无法找到两个列之间的相关数据

Question

我有一个数据集，基本上是一个列表列表

data = [[(datetime.datetime(2018, 12, 6, 10, 0), Decimal('7.0000000000000000')), (datetime.datetime(2018, 12, 6, 11, 0), Decimal('2.0000000000000000')), (datetime.datetime(2018, 12, 6, 12, 0), Decimal('43.6666666666666667')), (datetime.datetime(2018, 12, 6, 14, 0), Decimal('8.0000000000000000')), (datetime.datetime(2018, 12, 7, 9, 0), Decimal('12.0000000000000000')), (datetime.datetime(2018, 12, 7, 10, 0), Decimal('2.0000000000000000')), (datetime.datetime(2018, 12, 7, 11, 0), Decimal('2.0000000000000000')), (datetime.datetime(2018, 12, 7, 17, 0), Decimal('2.0000000000000000'))], [(datetime.datetime(2018, 12, 6, 10, 0), 28.5), (datetime.datetime(2018, 12, 6, 11, 0), 12.75), (datetime.datetime(2018, 12, 6, 12, 0), 12.15), (datetime.datetime(2018, 12, 6, 14, 0), 12.75), (datetime.datetime(2018, 12, 7, 9, 0), 12.75), (datetime.datetime(2018, 12, 7, 10, 0), 12.75), (datetime.datetime(2018, 12, 7, 11, 0), 12.75), (datetime.datetime(2018, 12, 7, 17, 0), 12.75)]]

它基本上包含两个列表，每个列表都有date和metric列。我需要提取每个列表的公制列值，并找到它们之间的相互关系。在

注意：每个列表中的日期都是相似的

因此，首先我将每个列表加载到pandas中并设置日期索引。在

^{pr2}$

现在我合并两个数据帧（它们共享相同的日期）。在

^{3}$

现在我的数据框看起来像这样

                                     1_x    1_y
0                                              
2018-12-06 10:00:00   7.0000000000000000  28.50
2018-12-06 11:00:00   2.0000000000000000  12.75
2018-12-06 12:00:00  43.6666666666666667  12.15
2018-12-06 14:00:00   8.0000000000000000  12.75
2018-12-07 09:00:00  12.0000000000000000  12.75
2018-12-07 10:00:00   2.0000000000000000  12.75
2018-12-07 11:00:00   2.0000000000000000  12.75
2018-12-07 17:00:00   2.0000000000000000  12.75

现在我需要找到两列1_x和{}之间的相关性。所以我就这么做了

df.iloc[:,0].corr(df.iloc[:,1])

但是我得到了以下错误

Traceback (most recent call last):
  File "/home/souvik/Music/UI_Server2/test61.py", line 71, in <module>
    print(df.iloc[:,0].corr(df.iloc[:,1]))
  File "/home/souvik/django_test/webdev/lib/python3.5/site-packages/pandas/core/series.py", line 1911, in corr
    min_periods=min_periods)
  File "/home/souvik/django_test/webdev/lib/python3.5/site-packages/pandas/core/nanops.py", line 77, in _f
    return f(*args, **kwargs)
  File "/home/souvik/django_test/webdev/lib/python3.5/site-packages/pandas/core/nanops.py", line 762, in nancorr
    return f(a, b)
  File "/home/souvik/django_test/webdev/lib/python3.5/site-packages/pandas/core/nanops.py", line 770, in _pearson
    return np.corrcoef(a, b)[0, 1]
  File "/home/souvik/django_test/webdev/lib/python3.5/site-packages/numpy/lib/function_base.py", line 2392, in corrcoef
    c = cov(x, y, rowvar)
  File "/home/souvik/django_test/webdev/lib/python3.5/site-packages/numpy/lib/function_base.py", line 2302, in cov
    avg, w_sum = average(X, axis=1, weights=w, returned=True)
  File "/home/souvik/django_test/webdev/lib/python3.5/site-packages/numpy/lib/function_base.py", line 391, in average
    if scl.shape != avg.shape:
AttributeError: 'float' object has no attribute 'shape'

我不知道发生了什么事。我在网上看到的例子使用df['A].corr(df['B'])来得到A和{}之间的相关性。我做错什么了？在

Answer 1

您的列1_x有{}，可以从这里观察到：

df.info()
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 8 entries, 2018-12-06 10:00:00 to 2018-12-07 17:00:00
Data columns (total 2 columns):
1_x    8 non-null object
1_y    8 non-null float64
dtypes: float64(1), object(1)
memory usage: 512.0+ bytes

因此，将列1_x转换为float。在

使用：

^{pr2}$