您好，我是 Pandas DataFrame 的新手。我有 150 个用户数据文件。其中一份用户数据文件如下:
SL_NO KP KPT KR KRT FA FP
0 1 9.84278E+12 1 9.84286E+12 0.16862746 1
0 3 9.84309E+12 3 9.84317E+12 0.16862746 1
0 7 9.84353E+12 7 9.84358E+12 0.14117648 1
0 9 9.84383E+12 9 9.84394E+12 0.16470589 1
0 6 9.84413E+12 6 9.84422E+12 0.1764706 1
0 6 9.8443E+12 6 9.84438E+12 0.16470589 1
0 6 9.84448E+12 6 9.84456E+12 0.16862746 1
0 6 9.84479E+12 6 9.84489E+12 0.16470589 1
0 2 9.84511E+12 2 9.8452E+12 0.18431373 1
0 4 9.84544E+12 4 9.84553E+12 0.16470589 1
0 6 9.8459E+12 6 9.84598E+12 0.16862746 1
0 8 9.84617E+12 8 9.84625E+12 0.16470589 1
0 0 9.84649E+12 0 9.8466E+12 0.16078432 1
0 8 9.84713E+12 8 9.84722E+12 0.15686275 1
0 5 9.84733E+12 5 9.84741E+12 0.16078432 1
0 2 9.84755E+12 2 9.84762E+12 0.18431373 1
0 E 9.84936E+12 E 9.84943E+12 0.16470589 1
1 1 9.85852E+12 1 9.85859E+12 0.19607845 1
1 3 9.85877E+12 3 9.85886E+12 0.19607845 1
1 7 9.85919E+12 7 9.85925E+12 0.15294118 1
1 9 9.85961E+12 9 9.85969E+12 0.16862746 1
1 6 9.85986E+12 6 9.85994E+12 0.1764706 1
1 6 9.86002E+12 6 9.86012E+12 0.18431373 1
1 6 9.8602E+12 6 9.86029E+12 0.1764706 1
1 6 9.8605E+12 6 9.86059E+12 0.18039216 1
1 2 9.86083E+12 2 9.86091E+12 0.18431373 1
1 4 9.86116E+12 4 9.86123E+12 0.1764706 1
1 6 9.86167E+12 6 9.86177E+12 0.18431373 1
1 8 9.86192E+12 8 9.862E+12 0.18039216 1
1 0 9.86219E+12 0 9.86229E+12 0.15686275 1
1 8 9.86272E+12 8 9.86278E+12 0.16078432 1
1 5 9.86294E+12 5 9.86302E+12 0.1764706 1
1 2 9.86315E+12 2 9.86323E+12 0.17254902 1
1 E 9.86425E+12 E 9.86433E+12 0.14117648 1
2 1 9.86495E+12 1 9.86503E+12 0.1764706 1
2 3 9.86528E+12 3 9.86536E+12 0.19215688 1
2 7 9.86569E+12 7 9.86575E+12 0.1254902 1
2 9 9.86611E+12 9 9.8662E+12 0.16470589 1
2 6 9.86658E+12 6 9.86666E+12 0.16862746 1
2 6 9.86675E+12 6 9.86685E+12 0.17254902 1
2 6 9.86699E+12 6 9.86705E+12 0.1764706 1
2 6 9.86723E+12 6 9.86734E+12 0.17254902 1
2 2 9.8676E+12 2 9.86768E+12 0.18039216 1
2 4 9.86782E+12 4 9.86792E+12 0.16862746 1
2 6 9.8682E+12 6 9.86831E+12 0.16862746 1
2 8 9.86862E+12 8 9.86869E+12 0.18039216 1
2 0 9.86891E+12 0 9.869E+12 0.16470589 1
2 8 9.86933E+12 8 9.86941E+12 0.17254902 1
2 5 9.86953E+12 5 9.86961E+12 0.1764706 1
2 2 9.86974E+12 2 9.86982E+12 0.19215688 1
2 E 9.87035E+12 E 9.87044E+12 0.17254902 1
3 1 9.87104E+12 1 9.87111E+12 0.1764706 1
3 3 9.87136E+12 3 9.87146E+12 0.18823531 1
3 7 9.87177E+12 7 9.87185E+12 0.15294118 1
3 9 9.87226E+12 9 9.87235E+12 0.16470589 1
3 6 9.87276E+12 6 9.87284E+12 0.18431373 1
3 6 9.87298E+12 6 9.87306E+12 0.1764706 1
3 6 9.87323E+12 6 9.87332E+12 0.17254902 1
3 6 9.8735E+12 6 9.87361E+12 0.18039216 1
3 2 9.87384E+12 2 9.87392E+12 0.1764706 1
3 4 9.87416E+12 4 9.87424E+12 0.17254902 1
3 6 9.87492E+12 6 9.875E+12 0.16862746 1
3 8 9.87517E+12 8 9.87525E+12 0.16470589 1
3 0 9.87546E+12 0 9.87554E+12 0.15294118 1
3 8 9.87633E+12 8 9.87641E+12 0.16862746 1
3 5 9.87655E+12 5 9.87663E+12 0.1764706 1
3 2 9.87675E+12 2 9.87683E+12 0.18823531 1
3 E 9.87744E+12 E 9.87753E+12 0.17254902 1
4 1 9.8781E+12 1 9.87817E+12 0.17254902 1
4 3 9.87841E+12 3 9.87849E+12 0.1764706 1
4 7 9.87875E+12 7 9.87882E+12 0.15686275 1
4 9 9.8797E+12 9 9.87978E+12 0.16862746 1
4 6 9.8802E+12 6 9.88026E+12 0.17254902 1
4 6 9.88039E+12 6 9.88047E+12 0.18039216 1
4 6 9.88067E+12 6 9.88075E+12 0.1764706 1
4 6 9.88094E+12 6 9.88104E+12 0.17254902 1
4 2 9.88129E+12 2 9.8814E+12 0.1764706 1
4 4 9.88162E+12 4 9.8817E+12 0.15686275 1
4 6 9.88224E+12 6 9.8823E+12 0.16862746 1
4 8 9.88252E+12 8 9.88258E+12 0.17254902 1
4 0 9.88279E+12 0 9.88288E+12 0.15686275 1
4 8 9.88374E+12 8 9.88382E+12 0.16078432 1
4 5 9.88394E+12 5 9.88402E+12 0.17254902 1
4 2 9.88413E+12 2 9.88421E+12 0.18039216 1
4 E 9.88469E+12 E 9.88477E+12 0.16078432 1
5 1 9.88538E+12 1 9.88546E+12 0.18039216 1
5 3 9.88647E+12 3 9.88653E+12 0.18039216 1
5 7 9.88689E+12 7 9.88695E+12 0.15686275 1
5 9 9.88728E+12 9 9.88738E+12 0.16862746 1
5 6 9.88761E+12 6 9.88769E+12 0.16078432 1
5 6 9.88778E+12 6 9.88788E+12 0.17254902 1
5 6 9.88801E+12 6 9.8881E+12 0.1764706 1
5 6 9.88826E+12 6 9.88835E+12 0.18431373 1
5 2 9.88856E+12 2 9.88867E+12 0.1764706 1
5 4 9.88889E+12 4 9.88897E+12 0.16862746 1
5 6 9.8893E+12 6 9.88939E+12 0.18823531 1
5 8 9.8898E+12 8 9.88986E+12 0.16862746 1
5 0 9.89011E+12 0 9.89021E+12 0.16470589 1
5 8 9.8907E+12 8 9.89078E+12 0.16078432 1
5 5 9.89091E+12 5 9.891E+12 0.1764706 1
