本文介绍了重采样多索引数据帧中的时间序列的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！
问题描述
我在 MultiIndex DataFrame 中有一个包含时间序列数据的分层表 - 请参阅下面的示例数据 - 请注意，第 1 级索引的填充不均匀.
I have a hierarchical table with time series data in a MultiIndex DataFrame - see sample data below - note that Level 1 index is unevenly populated.
如何重新采样 DataFrame 以生成每周时间序列，从当前日期开始每周向后重新采样.
How do I resample the DataFrame to result in a weekly time series that resamples weekly backwards from the current date.
我已经尝试过但失败了:
I have tried this but failed:
df.index.levels[0].name = 'date'
df.reset_index().groupby(pandas.Grouper(key='date', freq='W'))
示例数据:
Price Sector
2016-08-08 Equity(24 [AAPL]) 107.47 311
Equity(4151 [JNJ]) 124.19 206
Equity(5061 [MSFT]) 57.95 311
Equity(8347 [XOM]) 87.52 309
Equity(11100 [BRK_B]) 145.52 103
Equity(16841 [AMZN]) 765.87 102
Equity(26578 [GOOG_L]) 806.93 311
Equity(42950 [FB]) 125.11 311
2016-08-09 Equity(24 [AAPL]) 108.37 311
Equity(4151 [JNJ]) 123.69 206
Equity(5061 [MSFT]) 58.06 311
Equity(8347 [XOM]) 88.55 309
Equity(11100 [BRK_B]) 145.45 103
Equity(16841 [AMZN]) 766.56 102
Equity(26578 [GOOG_L]) 805.39 311
Equity(42950 [FB]) 125.26 311
2016-08-10 Equity(24 [AAPL]) 108.81 311
Equity(4151 [JNJ]) 123.44 206
Equity(5061 [MSFT]) 58.20 311
Equity(8347 [XOM]) 87.98 309
Equity(11100 [BRK_B]) 147.30 103
Equity(16841 [AMZN]) 768.32 102
Equity(26578 [GOOG_L]) 807.47 311
Equity(42950 [FB]) 125.06 311
2016-08-11 Equity(24 [AAPL]) 108.00 311
Equity(4151 [JNJ]) 123.43 206
Equity(5061 [MSFT]) 58.02 311
Equity(8347 [XOM]) 86.41 309
Equity(11100 [BRK_B]) 146.73 103
Equity(16841 [AMZN]) 768.44 102
Equity(26578 [GOOG_L]) 808.66 311
Equity(42950 [FB]) 124.87 311
2016-08-12 Equity(24 [AAPL]) 107.93 311
Equity(4151 [JNJ]) 123.76 206
Equity(5061 [MSFT]) 58.31 311
Equity(8347 [XOM]) 86.73 309
Equity(11100 [BRK_B]) 147.72 103
Equity(16841 [AMZN]) 771.30 102
Equity(26578 [GOOG_L]) 808.20 311
Equity(42950 [FB]) 124.91 311
2016-08-15 Equity(24 [AAPL]) 108.16 311
Equity(4151 [JNJ]) 123.21 206
Equity(5061 [MSFT]) 57.93 311
Equity(8347 [XOM]) 87.80 309
Equity(11100 [BRK_B]) 147.64 103
Equity(16841 [AMZN]) 772.56 102
Equity(26578 [GOOG_L]) 807.05 311
Equity(42950 [FB]) 124.87 311
2016-08-16 Equity(24 [AAPL]) 109.50 311
Equity(4151 [JNJ]) 122.33 206
Equity(5061 [MSFT]) 57.76 311
Equity(8347 [XOM]) 87.81 309
Equity(11100 [BRK_B]) 147.78 103
Equity(16841 [AMZN]) 768.49 102
Equity(26578 [GOOG_L]) 806.00 311
Equity(42950 [FB]) 123.90 311
2016-08-17 Equity(24 [AAPL]) 109.38 311
Equity(4151 [JNJ]) 120.34 206
Equity(5061 [MSFT]) 57.44 311
Equity(8347 [XOM]) 87.94 309
…… …… …… ……
2017-07-31 Equity(8347 [XOM]) 79.60 309
Equity(11100 [BRK_B]) 173.99 103
Equity(16841 [AMZN]) 1020.01 102
Equity(25006 [JPM]) 91.27 103
Equity(26578 [GOOG_L]) 958.20 311
Equity(42950 [FB]) 172.46 311
2017-08-01 Equity(24 [AAPL]) 148.77 311
Equity(4151 [JNJ]) 132.72 206
Equity(5061 [MSFT]) 72.72 311
Equity(8347 [XOM]) 80.04 309
Equity(11100 [BRK_B]) 175.07 103
Equity(16841 [AMZN]) 987.50 102
Equity(25006 [JPM]) 91.81 103
Equity(26578 [GOOG_L]) 945.83 311
Equity(42950 [FB]) 169.28 311
2017-08-02 Equity(24 [AAPL]) 150.05 311
Equity(4151 [JNJ]) 132.55 206
Equity(5061 [MSFT]) 72.55 311
