@merurial9 14 December 2020 more from that month

Which Splinterlands cards have the highest spread %? || Ep. #48 || Splinterlands

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. https://www.splintertalk.io/hive-13323/@mercurial9/the-biggest-mistake-all-new-players-should-avoid-or-or-ep-20-or-or-splinterlands 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') I recently did a compilation post titled, The New Players Guide to Splinterlands: A Collection of Articles and Guides. This post serves as a summary of articles and guides I have written for new players starting fresh on Splinterlands. If you enjoy reading my Splinterlands content, please follow and support me by signing up to playing Splinterlands through my affiliate link: https://splinterlands.com?ref=mercurial9. https://www.splintertalk.io/hive-13323/@mercurial9/the-new-players-guide-to-splinterlands-a-collection-of-articles-and-guides-or-or-ep-34-or-or-splinterlands Thank you for reading and hope you have a good rest of the day! Follow me on these other platforms where I also post my content: Publish0x || Hive || Steem || Read.Cash || Uptrennd || Instagram || Twitter || Pinterest https://www.publish0x.com/@aftershock9?a=Vyb82ANmev https://hive.blog/@mercurial9 https://steempeak.com/@mercurial9 https://read.cash/@merurial9 https://www.uptrennd.com/user/mercurial9 https://www.instagram.com/minimalistpixel/ https://twitter.com/minimalistpixel https://za.pinterest.com/minimalistpixel/boards/