5 2 9.89113E+12 2 9.89119E+12 0.19215688 1
5 E 9.89155E+12 E 9.89163E+12 0.16470589 1
6 1 9.89229E+12 1 9.89236E+12 0.16078432 1
6 3 9.89264E+12 3 9.89271E+12 0.18431373 1
6 7 9.89311E+12 7 9.89316E+12 0.15686275 1
6 9 9.89369E+12 9 9.89377E+12 0.17254902 1
6 6 9.89412E+12 6 9.8942E+12 0.18431373 1
6 6 9.89431E+12 6 9.89439E+12 0.19215688 1
6 6 9.89453E+12 6 9.89461E+12 0.19607845 1
6 6 9.89477E+12 6 9.89485E+12 0.1764706 1
6 2 9.89505E+12 2 9.89515E+12 0.17254902 1
6 4 9.8954E+12 4 9.89546E+12 0.16470589 1
6 6 9.89576E+12 6 9.89584E+12 0.17254902 1
6 8 9.89604E+12 8 9.89612E+12 0.17254902 1
6 0 9.89633E+12 0 9.8964E+12 0.16470589 1
6 8 9.89709E+12 8 9.89717E+12 0.15294118 1
6 5 9.89731E+12 5 9.89739E+12 0.1764706 1
6 2 9.89753E+12 2 9.89759E+12 0.18039216 1
6 E 9.89804E+12 E 9.89809E+12 0.10196079 1
7 1 9.89882E+12 1 9.8989E+12 0.16078432 1
7 3 9.8992E+12 3 9.89928E+12 0.18039216 1
7 7 9.89965E+12 7 9.89973E+12 0.15294118 1
7 9 9.90026E+12 9 9.90035E+12 0.16470589 1
7 6 9.90063E+12 6 9.90071E+12 0.17254902 1
7 6 9.90084E+12 6 9.90092E+12 0.16862746 1
7 6 9.90108E+12 6 9.90115E+12 0.18039216 1
7 6 9.90133E+12 6 9.90142E+12 0.18039216 1
7 2 9.90172E+12 2 9.90178E+12 0.18039216 1
7 4 9.90199E+12 4 9.90207E+12 0.18039216 1
7 6 9.90238E+12 6 9.90251E+12 0.17254902 1
7 8 9.90316E+12 8 9.90324E+12 0.16862746 1
7 0 9.90347E+12 0 9.90357E+12 0.1764706 1
7 8 9.90393E+12 8 9.90399E+12 0.16470589 1
7 5 9.90413E+12 5 9.90421E+12 0.1764706 1
7 2 9.90432E+12 2 9.9044E+12 0.18823531 1
7 E 9.90479E+12 E 9.90488E+12 0.16078432 1
8 1 9.90574E+12 1 9.90582E+12 0.17254902 1
8 3 9.90614E+12 3 9.90622E+12 0.1764706 1
8 7 9.90655E+12 7 9.90663E+12 0.16470589 1
8 9 9.90707E+12 9 9.90716E+12 0.1764706 1
8 6 9.90752E+12 6 9.9076E+12 0.1764706 1
8 6 9.90774E+12 6 9.90784E+12 0.18431373 1
8 6 9.90804E+12 6 9.9081E+12 0.16862746 1
8 6 9.90835E+12 6 9.90845E+12 0.18039216 1
8 2 9.90875E+12 2 9.90882E+12 0.18431373 1
8 4 9.90911E+12 4 9.90917E+12 0.16470589 1
8 6 9.90956E+12 6 9.90964E+12 0.1764706 1
8 8 9.90979E+12 8 9.90987E+12 0.17254902 1
8 0 9.91011E+12 0 9.91017E+12 0.14509805 1
8 8 9.91064E+12 8 9.91072E+12 0.17254902 1
8 5 9.91084E+12 5 9.91092E+12 0.17254902 1
8 2 9.91108E+12 2 9.91118E+12 0.18823531 1
8 E 9.91164E+12 E 9.91174E+12 0.16862746 1
9 1 9.91869E+12 1 9.91875E+12 0.1764706 1
9 3 9.91966E+12 3 9.91973E+12 0.16862746 1
9 7 9.92011E+12 7 9.92019E+12 0.16470589 1
9 9 9.92056E+12 9 9.92064E+12 0.18039216 1
9 6 9.92095E+12 6 9.92103E+12 0.1764706 1
9 6 9.92116E+12 6 9.92125E+12 0.17254902 1
9 6 9.92141E+12 6 9.92149E+12 0.17254902 1
9 6 9.92166E+12 6 9.92176E+12 0.18039216 1
9 2 9.92201E+12 2 9.92209E+12 0.18431373 1
9 4 9.92229E+12 4 9.92239E+12 0.16470589 1
9 6 9.92271E+12 6 9.92281E+12 0.16470589 1
9 8 9.92339E+12 8 9.92345E+12 0.1764706 1
9 0 9.92362E+12 0 9.92372E+12 0.17254902 1
9 8 9.92451E+12 8 9.92459E+12 0.16862746 1
9 5 9.92524E+12 5 9.92532E+12 0.16862746 1
9 2 9.92571E+12 2 9.92577E+12 0.16862746 1
9 E 9.92647E+12 E 9.92652E+12 0.14117648 1
我正在将上面的每个数据文件读取到数据帧 df 中，并将其转发到 calculateFeatures 函数以执行一些计算，并将其保存并以新的形式返回数据帧df1。 calculateFeatures 函数如下:
import pandas as pd
df1 = pd.DataFrame()
def calculateFeatures(df,user_count):
print(df)
digit_count=1
counter=1
while counter!=11:
df1['subject'] = "USER{}".format(user_count)
df1['count'] = counter
for index, row in df.head(n=17).iterrows():
df1['KDT.{}'.format(digit_count)] = df['KRT'] - df['KPT']
df1['PPT.{}'.format(digit_count)] = (df['KPT'].shift(-1)) - df['KPT']
df1['PRT.{}'.format(digit_count)] = (df['KRT'].shift(-1)) - df['KPT']
df1['RPT.{}'.format(digit_count)] = (df['KPT'].shift(-1)) - df['KRT']
df1['RRT.{}'.format(digit_count)] = (df['KRT'].shift(-1)) - df['KPT']
df1['KDT(n+1).{}'.format(digit_count)] = (df['KRT'].shift(-1)) - (df['KPT'].shift(-1))
df1['FA.{}'.format(digit_count)] = df['FA']
df1['FA(n+1).{}'.format(digit_count)] = df['FA'].shift(-1)
digit_count +=1
counter +=1
digit_count = 1
print(df1)
return df1
第一个代码段中提到的用户数据帧 df 包含 10 (0-9) 组信息，每组包含 17 行(每组以第二个行值结束)列“KP”= E)。新的数据帧 df1 应包含来自 df 的 8 个基本计算，在内部循环中执行，对每组用户数据的 17 行重复(我试图控制这些数据)在外循环中)并将此信息作为一行存储在 df1 中，总计 8 * 17 = 136 + (1 USER COLUMN + 1 COUNT COLUMN) = 138 列。
我在新数据帧 df1 中获得的列数很好，如上所述，为 138。
但问题是，我在 df1 中为每个用户获取了 170 行，这等于 df 的行数。而我应该得到 10 行(如在外循环中)。 1 行 138 列，对于 df 中的每组(17 行)用户数据。我知道，由于我是 pandas 数据框的新手，我在解释数据框时犯了一些错误，但我无法弄清楚。请帮忙。非常感谢!
