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 requestsfrom pandas import json_normalizeimport numpy as npimport pandas as pd# splinterlands grouped market summaryurl = '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 summaryurl = 'https://api.splinterlands.io/cards/get_details'response = requests.get(url)data_card_details = response.json()df_card_details = json_normalize(data_card_details)# mergedf_grouped.rename(columns={'card_detail_id':'id'}, inplace=True)df_merged = df_grouped.merge(df_card_details[['id', 'name']], on='id', how='inner')# update columnsdf_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 columnsdf_merged.drop(['qty','high_price'], axis=1,inplace=True)# order columnsdf_merged = df_merged[['id','name','gold','edition','low_price','low_price_bcx']]# add new column: spreaddf_merged['spread %'] = ((df_merged['low_price'] / df_merged['low_price_bcx']) - 1) * 100# sort by spreaddf_merged.sort_values('spread %', inplace=True, ascending=False)# export to exceldf_merged.to_excel(r'C:\Development\splinterlands\splinterlands.xlsx')
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