我有两个索引相同但形状不同的数据框，无法将数据框的列与数据框中df1的列分开df2。
预期结果是df1 / df2。
df1.head()
volume volume volume volume \
timestamp
2016-07-24 00:00:00+00:00 NaN NaN NaN NaN
2016-07-25 00:00:00+00:00 NaN NaN NaN NaN
2016-07-26 00:00:00+00:00 NaN NaN NaN 102720.829507
2016-07-27 00:00:00+00:00 NaN NaN 3.729644e+05 398346.509801
2016-07-28 00:00:00+00:00 NaN NaN 1.326648e+06 244165.794698
volume volume volume volume
timestamp
2016-07-24 00:00:00+00:00 NaN NaN NaN 1.734943e+07
2016-07-25 00:00:00+00:00 NaN NaN NaN 1.365341e+07
2016-07-26 00:00:00+00:00 NaN NaN NaN 5.199938e+07
2016-07-27 00:00:00+00:00 NaN 2.471076e+06 NaN 2.558753e+07
2016-07-28 00:00:00+00:00 NaN 1.642990e+06 NaN 3.118785e+06
df2.head()
timestamp
2016-07-24 00:00:00+00:00 1.734943e+07
2016-07-25 00:00:00+00:00 1.365341e+07
2016-07-26 00:00:00+00:00 5.210210e+07
2016-07-27 00:00:00+00:00 2.882991e+07
2016-07-28 00:00:00+00:00 6.332589e+06
Freq: D, dtype: float64
df1.shape
Out[2126]: (723, 8)
df2.shape
Out[2127]: (723,)
df1.divide(df2, axis= 'index')
ValueError: operands could not be broadcast together with shapes (5784,) (723,)
两个数据帧具有不同的结构，但索引相同。
type(df1)
Out[2143]: pandas.core.frame.DataFrame
type(df2)
Out[2144]: pandas.core.series.Series
我读到我需要重塑一个数据框，所以我尝试了以下方法：
df1.divide(df2.reshape(723,1), axis= 'index')
但是它返回一个错误：
ValueError: Unable to coerce to DataFrame, shape must be (723, 8): given (723, 1)
当我转换时df2，pd.DataFrame(df2)它将引发错误：
TypeError: '<' not supported between instances of 'str' and 'int'
我想念什么，我该怎么办？
试试这个方法。我使用了一个简单的示例，但请告诉我这是否无效。
import pandas as pd
import numpy as np
from IPython.display import display, HTML
CSS = """
.output {
flex-direction: row;
}
"""
HTML('<style>{}</style>'.format(CSS))
data1 = {"a":[1.,7.,12.],
"b":[4.,8.,3.],
"c":[5.,45.,67.]}
data2 = {"a":[3.],
"b":[2.],
"c":[8.]}
df1 = pd.DataFrame(data1)
df2 = pd.DataFrame(data2)
df2 = df2.T
df2 = df2.reset_index()
del df2['index']
display(df1)
display(df2)
display(df1.iloc[:,0:].truediv(df2[0], axis=0)) # this portion of code you want
abc
0 1.0 4.0 5.0
1 7.0 8.0 45.0
2 12.0 3.0 67.0
0
0 3.0
1 2.0
2 8.0
abc
0 0.333333 1.333333 1.666667
1 3.500000 4.000000 22.500000
2 1.500000 0.375000 8.375000
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我来说两句