Pygsheets: Google Sheets Python API v4

pygsheets - Google Spreadsheets Python API v4

A simple, intuitive library for google sheets which gets your work done.

Features:

• Open, create, delete and share spreadsheets using title or key
• Intuitive models - spreadsheet, worksheet, cell, datarange
• Control permissions of spreadsheets.
• Set cell format, text format, color, write notes
• Named and Protected Ranges Support
• Work with range of cells easily with DataRange and Gridrange
• Data validation support. checkboxes, drop-downs etc.
• Conditional formatting support
• get multiple ranges with get_values_batch and update wit update_values_batch

Updates

• version 2.0.5 released

Installation

From PyPi (Stable)

pip install pygsheets

If you are installing from pypi please see the docs here.

From GitHub (Recommended)

pip install https://github.com/nithinmurali/pygsheets/archive/staging.zip

If you are installing from github please see the docs here.

Basic Usage

Basic features are shown here, for complete set of features see the full documentation here.

Obtain OAuth2 credentials from Google Developers Console for google spreadsheet api and drive api and save the file as client_secret.json in same directory as project. read more here.

Start using pygsheets:

Sample scenario : you want to share a numpy array with your remote friend

import pygsheets
import numpy as np

gc = pygsheets.authorize()

# Open spreadsheet and then worksheet
sh = gc.open('my new sheet')
wks = sh.sheet1

# Update a cell with value (just to let him know values is updated ;) )
wks.update_value('A1', "Hey yank this numpy array")
my_nparray = np.random.randint(10, size=(3, 4))

# update the sheet with array
wks.update_values('A2', my_nparray.tolist())

# share the sheet with your friend
sh.share("myFriend@gmail.com")

Sample Scenario: you want to fill height values of students

## import pygsheets and open the sheet as given above

header = wks.cell('A1')
header.value = 'Names'
header.text_format['bold'] = True # make the header bold
header.update()

# or achive the same in oneliner
wks.cell('B1').set_text_format('bold', True).value = 'heights'

# set the names
wks.update_values('A2:A5',[['name1'],['name2'],['name3'],['name4']])

# set the heights
heights = wks.range('B2:B5', returnas='range')  # get the range as DataRange object
heights.name = "heights"  # name the range
heights.update_values([[50],[60],[67],[66]]) # update the vales
wks.update_value('B6','=average(heights)') # set the avg value of heights using named range

More Examples

Opening a Spreadsheet

# You can open a spreadsheet by its title as it appears in Google Docs 
sh = gc.open("pygsheetTest")

# If you want to be specific, use a key
sht1 = gc.open_by_key('1mwA-NmvjDqd3A65c8hsxOpqdfdggPR0fgfg5nXRKScZAuM')

# create a spreadsheet in a folder (by id)
sht2 = gc.create("new sheet", folder_name="my worksheets")

# open enable TeamDrive support
gc.drive.enable_team_drive("Dqd3A65c8hsxOpqdfdggPR0fgfg")

Operations on Spreadsheet doc

import pygsheets
c = pygsheets.authorize()
sh = c.open('spreadsheet')

# create a new sheet with 50 rows and 60 colums
wks = sh.add_worksheet("new sheet",rows=50,cols=60)

# create a new sheet with 50 rows and 60 colums at the begin of worksheets
wks = sh.add_worksheet("new sheet",rows=50,cols=60,index=0)

# or copy from another worksheet
wks = sh.add_worksheet("new sheet", src_worksheet='<other worksheet instance>')

# delete this wroksheet
sh.del_worksheet(wks)

# unshare the sheet
sh.remove_permissions("myNotSoFriend@gmail.com")

Selecting a Worksheet

import pygsheets
c = pygsheets.authorize()
sh = c.open('spreadsheet')

