Good evening to everyone , I hope you all are having a great day .

Onboarding

We all know how much LeoFinance team has done regarding onboarding of people . I am not here to talk about that , the thing I want to mention in this post is - how can we help LeoFinance team in this regard?

One obvious way is to delegate our HP to @leo.voter so that HP available in that account is used to delegate to new users .

I am here to talk about the second way , let's get LeoFinance in top 20 - that way the Block producer reward will keep on adding HP to LeoFinance more than what they are gaining right now at 30th position .

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Note: For those who isn't interested in codes , skip to the end of post for results.

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# Proxy and No proxy accounts

Proxy accounts are those which haven't directly voted for witness but have used proxy to vote for witnesses indirectly .
Non-Proxy are those who have voted by themselves.

First , I gathered all the data from TxAccount table from HiveSQL and bifurcated the account with proxy and without proxy and stored it in CSV file .

The following data is used to retrieve the data from CSV and store it in DataFrame.

import pandas as pd
from datetime import datetime as dt
without_proxy = pd.read_csv('Without_proxy.csv')

Table looks like this -

What we need to look for is - witness_votes column -

No Proxy account

witness_list=[]
for j in range(0,len(without_proxy)):
    list_generator= list(without_proxy['witness_votes'][j].split('\r\n')) 
    if list_generator:
        for i in range(0,len(list_generator)):
            list_generator[i]=''.join(e for e in list_generator[i] if e.isalnum())
        list_generator= list(filter(None, list_generator))
    
    witness_list.append([without_proxy['name'][j],list_generator])
    

What have I done above?

• going through every row ( which contains details of who a particular user has voted for as witness)

• it contains some unnecessary data like \r\n so I have cleansed it and stored it in pure username form

• I have stored that in a list in the following format

  • Username : Witness votes
  • Ex :
  • Here 'hope777' is the account name and the next list is whom he has voted for as witness.

voted_for_leo_list=[]
not_voted_list=[]
for i in range(0,len(witness_list)):
    if 'leofinance' in (witness_list[i][1]):
        voted_for_leo_list.append(witness_list[i][0])
    else:
        not_voted_list.append(witness_list[i][0])
    

In the above code

• I have just checked if LeoFinance is one of the votes by the user .
• If yes , I have stored his name in voted_for_leo_list
• If no , I have stored his name in not_voted_list

That is it for ' No proxy account votes ' . Now let's jump to ' Proxy accounts '

with_proxy = pd.read_csv('With_proxy.csv')
for i in range(0,len(with_proxy)):
    proxy_name=with_proxy['proxy'][i]
    
    if proxy_name in voted_for_leo_list:
        voted_for_leo_list.append(with_proxy['name'][i])
    else:
        not_voted_list.append(with_proxy['name'][i])

Steps

• I have stored taken user and his proxy .
• I have stored the proxy name in proxy_name .
• I have checked if the proxy_name have voted for leofinance by referring to above voted_for_leo_list
• If yes , I have appended the user name to voted_for_leo_list
• If no , I have stored his name in not_voted_list .

Now I have used Hive-Engine API to gather the data of how much they have earned from Jan 1 to Jan 26.

import shelve
s=shelve.open('Blocks\Blockchain') # Where I store all 2nd layer tx details
df=pd.DataFrame.from_dict(s.items()) # Converting to DataFrame
df.columns=['Blocks','Transactions'] # Renaming the columns 


import json
leo_earned_list=[]
for i in range(0,len(df)):
    if(df['Transactions'][i]['Transaction']['action']=='issue'):
        json_logs=json.loads(df['Transactions'][i]['Transaction']['logs'])
        if 'events' in json_logs:
            if(json_logs['events'][0]['data']['symbol']=='LEO'):
                leo_earned_list.append([json_logs['events'][0]['data']['to'],json_logs['events'][0]['data']['quantity'],df['Transactions'][i]['Transaction']['sender']])
                
        

Steps

• I have filtered first by taking transactions which has action as 'issue'
• I have taken logs and have stored the data for only LEO symbol .

The output looks like this -

Since we need all the LEO issued to particular user from Jan 1 to Jan 25 , I have used groupby username and sum() , this will give us -

df_total_leo_earned=df_leo_earned.groupby('to').sum().reset_index()

output -

LEO earned in this month from Jan 1 to Jan 25

list_less_100=[]
list_100_1000=[]
list_more_1000=[]
for i in range(0,len(df_total_leo_earned)):
    if(df_total_leo_earned['quantity'][i]>100 and df_total_leo_earned['quantity'][i]<1000):
        list_100_1000.append(df_total_leo_earned['to'][i])
    elif(df_total_leo_earned['quantity'][i]<=100):
        list_less_100.append(df_total_leo_earned['to'][i])
    else:
        list_more_1000.append(df_total_leo_earned['to'][i])

This is pretty simple , I have segregated accounts based on amount of LEO earned .

• Those who have earned less than 100 , their names are stored in list_less_100 .
• Those who have earned between 100 and 1000 , I have stored in list_100_1000 .
• Those who have earned more than 1000 , I have stored in list_more_1000 .

If I count the number of users -

print('Less than 100 LEO earned = '+str(len(list_less_100)))
print('Earned between 100 and 1000 = '+str(len(list_100_1000)))
print('Leo Earned more than 1000 = '+str(len(list_more_1000)))

Output -

Less than 100 LEO earned = 2421
Earned between 100 and 1000 = 152
Leo Earned more than 1000 = 45

Now comes the main part - How many users have voted to LeoFinance ?

count_less_100=0
count_100_1000=0
count_more_1000=0
for i in list_less_100:
    if i in voted_for_leo_list:
        count_less_100+=1

for i in list_100_1000:
    if i in voted_for_leo_list:
        count_100_1000 +=1

for i in list_more_1000:
    if i in voted_for_leo_list:
        count_more_1000 +=1

Steps

• I have just taken users and seen if they have voted or not , that's it .

Data and Charts

So for all those people who just hated the above part and just want to know the final result -
this is it -

Total voted for Leofinance who have earned less than 100 :535
Total voted for Leofinance who have earned between 100 and 1000 :93
Total voted for Leofinance who have earned more than 1000 :19

Not voted for Leofinance but who have earned less than 100 :1886
Not voted for Leofinance but who have earned between 100 and 1000 :59
Not voted for Leofinance but who have earned more than 1000 :26

Those who have earned less than 100 LEO , witness votes proportion.

Those who have earned between 100 and 1000 LEO , witness votes proportion

Those who have earned more than 1000 LEO .

What do you think of the above ?

I also tried to see how much Vests resides in the accounts who have earned over 1000 LEO .

*Note: Only over 1000 LEO , not the other two , if we calculate the total VESTS - we get this '

sum_vests=0
from beem.account import Account
for i in final_list1:
    account = Account(i)
    sum_vests=sum_vests+(float(str(account.balances['total'][2]).split(' ')[0]))

for i in final_list2:
    account = Account(i)
    sum_vests=sum_vests+(float(str(account.balances['total'][2]).split(' ')[0]))
   
    
for i in final_list3:
    account = Account(i)

    sum_vests=sum_vests+(float(str(account.balances['total'][2]).split(' ')[0]))
    

sum_vests 

Update : I have just updated for all 3 lists -
Output - 35242666163.46122 VESTS .

In HP - 18 M HP.

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Thank you for reading , let me know what you think about this in comments .

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Regards,
MR.

Posted Using LeoFinance ^Beta