我有一個DataFrame帶有多個列的，一個Series。兩者具有相同的特征DateTimeIndex。我想從中的每一行的Series所有值中減去的每一行的值DataFrame這是我的示例數據：dates = pandas.date_range('20180101', periods=10)stocks = ['AAPL', 'GOOG', 'MSFT', 'AMZN', 'FB']data = numpy.random.randn(10,5)prices = pandas.DataFrame(index=dates, columns=stocks, data=data)returns = prices.pct_change(1)這使我DataFrame類似于以下內容然后創建我的Series，這是一籃子股票的回報basket = returns.mean(axis=1)這使我Series類似于以下內容現在我想從每只股票的收益中減去一攬子收益：excess_ret = returns - basket我收到以下警告：RuntimeWarning: Cannot compare type 'Timestamp' with type 'str', sort order isundefined for incomparable objects return this.join(other, how=how, return_indexers=return_indexers)這是結果DataFrame：這曾經在中工作pandas-0.16.2，但我現在正在使用pandas-0.22.0，看來我現在無法Series從DataFrame具有匹配項的a中減去a Indexes？問題：我當前正在執行的減法操作中發生了什么？如何從中的每一行的Series所有值中減去中的每一行的值DataFrame？
1 回答
Cats萌萌
TA貢獻1805條經驗 獲得超9個贊
我認為需要sub帶有參數axis=0的匹配索引，DataFrame該索引的索引為Series：
軸：{0，1，'索引'，'列'}
對于系列輸入，軸與系列索引匹配
excess_ret = returns.sub(basket, axis=0)
print (excess_ret)
AAPL GOOG MSFT AMZN FB
2018-01-01 NaN NaN NaN NaN NaN
2018-01-02 -1.833226 -0.110935 0.455586 -0.173553 1.662127
2018-01-03 -0.662713 1.737714 -1.295243 1.381853 -1.161611
2018-01-04 3.269817 -0.824819 0.377973 -0.788368 -2.034604
2018-01-05 -0.082528 1.814466 2.295359 -3.543489 -0.483808
2018-01-06 0.295950 2.978380 1.000856 1.346977 -5.622164
2018-01-07 1.988864 -2.316191 0.633370 1.043901 -1.349943
2018-01-08 -2.640122 -0.861669 -1.472634 -1.559951 6.534376
2018-01-09 8.062484 -1.712583 -2.497513 -0.807566 -3.044822
2018-01-10 -1.823915 0.370618 -0.883559 0.888679 1.448177
如果要按列匹配：
a = returns.mean(axis=0)
print (a)
AAPL 0.088224
GOOG -1.301244
MSFT -2.436290
AMZN -1.009339
FB -0.102484
dtype: float64
excess_ret = returns.sub(a, axis=1)
print (excess_ret)
AAPL GOOG MSFT AMZN FB
2018-01-01 NaN NaN NaN NaN NaN
2018-01-02 -1.353102 1.441870 5.759181 0.421661 -0.608508
2018-01-03 -0.434575 -0.969659 0.665239 0.823154 4.917633
2018-01-04 8.771575 -2.722012 0.409977 -2.113780 -1.164615
2018-01-05 -0.220083 0.213942 1.329937 -0.372537 0.037217
2018-01-06 -0.633686 6.371478 -14.157027 -0.831583 1.226992
2018-01-07 -2.363521 0.130848 1.743317 -1.381718 -1.929583
2018-01-08 -3.062185 -6.431137 0.438800 0.956752 -1.641623
2018-01-09 -0.450300 2.093572 2.965726 -0.617335 1.042234
2018-01-10 -0.254123 -0.128903 0.844849 3.115386 -1.879747
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