# Select worksheet by id, index, title.
wks = sh.worksheet_by_title("my test sheet")

# By any property
wks = sh.worksheet('index', 0)

# Get a list of all worksheets
wks_list = sh.worksheets()

# Or just
wks = sh[0]

Operations on Worksheet doc

# Get values as 2d array('matrix') which can easily be converted to an numpy aray or as 'cell' list
values_mat = wks.get_values(start=(1,1), end=(20,20), returnas='matrix')

# Get values of - rows A1 to B10, column C, 1st row, 10th row
wks.get_values_batch(['A1:B10', 'C', '1', (10, None)])

# Get all values of sheet as 2d list of cells
cell_matrix = wks.get_all_values(returnas='matrix')

# update a range of values with a cell list or matrix
wks.update_values(crange='A1:E10', values=values_mat)

# update multiple ranges with bath update
wks.update_values_batch(['A1:A2', 'B1:B2'], [[[1],[2]], [[3],[4]]])

# Insert 2 rows after 20th row and fill with values
wks.insert_rows(row=20, number=2, values=values_list)

# resize by changing rows and colums
wks.rows=30

# use the worksheet as a csv
for row in wks:
    print(row)

# get values by indexes
 A1_value = wks[0][0]

# clear all values
wks.clear()

# Search for a table in the worksheet and append a row to it
wks.append_table(values=[1,2,3,4])

# export a worksheet as csv
wks.export(pygsheets.ExportType.CSV)

# Find/Replace cells with string value
cell_list = worksheet.find("query string")

# Find/Replace cells with regexp
filter_re = re.compile(r'(small|big) house')
cell_list = worksheet.find(filter_re, searchByRegex=True)
cell_list = worksheet.replace(filter_re, 'some house', searchByRegex=True)

# Move a worksheet in the same spreadsheet (update index)
wks.index = 2 # index start at 1 , not 0

# Update title
wks.title = "NewTitle"

# Update hidden state
wks.hidden = False

# working with named ranges
wks.create_named_range('A1', 'A10', 'prices')
wks.get_named_range('prices')
wks.get_named_ranges()  # will return a list of DataRange objects
wks.delete_named_range('prices')

# Plot a chart/graph
wks.add_chart(('A1', 'A6'), [('B1', 'B6')], 'Health Trend')

# create drop-downs
wks.set_data_validation(start='C4', end='E7', condition_type='NUMBER_BETWEEN', condition_values=[2,10], strict=True, inputMessage="inut between 2 and 10")

Pandas integration

If you work with pandas, you can directly use the dataframes

#set the values of a pandas dataframe to sheet
wks.set_dataframe(df,(1,1))

#you can also get the values of sheet as dataframe
df = wks.get_as_df()

Cell Object doc

Each cell has a value and cordinates (row, col, label) properties.

Getting cell objects

c1 = Cell('A1',"hello")  # create a unlinked cell
c1 = worksheet.cell('A1')  # creates a linked cell whose changes syncs instantanously
cl.value  # Getting cell value
c1.value_unformatted #Getting cell unformatted value
c1.formula # Getting cell formula if any
c1.note # any notes on the cell
c1.address # address object with cell position

cell_list = worksheet.range('A1:C7')  # get a range of cells 
cell_list = worksheet.col(5, returnas='cell')  # return all cells in 5th column(E)

Most of the functions has returnas param, if whose value is cell it will return a list of cell objects. Also you can use label or (row,col) tuple interchangbly as a cell adress

Cell Operations

Each cell is directly linked with its cell in spreadsheet, hence changing the value of cell object will update the corresponding cell in spreadsheet unless you explictly unlink it Also not that bu default only the value of cell is fetched, so if you are directly accessing any cell properties call cell.fetch() beforehand.

