PyVisualizer 1.0.4

A simple static visualizer.

PyVisualizer_Task2

A simple static visualizer, Wrapper around Gspread lib for reading the google sheets.

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.connectAndAutorizeToSeriveAccount("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 = com.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 passing 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)