Home: https://github.com/segmentio/analytics-python
Package license: MIT
Feedstock license: BSD 3-Clause
Summary: The hassle-free way to integrate analytics into any python application, Segment
analytics-python is a python client for Segment
You can't fix what you can't measure
Analytics helps you measure your users, product, and business. It unlocks insights into your app's funnel, core business metrics, and whether you have product-market fit.
- Collect analytics data from your app(s).
- The top 200 Segment companies collect data from 5+ source types (web, mobile, server, CRM, etc.).
- Send the data to analytics tools (for example, Google Analytics, Amplitude, Mixpanel).
- Over 250+ Segment companies send data to eight categories of destinations such as analytics tools, warehouses, email marketing and remarketing systems, session recording, and more.
- Explore your data by creating metrics (for example, new signups, retention cohorts, and revenue generation).
- The best Segment companies use retention cohorts to measure product market fit. Netflix has 70% paid retention after 12 months, 30% after 7 years.
Segment collects analytics data and allows you to send it to more than 250 apps (such as Google Analytics, Mixpanel, Optimizely, Facebook Ads, Slack, Sentry) just by flipping a switch. You only need one Segment code snippet, and you can turn integrations on and off at will, with no additional code. Sign up with Segment today.
-
Power all your analytics apps with the same data. Instead of writing code to integrate all of your tools individually, send data to Segment, once.
-
Install tracking for the last time. We're the last integration you'll ever need to write. You only need to instrument Segment once. Reduce all of your tracking code and advertising tags into a single set of API calls.
-
Send data from anywhere. Send Segment data from any device, and we'll transform and send it on to any tool.
-
Query your data in SQL. Slice, dice, and analyze your data in detail with Segment SQL. We'll transform and load your customer behavioral data directly from your apps into Amazon Redshift, Google BigQuery, or Postgres. Save weeks of engineering time by not having to invent your own data warehouse and ETL pipeline.
For example, you can capture data on any app:
analytics.track('Order Completed', { price: 99.84 })Then, query the resulting data in SQL: `select * from app.order_completed order by price desc`
<td>All platforms:</td>
<td>
<a href="https://dev.azure.com/conda-forge/feedstock-builds/_build/latest?definitionId=6616&branchName=master">
<img src="https://dev.azure.com/conda-forge/feedstock-builds/_apis/build/status/analytics-python-feedstock?branchName=master">
</a>
</td>
| Name | Downloads | Version | Platforms |
|---|---|---|---|
 |
 |
 |
 |
Installing analytics-python from the conda-forge channel can be achieved by adding conda-forge to your channels with:
conda config --add channels conda-forge
Once the conda-forge channel has been enabled, analytics-python can be installed with:
conda install analytics-python
It is possible to list all of the versions of analytics-python available on your platform with:
conda search analytics-python --channel conda-forge
conda-forge is a community-led conda channel of installable packages. In order to provide high-quality builds, the process has been automated into the conda-forge GitHub organization. The conda-forge organization contains one repository for each of the installable packages. Such a repository is known as a feedstock.
A feedstock is made up of a conda recipe (the instructions on what and how to build the package) and the necessary configurations for automatic building using freely available continuous integration services. Thanks to the awesome service provided by CircleCI, AppVeyor and TravisCI it is possible to build and upload installable packages to the conda-forge Anaconda-Cloud channel for Linux, Windows and OSX respectively.
To manage the continuous integration and simplify feedstock maintenance
conda-smithy has been developed.
Using the conda-forge.yml within this repository, it is possible to re-render all of
this feedstock's supporting files (e.g. the CI configuration files) with conda smithy rerender.
For more information please check the conda-forge documentation.
feedstock - the conda recipe (raw material), supporting scripts and CI configuration.
conda-smithy - the tool which helps orchestrate the feedstock.
Its primary use is in the construction of the CI .yml files
and simplify the management of many feedstocks.
conda-forge - the place where the feedstock and smithy live and work to produce the finished article (built conda distributions)
If you would like to improve the analytics-python recipe or build a new
package version, please fork this repository and submit a PR. Upon submission,
your changes will be run on the appropriate platforms to give the reviewer an
opportunity to confirm that the changes result in a successful build. Once
merged, the recipe will be re-built and uploaded automatically to the
conda-forge channel, whereupon the built conda packages will be available for
everybody to install and use from the conda-forge channel.
Note that all branches in the conda-forge/analytics-python-feedstock are
immediately built and any created packages are uploaded, so PRs should be based
on branches in forks and branches in the main repository should only be used to
build distinct package versions.
In order to produce a uniquely identifiable distribution:
- If the version of a package is not being increased, please add or increase
the
build/number. - If the version of a package is being increased, please remember to return
the
build/numberback to 0.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent