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---
license: apache-2.0
task_categories:
- other
language:
- en
tags:
- dataset
- pandas
- parquet
size_categories:
- 1M<n<10M
pretty_name: Plotqa V1
---
# Plotqa V1
## Dataset Description
This dataset was uploaded from a pandas DataFrame.
## Dataset Structure
### Overview
- **Total Examples**: 5,733,893
- **Total Features**: 9
- **Dataset Size**: ~2805.4 MB
- **Format**: Parquet files
- **Created**: 2025-09-22 20:12:01 UTC
### Data Instances
The dataset contains 5,733,893 rows and 9 columns.
### Data Fields
- **image_index** (int64): 0 null values (0.0%), Range: [0.00, 157069.00], Mean: 78036.26
- **qid** (object): 0 null values (0.0%), 74 unique values
- **question_string** (object): 0 null values (0.0%), 1,502,530 unique values
- **answer_bbox** (object): 0 null values (0.0%), 798,805 unique values
- **template** (object): 0 null values (0.0%), 6 unique values
- **answer** (object): 0 null values (0.0%), 1,002,651 unique values
- **answer_id** (int64): 0 null values (0.0%), Range: [0.00, 1481788.00], Mean: 185454.21
- **type** (object): 0 null values (0.0%), 4 unique values
- **question_id** (int64): 0 null values (0.0%), Range: [0.00, 2170651.00], Mean: 441648.27
### Data Splits
| Split | Number of Examples |
|-------|-------------------|
| train | 5,733,893 |
## Dataset Creation
This dataset was created by uploading a pandas DataFrame to Hugging Face Hub using the `datasets` library.
### Source Data
The data was processed and uploaded as parquet files for efficient storage and loading.
## Usage
### Loading the Dataset
```python
from datasets import load_dataset
# Load the dataset
dataset = load_dataset("Abd223653/PlotQA_V1")
# Convert to pandas DataFrame
df = dataset["train"].to_pandas()
print(f"Dataset shape: {df.shape}")
print(f"Columns: {list(df.columns)}")
```
### Streaming (Memory Efficient)
```python
from datasets import load_dataset
# Load dataset in streaming mode
dataset = load_dataset("Abd223653/PlotQA_V1", streaming=True)
train_stream = dataset["train"]
# Process in batches
for batch in train_stream.iter(batch_size=1000):
# Process your batch here
print(f"Processing batch with {len(batch['column_name'])} examples")
```
### Basic Data Analysis
```python
import pandas as pd
from datasets import load_dataset
# Load and explore the dataset
dataset = load_dataset("Abd223653/PlotQA_V1")
df = dataset["train"].to_pandas()
# Basic statistics
print(df.info())
print(df.describe())
# Check for missing values
print("Missing values per column:")
print(df.isnull().sum())
```
## Data Quality
### Missing Values
- **Total missing values**: 0
- **Columns with missing values**: 0
- **Percentage of complete rows**: 100.0%
### Data Types
- **int64**: 3 columns
- **object**: 6 columns
## Limitations and Considerations
- This dataset is provided as-is without warranty
- Users should validate data quality for their specific use cases
- Consider the licensing terms when using this dataset
- Large datasets may require streaming or chunked processing
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