Different ways of updating Cells

# using linked cells
c1 = worksheet.cell('B1') # created from worksheet, so linked cell
c1.col = 5  # Now c1 correponds to E1
c1.value = "hoho"  # will change the value of E1

# Or onliner
worksheet.update_value('B1', 'hehe')

# get a range of cells
cell_list = worksheet.range('A1:C7')
cell_list = worksheet.get_values(start='A1', end='C7', returnas='cells')
cell_list = worksheet.get_row(2, returnas='cells')


# add formula
c1.formula = 'A1+C2'
c1.formula # '=A1+C2'

# get neighbouring cells
c2 = c1.neighbour('topright') # you can also specify relative position as tuple eg (1,1)

# set cell format
c1.set_number_format(pygsheets.FormatType.NUMBER, '00.0000')

# write notes on cell
c1.note = "yo mom"

# set cell color
c1.color = (1.0, 1.0, 1.0, 1.0) # Red, Green, Blue, Alpha

# set text format
c1.text_format['fontSize'] = 14
c1.set_text_format('bold', True)

# sync the changes
 c1.update()

# you can unlink a cell and set all required properties and then link it
# So yu could create a model cell and update multiple sheets
c.unlink()
c.note = "offine note"
c.link(wks1, True)
c.link(wks2, True)

DataRange Object doc

The DataRange is used to represent a range of cells in a worksheet. They can be named or protected. Almost all get_ functions has a returnas param, set it to range to get a range object.

# Getting a Range object
rng = wks.get_values('A1', 'C5', returnas='range')
rng.start_addr = 'A' # make the range unbounded on rows <Datarange Sheet1!A:B>
drange.end_addr = None # make the range unbounded on both axes <Datarange Sheet1>

# Named ranges
rng.name = 'pricesRange'  # will make this range a named range
rng = wks.get_named_ranges('commodityCount') # directly get a named range
rng.name = ''  # will delete this named range

#Protected ranges
rng.protected = True
rng.editors = ('users', 'someemail@gmail.com')

# Setting Format
 # first create a model cell with required properties
model_cell = Cell('A1')
model_cell.color = (1.0,0,1.0,1.0) # rose color cell
model_cell.format = (pygsheets.FormatType.PERCENT, '')

 # Setting format to multiple cells in one go
rng.apply_format(model_cell)  # will make all cell in this range rose color and percent format
# Or if you just want to apply format, you can skip fetching data while creating datarange
Datarange('A1','A10', worksheet=wks).apply_format(model_cell)

# get cells in range
cell = rng[0][1]

Batching calls

If you are calling a lot of spreadsheet modification functions (non value update). you can merge them into a single call. By doing so all the requests will be merged into a single call.

gc.set_batch_mode(True)
wks.merge_cells("A1", "A2")
wks.merge_cells("B1", "B2")
Datarange("D1", "D5", wks).apply_format(cell)
gc.run_batch() # All the above requests are executed here
gc.set_batch_mode(False)

Batching also happens when you unlink worksheet. But in that case the requests are not merged.

How to Contribute

This library is still in development phase.

• Follow the Contributing to Open Source Guide.
• Branch off of the staging branch, and submit Pull Requests back to that branch. Note that the master branch is used for version bumps and hotfixes only.
• For quick testing the changes you have made to source, run the file tests/manual_testing.py. It will give you an IPython shell with lastest code loaded.

Report Issues/Features

• Please report bugs and suggest features via the GitHub Issues.
• Before opening an issue, search the tracker for possible duplicates.
• If you have any usage questions, ask a question on stackoverflow with pygsheets Tag

Run Tests

• install py.test
• run make test

Now that you have scrolled all the way down, finding this library useful? 

Author: Nithinmurali
Source Code: https://github.com/nithinmurali/pygsheets 
License: View license

#python #googlesheets 

What is GEEK

Buddha Community

Google Sheets, Meet Pandas DataFrame

Maybe it’s from too many hours behind the keyboard. But sometimes my reading and watching on the internet gives me the feeling I’m one of an endangered species of developer. One who’s working inside spreadsheets. Every single day.

Can you believe it? Developers, going about their lives like spreadsheets don’t exist. If you are one of these people, I’d like to meet you. To know that you exist, and this isn’t all a paranoid rant raging between my ears.