在评论中提出建议后添加:
subject count KDT.1 PPT.1 PRT.1 RPT.1 RRT.1 KDT(n+1).1 FA.1 FA(n+1).1 KDT.2 PPT.2 PRT.2 RPT.2 RRT.2 KDT(n+1).2 FA.2 FA(n+1).2 KDT.3 PPT.3 PRT.3 RPT.3 RRT.3 KDT(n+1).3 FA.3 FA(n+1).3 KDT.4 PPT.4 PRT.4 RPT.4 RRT.4 KDT(n+1).4 FA.4 FA(n+1).4 KDT.5 PPT.5 PRT.5 RPT.5 RRT.5 KDT(n+1).5 FA.5 FA(n+1).5 KDT.6 PPT.6 PRT.6 RPT.6 RRT.6 KDT(n+1).6 FA.6 FA(n+1).6 KDT.7 PPT.7 PRT.7 RPT.7 RRT.7 KDT(n+1).7 FA.7 FA(n+1).7 KDT.8 PPT.8 PRT.8 RPT.8 RRT.8 KDT(n+1).8 FA.8 FA(n+1).8 KDT.9 PPT.9 PRT.9 RPT.9 RRT.9 KDT(n+1).9 FA.9 FA(n+1).9 KDT.10 PPT.10 PRT.10 RPT.10 RRT.10 KDT(n+1).10 FA.10 FA(n+1).10 KDT.11 PPT.11 PRT.11 RPT.11 RRT.11 KDT(n+1).11 FA.11 FA(n+1).11 KDT.12 PPT.12 PRT.12 RPT.12 RRT.12 KDT(n+1).12 FA.12 FA(n+1).12 KDT.13 PPT.13 PRT.13 RPT.13 RRT.13 KDT(n+1).13 FA.13 FA(n+1).13 KDT.14 PPT.14 PRT.14 RPT.14 RRT.14 KDT(n+1).14 FA.14 FA(n+1).14 KDT.15 PPT.15 PRT.15 RPT.15 RRT.15 KDT(n+1).15 FA.15 FA(n+1).15 KDT.16 PPT.16 PRT.16 RPT.16 RRT.16 KDT(n+1).16 FA.16 FA(n+1).16
USER1 1 75863000 310189999 389853999 234326999 389853999 79664000 0.16862746 0.16862746 75863000 310189999 389853999 234326999 389853999 79664000 0.16862746 0.16862746 75863000 310189999 389853999 234326999 389853999 79664000 0.16862746 0.16862746 75863000 310189999 389853999 234326999 389853999 79664000 0.16862746 0.16862746 75863000 310189999 389853999 234326999 389853999 79664000 0.16862746 0.16862746 75863000 310189999 389853999 234326999 389853999 79664000 0.16862746 0.16862746 75863000 310189999 389853999 234326999 389853999 79664000 0.16862746 0.16862746 75863000 310189999 389853999 234326999 389853999 79664000 0.16862746 0.16862746 75863000 310189999 389853999 234326999 389853999 79664000 0.16862746 0.16862746 75863000 310189999 389853999 234326999 389853999 79664000 0.16862746 0.16862746 75863000 310189999 389853999 234326999 389853999 79664000 0.16862746 0.16862746 75863000 310189999 389853999 234326999 389853999 79664000 0.16862746 0.16862746 75863000 310189999 389853999 234326999 389853999 79664000 0.16862746 0.16862746 75863000 310189999 389853999 234326999 389853999 79664000 0.16862746 0.16862746 75863000 310189999 389853999 234326999 389853999 79664000 0.16862746 0.16862746 75863000 310189999 389853999 234326999 389853999 79664000 0.16862746 0.16862746
USER1 1 79664000 438039000 484985000 358375000 484985000 46946000 0.16862746 0.14117648 79664000 438039000 484985000 358375000 484985000 46946000 0.16862746 0.14117648 79664000 438039000 484985000 358375000 484985000 46946000 0.16862746 0.14117648 79664000 438039000 484985000 358375000 484985000 46946000 0.16862746 0.14117648 79664000 438039000 484985000 358375000 484985000 46946000 0.16862746 0.14117648 79664000 438039000 484985000 358375000 484985000 46946000 0.16862746 0.14117648 79664000 438039000 484985000 358375000 484985000 46946000 0.16862746 0.14117648 79664000 438039000 484985000 358375000 484985000 46946000 0.16862746 0.14117648 79664000 438039000 484985000 358375000 484985000 46946000 0.16862746 0.14117648 79664000 438039000 484985000 358375000 484985000 46946000 0.16862746 0.14117648 79664000 438039000 484985000 358375000 484985000 46946000 0.16862746 0.14117648 79664000 438039000 484985000 358375000 484985000 46946000 0.16862746 0.14117648 79664000 438039000 484985000 358375000 484985000 46946000 0.16862746 0.14117648 79664000 438039000 484985000 358375000 484985000 46946000 0.16862746 0.14117648 79664000 438039000 484985000 358375000 484985000 46946000 0.16862746 0.14117648 79664000 438039000 484985000 358375000 484985000 46946000 0.16862746 0.14117648
USER1 1 46946000 296481000 408431000 249535000 408431000 111950000 0.14117648 0.16470589 46946000 296481000 408431000 249535000 408431000 111950000 0.14117648 0.16470589 46946000 296481000 408431000 249535000 408431000 111950000 0.14117648 0.16470589 46946000 296481000 408431000 249535000 408431000 111950000 0.14117648 0.16470589 46946000 296481000 408431000 249535000 408431000 111950000 0.14117648 0.16470589 46946000 296481000 408431000 249535000 408431000 111950000 0.14117648 0.16470589 46946000 296481000 408431000 249535000 408431000 111950000 0.14117648 0.16470589 46946000 296481000 408431000 249535000 408431000 111950000 0.14117648 0.16470589 46946000 296481000 408431000 249535000 408431000 111950000 0.14117648 0.16470589 46946000 296481000 408431000 249535000 408431000 111950000 0.14117648 0.16470589 46946000 296481000 408431000 249535000 408431000 111950000 0.14117648 0.16470589 46946000 296481000 408431000 249535000 408431000 111950000 0.14117648 0.16470589 46946000 296481000 408431000 249535000 408431000 111950000 0.14117648 0.16470589 46946000 296481000 408431000 249535000 408431000 111950000 0.14117648 0.16470589 46946000 296481000 408431000 249535000 408431000 111950000 0.14117648 0.16470589 46946000 296481000 408431000 249535000 408431000 111950000 0.14117648 0.16470589
