README.md

---
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