For the rest of us, spreadsheets are very much a thing. Love them or hate them, those binary rows and columns are going nowhere. To cut the philosophy lesson short: spreadsheets are. Every non-developer employee save the cleaning staff lives inside spreadsheets.

“Spreadsheets are.” Submit this existential bombshell to a university; an honorary doctorate in philosophy is sure to follow.

Excel and Google Sheets, they’re going nowhere; neither is our productivity until we put automation between us and the knowledge worker ritual of passing the spreadsheet.

My Two Cents in Three Paragraphs

Personally, I love a good spreadsheet. For munging data, quick and dirty calculations, visualizing 1-dimensional times series… 4 out of 5 times I will open Excel or Google Sheets, rather than fire up a Jupyter server.

Except, spreadsheetitis in a business is a symptom of not knowing how to move data. I have personally seen the “human router” problem so bad where more than 20 (twenty!) people touched a version of a spreadsheet before it was finally–mercifully–put to rest in “the database” (another spreadsheet).

And THE Spreadsheet. You know the business is in data hell when there’s The Spreadsheet, with a capital “S”. The one managers think is the business.

Automate Your Way to Sanity

There’s a few libraries we will talk about using for Google Sheets I/O in Python. No matter the approach, you have to enable the Google Sheets API inside the maze-like console for Google Cloud Platform (Google’s version of AWS and Azure). Fortunately Google–go figure–has a great search feature in the console that we’ll use to get you set up.

Insanity First — Getting a credentials.json file from Google Cloud Platform

Go to console.cloud.google.com/, sign in with the Google account with access to the spreadsheet you want to automate. The API is available for both GSuite and free Google accounts. If it’s a work-owned spreadsheet, the admin with GCP access is going to have to follow these steps and give you the credentials file.

Select the project in the navbar on the top. Every resource is kept inside a project, like resource groups in Azure. If you do not have one yet, select the project dropdown and “NEW PROJECT”. Then search for “Google Sheets API”, which if you are a sadist and want to explore the dashboard by mouse, is under “APIs & Services”.

“Enable” the API.

Now you need to do the same for Google Drive API.

Next up, you need to create a service account, which you will then create credentials for.

Steps:

• You are in Google Cloud Platform > Google Sheets API at console.cloud.google.com/apis/api/sheets.googleapis.com
• Go to credentials 🗝 Credentials on the sidebar.
• Select + CREATE CREDENTIALS > Service Account. OAuth is a different story. This article covers a server-side, in-house use. Create the service account.
• Next view is to select an IAM role for the service account. This is optional, skip it.
• Next up is “Grant users access to this service account (optional)”. Also skip.

Now you are back on the “Service accounts for project [PROJECT NAME]” screen. Select the service account in the table, either clicking on the hyperlinked Email field, or hamburger menu (three dots) > Create key.

Either way, ADD KEY or Create key, select JSON key type and CREATE. This downloads a JSON file to your computer.

Last thing: the spreadsheet. Here I created one for this example. Note the highlighted section of the URL in the Omnibar. That is the document’s ID. Most libraries give the choice of referencing the whole URL or just this ID.

You can take a look at and download the spreadsheet from here.

Open the credentials.json file you downloaded from GCP for the service account. Copy the email value in the client_email property. Share the Google Sheet with this email. This gives the service account access to the spreadsheet, which was created on another account (not the service account).

Phew. That’s all the Google infrastructure stuff. I promise.

Now we can get down to business writing Python code.

#google-sheets #python-programming #python-libraries #google-drive-api #google-cloud-platform #api

Top 10 API Security Threats Every API Team Should Know

As more and more data is exposed via APIs either as API-first companies or for the explosion of single page apps/JAMStack, API security can no longer be an afterthought. The hard part about APIs is that it provides direct access to large amounts of data while bypassing browser precautions. Instead of worrying about SQL injection and XSS issues, you should be concerned about the bad actor who was able to paginate through all your customer records and their data.