USER1 1 111950000 300217000 395692000 188267000 395692000 95475000 0.16470589 0.1764706 111950000 300217000 395692000 188267000 395692000 95475000 0.16470589 0.1764706 111950000 300217000 395692000 188267000 395692000 95475000 0.16470589 0.1764706 111950000 300217000 395692000 188267000 395692000 95475000 0.16470589 0.1764706 111950000 300217000 395692000 188267000 395692000 95475000 0.16470589 0.1764706 111950000 300217000 395692000 188267000 395692000 95475000 0.16470589 0.1764706 111950000 300217000 395692000 188267000 395692000 95475000 0.16470589 0.1764706 111950000 300217000 395692000 188267000 395692000 95475000 0.16470589 0.1764706 111950000 300217000 395692000 188267000 395692000 95475000 0.16470589 0.1764706 111950000 300217000 395692000 188267000 395692000 95475000 0.16470589 0.1764706 111950000 300217000 395692000 188267000 395692000 95475000 0.16470589 0.1764706 111950000 300217000 395692000 188267000 395692000 95475000 0.16470589 0.1764706 111950000 300217000 395692000 188267000 395692000 95475000 0.16470589 0.1764706 111950000 300217000 395692000 188267000 395692000 95475000 0.16470589 0.1764706 111950000 300217000 395692000 188267000 395692000 95475000 0.16470589 0.1764706 111950000 300217000 395692000 188267000 395692000 95475000 0.16470589 0.1764706
USER1 1 95475000 175107000 254849000 79632000 254849000 79742000 0.1764706 0.16470589 95475000 175107000 254849000 79632000 254849000 79742000 0.1764706 0.16470589 95475000 175107000 254849000 79632000 254849000 79742000 0.1764706 0.16470589 95475000 175107000 254849000 79632000 254849000 79742000 0.1764706 0.16470589 95475000 175107000 254849000 79632000 254849000 79742000 0.1764706 0.16470589 95475000 175107000 254849000 79632000 254849000 79742000 0.1764706 0.16470589 95475000 175107000 254849000 79632000 254849000 79742000 0.1764706 0.16470589 95475000 175107000 254849000 79632000 254849000 79742000 0.1764706 0.16470589 95475000 175107000 254849000 79632000 254849000 79742000 0.1764706 0.16470589 95475000 175107000 254849000 79632000 254849000 79742000 0.1764706 0.16470589 95475000 175107000 254849000 79632000 254849000 79742000 0.1764706 0.16470589 95475000 175107000 254849000 79632000 254849000 79742000 0.1764706 0.16470589 95475000 175107000 254849000 79632000 254849000 79742000 0.1764706 0.16470589 95475000 175107000 254849000 79632000 254849000 79742000 0.1764706 0.16470589 95475000 175107000 254849000 79632000 254849000 79742000 0.1764706 0.16470589 95475000 175107000 254849000 79632000 254849000 79742000 0.1764706 0.16470589
USER1 1 79742000 174423000 254294000 94681000 254294000 79871000 0.16470589 0.16862746 79742000 174423000 254294000 94681000 254294000 79871000 0.16470589 0.16862746 79742000 174423000 254294000 94681000 254294000 79871000 0.16470589 0.16862746 79742000 174423000 254294000 94681000 254294000 79871000 0.16470589 0.16862746 79742000 174423000 254294000 94681000 254294000 79871000 0.16470589 0.16862746 79742000 174423000 254294000 94681000 254294000 79871000 0.16470589 0.16862746 79742000 174423000 254294000 94681000 254294000 79871000 0.16470589 0.16862746 79742000 174423000 254294000 94681000 254294000 79871000 0.16470589 0.16862746 79742000 174423000 254294000 94681000 254294000 79871000 0.16470589 0.16862746 79742000 174423000 254294000 94681000 254294000 79871000 0.16470589 0.16862746 79742000 174423000 254294000 94681000 254294000 79871000 0.16470589 0.16862746 79742000 174423000 254294000 94681000 254294000 79871000 0.16470589 0.16862746 79742000 174423000 254294000 94681000 254294000 79871000 0.16470589 0.16862746 79742000 174423000 254294000 94681000 254294000 79871000 0.16470589 0.16862746 79742000 174423000 254294000 94681000 254294000 79871000 0.16470589 0.16862746 79742000 174423000 254294000 94681000 254294000 79871000 0.16470589 0.16862746
USER1 1 79871000 314216000 409585000 234345000 409585000 95369000 0.16862746 0.16470589 79871000 314216000 409585000 234345000 409585000 95369000 0.16862746 0.16470589 79871000 314216000 409585000 234345000 409585000 95369000 0.16862746 0.16470589 79871000 314216000 409585000 234345000 409585000 95369000 0.16862746 0.16470589 79871000 314216000 409585000 234345000 409585000 95369000 0.16862746 0.16470589 79871000 314216000 409585000 234345000 409585000 95369000 0.16862746 0.16470589 79871000 314216000 409585000 234345000 409585000 95369000 0.16862746 0.16470589 79871000 314216000 409585000 234345000 409585000 95369000 0.16862746 0.16470589 79871000 314216000 409585000 234345000 409585000 95369000 0.16862746 0.16470589 79871000 314216000 409585000 234345000 409585000 95369000 0.16862746 0.16470589 79871000 314216000 409585000 234345000 409585000 95369000 0.16862746 0.16470589 79871000 314216000 409585000 234345000 409585000 95369000 0.16862746 0.16470589 79871000 314216000 409585000 234345000 409585000 95369000 0.16862746 0.16470589 79871000 314216000 409585000 234345000 409585000 95369000 0.16862746 0.16470589 79871000 314216000 409585000 234345000 409585000 95369000 0.16862746 0.16470589 79871000 314216000 409585000 234345000 409585000 95369000 0.16862746 0.16470589
