Sushi pairs on Avalanche and BSC

Q108. Let's dive deep into Sushi pairs on Avalanche and BSC

In this dashboard we will dive into:

Create a document that includes all addresses for all Swap pairs on Avalanche for sushi, plus the addresses of the two tokens belonging to each pair.
Create a document that includes all addresses for all Swap pairs on the BSC chain for sushi, plus the addresses of the two tokens belonging to each pair.
Create a document that includes all addresses for all Kashi pairs on Avalanche for sushi, plus the addresses of the two tokens belonging to each pair. Ideally, define which one is the main asset and which one is the collateral.
Create a document that includes all addresses for all Kashi pairs on the BSC chain for sushi, plus the addresses of the two tokens belonging to each pair. Ideally, define which one is the main asset and which one is the collateral.

Methodology

I chose to go through this dashboard by scrapping and using the links of pools in sushiswap website and to be more precise, I used the info from the following links:

After retrieving the necessary data, from the above links, I needed to work on the JSON retrieved data so I can select the related columns to show. I used pandas library of python for this task. I will put a sample code for doing this part.

And finally I figured probably the best way to show the results in a neat way is to share my analytics in Google sheets.

So here are the results to four needed categories:

Swap pairs of Avalanche

Swap pairs of BSC

Kashi pairs on Avalanche

Kashi pairs on BSC

On the next parts, I will put a sample screenshot of how my result table looks like.

Note: As I used the data from outside of Flipside, I didn’t write queries, but in order to be eligible to submit my dashboard I added one simple query to calculate the number of Kashi pools on Avalanche.

The sample code for tables

import json, pandas as pd

data = {}
with open('avax.json', 'r') as f:
   data = json.load(f)

df = pd.DataFrame(columns=['pair-name', 'swap-address', 'token0_symbol','token0_name',
 'token0_contract_address', 'token0_decimals', 'token1_symbol', 
 'token1_name', 'token1_contract_address', 'token1_decimals' ])

for i in range(len(data)):
    df = df.append({ 'pair-name': data[i]['pair']['token0']['symbol'] + '-' + data[i]['pair']['token1']['symbol'],
     'swap-address' : data[i]['pair']['id'],
    'token0_symbol' : data[i]['pair']['token0']['symbol'],
        'token0_name' : data[i]['pair']['token0']['name'],
        'token0_contract_address' : data[i]['pair']['token0']['id'],
        'token0_decimals' : data[i]['pair']['token0']['decimals'],
        'token1_symbol' : data[i]['pair']['token1']['symbol'],
        'token1_name' : data[i]['pair']['token1']['name'],
        'token1_contract_address' : data[i]['pair']['token1']['id'],
        'token1_decimals' : data[i]['pair']['token1']['decimals']}, ignore_index=True)

df.to_csv (r'avax_final.csv', index = None)

So here, I took every JSON files related to the raw data from Sushiswap, and I created a Dataframe in a way that I wanted so I could have the columns I need from it.

Swap pairs

In the results of swap pairs for both Avalanche and BSC, we can see these columns:

pair-name	swap-address	token0_symbol	token0_name	token0_contract_address	token0_decimals	token1_symbol	token1_name	token1_contract_address	token1_decimals

There is a screenshot of swap pairs on BSC.

Kashi Pairs

In the results of Kashi pairs for both Avalanche and BSC, we can see the these columns:

Pair Address	main_asset Name	main_asset Symbol	main_asset Contract Address	main_asset Decimals	collateral Name	collateral Symbol	collateral Contract Address	collateral Decimals

So as the question asked, I made the main asset and collateral clear here, also added decimals for each one.

There is a screenshot of Kashi pairs on Avalanche.

The results links

I once again put the results link from my Google sheet account: