semvec/pypi-packages huggingface dataset:
d2-dome 1.0.2
Product class use Dome API's
Requires Python: >=3.8

# d2-dome
Dome from Declarative Data Inc. contains the required modules to interactive with Dome Data Product and management Intents 

## Getting Started

Follow the steps described below to use this module 


### Prerequisites

+ python3
+ pandas
+ requests 

### Usage 

#### Installation
```
pip install d2-dome
```


#### Import and Initialize a Dome Product 
```
from dome import Product
my_product = Product(productId="12345678")
```


#### Get Product and Attribute Info 
```
product_df, attributes_df = my_product.info()
```

#### Get Management Intents that govern the lifecycle of a Product
```
product_intents = my_product.intents()
```

#### Get Series Members (Partition Keys and Values) of the Product
```
product_series_members_df = my_product.series_members()
```


#### Read Product Data 

The read operation takes following optional parameters 

+columns: Optional[List[str | dict]] - A list of all columns to be read. The List can contain a string with the column name or a dict with column name as the key and grouping function to be applied as value 
+where: Optional[str] - A string containing a filter expression to be added. Note: The expression should not include the where key word
+group_by: Optional[List[str]] - A list of column names for grouping the results 
+order_by: Optional[List[str]] - A list of column names for grouping the results
+limit: Optional[int] - A limit to number of records to be returned

Here are few examples 

##### Read all product data 

```
product_read_df = my_product.read()
```

##### Read only 10 rows of my product 

```
product_read_df = my_product.read(limit=10)
```

##### Read only 2 columns with a where condition and limit to 10 rows 

```
product_read_df = my_product.read(columns=["col1","col2"], where="col3 = 'col3_value'", limit=10)
```

##### Read with results grouped 

```
product_read_df = my_product.read(columns=["col1",{"col2":"sum"}], where="col3 = 'col3_value'", group_by=["col1"], order_by=["col1"], limit=10)
```


## Authors 
+ Bhavin Dave 

## Support 
Please reach out to info@declarativedata.com
coco-agent 0.3.13
coco-agent
Requires Python: >=3.7

A set of tools and utilities for extracting and shipping raw data to ConnectedCompany ("CC").

## Pre-requisites

- python 3.6+ (`python3 --version` to check)
- CC connector ID - this is a string provided by CC, strucured like `customer-id>/<source-type>/<source-id>`
- optionally, a credentials file, to push data extracts and / or logs to CC

## Installation

- Create a new directory for this tool, with a Python virtual environment (venv), then activate the venv:

  ```
  mkdir coco-agent
  cd coco-agent
  python3 -m venv venv
  source venv/bin/activate
  ```

- Install the latest version of the tool the virtual environment:

  ```
  pip install -U coco-agent
  ```

## Extract metadata from a Git repository

To extract metadata from a cloned repo accessible via the file system:

```
coco-agent extract git-repo --connector-id=<connector id> repo-dir
```

where

- `connector id` is the connector ID mentioned above, provided by CC
- `repo-dir` is the directory of the Git repository

By default, output is written to the `out` directory.

For additional options, including specifying date ranges, see `./coco-agent extract git-repo --help`

#### Additional data sources

Each will have its own connector-id. Simply re-run the `extract` command, pointing at each additional source as desired.

## Upload data

Once desired data has been extracted, it can be securely uploaded to CC from the output directory:

```
coco-agent upload data --credentials-file=<credentials file path> <connector id> <directory>
```

where

- `credentials file path` is the location of the upload credentials JSON file, provided by CC
- `connector id` is, as before, the connector ID for the data source
- `directory` is the directory where data was previously extracted (`./out` by default)

---

## Supported options

Invoking `coco-agent` without arguments will display supported commands and options.

`coco-agent version` will display the current version.

In the same way, description and options for each sub-command can be seen by passing the `--help` argument - e.g. `coco-agent extract git-repo --help`.