Typical prevention mechanisms like Captchas and browser fingerprinting won’t work since APIs by design need to handle a very large number of API accesses even by a single customer. So where do you start? The first thing is to put yourself in the shoes of a hacker and then instrument your APIs to detect and block common attacks along with unknown unknowns for zero-day exploits. Some of these are on the OWASP Security API list, but not all.

Insecure pagination and resource limits

Most APIs provide access to resources that are lists of entities such as /users or /widgets. A client such as a browser would typically filter and paginate through this list to limit the number items returned to a client like so:

First Call: GET /items?skip=0&take=10 
Second Call: GET /items?skip=10&take=10

However, if that entity has any PII or other information, then a hacker could scrape that endpoint to get a dump of all entities in your database. This could be most dangerous if those entities accidently exposed PII or other sensitive information, but could also be dangerous in providing competitors or others with adoption and usage stats for your business or provide scammers with a way to get large email lists. See how Venmo data was scraped

A naive protection mechanism would be to check the take count and throw an error if greater than 100 or 1000. The problem with this is two-fold:

1. For data APIs, legitimate customers may need to fetch and sync a large number of records such as via cron jobs. Artificially small pagination limits can force your API to be very chatty decreasing overall throughput. Max limits are to ensure memory and scalability requirements are met (and prevent certain DDoS attacks), not to guarantee security.
2. This offers zero protection to a hacker that writes a simple script that sleeps a random delay between repeated accesses.

skip = 0
while True:    response = requests.post('https://api.acmeinc.com/widgets?take=10&skip=' + skip),                      headers={'Authorization': 'Bearer' + ' ' + sys.argv[1]})    print("Fetched 10 items")    sleep(randint(100,1000))    skip += 10

How to secure against pagination attacks

To secure against pagination attacks, you should track how many items of a single resource are accessed within a certain time period for each user or API key rather than just at the request level. By tracking API resource access at the user level, you can block a user or API key once they hit a threshold such as “touched 1,000,000 items in a one hour period”. This is dependent on your API use case and can even be dependent on their subscription with you. Like a Captcha, this can slow down the speed that a hacker can exploit your API, like a Captcha if they have to create a new user account manually to create a new API key.

Insecure API key generation

Most APIs are protected by some sort of API key or JWT (JSON Web Token). This provides a natural way to track and protect your API as API security tools can detect abnormal API behavior and block access to an API key automatically. However, hackers will want to outsmart these mechanisms by generating and using a large pool of API keys from a large number of users just like a web hacker would use a large pool of IP addresses to circumvent DDoS protection.

How to secure against API key pools

The easiest way to secure against these types of attacks is by requiring a human to sign up for your service and generate API keys. Bot traffic can be prevented with things like Captcha and 2-Factor Authentication. Unless there is a legitimate business case, new users who sign up for your service should not have the ability to generate API keys programmatically. Instead, only trusted customers should have the ability to generate API keys programmatically. Go one step further and ensure any anomaly detection for abnormal behavior is done at the user and account level, not just for each API key.

Accidental key exposure

APIs are used in a way that increases the probability credentials are leaked:

1. APIs are expected to be accessed over indefinite time periods, which increases the probability that a hacker obtains a valid API key that’s not expired. You save that API key in a server environment variable and forget about it. This is a drastic contrast to a user logging into an interactive website where the session expires after a short duration.
2. The consumer of an API has direct access to the credentials such as when debugging via Postman or CURL. It only takes a single developer to accidently copy/pastes the CURL command containing the API key into a public forum like in GitHub Issues or Stack Overflow.
3. API keys are usually bearer tokens without requiring any other identifying information. APIs cannot leverage things like one-time use tokens or 2-factor authentication.

If a key is exposed due to user error, one may think you as the API provider has any blame. However, security is all about reducing surface area and risk. Treat your customer data as if it’s your own and help them by adding guards that prevent accidental key exposure.

