PyVisualizer 1.0.5

A simple static visualizer.

PyVisualizer_Task2

A simple static visualizer, Wrapper around Gspread lib for reading the google sheets. https://pypi.org/project/PyVisualizer/1.0.4/

Installation

pip install PyVisualizer

Points to keep in mind

You can access any Google sheet, if that google sheet is shared with this user

"[e-mail]"

. This is because test account has been registered to access a google service account and configured accordingly.

Overview

This lib has 3 parts
1 - Connect to the Google sheet by passing the service_account.json file path as a parameter
2 - After successfully connection, Read the Google sheet with the connection object and get the specified sheetobject in return.
3 - Once the data has been read successfully, Pass the data to pre-defined methods of Visualizer class for plotting.

QuickStart

1 - Import 2 classes of PyVisualizer package

CommonUtilities class holds 2 methods.
1 - connectAndAutorizeToServiceAccount() : This method accepts a service_account.json file path as a parameter and return clientConnectionObject.
2 - read_google_sheet() : This method accepts 3 parameters. clientConnectionObject, Google Sheet file name, Worksheet name. It returns sheetObject for access the data.

>>> from PyVisualizer.CommonUtilities import CommonUtilities
>>> from PyVisualizer.Visualizer import Visualizer

2 - Create a connection and read the data.

>>> commonUtils = CommonUtilities()
>>> connectionClient = commonUtils.connectAndAutorizeToServiceAccount("pass_the_service_account.json file path, you can download it from this repo /PyVisualizer/service_account.json.")
Successfully Authorized and Connected to Google Service Account. :)
>>> sheetObj = commonUtils.read_google_sheet(connectionClient,"Copy_of_Greendeck_SE_Assignment_Task_2","Sheet1") # 2nd parameter is the file name, which is already shared with test account
>>> sheet1_data = sheetObj.get_all_records() # This return the json array

3 - Converting the sheet data into pandas DataFrame and pass it to pre-defined methods

>>> import pandas as pd
>>> df = pd.DataFrame(sheet1_data)
>>> Visualizer().barPlotSalesByYear(pandasDataframe=df,xColName="timestamp",yColName="average_sales",sSaveWithFileName="salesByYear.png")
>>> Visualizer().barPlotSalesByMonth(pandasDataframe=df,xColName="timestamp",yColName="average_sales",sSaveWithFileName="salesByMonth.png")
>>> Visualizer().getSalesByYearsAndMonths(df)