semvec/pypi-packages huggingface dataset:
logsnag 1.0.1
LogSnag API Wrapper
License: MIT
Requires Python: >=3.6

<div align="center">
	<img src="https://logsnag.com/og-image.png" alt="LogSnag"/>
	<br>
    <h1>LogSnag</h1>
	<p>Get notifications and track your project events.</p>
	<a href="https://discord.gg/dY3pRxgWua"><img src="https://img.shields.io/discord/922560704454750245?color=%237289DA&label=Discord" alt="Discord"></a>
	<a href="https://docs.logsnag.com"><img src="https://img.shields.io/badge/Docs-LogSnag" alt="Documentation"></a>
	<br>
	<br>
</div>


## Installation

```sh
pip3 install logsnag
```

## Usage

### Import Library

```python
from logsnag import LogSnag
```

### Initialize Client

```python
logsnag = LogSnag(token='7f568d735724351757637b1dbf108e5', project="my-saas")
```

### Publish Event

```python
logsnag.track(
    channel="waitlist",
    event="User Joined",
    user_id="user_123",
    description="Email: john@doe.com",
    icon="🎉",
    tags={
      "source": "google",
    },
    notify=True
)
```

### User Properties

```python
logsnag.identify(
    user_id="user_123",
    properties={
        "name": "John Doe",
        "email": "john@doe.com",
        "plan": "free",
    }
)
```

### Group Properties

```python
logsnag.group(
    user_id="user_123",
    group_id="group_123",
    properties={
        "name": "ACME Inc.",
        "plan": "enterprise",
        "fund": "Series A",
    }
)
```

### Publish Insight

```python
logsnag.insight.track(
    title='User Count',
    value=100,
    icon='👨',
)
```

### Increment Insight

```python
logsnag.insight.increment(
    title='User Count',
    value=1,
    icon='👨',
)
```
merdb 0.0.2
Datastore supporting merql
License: MIT
Requires Python: >=3.10

I took a career break to build this. If you like it and are looking to hire a ML engineer, please contact me :) 

CAUTION: PROTOTYPE! NO PRODUCTION USE, not even development. 

Merdb is a data processing library that
* is a relational api like SQL to query data
* has Unix like pipes to compose operators using the `|` syntax
* scales to multi core or a cluster(via Modin)
* processes data too big to fit into memory
* support interactive and optimized processing(optimizations in roadmap)

# Install
```shell
pip install merdb
```
# Example

```python
import pandas as pd
from merdb.interactive import *
# for lazy(TBD) use `from merdb.lazy import *`

def is_senior(row) -> bool:
    return row['age'] > 35


def double_age(row) -> int:
    return row["age"] * 2


cols = ["name", "age"]
people_df = pd.DataFrame([
    ["Raj", 35],
    ["Sona", 20],
    ["Abby", 70],
    ["Abba", 90],
], columns=cols)

# One can specify functions without any source data like quadruple age
quadruple_age = map(double_age, "age") | map(double_age, "age")

result = (t(people_df) # convert people_df to a merdb table
          | where(is_senior)
          | order_by("name", "asc")
          | quadruple_age # Unix like pipe syntax making it easy to refactor out intermediate processing
          | select("age")
          | rename({"age": "new_age"})
          )

# Convert to Pandas Dataframe and print
print(result.df())

```