XET_USER -> XET_USERNAME

- removed mount docs
- added ML example to README
- cleaned up index.rst

Author: Rajat Arya

README.md

@@ -35,12 +35,7 @@ pyxet is a Python library that provides a lightweight interface for the [XetHub]
     * [glob](https://docs.python.org/3/library/glob.html)
     * [pathlib.Path](https://docs.python.org/3/library/pathlib.html)(WIP)
 
-2. Mount:
-    * Read-only optimize for speed; perfect for data exploration and analysis and building data-apps and model
-      inference.
-    * Read-write for data ingestion and preparation; optimal for database backups and training and monitoring logs. _(coming soon)_
-
-3. Integrations:
+2. Integrations:
     - [x] [GitHub](https://github.com) [submodule](https://git-scm.com/book/en/v2/Git-Tools-Submodules)
     - [x] [pandas](https://pandas.pydata.org)
     - [x] [polars](https://pola-rs.github.io/polars-book/)
@@ -49,7 +44,6 @@ pyxet is a Python library that provides a lightweight interface for the [XetHub]
     - [ ] [dask](https://dask.org/)
     - [ ] [ray](https://ray.io/)
 
-
 ## Documentation
 For API documentation and full examples, please see [here](https://pyxet.readthedocs.io/en/latest/).
 
@@ -82,35 +76,84 @@ git config --global user.email "[email protected]"
 To verify that pyxet is working, let's load a CSV file directly into a Pandas dataframe, leveraging pyxet's support for Python fsspec.
 
 ```python
+# assumes you have already done pip install pandas
 import pandas as pd
 import pyxet
 
 df = pd.read_csv('xet://xdssio/titanic/main/titanic.csv')
 df
 ```
 
+should return something like:
+
+```
+Out[3]:
+     PassengerId  Survived  Pclass  ...     Fare Cabin  Embarked
+0              1         0       3  ...   7.2500   NaN         S
+1              2         1       1  ...  71.2833   C85         C
+2              3         1       3  ...   7.9250   NaN         S
+3              4         1       1  ...  53.1000  C123         S
+4              5         0       3  ...   8.0500   NaN         S
+..           ...       ...     ...  ...      ...   ...       ...
+886          887         0       2  ...  13.0000   NaN         S
+887          888         1       1  ...  30.0000   B42         S
+888          889         0       3  ...  23.4500   NaN         S
+889          890         1       1  ...  30.0000  C148         C
+890          891         0       3  ...   7.7500   NaN         Q
+
+[891 rows x 12 columns]
+```
+
 ### Next Steps - Working with private repos (How to set pyxet credentials)
 To start working with private repositories, you need to set up credentials for pyxet. The steps to do this are as follows:
 
 1. Sign up for [XetHub](https://xethub.com/user/sign_up)
 2. Install [git-xet client](https://xethub.com/explore/install)
 3. Create a [Personal Access Token](https://xethub.com/explore/install). Click on 'CREATE TOKEN' button.
 4. Copy & Execute Login command, it should look like: `git xet login -u rajatarya -e [email protected] -p **********`
-5. To make these credentials available to pyxet, set the -u param (rajatarya above) and the -p param as XET_USER and XET_TOKEN environment variables. Also, for your python session, `pyxet.login()` will set the environment variables for you.
+5. To make these credentials available to pyxet, set the -u param (rajatarya above) and the -p param as XET_USERNAME and XET_TOKEN environment variables. Also, for your python session, `pyxet.login()` will set the environment variables for you.
 
 ```sh
 # Note: set this environment variable into your shell config (ex. .zshrc) so not lost.
-export XET_USER=<YOUR XETHUB USERNAME>
+export XET_USERNAME=<YOUR XETHUB USERNAME>
 export XET_TOKEN=<YOUR PERSONAL ACCESS TOKEN PASSWORD>
 ```
 
+### ML Demo
+
+A slightly more complete demo doing some basic ML is as simple as setting up your virtualenv with:
+
+```sh
+pip install scikit-learn ipython pandas
+```
 ```python
-import pandas as pd
 import pyxet
 
-df = pd.read_csv("xet://xdssio/titanic/main/titanic.csv")  # All files on the platform are available with permissions
-# or
-pyxet.copy("xet://xdssio/titanic/main/titanic.csv", 'titanic.csv')
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.metrics import classification_report
+
+# make sure to set your XET_USERNAME and XET_TOKEN environment variables, or run:
+# pyxet.login('username', 'token')
+
+df = pd.read_csv("xet://xdssio/titanic.git/main/titanic.csv")  # read data from XetHub
+target_names, features, target = ['die', 'survive'], ["Pclass", "SibSp", "Parch"], "Survived"
+
+test_size, random_state = 0.2, 42
+train, test = train_test_split(df, test_size=test_size, random_state=random_state)
+model = RandomForestClassifier().fit(train[features], train[target])
+predictions = model.predict(test[features])
+print(classification_report(test[target], predictions, target_names=target_names))
+
+# Any parameters we want to save
+info = classification_report(test[target], predictions,
+                             target_names=target_names,
+                             output_dict=True)
+info["test_size"] = test_size
+info["random_state"] = random_state
+info['features'] = features
+info['target'] = target
 ```
 