USER1 1 95369000 314901000 410284000 219532000 410284000 95383000 0.16470589 0.18431373 95369000 314901000 410284000 219532000 410284000 95383000 0.16470589 0.18431373 95369000 314901000 410284000 219532000 410284000 95383000 0.16470589 0.18431373 95369000 314901000 410284000 219532000 410284000 95383000 0.16470589 0.18431373 95369000 314901000 410284000 219532000 410284000 95383000 0.16470589 0.18431373 95369000 314901000 410284000 219532000 410284000 95383000 0.16470589 0.18431373 95369000 314901000 410284000 219532000 410284000 95383000 0.16470589 0.18431373 95369000 314901000 410284000 219532000 410284000 95383000 0.16470589 0.18431373 95369000 314901000 410284000 219532000 410284000 95383000 0.16470589 0.18431373 95369000 314901000 410284000 219532000 410284000 95383000 0.16470589 0.18431373 95369000 314901000 410284000 219532000 410284000 95383000 0.16470589 0.18431373 95369000 314901000 410284000 219532000 410284000 95383000 0.16470589 0.18431373 95369000 314901000 410284000 219532000 410284000 95383000 0.16470589 0.18431373 95369000 314901000 410284000 219532000 410284000 95383000 0.16470589 0.18431373 95369000 314901000 410284000 219532000 410284000 95383000 0.16470589 0.18431373 95369000 314901000 410284000 219532000 410284000 95383000 0.16470589 0.18431373
USER1 1 95383000 330080000 425431000 234697000 425431000 95351000 0.18431373 0.16470589 95383000 330080000 425431000 234697000 425431000 95351000 0.18431373 0.16470589 95383000 330080000 425431000 234697000 425431000 95351000 0.18431373 0.16470589 95383000 330080000 425431000 234697000 425431000 95351000 0.18431373 0.16470589 95383000 330080000 425431000 234697000 425431000 95351000 0.18431373 0.16470589 95383000 330080000 425431000 234697000 425431000 95351000 0.18431373 0.16470589 95383000 330080000 425431000 234697000 425431000 95351000 0.18431373 0.16470589 95383000 330080000 425431000 234697000 425431000 95351000 0.18431373 0.16470589 95383000 330080000 425431000 234697000 425431000 95351000 0.18431373 0.16470589 95383000 330080000 425431000 234697000 425431000 95351000 0.18431373 0.16470589 95383000 330080000 425431000 234697000 425431000 95351000 0.18431373 0.16470589 95383000 330080000 425431000 234697000 425431000 95351000 0.18431373 0.16470589 95383000 330080000 425431000 234697000 425431000 95351000 0.18431373 0.16470589 95383000 330080000 425431000 234697000 425431000 95351000 0.18431373 0.16470589 95383000 330080000 425431000 234697000 425431000 95351000 0.18431373 0.16470589 95383000 330080000 425431000 234697000 425431000 95351000 0.18431373 0.16470589
USER1 1 95351000 469414000 549249000 374063000 549249000 79835000 0.16470589 0.16862746 95351000 469414000 549249000 374063000 549249000 79835000 0.16470589 0.16862746 95351000 469414000 549249000 374063000 549249000 79835000 0.16470589 0.16862746 95351000 469414000 549249000 374063000 549249000 79835000 0.16470589 0.16862746 95351000 469414000 549249000 374063000 549249000 79835000 0.16470589 0.16862746 95351000 469414000 549249000 374063000 549249000 79835000 0.16470589 0.16862746 95351000 469414000 549249000 374063000 549249000 79835000 0.16470589 0.16862746 95351000 469414000 549249000 374063000 549249000 79835000 0.16470589 0.16862746 95351000 469414000 549249000 374063000 549249000 79835000 0.16470589 0.16862746 95351000 469414000 549249000 374063000 549249000 79835000 0.16470589 0.16862746 95351000 469414000 549249000 374063000 549249000 79835000 0.16470589 0.16862746 95351000 469414000 549249000 374063000 549249000 79835000 0.16470589 0.16862746 95351000 469414000 549249000 374063000 549249000 79835000 0.16470589 0.16862746 95351000 469414000 549249000 374063000 549249000 79835000 0.16470589 0.16862746 95351000 469414000 549249000 374063000 549249000 79835000 0.16470589 0.16862746 95351000 469414000 549249000 374063000 549249000 79835000 0.16470589 0.16862746
对于 csv 数据的格式错误，我们深表歉意。但上面的数据样本应该是输出的样子。正如你所看到的。第一列指示用户，第二列应指示计数(df 中的集合中的 1-10 或 0-9)。上面的示例仅根据我在顶部提供的 df 中的 USER1 原始数据计算得出(不是精确数据)。当 df 更新为 USER2 原始数据时，df1 将提供 USER2 的计算用户数据，共 10 行 138 列，类似于上面显示的数据形式用户1。谢谢。
最佳答案
考虑一种大流行(pandas 的 python-ic)解决方案，该解决方案避免逐行循环，而是在矢量化过程中处理数据帧和系列。具体来说:
- 在一次调用中计算所需的列。
- 使用
melt将数据 reshape 为 long，以实现并排变量和值组合。 - 使用
pivot_table将数据 reshape 为宽型。
将所有三个步骤集成到您的用户定义方法 calculateFeatures 中。 注意:仔细检查输出并测试计算是否正确处理，尽管有所需的最终结构 (10 X 138)。
分配新的计算列
### ORIGINAL COLUMNS
df = ...
orig_cols = df.columns[1:].to_list()
### NEW COLUMNS
df['subject'] = "USER{}".format(user_count)
df['counter'] = list(range(1,18)) * (df['SL_NO'].max()+1)
df['KDT.'] = df['KRT'] - df['KPT']
df['PPT.'] = (df['KPT'].shift(-1)) - df['KPT']
df['PRT.'] = (df['KRT'].shift(-1)) - df['KPT']
df['RPT.'] = (df['KPT'].shift(-1)) - df['KRT']
df['RRT.'] = (df['KRT'].shift(-1)) - df['KPT']
df['KDT(n+1).'] = (df['KRT'].shift(-1)) - (df['KPT'].shift(-1))
df['FA.'] = df['FA']
df['FA(n+1).'] = df['FA'].shift(-1)
new_cols = df.columns[9:]
print(df.loc[:, df.columns[7:]].tail(20))
