Pre-release

This release is a pre-release and may not be stable for production use.

PyTrendy Logo

PyTrendy

PyPI version Python License: MIT
Tests Release
codecov Downloads

PyTrendy is a robust solution for identifying and analysing trends in time series. Unlike other trend detection packages, it is robust to noisy and flat segments, and handles gradual and abrupt trend cases with high precision. It aims to be the best package for trend detection in Python.

Read more in the documentation: russellsb.github.io/pytrendy/main

Why PyTrendy?

Most time series tools give you either a "trend component" (via decomposition) or "changepoints" (the moments of shift). PyTrendy is built for automated trend detection, answering what trends existed, how strong were they, and when did they start and end?

  • Beyond step changes - ruptures is the gold standard for abrupt shifts, but it doesn't handle gradual slope changes (digital marketing, stocks, energy). PyTrendy detects both in a single run.
  • The flat/noise problem - closest peers (pytrendseries, trendet, tstrends) over-fit trends on flat or noisy periods. PyTrendy's signal-processing and post-processing logic ensures trends are only detected when they are precise and valid.
  • Strategic value - where dozens of time series interact, knowing how they align or confound at specific times is invaluable for experiment design.

Features

Quickstart

Install the package from PyPi.

pip install pytrendy

Import pytrendy, and apply trend detection on daily time series data.

import pytrendy as pt
df = pt.load_data('series_synthetic')
results = pt.detect_trends(df, date_col='date', value_col='gradual', plot=True)
results.print_summary()


Detected: 
- 3 Uptrends. 
- 3 Downtrends.
- 3 Flats.
- 0 Noise.

The best detected trend is Down between dates 2025-05-09 - 2025-06-17

Full Results:
-------------------------------------------------------------------------------
            direction       start         end  days  total_change  change_rank trend_class
time_index                                                                               
1                 Up  2025-01-02  2025-01-24    22     14.013348            5     gradual
2               Down  2025-01-25  2025-02-05    11    -13.564214            6     gradual
3               Flat  2025-02-06  2025-02-09     3     -1.168831            9         NaN
4                 Up  2025-02-10  2025-03-14    32     24.632035            3     gradual
5               Flat  2025-03-15  2025-03-17     2      5.660173            7         NaN
6               Down  2025-03-18  2025-04-01    14    -22.721861            4     gradual
7                 Up  2025-04-02  2025-05-08    36     72.611833            2     gradual
8               Down  2025-05-09  2025-06-17    39    -73.253968            1     gradual
9               Flat  2025-06-18  2025-06-30    12      3.910534            8     NaN 
-------------------------------------------------------------------------------

Explore the strongest uptrends:

results.filter_segments(direction='Up', sort_by='change_rank')[:3]
time_index direction start end trend_class change pct_change days total_change SNR change_rank
7 Up 2025-04-02 2025-05-08 gradual 72.61 367.50% 36 72.61 21.70 2
4 Up 2025-02-10 2025-03-14 gradual 24.63 169.22% 32 24.63 18.87 3
1 Up 2025-01-02 2025-01-24 gradual 14.01 104.41% 22 14.01 22.21 5

filter_segments ranks segments by magnitude (change_rank). See the API reference for all filter and sort options.

For the full per-segment metrics table, use results.df.

For more examples on interpreting the results, see Detect Gradual Trends.

Project links

Data verified by PyPI on Jul 14, 2026
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Stable release available (1.3.1)

2 maintainers

PyPI data
Data sourced directly from PyPI's database.
Avatar for BarshaDeb from gravatar.com BarshaDeb Avatar for russellsb from gravatar.com russellsb

Credits

Author/Maintainer: Russell Sammut Bonnici

GitHub Statistics

Data verified by PyPI on Jul 14, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License

MIT License (MIT License)

Requires

Python >=3.10

Provides Extra

dev docs

Tags

trend detection time series time series analysis signal processing uptrend downtrend changepoint detection trend classification stock analysis marketing analytics causal inference

Classifiers

Report project as malware

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

pytrendy-1.4.0.dev1.tar.gz (36.5 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

pytrendy-1.4.0.dev1-py3-none-any.whl (44.4 kB view details)

Uploaded Python 3

File details

Details for the file pytrendy-1.4.0.dev1.tar.gz.

File metadata

  • Download URL: pytrendy-1.4.0.dev1.tar.gz
  • Upload date:
  • Size: 36.5 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.12

File hashes

Hashes for pytrendy-1.4.0.dev1.tar.gz
Algorithm Hash digest
SHA256 b289be6ced58dd5f99789c5248bb831ccb7f78084d2eb55f8cd12f0a8c2693ef
MD5 8fc37b90babd45ef0768793ac6f52c91
BLAKE2b-256 483fc531f2edcc957025eae4c44bd64b92ac3a17cbc9608381dd948e61ca6b39

See more details on using hashes here.

Provenance

The following attestation bundles were made for pytrendy-1.4.0.dev1.tar.gz:

Publisher: release.yaml on RussellSB/pytrendy

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file pytrendy-1.4.0.dev1-py3-none-any.whl.

File metadata

  • Download URL: pytrendy-1.4.0.dev1-py3-none-any.whl
  • Upload date:
  • Size: 44.4 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.12

File hashes

Hashes for pytrendy-1.4.0.dev1-py3-none-any.whl
Algorithm Hash digest
SHA256 b4195380f72d110cbdd15f26206975eddd05b7404fa34bcf03d1990f02013d3a
MD5 b9e75ca552a91456b76afdf93f9fb281
BLAKE2b-256 a56a572392d8afabcb3f861be41f5646e4d7fd32b7e2a69e8cf64c06a631b968

See more details on using hashes here.

Provenance

The following attestation bundles were made for pytrendy-1.4.0.dev1-py3-none-any.whl:

Publisher: release.yaml on RussellSB/pytrendy

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Release history Release notifications | RSS feed

Pre-release

1.4.0.dev4

2 files

Pre-release

1.4.0.dev3

2 files

Pre-release

1.4.0.dev2

2 files

This release
Pre-release

1.4.0.dev1 This release

2 files

1.3.1

2 files

1.3.0

2 files

Pre-release

1.3.0.dev1

2 files

1.2.0

2 files

Pre-release

1.2.0.dev6

2 files

Pre-release

1.2.0.dev5

2 files

Pre-release

1.2.0.dev4

2 files

Pre-release

1.2.0.dev3

2 files

Pre-release

1.2.0.dev2

2 files

Pre-release

1.2.0.dev1

2 files

1.1.11

2 files

Pre-release

1.1.11.dev4

2 files

Pre-release

1.1.11.dev3

2 files

Pre-release

1.1.11.dev2

2 files

Pre-release

1.1.11.dev1

2 files

1.1.10

2 files

1.1.9

2 files

1.1.8

2 files

1.1.7

2 files

1.1.6

2 files

1.1.5

2 files

1.1.4

2 files

1.1.3

2 files

1.1.2

2 files

1.1.1

2 files

1.0.8

2 files

1.0.7

2 files

1.0.6

2 files

1.0.5

2 files

1.0.4

2 files

1.0.3

2 files

1.0.2

2 files

1.0.1

2 files

0.0.12

2 files

0.0.11

2 files

0.0.10

2 files

0.0.9

2 files

0.0.8

2 files

0.0.7

2 files

0.0.5

2 files

0.0.4

2 files

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page