size_categories:n<1K format:parquet modality:text library:datasets library:pandas library:mlcroissant library:polars library:distilabel region:us synthetic distilabel rlaif datacraft
---
size_categories: n<1K
dataset_info:
  features:
  - name: prompt
    dtype: string
  - name: completion
    dtype: string
  - name: system_prompt
    dtype: string
  splits:
  - name: train
    num_bytes: 40425
    num_examples: 10
  download_size: 29512
  dataset_size: 40425
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
tags:
- synthetic
- distilabel
- rlaif
- datacraft
---

<p align="left">
  <a href="https://github.com/argilla-io/distilabel">
    <img src="https://raw.githubusercontent.com/argilla-io/distilabel/main/docs/assets/distilabel-badge-light.png" alt="Built with Distilabel" width="200" height="32"/>
  </a>
</p>

# Dataset Card for my-distiset-7438dac8

This dataset has been created with [distilabel](https://distilabel.argilla.io/).



## Dataset Summary

This dataset contains a `pipeline.yaml` which can be used to reproduce the pipeline that generated it in distilabel using the `distilabel` CLI:

```console
distilabel pipeline run --config "https://huggingface.co/datasets/Hongv/my-distiset-7438dac8/raw/main/pipeline.yaml"
```

or explore the configuration:

```console
distilabel pipeline info --config "https://huggingface.co/datasets/Hongv/my-distiset-7438dac8/raw/main/pipeline.yaml"
```

## Dataset structure

The examples have the following structure per configuration:


<details><summary> Configuration: default </summary><hr>

```json
{
    "completion": "Here\u0027s the corrected and improved code with explanations for each step.\n\n```python\nimport numpy as np\nimport math\nimport matplotlib.pyplot as plt\n\ndef haversine_distance(lat1, lon1, lat2, lon2):\n    \"\"\"\n    Calculate the Haversine distance between two points on a sphere.\n\n    Parameters:\n    lat1 (float): Latitude of the first point in degrees.\n    lon1 (float): Longitude of the first point in degrees.\n    lat2 (float): Latitude of the second point in degrees.\n    lon2 (float): Longitude of the second point in degrees.\n\n    Returns:\n    float: The distance between the two points in kilometers.\n    \"\"\"\n\n    # Convert the latitude and longitude values from degrees to radians.\n    lat1, lon1, lat2, lon2 = map(math.radians, [lat1, lon1, lat2, lon2])\n\n    # The Haversine formula to calculate the distance between two points on a sphere.\n    a = math.sin((lat2 - lat1) / 2) ** 2 + math.cos(lat1) * math.cos(lat2) * math.sin((lon2 - lon1) / 2) ** 2\n    c = 2 * math.asin(math.sqrt(a))\n\n    # The radius of the Earth in kilometers.\n    R = 6371  # km\n\n    # Calculate the distance using the Haversine formula.\n    distance = R * c\n\n    return distance\n\ndef plot_distance(lat1, lon1, lat2, lon2):\n    \"\"\"\n    Plot the cities and a circle with the given radius.\n\n    Parameters:\n    lat1 (float): Latitude of the first point in degrees.\n    lon1 (float): Longitude of the first point in degrees.\n    lat2 (float): Latitude of the second point in degrees.\n    lon2 (float): Longitude of the second point in degrees.\n    \"\"\"\n\n    # Convert the latitude and longitude values from degrees to radians.\n    lat1, lon1, lat2, lon2 = map(math.radians, [lat1, lon1, lat2, lon2])\n\n    # Create a figure with a square aspect ratio.\n    plt.figure(figsize=(6,6))\n\n    # Plot the cities as points.\n    plt.scatter([lat1],[lon1],label=\u0027City 1\u0027)\n    plt.scatter([lat2],[lon2],label=\u0027City 2\u0027)\n    plt.legend()\n\n    # Calculate the distance between the two cities.\n    distance = haversine_distance(lat1, lon1, lat2, lon2)\n\n    # Define the central point and radius for the circle.\n    central_point = [0, 0]\n    radius = distance / 500  # km to miles\n\n    # Plot the circle.\n    circle = plt.Circle(central_point, radius, fill=False)\n    plt.gcf().gca().add_artist(circle)\n\n    # Set the limits of the plot to ensure the circle is visible.\n    plt.xlim(-0.1, 0.1)\n    plt.ylim(-0.1, 0.1)\n\n    # Set the aspect ratio to be equal so the circle appears as a circle.\n    plt.gca().set_aspect(\u0027equal\u0027, adjustable=\u0027box\u0027)\n\n    # Add the title and labels.\n    plt.xlabel(\u0027Longitude\u0027)\n    plt.ylabel(\u0027Latitude\u0027)\n    plt.title(\u0027Cities and plot\u0027)\n\n    plt.show()\n\n# Test the functions.\nlat1 = 40.7128\nlon1 = -74.0060\nlat2 = 34.0522\nlon2 = -118.2437\nprint(f\"The distance between cities is {haversine_distance(lat1, lon1, lat2, lon2)} km.\")\n\n# Plot the distance.\nplot_distance(lat1, lon1, lat2, lon2)\n```\n\nExplanation of changes:\n\n1.  The `haversine_distance` function now correctly uses the Haversine formula to calculate the distance between two points on a sphere. It converts the latitude and longitude values from degrees to radians using the `math.radians` function. It then uses the Haversine formula to calculate the distance, which is the central angle between the two points. The distance is then calculated by multiplying the central angle by the radius of the Earth (6371 km).\n\n2.  The `plot_distance` function has been improved to correctly plot the cities and a circle with the given radius. It now uses the same Haversine distance function to calculate the distance between the two cities. The circle is then plotted around the central point (0, 0) with a radius that is proportional to the distance between the two cities. The aspect ratio of the plot is set to be equal so the circle appears as a circle.\n\nI have also reformatted the code with proper docstrings, function names, and comments for better readability. I have also added a docstring to explain the usage of the functions.",
    "prompt": "I\u0027m having trouble writing a Python program to calculate the Haversine distance (calculate the distance between two points on a sphere like the Earth) between two cities and want to plot the distance using matplotlib.\n\nHere\u0027s my code:\n```python\nimport numpy as np\nimport math\nimport matplotlib.pyplot as plt\n\ndef haversine_distance(lat1, lon1, lat2, lon2):\n    # In degrees\n    lat1, lon1, lat2, lon2 = map(math.radians, [lat1, lon1, lat2, lon2])\n\n    a = math.sin((lat2-lat1)/2)**2 + math.cos(lat1) * math.cos(lat2) * math.sin((lon2-lon1)/2)**2\n    c = 2 * math.asin(math.sqrt(a))\n    return c\n\ndef plot_distance(lat1, lon1, lat2, lon1):\n    plt.figure()\n    plt.scatter([lat1],[lon1],label=\u0027City 1\u0027)\n    plt.scatter([lat2],[lon2],label=\u0027City 2\u0027)\n    plt.legend()\n    plt.draw()\n\n    # plot the circles around the points\n    u = [0, 2 * math.pi]\n    v = [(r*math.cos(t), r*math.sin(t)) for t in u]\n    plt.plot([r*math.sin(t)*math.cos(u), r*math.cos(t)*math.sin(u)], [r*math.sin(u)*math.cos(t), r*math.sin(u)*math.sin(t)])\n\n    plt.xlabel(\u0027Latitude\u0027)\n    plt.ylabel(\u0027Longitude\u0027)\n    plt.title(\u0027Cities and plot\u0027)\n\n    plt.show()\n\n# test the functions\nlat1 = 40.7128\nlon1 = -74.0060\nlat2 = 34.0522\nlon2 = -118.2437\nprint(f\"The distance between cities is {haversine_distance(lat1, lon1, lat2, lon2)} km.\")\n\n# Plotting the distance\nplot_distance(lat1, lon1, lat2, lon2)\n```\n\nHowever, your code has the following errors and issues:\n1. The function `haversine_distance` does not correctly use the haversine distance formula.\n\n2. The `plot_distance` function is not correctly implemented to plot the cities and a circle with the given radius.\n\nHere are the required corrections:\n\n## Code corrections and improvements:\n1. The correct haversine distance formula is: `c = 2*r*math.as",
    "system_prompt": "You are a Python-based AI assistant specializing in solving math puzzles and problems. Your purpose is to assist users in breaking down complex mathematical challenges, applying algorithms and formulas, and finding efficient solutions using Python programming language. Provide step-by-step code snippets, explain mathematical concepts, and help users debug their code when needed. Guide users in implementing popular mathematical algorithms, data structures, and techniques such as dynamic programming, recursion, and combinatorics. Offer suggestions for real-world applications, extensions, and optimizations of math-based solutions. User questions are direct and concise."
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

ds = load_dataset("Hongv/my-distiset-7438dac8", "default")
```

Or simply as it follows, since there's only one configuration and is named `default`: 

```python
from datasets import load_dataset

ds = load_dataset("Hongv/my-distiset-7438dac8")
```


</details>