# subject counter KDT. PPT. PRT. RPT. RRT. KDT(n+1). FA. FA(n+1).
# 150 USER150 15 80000000.0 2.400000e+08 3.400000e+08 1.600000e+08 3.400000e+08 100000000.0 0.172549 0.188235
# 151 USER150 16 100000000.0 5.600000e+08 6.600000e+08 4.600000e+08 6.600000e+08 100000000.0 0.188235 0.168627
# 152 USER150 17 100000000.0 7.050000e+09 7.110000e+09 6.950000e+09 7.110000e+09 60000000.0 0.168627 0.176471
# 153 USER150 1 60000000.0 9.700000e+08 1.040000e+09 9.100000e+08 1.040000e+09 70000000.0 0.176471 0.168627
# 154 USER150 2 70000000.0 4.500000e+08 5.300000e+08 3.800000e+08 5.300000e+08 80000000.0 0.168627 0.164706
# 155 USER150 3 80000000.0 4.500000e+08 5.300000e+08 3.700000e+08 5.300000e+08 80000000.0 0.164706 0.180392
# 156 USER150 4 80000000.0 3.900000e+08 4.700000e+08 3.100000e+08 4.700000e+08 80000000.0 0.180392 0.176471
# 157 USER150 5 80000000.0 2.100000e+08 3.000000e+08 1.300000e+08 3.000000e+08 90000000.0 0.176471 0.172549
# 158 USER150 6 90000000.0 2.500000e+08 3.300000e+08 1.600000e+08 3.300000e+08 80000000.0 0.172549 0.172549
# 159 USER150 7 80000000.0 2.500000e+08 3.500000e+08 1.700000e+08 3.500000e+08 100000000.0 0.172549 0.180392
# 160 USER150 8 100000000.0 3.500000e+08 4.300000e+08 2.500000e+08 4.300000e+08 80000000.0 0.180392 0.184314
# 161 USER150 9 80000000.0 2.800000e+08 3.800000e+08 2.000000e+08 3.800000e+08 100000000.0 0.184314 0.164706
# 162 USER150 10 100000000.0 4.200000e+08 5.200000e+08 3.200000e+08 5.200000e+08 100000000.0 0.164706 0.164706
# 163 USER150 11 100000000.0 6.800000e+08 7.400000e+08 5.800000e+08 7.400000e+08 60000000.0 0.164706 0.176471
# 164 USER150 12 60000000.0 2.300000e+08 3.300000e+08 1.700000e+08 3.300000e+08 100000000.0 0.176471 0.172549
# 165 USER150 13 100000000.0 8.900000e+08 9.700000e+08 7.900000e+08 9.700000e+08 80000000.0 0.172549 0.168627
# 166 USER150 14 80000000.0 7.300000e+08 8.100000e+08 6.500000e+08 8.100000e+08 80000000.0 0.168627 0.168627
# 167 USER150 15 80000000.0 4.700000e+08 5.300000e+08 3.900000e+08 5.300000e+08 60000000.0 0.168627 0.168627
# 168 USER150 16 60000000.0 7.600000e+08 8.100000e+08 7.000000e+08 8.100000e+08 50000000.0 0.168627 0.141176
# 169 USER150 17 50000000.0 NaN NaN NaN NaN NaN 0.141176 NaN
reshape 长数据
mdf = (pd.melt(df.drop(columns=orig_cols),
id_vars = ['subject', 'SL_NO', 'counter'])
.assign(variable = lambda x: x['variable'] + x['counter'].astype('str'))
)
print(mdf.tail(20))
# subject SL_NO counter variable value
# 1340 USER150 8 15 FA(n+1).15 0.188235
# 1341 USER150 8 16 FA(n+1).16 0.168627
# 1342 USER150 8 17 FA(n+1).17 0.176471
# 1343 USER150 9 1 FA(n+1).1 0.168627
# 1344 USER150 9 2 FA(n+1).2 0.164706
# 1345 USER150 9 3 FA(n+1).3 0.180392
# 1346 USER150 9 4 FA(n+1).4 0.176471
# 1347 USER150 9 5 FA(n+1).5 0.172549
# 1348 USER150 9 6 FA(n+1).6 0.172549
# 1349 USER150 9 7 FA(n+1).7 0.180392
# 1350 USER150 9 8 FA(n+1).8 0.184314
# 1351 USER150 9 9 FA(n+1).9 0.164706
# 1352 USER150 9 10 FA(n+1).10 0.164706
# 1353 USER150 9 11 FA(n+1).11 0.176471
# 1354 USER150 9 12 FA(n+1).12 0.172549
# 1355 USER150 9 13 FA(n+1).13 0.168627
# 1356 USER150 9 14 FA(n+1).14 0.168627
# 1357 USER150 9 15 FA(n+1).15 0.168627
# 1358 USER150 9 16 FA(n+1).16 0.141176
# 1359 USER150 9 17 FA(n+1).17 NaN
reshape 数据范围 (结果为 10 行 X 138 列)
from itertools import product # TO RE-ORDER COLUMNS USING reindex
...
pvt_df = (mdf.pivot_table(index = ['subject', 'SL_NO'],
columns = 'variable',
values = 'value',
aggfunc = 'max')
.reindex([j+str(i) for i,j in list(product(list(range(1,18)), new_cols))],
axis = 1)
)
print(pvt_df)
# subject SL_NO KDT.1 PPT.1 PRT.1 RPT.1 RRT.1 KDT(n+1).1 FA.1 FA(n+1).1 KDT.2 PPT.2 PRT.2 RPT.2 RRT.2 KDT(n+1).2 FA.2 FA(n+1).2 KDT.3 PPT.3 PRT.3 RPT.3 RRT.3 KDT(n+1).3 FA.3 FA(n+1).3 KDT.4 PPT.4 PRT.4 RPT.4 RRT.4 KDT(n+1).4 FA.4 FA(n+1).4 KDT.5 PPT.5 PRT.5 RPT.5 RRT.5 KDT(n+1).5 FA.5 FA(n+1).5 KDT.6 PPT.6 PRT.6 RPT.6 RRT.6 KDT(n+1).6 FA.6 FA(n+1).6 KDT.7 PPT.7 PRT.7 RPT.7 RRT.7 KDT(n+1).7 FA.7 FA(n+1).7 KDT.8 PPT.8 PRT.8 RPT.8 RRT.8 KDT(n+1).8 FA.8 FA(n+1).8 KDT.9 PPT.9 PRT.9 RPT.9 RRT.9 KDT(n+1).9 FA.9 FA(n+1).9 KDT.10 PPT.10 PRT.10 RPT.10 RRT.10 KDT(n+1).10 FA.10 FA(n+1).10 KDT.11 PPT.11 PRT.11 RPT.11 RRT.11 KDT(n+1).11 FA.11 FA(n+1).11 KDT.12 PPT.12 PRT.12 RPT.12 RRT.12 KDT(n+1).12 FA.12 FA(n+1).12 KDT.13 PPT.13 PRT.13 RPT.13 RRT.13 KDT(n+1).13 FA.13 FA(n+1).13 KDT.14 PPT.14 PRT.14 RPT.14 RRT.14 KDT(n+1).14 FA.14 FA(n+1).14 KDT.15 PPT.15 PRT.15 RPT.15 RRT.15 KDT(n+1).15 FA.15 FA(n+1).15 KDT.16 PPT.16 PRT.16 RPT.16 RRT.16 KDT(n+1).16 FA.16 FA(n+1).16 KDT.17 PPT.17 PRT.17 RPT.17 RRT.17 KDT(n+1).17 FA.17 FA(n+1).17
# USER150 0 80000000 310000000 390000000 230000000 390000000 80000000 0.16862746 0.16862746 80000000 440000000 490000000 360000000 490000000 50000000 0.16862746 0.14117648 50000000 300000000 410000000 250000000 410000000 110000000 0.14117648 0.16470589 110000000 300000000 390000000 190000000 390000000 90000000 0.16470589 0.1764706 90000000 170000000 250000000 80000000 250000000 80000000 0.1764706 0.16470589 80000000 180000000 260000000 100000000 260000000 80000000 0.16470589 0.16862746 80000000 310000000 410000000 230000000 410000000 100000000 0.16862746 0.16470589 100000000 320000000 410000000 220000000 410000000 90000000 0.16470589 0.18431373 90000000 330000000 420000000 240000000 420000000 90000000 0.18431373 0.16470589 90000000 460000000 540000000 370000000 540000000 80000000 0.16470589 0.16862746 80000000 270000000 350000000 190000000 350000000 80000000 0.16862746 0.16470589 80000000 320000000 430000000 240000000 430000000 110000000 0.16470589 0.16078432 110000000 640000000 730000000 530000000 730000000 90000000 0.16078432 0.15686275 90000000 200000000 280000000 110000000 280000000 80000000 0.15686275 0.16078432 80000000 220000000 290000000 140000000 290000000 70000000 0.16078432 0.18431373 70000000 1810000000 1880000000 1740000000 1880000000 70000000 0.18431373 0.16470589 70000000 9160000000 9230000000 9090000000 9230000000 70000000 0.16470589 0.19607845
# USER150 1 70000000 250000000 340000000 180000000 340000000 90000000 0.19607845 0.19607845 90000000 420000000 480000000 330000000 480000000 60000000 0.19607845 0.15294118 60000000 