Previously I did a post about a major mistake I made when I started buying cards on the market. Since doing that post, I have always wondered which cards have the highest spread regarding USD value between its lowest and lowest per base card XP. In this post, we will explore which splinterlands cards have the highest spread.
Top 10 Monster Cards with the highest spread % id
I am not surprised with the top 5 cards with the highest spread, but I am surprised to see Divine and Skeleton Assassin's likes in the top 10.
Python Code
I am a hobbyist programmer and used python to query and pull the data. You can run the following to produce the table above.
import requests
from pandas import json_normalize
import numpy as np
import pandas as pd
# splinterlands grouped market summary
url = 'https://api.splinterlands.io/market/for_sale_grouped'
response = requests.get(url)
data_grouped = response.json()
df_grouped = json_normalize(data_grouped)
# splinterlands card details market summary
url = 'https://api.splinterlands.io/cards/get_details'
response = requests.get(url)
data_card_details = response.json()
df_card_details = json_normalize(data_card_details)
# merge
df_grouped.rename(columns={'card_detail_id':'id'}, inplace=True)
df_merged = df_grouped.merge(df_card_details[['id', 'name']], on='id', how='inner')
# update columns
df_merged.loc[df_merged['edition'] == 0, ['edition']] = 'Alpha'
df_merged.loc[df_merged['edition'] == 1, ['edition']] = 'Beta'
df_merged.loc[df_merged['edition'] == 2, ['edition']] = 'Promo'
df_merged.loc[df_merged['edition'] == 3, ['edition']] = 'Reward'
df_merged.loc[df_merged['edition'] == 4, ['edition']] = 'Untamed'
df_merged.loc[df_merged['edition'] == 5, ['edition']] = 'Dice'
# drop columns
df_merged.drop(['qty','high_price'], axis=1,inplace=True)
# order columns
df_merged = df_merged[['id','name','gold','edition','low_price','low_price_bcx']]
# add new column: spread
df_merged['spread %'] = ((df_merged['low_price'] / df_merged['low_price_bcx']) - 1) * 100
# sort by spread
df_merged.sort_values('spread %', inplace=True, ascending=False)
# export to excel
df_merged.to_excel(r'C:\Development\splinterlands\splinterlands.xlsx')
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