我有两个数据集，我想将它们组合成一个 pandas 数据框。它们看起来像这样:
df1 = pandas.DataFrame({
'protein': ['A']*4 + ['B']*4,
'repeat':range(1, 9),
'measurement1': [numpy.nan]*4 + list(numpy.random.uniform(0, 1, 4)),
'measurement2': list(numpy.random.uniform(0, 1, 4)) + [numpy.nan]*4,
'measurement3': list(numpy.random.uniform(0, 1, 4)) + [numpy.nan]*4,
})
df2 = pandas.DataFrame({
'protein': ['A']*2 + ['B']*2,
'repeat':range(1, 5),
'measurement1': list(numpy.random.uniform(0, 1, 4)),
'measurement4': list(numpy.random.uniform(0, 1, 4)),
'measurement5': list(numpy.random.uniform(0, 1, 4)),
})
idx = ['protein', 'repeat']
df1.set_index(idx, inplace=True)
df2.set_index(idx, inplace=True)
第一个:
>>> df1
measurement1 measurement2 measurement3
protein repeat
A 1 NaN 0.757366 0.858163
2 NaN 0.453202 0.287777
3 NaN 0.434762 0.044638
4 NaN 0.825710 0.653887
B 5 0.732218 NaN NaN
6 0.380481 NaN NaN
7 0.444811 NaN NaN
8 0.569743 NaN NaN
第二个
>>> df2
measurement1 measurement4 measurement5
protein repeat
A 1 0.342011 0.174242 0.071223
2 0.416247 0.820345 0.048176
B 3 0.240464 0.767659 0.328830
4 0.985637 0.459141 0.089130
我怎样才能合并这些数据帧，以便我有这样的东西:
measurement1 measurement2 measurement3 measurement4 measurement5
protein repeat
A 1 0.721179 0.019207 0.189169 0.186984 0.316553
2 0.425959 0.301376 0.677409 0.794600 0.668739
3 0.675156 0.834304 0.022280 0.414653 0.263979
4 0.667983 0.563201 0.841316 0.062459 0.584332
B 5 0.598407 NaN NaN NaN NaN
6 0.658570 NaN NaN NaN NaN
7 0.226620 NaN NaN NaN NaN
8 0.958272 NaN NaN NaN NaN
请您参考如下方法:
看来df2是错误的，只有A级别:
df2 = pd.DataFrame({
'protein': ['A']*4,
'repeat':range(1, 5),
'measurement1': list(np.random.uniform(0, 1, 4)),
'measurement4': list(np.random.uniform(0, 1, 4)),
'measurement5': list(np.random.uniform(0, 1, 4)),
})
idx = ['protein', 'repeat']
df2.set_index(idx, inplace=True)
print (df2)
measurement1 measurement4 measurement5
protein repeat
A 1 0.927584 0.741862 0.165938
2 0.569004 0.048579 0.780998
3 0.457412 0.708697 0.286537
4 0.753526 0.839243 0.306470
也是可能的用途:
df = df2.combine_first(df1).reset_index()
df = df[df.columns[2:].tolist() + df.columns[:2].tolist()]
print (df)
measurement1 measurement2 measurement3 measurement4 measurement5 \
0 0.539505 0.241686 0.894978 0.988329 0.963004
1 0.626309 0.095530 0.043223 0.375186 0.341831
2 0.005545 0.238250 0.301947 0.097038 0.798923
3 0.484909 0.807791 0.980582 0.461909 0.798846
4 0.463653 NaN NaN NaN NaN
5 0.502216 NaN NaN NaN NaN
6 0.313669 NaN NaN NaN NaN
7 0.047340 NaN NaN NaN NaN
protein repeat
0 A 1
1 A 2
2 A 3
3 A 4
4 B 5
5 B 6
6 B 7
7 B 8