Equity(8347 [XOM]) 80.15 309
Equity(11100 [BRK_B]) 176.29 103
Equity(16841 [AMZN]) 996.19 102
Equity(25006 [JPM]) 93.00 103
Equity(26578 [GOOG_L]) 946.56 311
Equity(42950 [FB]) 169.85 311
2017-08-03 Equity(24 [AAPL]) 157.15 311
Equity(4151 [JNJ]) 132.14 206
Equity(5061 [MSFT]) 72.25 311
Equity(8347 [XOM]) 80.59 309
Equity(11100 [BRK_B]) 177.78 103
Equity(16841 [AMZN]) 995.32 102
Equity(25006 [JPM]) 93.08 103
Equity(26578 [GOOG_L]) 947.18 311
Equity(42950 [FB]) 169.25 311
2017-08-04 Equity(24 [AAPL]) 155.53 311
Equity(4151 [JNJ]) 133.35 206
Equity(5061 [MSFT]) 72.14 311
Equity(8347 [XOM]) 80.46 309
Equity(11100 [BRK_B]) 178.94 103
Equity(16841 [AMZN]) 987.14 102
Equity(25006 [JPM]) 92.46 103
Equity(26578 [GOOG_L]) 940.63 311
Equity(42950 [FB]) 168.58 311
2017-08-07 Equity(24 [AAPL]) 156.34 311
Equity(4151 [JNJ]) 133.11 206
Equity(5061 [MSFT]) 72.66 311
Equity(8347 [XOM]) 80.17 309
Equity(11100 [BRK_B]) 179.79 103
Equity(16841 [AMZN]) 987.41 102
Equity(25006 [JPM]) 93.68 103
Equity(26578 [GOOG_L]) 945.34 311
Equity(42950 [FB]) 169.59 311
2017-08-08 Equity(24 [AAPL]) 158.79 311
Equity(4151 [JNJ]) 132.84 206
Equity(5061 [MSFT]) 72.40 311
Equity(8347 [XOM]) 80.15 309
Equity(11100 [BRK_B]) 178.00 103
Equity(16841 [AMZN]) 991.80 102
Equity(25006 [JPM]) 94.02 103
Equity(26578 [GOOG_L]) 945.31 311
Equity(42950 [FB]) 171.96 311
推荐答案
根据您希望如何汇总价格，resample 为您提供了多种选择.以下是涵盖 2 个工作日部分时间的 2 只股票的示例数据框:
Depending on how you want to aggregate the prices, resample gives you many options. Here's a sample dataframe for 2 stocks covering parts of 2 business weeks:
np.random.seed(123)
df1 = pd.DataFrame({ 'price':abs(np.random.randn(5)).cumsum(), 'ticker':'GOOG_L' },
index=pd.date_range('1-4-2017', periods=5, freq='B') )
df2 = pd.DataFrame({ 'price':abs(np.random.randn(5)).cumsum(), 'ticker':'AAPL' },
index=pd.date_range('1-4-2017', periods=5, freq='B') )
df = df1.append(df2).sort_index()
price ticker
2017-01-04 1.085631 GOOG_L
2017-01-04 1.651437 AAPL
2017-01-05 2.082976 GOOG_L
2017-01-05 4.078116 AAPL
2017-01-06 2.365955 GOOG_L
2017-01-06 4.507028 AAPL
2017-01-09 3.872249 GOOG_L
2017-01-09 5.772965 AAPL
2017-01-10 4.450850 GOOG_L
2017-01-10 6.639705 AAPL
您可以从那里以多种方式(平均值、最大值、第一等)重新采样:
From there you can resample in a variety of ways (mean, max, first, etc.):
df.groupby('ticker')['price'].resample('W').mean()
ticker
AAPL 2017-01-08 3.412194
2017-01-15 6.206335
GOOG_L 2017-01-08 1.844854
2017-01-15 4.161549
Name: price, dtype: float64
df.groupby('ticker')['price'].resample('W').max()
ticker
AAPL 2017-01-08 4.507028
2017-01-15 6.639705
GOOG_L 2017-01-08 2.365955
2017-01-15 4.450850
Name: price, dtype: float64
df.groupby('ticker')['price'].resample('W').first()
ticker
AAPL 2017-01-08 1.651437
2017-01-15 5.772965
GOOG_L 2017-01-08 1.085631
2017-01-15 3.872249
Name: price, dtype: float64
编辑添加:您可以使用loffset"可选参数调整星期几.比如上面的日期都是星期日.要使其成为星期一，请使用:
edit to add: You can adjust the day of the week with the "loffset" optional argument. For example, the above dates are all Sundays. To make it Monday, use:
df.groupby('ticker')['price'].resample('W',loffset='1D').first()
这篇关于重采样多索引数据帧中的时间序列的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！