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<p>我有以下代码</p>
<pre class="lang-py prettyprint-override"><code>import yfinance as yf
data = yf.download("EURUSD=X", period = "max", group_by = "ticker")
for label, content in data.items():
print('label:', label)
print('content:', content, sep='\n')`
</code></pre>
<p>我试图通过这个数据集,计算欧元兑美元外汇对在同一个日历日的表现,例如“3/11”,因为从一开始,计算对上涨的天数和下跌的天数意味着什么。我试着引用熊猫文档,但代码似乎没有意义</p>
<pre class="lang-py prettyprint-override"><code>data looks like that:
</code></pre>
<pre class="lang-py prettyprint-override"><code> Open High Low Close Adj Close Volume
Date
2003-12-01 1.203398 1.204007 1.194401 1.196501 1.196501 0
2003-12-02 1.196101 1.210903 1.194600 1.208897 1.208897 0
2003-12-03 1.209000 1.213003 1.207700 1.212298 1.212298 0
2003-12-04 1.212004 1.214403 1.204398 1.208094 1.208094 0
2003-12-05 1.207802 1.219096 1.206593 1.218695 1.218695 0
... ... ... ... ... ... ...
2019-11-18 1.105400 1.109100 1.105400 1.105510 1.105510 0
2019-11-19 1.107224 1.108525 1.106427 1.107236 1.107236 0
2019-11-20 1.108033 1.108279 1.105363 1.108000 1.108000 0
2019-11-21 1.107898 1.109644 1.106378 1.107886 1.107886 0
2019-11-22 1.106562 1.107861 1.102864 1.106586 1.106586 0
[4140 rows x 6 columns]
</code></pre>
<p>我想要的输出:</p>
<pre class="lang-py prettyprint-override"><code>[["2019-11-20", 1.108033, 1.108279, 1.105363, 1.108000, 1.108000, 0.0],
["2018-11-20", 1,.108033, 1.108279, 1.105363, 1.108000, 1.108000, 0.0],
["2017-11-20", 1.108033, 1.108279, 1.105363, 1.108000, 1.108000, 0.0],
["2016-11-20", 1.108033, 1.108279, 1.105363, 1.108000, 1.108000, 0.0],
["2015-11-20", 1.108033, 1.108279, 1.105363, 1.108000, 1.108000, 0.0],
["2014-11-20", 1.108033, 1.108279, 1.105363, 1.108000, 1.108000, 0.0]]
</code></pre>