timeseriesdatasets
The timeseriesdatasets package provides a curated collection of time series
datasets for data analysis, forecasting, statistical analysis, and machine learning
in Python. It includes stock market prices, climate and weather data,
energy consumption, air passengers, cryptocurrency, sensor readings, sales records,
and more — sourced from Kaggle.
Installation
You can install the timeseriesdatasets package from PyPI:
pip install timeseriesdatasets
Usage
import timeseriesdatasets as ts
# List all available datasets
datasets = ts.list_datasets()
print(datasets)
# Load a specific dataset
df = ts.load_dataset('microsoft_stock')
print(df.head())
# Describe dataset
df_01 = ts.describe('nvidia_stock')
print(df_01)
📊 Some Available Datasets
| Dataset | Description |
|---|---|
microsoft_stock |
Microsoft stock price time series from 2015 to 2021. |
yahoo_stock |
Yahoo stock price time series data for forecasting. |
tesla_stock |
Tesla stock price time series data for daily trading and forecasting. |
nvidia_stock |
NVIDIA stock price time series from 1999 to 2025. |
Run
timeseriesdatasets.list_datasets()orts.list_datasets()(usingtsas alias) to see the full list of available datasets.
License
The timeseriesdatasets library is released under the MIT License, allowing free use for both commercial and non-commercial purposes.
See the LICENSE file for details.
Metadata
Release files for timeseriesdatasets 0.1.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | |
|---|---|---|---|---|
| timeseriesdatasets-0.1.0.tar.gz | 1.1 MB | Without provenance | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| timeseriesdatasets-0.1.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 2.1 MB
Release files / timeseriesdatasets-0.1.0.tar.gz
| Download URL | timeseriesdatasets-0.1.0.tar.gz |
|---|---|
| Size | 1.1 MB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
f2a21931519ce6699933616c5a9724f7c88ecac4f472469aa6e801191e9d2f5a
|
|
BLAKE2b-256 checksum How to use checksums |
445b2d610b38eebf38d35b0890500e4541f68d88ad30b38c3957f7a7fd10fbb6
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.2.0 CPython/3.13.5
|
Release files / timeseriesdatasets-0.1.0-py3-none-any.whl
| Download URL | timeseriesdatasets-0.1.0-py3-none-any.whl |
|---|---|
| Size | 1.0 MB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
7e2bbfc17ef0ac6f8cf81ec5bd224216e8a883a06e2d6231e8a080e8072de832
|
|
BLAKE2b-256 checksum How to use checksums |
5e4afef9ac1548a748ff3c964a9b24d8456eb5deeee0d1f88a847fff3faa6c87
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.2.0 CPython/3.13.5
|
Security for timeseriesdatasets 0.1.0
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
No provenance found
None of the files in this release were uploaded with provenance metadata.
A note on package security
PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.
To learn more about protecting yourself from malicious actors, visit our package security documentation.