semvec/pypi-packages huggingface dataset:
pyplatform-reporting 2020.12.1
Pyplatform-reporting package provides function for managing  hyper datasources on Tableau server.
License: BSD
Keywords: google bigquery cloud functions storage jupyterlab python SQL

### Pyplatform-reporting package provides function for managing  hyper datasources on Tableau server.

## Installation
```python
pip install pyplatform-reporting
```

## Authentication and environment variables
Refer to [main page](https://github.com/mhadi813/pyplatform) for documentation on authentication

## Exploring the modules
```python

from pyplatform.reporting import *
show_me()
```

## Usage
### listing Tableau server datasources and reading hyper datasource into pandas dataframe
```python
from pyplatform.reporting import *

print(tableau_server_list_resources())
datasource_name = 'sample superstore'
hyper_filepath = tableau_server_download_hyper(datasource_name)
df= hyper_to_df(hyper_filepath)

```

### uploading pandas dataframe as hyper datasource on Tableau server
```python
from pyplatform.reporting import *
# df #TODO create dataframe as source
hyper_filepath = df_to_hyper(df,filepath='Sample superstore.hyper')
datasource_name = tableau_server_upload_hyper(hyper_filepath)

```
pyplatform-datawarehouse 2020.12.1
Pyplatform-datawarehouse package provides functions for querying, writing and managing data in Google BiqQuery.
License: BSD
Keywords: google bigquery cloud functions storage jupyterlab python SQL

### Pyplatform-datawarehouse package provides functions for querying, writing and managing data in Google BiqQuery.

## Installation
```python
pip install pyplatform-datawarehouse
```
## Authentication and environment variables
Refer to [main page](https://github.com/mhadi813/pyplatform) for documentation on authentication

## Exploring the modules
```python

from pyplatform.datawarehouse import *
show_me()
```

## Usage
### querying BigQuery table
```python
import pyplatform.datawarehouse as dw
sql = """SELECT Order_ID, Order_Date, Ship_Date, Ship_Mode, Customer_ID, Customer_Name, Segment 
FROM `project_id.dataset.sample_superstore` """
df = dw.bq_to_df(sql)
df.head()

# select statemet is enclosed in the stored procedure
sql = """CALL `project_id.dataset.spoc_sample_superstore`();"""
df = dw.bq_to_df(sql)
df.head()

```
### writing dataframe to BigQuery table
```python
import pyplatform.datawarehouse as dw
# df #TODO create dataframe as source
table_id = 'project_id.dataset.sample_superstore'
job = dw.df_to_bq(df,table_id)

print(dw.bq_get_job_info(job))

```
pyplatform-datalake 2020.12.1
Pyplatform-datalake package provides functions for Google Cloud Storage and Microsoft Storage services.
License: BSD
Keywords: google bigquery cloud functions storage jupyterlab python SQL

### Pyplatform-datalake package provides functions for Google Cloud Storage and Microsoft Storage services.

## Installation
```python
pip install pyplatform-datalake
```

## Authentication and environment variables
Refer to [main page](https://github.com/mhadi813/pyplatform) for documentation on authentication

## Exploring the modules
```python

from pyplatform.datalake import *
show_me()
```

## Usage
### uploading in-memory data to Google Cloud Storage
```python
import pyplatform.datalake as dlk
from io import BytesIO
# df #TODO create dataframe as source
in_mem_file = BytesIO()
df.to_excel(in_mem_file,index=False)
in_mem_file.seek(0)
dlk.gcs_upload_blob(in_mem_file,bucket_id='bucket',blobname='test.xlsx')

```

### downloading and loading trained forecasting model from Google Cloud Storage
```python
import pyplatform.datalake as dlk
from io import BytesIO
import pickle

gcs_uri = 'gs://bucket/folder_name/model_name'
model_name="fbprophet_forecast_daily"
dlk.gcs_download_blob(gcs_uri,filepath=model_name)
with open(model_name,"rb") as model:
    my_model=pickle.load(model)

```
pyplatform-database 2020.12.1
Pyplatform-database package provides read and write functions for Microsoft SQL database.
License: BSD
Keywords: google bigquery cloud functions storage jupyterlab python SQL

### Pyplatform-database package provides read and write functions for Microsoft SQL database.

## Installation
```python
pip install pyplatform-database
```

## Authentication and environment variables
Refer to [main page](https://github.com/mhadi813/pyplatform) for documentation on authentication

## Exploring the modules
```python

from pyplatform.database import *
show_me()
```

## Usage
### reading from SQL table, default database provided in credential file
```python
from pyplatform.database import *
query = """SELECT * FROM dbo.fact_sample_superstore"""
df = az_to_df(query)
df.head()

```

### writing pandas dataframe to sql table with non default setting
```python

from pyplatform.database import *
# df #TODO create dataframe as source
table_name = 'dbo.fact_sample_superstore'
engine = azure_sql_engine(database='testDB')  #changing database only
df_to_azure_sql(df,table_name, engine=engine)

```