table, tidyverse)
- Excel and other spreadsheet applications
Updates
Dataset last updated: January 2026
For questions or issues, please open a discussion on the Hugging Face Hub.
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