420000000 500000000 360000000 500000000 80000000 0.15294118 0.16862746 80000000 250000000 330000000 170000000 330000000 80000000 0.16862746 0.1764706 80000000 160000000 260000000 80000000 260000000 100000000 0.1764706 0.18431373 100000000 180000000 270000000 80000000 270000000 90000000 0.18431373 0.1764706 90000000 300000000 390000000 210000000 390000000 90000000 0.1764706 0.18039216 90000000 330000000 410000000 240000000 410000000 80000000 0.18039216 0.18431373 80000000 330000000 400000000 250000000 400000000 70000000 0.18431373 0.1764706 70000000 510000000 610000000 440000000 610000000 100000000 0.1764706 0.18431373 100000000 250000000 330000000 150000000 330000000 80000000 0.18431373 0.18039216 80000000 270000000 370000000 190000000 370000000 100000000 0.18039216 0.15686275 100000000 530000000 590000000 430000000 590000000 60000000 0.15686275 0.16078432 60000000 220000000 300000000 160000000 300000000 80000000 0.16078432 0.1764706 80000000 210000000 290000000 130000000 290000000 80000000 0.1764706 0.17254902 80000000 1100000000 1180000000 1020000000 1180000000 80000000 0.17254902 0.14117648 80000000 700000000 780000000 620000000 780000000 80000000 0.14117648 0.1764706
# USER150 2 80000000 330000000 410000000 250000000 410000000 80000000 0.1764706 0.19215688 80000000 410000000 470000000 330000000 470000000 60000000 0.19215688 0.1254902 60000000 420000000 510000000 360000000 510000000 90000000 0.1254902 0.16470589 90000000 470000000 550000000 380000000 550000000 80000000 0.16470589 0.16862746 80000000 170000000 270000000 90000000 270000000 100000000 0.16862746 0.17254902 100000000 240000000 300000000 140000000 300000000 60000000 0.17254902 0.1764706 60000000 240000000 350000000 180000000 350000000 110000000 0.1764706 0.17254902 110000000 370000000 450000000 260000000 450000000 80000000 0.17254902 0.18039216 80000000 220000000 320000000 140000000 320000000 100000000 0.18039216 0.16862746 100000000 380000000 490000000 280000000 490000000 110000000 0.16862746 0.16862746 110000000 420000000 490000000 310000000 490000000 70000000 0.16862746 0.18039216 70000000 290000000 380000000 220000000 380000000 90000000 0.18039216 0.16470589 90000000 420000000 500000000 330000000 500000000 80000000 0.16470589 0.17254902 80000000 200000000 280000000 120000000 280000000 80000000 0.17254902 0.1764706 80000000 210000000 290000000 130000000 290000000 80000000 0.1764706 0.19215688 80000000 610000000 700000000 530000000 700000000 90000000 0.19215688 0.17254902 90000000 690000000 760000000 600000000 760000000 70000000 0.17254902 0.1764706
# USER150 3 70000000 320000000 420000000 250000000 420000000 100000000 0.1764706 0.18823531 100000000 410000000 490000000 310000000 490000000 80000000 0.18823531 0.15294118 80000000 490000000 580000000 410000000 580000000 90000000 0.15294118 0.16470589 90000000 500000000 580000000 410000000 580000000 80000000 0.16470589 0.18431373 80000000 220000000 300000000 140000000 300000000 80000000 0.18431373 0.1764706 80000000 250000000 340000000 170000000 340000000 90000000 0.1764706 0.17254902 90000000 270000000 380000000 180000000 380000000 110000000 0.17254902 0.18039216 110000000 340000000 420000000 230000000 420000000 80000000 0.18039216 0.1764706 80000000 320000000 400000000 240000000 400000000 80000000 0.1764706 0.17254902 80000000 760000000 840000000 680000000 840000000 80000000 0.17254902 0.16862746 80000000 250000000 330000000 170000000 330000000 80000000 0.16862746 0.16470589 80000000 290000000 370000000 210000000 370000000 80000000 0.16470589 0.15294118 80000000 870000000 950000000 790000000 950000000 80000000 0.15294118 0.16862746 80000000 220000000 300000000 140000000 300000000 80000000 0.16862746 0.1764706 80000000 200000000 280000000 120000000 280000000 80000000 0.1764706 0.18823531 80000000 690000000 780000000 610000000 780000000 90000000 0.18823531 0.17254902 90000000 660000000 730000000 570000000 730000000 70000000 0.17254902 0.17254902
# USER150 4 70000000 310000000 390000000 240000000 390000000 80000000 0.17254902 0.1764706 80000000 340000000 410000000 260000000 410000000 70000000 0.1764706 0.15686275 70000000 950000000 1030000000 880000000 1030000000 80000000 0.15686275 0.16862746 80000000 500000000 560000000 420000000 560000000 60000000 0.16862746 0.17254902 60000000 190000000 270000000 130000000 270000000 80000000 0.17254902 0.18039216 80000000 280000000 360000000 200000000 360000000 80000000 0.18039216 0.1764706 80000000 270000000 370000000 190000000 370000000 100000000 0.1764706 0.17254902 100000000 350000000 460000000 250000000 460000000 110000000 0.17254902 0.1764706 110000000 330000000 410000000 220000000 410000000 80000000 0.1764706 0.15686275 80000000 620000000 680000000 540000000 680000000 60000000 0.15686275 0.16862746 60000000 280000000 340000000 220000000 340000000 60000000 0.16862746 0.17254902 60000000 270000000 360000000 210000000 360000000 90000000 0.17254902 0.15686275 90000000 950000000 1030000000 860000000 1030000000 80000000 0.15686275 0.16078432 80000000 200000000 280000000 120000000 280000000 80000000 0.16078432 0.17254902 80000000 190000000 270000000 110000000 270000000 80000000 0.17254902 0.18039216 80000000 560000000 640000000 480000000 640000000 80000000 0.18039216 0.16078432 80000000 690000000 770000000 610000000 770000000 80000000 0.16078432 0.18039216
# USER150 5 80000000 1090000000 1150000000 1010000000 1150000000 60000000 0.18039216 0.18039216 60000000 420000000 480000000 360000000 480000000 60000000 0.18039216 0.15686275 60000000 390000000 490000000 330000000 490000000 100000000 0.15686275 0.16862746 100000000 330000000 410000000 230000000 410000000 80000000 0.16862746 0.16078432 80000000 170000000 270000000 90000000 270000000 100000000 0.16078432 0.17254902 100000000 230000000 320000000 130000000 320000000 90000000 0.17254902 0.1764706 90000000 250000000 340000000 160000000 340000000 90000000 0.1764706 0.18431373 90000000 300000000 410000000 210000000 410000000 110000000 0.18431373 0.1764706 110000000 330000000 410000000 220000000 410000000 80000000 0.1764706 0.16862746 80000000 410000000 500000000 330000000 500000000 90000000 0.16862746 0.18823531 