我有几百个具有相同列名的数据框，如下所示：
wave num stlines fwhm EWs MeasredWave
0 4050.32 3 0.28269 0.07365 22.16080 4050.311360
1 4208.98 5 0.48122 0.08765 44.90035 4208.972962
2 4374.94 9 0.71483 0.11429 86.96497 4374.927110
3 4379.74 9 0.31404 0.09107 30.44271 4379.760601
4 4398.01 14 0.50415 0.09845 52.83236 4398.007473
5 5520.50 1 0.06148 0.12556 8.21685 5520.484742
wave num stlines fwhm EWs MeasredWave
0 4050.32 3 0.28616 0.07521 22.91064 4050.327388
1 4208.98 6 0.48781 0.08573 44.51609 4208.990029
2 4374.94 9 0.71548 0.11437 87.10152 4374.944513
3 4379.74 10 0.31338 0.09098 30.34791 4379.778009
4 4398.01 15 0.49950 0.08612 45.78707 4398.020367
5 4502.21 9 0.56362 0.10114 60.67868 4502.223123
6 4508.28 3 0.69554 0.11600 85.88428 4508.291777
7 4512.99 2 0.20486 0.08891 19.38745 4512.999332
8 5520.50 1 0.06148 0.12556 8.21685 5520.484742
这就是我阅读它们的方式
path_to_files = '/home/Desktop/computed_2d/'
lst = []
for filen in dir1:
df = pd.read_table(path_to_files+filen, skiprows=0, usecols=(0,1,2,3,4,8),names=['wave','num','stlines','fwhm','EWs','MeasredWave'],delimiter=r'\s+')
lst.append(df)
所需的结果应如下所示：
wave num stlines fwhm EWs MeasredWave
0 4050.32 3.0 0.284425 0.074430 22.535720 4050.319374
1 4208.98 5.5 0.484515 0.086690 44.708220 4208.981496
2 4374.94 9.0 0.715155 0.114330 87.033245 4374.935812
3 4379.74 9.5 0.313710 0.091025 30.395310 4379.769305
4 4398.01 14.5 0.501825 0.092285 49.309715 4398.013920
5 4502.21 9 0.56362 0.10114 60.67868 4502.223123
6 4508.28 3 0.69554 0.11600 85.88428 4508.291777
7 4512.99 2 0.20486 0.08891 19.38745 4512.999332
8 5520.50 1.0 0.061480 0.125560 8.216850 5520.484742
如您所见，行数不相同。现在，我想获取基于column1 wave的所有数据帧的平均值，并确保将wave的列df1的每个索引添加到{的正确索引中{1}}
答案 0 :(得分：1)
以下是执行所需操作的示例：
import pandas as pd
df1 = pd.DataFrame({'A': [0, 1, 2, 3],
'B': [0, 1, 2, 3],
'C': [0, 1, 2, 3],
'D': [0, 1, 2, 3]},
index=[0, 1, 2, 3])
df2 = pd.DataFrame({'A': [4, 5, 6, 7],
'B': [4, 5, 6, 7],
'C': [4, 5, 6, 7],
'D': [4, 5, 6, 7]},
index=[0, 1, 2, 3])
df3 = pd.DataFrame({'A': [8, 9, 10, 11],
'B': [8, 9, 10, 11],
'C': [8, 9, 10, 11],
'D': [8, 9, 10, 11]},
index=[0, 1, 2, 3])
df4 = pd.concat([df1, df2, df3])
df5 = pd.concat([df1, df2, df3], ignore_index=True)
print(df4)
print('\n\n')
print(df5)
print(f"Average of column A = {df4['A'].mean()}")
您将拥有
A B C D
0 0 0 0 0
1 1 1 1 1
2 2 2 2 2
3 3 3 3 3
0 4 4 4 4
1 5 5 5 5
2 6 6 6 6
3 7 7 7 7
0 8 8 8 8
1 9 9 9 9
2 10 10 10 10
3 11 11 11 11
A B C D
0 0 0 0 0
1 1 1 1 1
2 2 2 2 2
3 3 3 3 3
4 4 4 4 4
5 5 5 5 5
6 6 6 6 6
7 7 7 7 7
8 8 8 8 8
9 9 9 9 9
10 10 10 10 10
11 11 11 11 11
Average of column A = 5.5
答案 1 :(得分：1)
您可以使用pd.concat wich axis = 1将所有数据帧堆叠为一个，并取各列的平均值
df3 = pd.merge(df1,df2,on=['wave'],how ='outer',)
df4 = df3.rename(columns = lambda x: x.split('_')[0]).T
df4.groupby(df4.index).mean().T
出局：
EWs MeasredWave fwhm num stlines wave
0 22.535720 4050.319374 0.074430 3.0 0.284425 4050.32
1 44.708220 4208.981496 0.086690 5.5 0.484515 4208.98
2 87.033245 4374.935812 0.114330 9.0 0.715155 4374.94
3 30.395310 4379.769305 0.091025 9.5 0.313710 4379.74
4 49.309715 4398.013920 0.092285 14.5 0.501825 4398.01
5 8.216850 5520.484742 0.125560 1.0 0.061480 5520.50
6 60.678680 4502.223123 0.101140 9.0 0.563620 4502.21
7 85.884280 4508.291777 0.116000 3.0 0.695540 4508.28
8 19.387450 4512.999332 0.088910 2.0 0.204860 4512.9
答案 2 :(得分：1)
@Naga Kiran的回答很棒。我在这里更新了整个解决方案：
import pandas as pd
df1 = pd.DataFrame(
{'wave' : [4050.32, 4208.98, 4374.94, 4379.74, 4398.01, 5520.50],
'num' : [3, 5, 9, 9, 14, 1],
'stlines' : [0.28269, 0.48122, 0.71483, 0.31404, 0.50415, 0.06148],
'fwhm' : [0.07365, 0.08765, 0.11429, 0.09107, 0.09845, 0.12556],
'EWs' : [22.16080, 44.90035, 86.96497, 30.44271, 52.83236, 8.21685],
'MeasredWave' : [4050.311360, 4208.972962, 4374.927110, 4379.760601, 4398.007473, 5520.484742]},
index=[0, 1, 2, 3, 4, 5])
df2 = pd.DataFrame(
{'wave' : [4050.32, 4208.98, 4374.94, 4379.74, 4398.01, 4502.21, 4508.28, 4512.99, 5520.50],
'num' : [3, 6, 9, 10, 15, 9, 3, 2, 1],
'stlines' : [0.28616, 0.48781, 0.71548, 0.31338, 0.49950, 0.56362, 0.69554, 0.20486, 0.06148],
'fwhm' : [0.07521, 0.08573, 0.11437, 0.09098, 0.08612, 0.10114, 0.11600, 0.08891, 0.12556],
'EWs' : [22.91064, 44.51609, 87.10152, 30.34791, 45.78707, 60.67868, 85.88428, 19.38745, 8.21685],
'MeasredWave' : [4050.327388, 4208.990029, 4374.944513, 4379.778009, 4398.020367, 4502.223123, 4508.291777, 4512.999332, 5520.484742]},
index=[0, 1, 2, 3, 4, 5, 6, 7, 8])
df3 = pd.merge(df1, df2, on='wave', how='outer')
df4 = df3.rename(columns = lambda x: x.split('_')[0]).T
df5 = df4.groupby(df4.index).mean().T
df6 = df5[['wave', 'num', 'stlines', 'fwhm', 'EWs', 'MeasredWave']]
df7 = df6.sort_values('wave', ascending = True).reset_index(drop=True)
df7