How to prevent accidental key exposure

The easiest way to prevent key exposure is by leveraging two tokens rather than one. A refresh token is stored as an environment variable and can only be used to generate short lived access tokens. Unlike the refresh token, these short lived tokens can access the resources, but are time limited such as in hours or days.

The customer will store the refresh token with other API keys. Then your SDK will generate access tokens on SDK init or when the last access token expires. If a CURL command gets pasted into a GitHub issue, then a hacker would need to use it within hours reducing the attack vector (unless it was the actual refresh token which is low probability)

Exposure to DDoS attacks

APIs open up entirely new business models where customers can access your API platform programmatically. However, this can make DDoS protection tricky. Most DDoS protection is designed to absorb and reject a large number of requests from bad actors during DDoS attacks but still need to let the good ones through. This requires fingerprinting the HTTP requests to check against what looks like bot traffic. This is much harder for API products as all traffic looks like bot traffic and is not coming from a browser where things like cookies are present.

Stopping DDoS attacks

The magical part about APIs is almost every access requires an API Key. If a request doesn’t have an API key, you can automatically reject it which is lightweight on your servers (Ensure authentication is short circuited very early before later middleware like request JSON parsing). So then how do you handle authenticated requests? The easiest is to leverage rate limit counters for each API key such as to handle X requests per minute and reject those above the threshold with a 429 HTTP response. There are a variety of algorithms to do this such as leaky bucket and fixed window counters.

Incorrect server security

APIs are no different than web servers when it comes to good server hygiene. Data can be leaked due to misconfigured SSL certificate or allowing non-HTTPS traffic. For modern applications, there is very little reason to accept non-HTTPS requests, but a customer could mistakenly issue a non HTTP request from their application or CURL exposing the API key. APIs do not have the protection of a browser so things like HSTS or redirect to HTTPS offer no protection.

How to ensure proper SSL

Test your SSL implementation over at Qualys SSL Test or similar tool. You should also block all non-HTTP requests which can be done within your load balancer. You should also remove any HTTP headers scrub any error messages that leak implementation details. If your API is used only by your own apps or can only be accessed server-side, then review Authoritative guide to Cross-Origin Resource Sharing for REST APIs

Incorrect caching headers

APIs provide access to dynamic data that’s scoped to each API key. Any caching implementation should have the ability to scope to an API key to prevent cross-pollution. Even if you don’t cache anything in your infrastructure, you could expose your customers to security holes. If a customer with a proxy server was using multiple API keys such as one for development and one for production, then they could see cross-pollinated data.

#api management #api security #api best practices #api providers #security analytics #api management policies #api access tokens #api access #api security risks #api access keys

Why use Python for Software Development

No programming language is pretty much as diverse as Python. It enables building cutting edge applications effortlessly. Developers are as yet investigating the full capability of end-to-end Python development services in various areas. 

By areas, we mean FinTech, HealthTech, InsureTech, Cybersecurity, and that's just the beginning. These are New Economy areas, and Python has the ability to serve every one of them. The vast majority of them require massive computational abilities. Python's code is dynamic and powerful - equipped for taking care of the heavy traffic and substantial algorithmic capacities. 

Programming advancement is multidimensional today. Endeavor programming requires an intelligent application with AI and ML capacities. Shopper based applications require information examination to convey a superior client experience. Netflix, Trello, and Amazon are genuine instances of such applications. Python assists with building them effortlessly. 

5 Reasons to Utilize Python for Programming Web Apps 

Python can do such numerous things that developers can't discover enough reasons to admire it. Python application development isn't restricted to web and enterprise applications. It is exceptionally adaptable and superb for a wide range of uses.

Robust frameworks 

Python is known for its tools and frameworks. There's a structure for everything. Django is helpful for building web applications, venture applications, logical applications, and mathematical processing. Flask is another web improvement framework with no conditions. 

