以下代码
import pandas as pd
df = pd.load_csv('trace.data')
print(df.ix[0:1, :])
产生以下DataFrame
frame# X-1 Y-1 Angle-1 Error-1 X-5 Y-5 Angle-5 Error-5 X-12 \
0 1 NaN NaN NaN NaN NaN NaN NaN NaN NaN
1 2 NaN NaN NaN NaN NaN NaN NaN NaN NaN
... Angle-1355 Error-1355 X-1384 Y-1384 Angle-1384 Error-1384 \
0 ... NaN NaN NaN NaN NaN NaN
1 ... NaN NaN NaN NaN NaN NaN
X-1408 Y-1408 Angle-1408 Error-1408
0 853 2340 283.262859 0
1 NaN NaN NaN NaN
[2 rows x 801 columns]
每行对应于单个图片帧所进行的所有测量的集合。
第一列是帧的编号。
从第二列开始，每四个连续的列是该测量的X位置，Y位置，角度和误差。
i中的数字X-i Y-i Angle-i Error-i是该点的ID。我想将DataFrame改成这种形式的DataFrame：
帧＃
点ID（
i，X-i中的Y-i等）维度名称（例如
X，Y等）测量（实际测量，
float64）一只可敬的熊猫怎么做？
最佳答案
df = pd.DataFrame({'frame': [1, 2],
'Angle-1': [1.6288175485083471, -0.16980795008048055],
'Angle-1355': [-0.23364001238956567, 0.10508954185705043],
'Angle-1384': [-0.1055306764132989, 1.5766485876766343],
'Angle-5': [1.0530749477672805, -0.58051944875155881],
'Error-1': [-0.22597615373237354, -0.067869089031437124],
'Error-1355': [-1.1205136108736824, 1.5398343350154859],
'Error-1384': [0.2072177497820725, 1.5802856128691691],
'Error-5': [-0.054906215727689098, -0.115633635459458],
'X-1': [1.2374207482997275, -0.74052859017582551],
'X-12': [-0.10554748111840574, 0.51297919944988468],
'X-1384': [2.2710928129358541, 2.2873598143523743],
'X-5': [-0.68576722189220918, 1.480319768103725],
'Y-1': [-0.72686786051739416, 1.662550986420245],
'Y-1384': [-1.384276797510166, 0.89414830326943084],
'Y-5': [-0.12183746322452065, 1.0471295991115857]})
给定上面的示例数据框，您可以弹出
frames列，并使用列表推导将其重整为扁平化的结构。使用连字符拆分列并重新分配，以创建MultiIndex。然后将new_frames与熔化的数据框水平连接。瞧！
frames = df.pop('frame')
new_frames = [i for j in range(df.shape[1]) for i in frames]
df.columns = df.columns.str.split('-', expand=True)
>>> (pd.concat([pd.DataFrame(new_frames), pd.melt(df)], axis=1, ignore_index=True)
.rename(columns={0: 'frame', 1: 'dimension', 2: 'point', 3: 'measurement'}))
frame dimension point measurement
0 1 Angle 1 1.628818
1 2 Angle 1 -0.169808
2 1 Angle 1355 -0.233640
3 2 Angle 1355 0.105090
4 1 Angle 1384 -0.105531
5 2 Angle 1384 1.576649
6 1 Angle 5 1.053075
7 2 Angle 5 -0.580519
8 1 Error 1 -0.225976
9 2 Error 1 -0.067869
10 1 Error 1355 -1.120514
11 2 Error 1355 1.539834
12 1 Error 1384 0.207218
13 2 Error 1384 1.580286
14 1 Error 5 -0.054906
15 2 Error 5 -0.115634
16 1 X 1 1.237421
17 2 X 1 -0.740529
18 1 X 12 -0.105547
19 2 X 12 0.512979
20 1 X 1384 2.271093
21 2 X 1384 2.287360
22 1 X 5 -0.685767
23 2 X 5 1.480320
24 1 Y 1 -0.726868
25 2 Y 1 1.662551
26 1 Y 1384 -1.384277
27 2 Y 1384 0.894148
28 1 Y 5 -0.121837
29 2 Y 5 1.047130
关于python - 如何将pandas.read_csv结果改成以下格式？，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/35589992/