python - 创建多索引列数据框
问题描述
两个由 API 填充的 pandas DataFrame。需要以特定格式加入 DataFrame。
当前状态- 两个数据帧,每个数据帧都由时间戳索引
eth_df:
close symbol
timestamp
1541376000000 206.814430 eth
1541462400000 209.108877 eth
btc_df:
close symbol
timestamp
1541376000000 6351.06194 btc
1541462400000 6415.443409 btc
所需状态- 按时间戳索引和按符号多索引列
portfolio_df:
eth btc
close close
timestamp
1541376000000 206.814430 6351.06194
1541462400000 209.108877 6415.443409
编辑 1:来自社区的请求:您能否将 eth_df.to_dict() 和 btc_df.to_dict() 的结果添加到问题中?
这是两者的代码和结果:
btc = cg.get_coin_market_chart_range_by_id('bitcoin','usd', start_date, end_date)
portfolio_df = pd.DataFrame(data=btc['prices'], columns=['timestamp','close'])
portfolio_df['symbol'] = 'btc'
portfolio_df = portfolio_df.set_index('timestamp')
portfolio_df.to_dict()
{'close': {1541376000000: 6351.061941056285,
1541462400000: 6415.443408541094,
1541548800000: 6474.847290336688,
show more (open the raw output data in a text editor) ...
1627344000000: 'btc',
1627430400000: 'btc',
1627516800000: 'btc',
1627603200000: 'btc',
1627689600000: 'btc',
...}}
eth = cg.get_coin_market_chart_range_by_id('ethereum','usd', start_date, end_date)
eth_df = pd.DataFrame(data=eth['prices'], columns=['timestamp','close'])
eth_df['symbol'] = 'eth'
eth_df = eth_df.set_index('timestamp')
eth_df.to_dict()
{'close': {1541376000000: 206.8144295995958,
1541462400000: 209.10887661978714,
1541548800000: 219.16088708430863,
show more (open the raw output data in a text editor) ...
1627344000000: 'eth',
1627430400000: 'eth',
1627516800000: 'eth',
...}}
btc = cg.get_coin_market_chart_range_by_id('bitcoin','usd', start_date, end_date)
解决方案
我对 CoinGeckoAPI 不是很熟悉,所以假设你得到如下数据框,你不要先设置索引:
from pycoingecko import CoinGeckoAPI
from datetime import datetime
cg = CoinGeckoAPI()
start_date, end_date = 1497484800,1499138400
btc = cg.get_coin_market_chart_range_by_id('bitcoin','usd', start_date, end_date)
btc_df = pd.DataFrame(data=btc['prices'], columns=['timestamp','close'])
btc_df['symbol'] = 'btc'
eth = cg.get_coin_market_chart_range_by_id('ethereum','usd', start_date, end_date)
eth_df = pd.DataFrame(data=eth['prices'], columns=['timestamp','close'])
eth_df['symbol'] = 'eth'
您连接数据框并进行数据透视:
portfolio_df = pd.concat([btc_df,eth_df]).pivot_table(index="timestamp",columns="symbol")
然后交换级别:
portfolio_df = portfolio_df.swaplevel(axis=1)
portfolio_df
symbol btc eth
close close
timestamp
1497484800000 2444.493712 346.369070
1497571200000 2513.810348 358.284517
1497657600000 2683.571344 372.357011
1497744000000 2577.219361 359.438712
1497830400000 2620.136451 362.044289
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