Web2Py, CherryPy, and Falcon offer incredible capabilities to customize Python development services. A large portion of them are open-source frameworks that allow quick turn of events. 

Simple to read and compose 

Python has an improved sentence structure - one that is like the English language. New engineers for Python can undoubtedly understand where they stand in the development process. The simplicity of composing allows quick application building. 

The motivation behind building Python, as said by its maker Guido Van Rossum, was to empower even beginner engineers to comprehend the programming language. The simple coding likewise permits developers to roll out speedy improvements without getting confused by pointless subtleties. 

Utilized by the best 

Alright - Python isn't simply one more programming language. It should have something, which is the reason the business giants use it. Furthermore, that too for different purposes. Developers at Google use Python to assemble framework organization systems, parallel information pusher, code audit, testing and QA, and substantially more. Netflix utilizes Python web development services for its recommendation algorithm and media player. 

Massive community support 

Python has a steadily developing community that offers enormous help. From amateurs to specialists, there's everybody. There are a lot of instructional exercises, documentation, and guides accessible for Python web development solutions. 

Today, numerous universities start with Python, adding to the quantity of individuals in the community. Frequently, Python designers team up on various tasks and help each other with algorithmic, utilitarian, and application critical thinking. 

Progressive applications 

Python is the greatest supporter of data science, Machine Learning, and Artificial Intelligence at any enterprise software development company. Its utilization cases in cutting edge applications are the most compelling motivation for its prosperity. Python is the second most well known tool after R for data analytics.

The simplicity of getting sorted out, overseeing, and visualizing information through unique libraries makes it ideal for data based applications. TensorFlow for neural networks and OpenCV for computer vision are two of Python's most well known use cases for Machine learning applications.

Summary

Thinking about the advances in programming and innovation, Python is a YES for an assorted scope of utilizations. Game development, web application development services, GUI advancement, ML and AI improvement, Enterprise and customer applications - every one of them uses Python to its full potential. 

The disadvantages of Python web improvement arrangements are regularly disregarded by developers and organizations because of the advantages it gives. They focus on quality over speed and performance over blunders. That is the reason it's a good idea to utilize Python for building the applications of the future.

#python development services #python development company #python app development #python development #python in web development #python software development

Public ASX100 APIs: The Essential List

We’ve conducted some initial research into the public APIs of the ASX100 because we regularly have conversations about what others are doing with their APIs and what best practices look like. Being able to point to good local examples and explain what is happening in Australia is a key part of this conversation.

Method

The method used for this initial research was to obtain a list of the ASX100 (as of 18 September 2020). Then work through each company looking at the following:

1. Whether the company had a public API: this was found by googling “[company name] API” and “[company name] API developer” and “[company name] developer portal”. Sometimes the company’s website was navigated or searched.
2. Some data points about the API were noted, such as the URL of the portal/documentation and the method they used to publish the API (portal, documentation, web page).
3. Observations were recorded that piqued the interest of the researchers (you will find these below).
4. Other notes were made to support future research.
5. You will find a summary of the data in the infographic below.

Data

With regards to how the APIs are shared:

• Documentation is where just the Swagger/OpenAPI docs are hosted somewhere. e.g. https://api.linkgroup.com/member/docs/index
• Portal is where an interactive portal has been developed. e.g. https://dev.telstra.com/
• The web page is where the company has a single page that isn’t interactive explaining their Public APIs. e.g. https://www.bendigoadelaide.com.au/banking-products-api.asp

#api #api-development #api-analytics #apis #api-integration #api-testing #api-security #api-gateway

Reading Data From Google Sheet using The Google Sheet API V4 From Our Service - 5

In this video, we are going to create a structure of our service so that we can read data from Google Sheets. We will see how to get the dimensions of a google sheet that is required to read data.

We will read the entries through Laravel that we have added to the Google sheet.

Code: https://github.com/amitavdevzone/google-data-studio

#google sheet api v4 #google sheet #data