90000000 500000000 560000000 410000000 560000000 60000000 0.18823531 0.16862746 60000000 310000000 410000000 250000000 410000000 100000000 0.16862746 0.16470589 100000000 590000000 670000000 490000000 670000000 80000000 0.16470589 0.16078432 80000000 210000000 300000000 130000000 300000000 90000000 0.16078432 0.1764706 90000000 220000000 280000000 130000000 280000000 60000000 0.1764706 0.19215688 60000000 420000000 500000000 360000000 500000000 80000000 0.19215688 0.16470589 80000000 740000000 810000000 660000000 810000000 70000000 0.16470589 0.16078432
# USER150 6 70000000 350000000 420000000 280000000 420000000 70000000 0.16078432 0.18431373 70000000 470000000 520000000 400000000 520000000 50000000 0.18431373 0.15686275 50000000 580000000 660000000 530000000 660000000 80000000 0.15686275 0.17254902 80000000 430000000 510000000 350000000 510000000 80000000 0.17254902 0.18431373 80000000 190000000 270000000 110000000 270000000 80000000 0.18431373 0.19215688 80000000 220000000 300000000 140000000 300000000 80000000 0.19215688 0.19607845 80000000 240000000 320000000 160000000 320000000 80000000 0.19607845 0.1764706 80000000 280000000 380000000 200000000 380000000 100000000 0.1764706 0.17254902 100000000 350000000 410000000 250000000 410000000 60000000 0.17254902 0.16470589 60000000 360000000 440000000 300000000 440000000 80000000 0.16470589 0.17254902 80000000 280000000 360000000 200000000 360000000 80000000 0.17254902 0.17254902 80000000 290000000 360000000 210000000 360000000 70000000 0.17254902 0.16470589 70000000 760000000 840000000 690000000 840000000 80000000 0.16470589 0.15294118 80000000 220000000 300000000 140000000 300000000 80000000 0.15294118 0.1764706 80000000 220000000 280000000 140000000 280000000 60000000 0.1764706 0.18039216 60000000 510000000 560000000 450000000 560000000 50000000 0.18039216 0.10196079 50000000 780000000 860000000 730000000 860000000 80000000 0.10196079 0.16078432
# USER150 7 80000000 380000000 460000000 300000000 460000000 80000000 0.16078432 0.18039216 80000000 450000000 530000000 370000000 530000000 80000000 0.18039216 0.15294118 80000000 610000000 700000000 530000000 700000000 90000000 0.15294118 0.16470589 90000000 370000000 450000000 280000000 450000000 80000000 0.16470589 0.17254902 80000000 210000000 290000000 130000000 290000000 80000000 0.17254902 0.16862746 80000000 240000000 310000000 160000000 310000000 70000000 0.16862746 0.18039216 70000000 250000000 340000000 180000000 340000000 90000000 0.18039216 0.18039216 90000000 390000000 450000000 300000000 450000000 60000000 0.18039216 0.18039216 60000000 270000000 350000000 210000000 350000000 80000000 0.18039216 0.18039216 80000000 390000000 520000000 310000000 520000000 130000000 0.18039216 0.17254902 130000000 780000000 860000000 650000000 860000000 80000000 0.17254902 0.16862746 80000000 310000000 410000000 230000000 410000000 100000000 0.16862746 0.1764706 100000000 460000000 520000000 360000000 520000000 60000000 0.1764706 0.16470589 60000000 200000000 280000000 140000000 280000000 80000000 0.16470589 0.1764706 80000000 190000000 270000000 110000000 270000000 80000000 0.1764706 0.18823531 80000000 470000000 560000000 390000000 560000000 90000000 0.18823531 0.16078432 90000000 950000000 1030000000 860000000 1030000000 80000000 0.16078432 0.17254902
# USER150 8 80000000 400000000 480000000 320000000 480000000 80000000 0.17254902 0.1764706 80000000 410000000 490000000 330000000 490000000 80000000 0.1764706 0.16470589 80000000 520000000 610000000 440000000 610000000 90000000 0.16470589 0.1764706 90000000 450000000 530000000 360000000 530000000 80000000 0.1764706 0.1764706 80000000 220000000 320000000 140000000 320000000 100000000 0.1764706 0.18431373 100000000 300000000 360000000 200000000 360000000 60000000 0.18431373 0.16862746 60000000 310000000 410000000 250000000 410000000 100000000 0.16862746 0.18039216 100000000 400000000 470000000 300000000 470000000 70000000 0.18039216 0.18431373 70000000 360000000 420000000 290000000 420000000 60000000 0.18431373 0.16470589 60000000 450000000 530000000 390000000 530000000 80000000 0.16470589 0.1764706 80000000 230000000 310000000 150000000 310000000 80000000 0.1764706 0.17254902 80000000 320000000 380000000 240000000 380000000 60000000 0.17254902 0.14509805 60000000 530000000 610000000 470000000 610000000 80000000 0.14509805 0.17254902 80000000 200000000 280000000 120000000 280000000 80000000 0.17254902 0.17254902 80000000 240000000 340000000 160000000 340000000 100000000 0.17254902 0.18823531 100000000 560000000 660000000 460000000 660000000 100000000 0.18823531 0.16862746 100000000 7050000000 7110000000 6950000000 7110000000 60000000 0.16862746 0.1764706
# USER150 9 60000000 970000000 1040000000 910000000 1040000000 70000000 0.1764706 0.16862746 70000000 450000000 530000000 380000000 530000000 80000000 0.16862746 0.16470589 80000000 450000000 530000000 370000000 530000000 80000000 0.16470589 0.18039216 80000000 390000000 470000000 310000000 470000000 80000000 0.18039216 0.1764706 80000000 210000000 300000000 130000000 300000000 90000000 0.1764706 0.17254902 90000000 250000000 330000000 160000000 330000000 80000000 0.17254902 0.17254902 80000000 250000000 350000000 170000000 350000000 100000000 0.17254902 0.18039216 100000000 350000000 430000000 250000000 430000000 80000000 0.18039216 0.18431373 80000000 280000000 380000000 200000000 380000000 100000000 0.18431373 0.16470589 100000000 420000000 520000000 320000000 520000000 100000000 0.16470589 0.16470589 100000000 680000000 740000000 580000000 740000000 60000000 0.16470589 0.1764706 60000000 230000000 330000000 170000000 330000000 100000000 0.1764706 0.17254902 100000000 890000000 970000000 790000000 970000000 80000000 0.17254902 0.16862746 80000000 730000000 810000000 650000000 810000000 80000000 0.16862746 0.16862746 80000000 470000000 530000000 390000000 530000000 60000000 0.16862746 0.16862746 60000000 760000000 810000000 700000000 810000000 50000000 0.16862746 0.14117648 50000000 0.14117648
关于python - 无法使用 Pandas DataFrame 对象上的循环获得正确的 DataFrame 形状(行*列)，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/59156509/