metadata
license: mit
language:
- en
pretty_name: Nepal Mobile Prices (all_mobile_data5)
size_categories:
- n<1K
all_mobile_data5
Short description
all_mobile_data5 is a small CSV dataset of mobile phone models and their published prices in Nepal. It contains raw scraped price strings (with commas, ranges, and variants), a brand column, and a pre-parsed numeric "Price_clean" column that is partially populated. Use this dataset for experimentation with price-parsing, feature extraction, and simple price-prediction models.
Key facts
- Filename:
all_mobile_data5.csv - Columns:
Model,Price,Brand,Price_clean - Approximate rows: 127 (raw, includes header and informational rows)
- License: CC-BY-4.0 (recommended) — see LICENSE file
Column descriptions
- Model (string) — mobile model or descriptive text scraped from web pages (e.g., "Galaxy Z Fold 7", "12/256GB", or "Samsung Mobiles List").
- Price (string) — raw price field as scraped; may contain currency symbols, commas, multiple values, ranges, or storage/RAM annotations (e.g.,
Rs. 244,999 (12+256GB),NPR 239,999 (256GB),Price in Nepal). - Brand (string) — scraped brand name or page context (e.g., "Samsung", "Apple", "Xiaomi", or page slugs).
- Price_clean (float or string) — a pre-parsed numeric price in some rows; many entries remain messy or inconsistent and should be validated/cleaned before use.
Usage examples
Loading with pandas:
import pandas as pd
df = pd.read_csv("all_mobile_data5.csv")
print(df.head())
from datasets import load_dataset
# If you uploaded a dataset repository that contains only this CSV:
ds = load_dataset("sanojDD/all_mobile_data5", data_files="all_mobile_data5.csv", split="train")
df = ds.to_pandas()
import re
def parse_price(price_str):
s = str(price_str).replace(",", "")
nums = re.findall(r"\d{4,7}", s)
nums = [int(n) for n in nums if 5000 <= int(n) <= 500000]
return nums[0] if nums else None
```text name=MIT
Creative Commons Attribution 4.0 International (CC BY 4.0)
You are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
Under the following terms:
- Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made.
Full license text: https://creativecommons.org/licenses/by/4.0/