 ## Contributing & Getting Help

docs/index.rst

@@ -4,38 +4,30 @@ Welcome to pyxet's documentation!
 =================================
 
 pyxet is a Python library that provides a lightweight interface for the `XetHub <https://xethub.com/>`_  platform.
-XetHub is a blob-store with a file-system like interface and git capabilities, therefore pyxet implement both a CLI for both a file-system and git needs.
 
 Installation
 ~~~~~~~~~~~~
-``pip install pyxet``
+Assuming you are on a supported OS (MacOS or Linux) and are using a supported version of Python (3.7+), set up your virtualenv with:
+
+``python -m venv .venv``
+
+``. .venv/bin/activate``
 
+Then, install pyxet with:
+
+``pip install pyxet``
 
 Features
 ~~~~~~~~
 1. A file-system like interface:
     - [x] `fsspec <https://filesystem-spec.readthedocs.io>`_
     - [x] `pathlib.Path <https://docs.python.org/3/library/pathlib.html>`_
     - [ ] `glob <https://docs.python.org/3/library/glob.html>`_
-2. Mount:
-    - [x] read-only for data exploration and analysis
-    - [ ] read-write for data ingestion and preparation - optimal for database backups and logs _(coming soon)_
-3. Integrations:
+2. Integrations:
     - [x] `pandas <https://pandas.pydata.org>`_
     - [x] `polars <https://pola-rs.github.io/polars-book/>`_
     - [x] `pyarrow <https://arrow.apache.org/docs/python/>`_
     - [ ] `duckdb <https://duckdb.org>`_
-4. Extra features like login, copy, move, delete, rename, etc.
-5. Git capabilities:
-    * add, commit, push
-    * clone, fork
-    * merge, rebase
-    * pull, fetch
-    * checkout, reset
-    * stash, diff, log
-    * status, branch
-    * submodules
-    * etc.
 
 .. toctree::
    :maxdepth: 2
@@ -48,7 +40,6 @@ Features
    :caption: API Reference:
 
    markdowns/filesystem
-   markdowns/mount
    markdowns/integrations
 
 .. toctree::

docs/markdowns/integrations.md

@@ -3,7 +3,7 @@
 [XetHub](https://xethub.com) is aimed to simplify every part of the data science workflow.   
 Getting data in and out of XetHub is no exception.
 
-* You can setup `XET_USER` and `XET_TOKEN` as an environment variable to avoid passing it to every function.
+* You can setup `XET_USERNAME` and `XET_TOKEN` as an environment variable to avoid passing it to every function.
 
 ## Python Packages
 

docs/markdowns/quickstart.md

@@ -6,12 +6,19 @@ This library allows you to access XetHub from Python.
 
 ## Installation
 
-1. [Create an account or sign in](https://xethub.com)
-2. Get a personal access token [here](https://xethub.com/user/settings/pat) and set it `XET_USER`, `XET_TOKEN` environment
-   variables.
-3. Install the library
+Assuming you are on a supported OS (MacOS or Linux) and are using a supported version of Python (3.7+), set up your virtualenv with:
 
-`pip install pyxet`
+```sh
+$ python -m venv .venv
+...
+$ . .venv/bin/activate
+```
+
+Then, install pyxet with:
+
+```sh
+$ pip install pyxet
+```
 
 ## Usage
 
@@ -25,7 +32,7 @@ from sklearn.model_selection import train_test_split
 from sklearn.ensemble import RandomForestClassifier
 from sklearn.metrics import classification_report
 
-# make sure to set your XET_USER and XET_TOKEN environment variables, or run:
+# make sure to set your XET_USERNAME and XET_TOKEN environment variables, or run:
 # pyxet.login('username', 'token')
 
 df = pd.read_csv("xet://xdssio/titanic.git/main/titanic.csv")  # read data from XetHub
@@ -51,7 +58,7 @@ What do we care about? We care about the model, the data, the metrics and the co
 
 ### Setup
 
-Let's [create a new repo](https://xethub.com/xet/create) in the UI or programmatically:
+Let's [create a new repo](https://xethub.com/xet/create) in the UI.
 
 We clone the repo to our local filesystem, saving everything we want, and committing it: