我有8个带有16列的.csv文件和没有Header的“n”行。我想解析每个.csv并获取列[0,8] [#where列0是x，y，z等的值，第8列始终是＃的值]并将数据放入新的。 CSV。完成后，new.csv应该有16列（每个input.csv有2列）和“n”行。
现在，我想从new.csv中取出Column [1,3,5,7,9,11,13,15]的平均值并将其附加到另一个文件或此文件中。基本上在新的csv中，我希望从输入文件中获得colum [8]的平均值和输入文件中的每个列[0]。所以期望的最终输出应该具有9列和n行的形状。 示例输入文件：
a.csv:
5.42E+05 6.52E+05 2.17E+04 2.73E+04 2.58E+04 2.33E+04 2.81E+04 3.37E+04 1.08E+08 1.10E+08 2.54E+05 3.21E+05 2.99E+05 2.74E+05 3.39E+05 4.07E+05
4.64E+04 1.15E+06 1.96E+04 2.53E+04 2.39E+04 2.37E+04 1.98E+04 2.85E+04 6.18E+05 2.17E+08 2.30E+05 3.02E+05 2.75E+05 2.77E+05 2.33E+05 3.42E+05
4.36E+04 1.13E+06 5.72E+04 2.71E+04 2.77E+04 2.37E+04 2.62E+04 7.35E+04 5.78E+05 2.17E+08 9.26E+05 3.25E+05 3.20E+05 2.72E+05 3.20E+05 1.46E+06
4.32E+04 1.02E+06 1.47E+05 2.63E+04 3.05E+04 2.26E+04 2.89E+04 2.45E+04 5.70E+05 2.15E+08 2.78E+06 3.02E+05 3.58E+05 2.63E+05 3.49E+05 2.87E+05
4.44E+04 7.83E+05 1.58E+05 2.95E+04 2.71E+05 2.71E+04 3.67E+04 2.85E+04 5.86E+05 1.61E+08 2.89E+06 3.48E+05 5.39E+07 3.14E+05 4.49E+05 3.39E+05
1.47E+05 1.02E+06 2.09E+04 2.72E+04 2.66E+04 6.18E+04 3.50E+04 3.00E+04 2.72E+06 2.15E+08 2.46E+05 3.18E+05 3.07E+05 9.91E+05 7.18E+05 3.71E+05
1.81E+05 7.67E+05 1.94E+04 5.05E+04 2.62E+04 4.50E+04 2.92E+04 2.86E+04 3.16E+06 1.61E+08 2.28E+05 4.84E+06 3.10E+05 5.31E+06 3.49E+05 3.58E+05
4.94E+05 1.34E+05 6.99E+04 8.76E+05 5.51E+04 5.27E+04 3.34E+05 1.30E+05 1.35E+07 3.59E+06 1.66E+06 1.64E+08 1.03E+06 1.12E+06 5.56E+07 3.37E+06
4.79E+04 1.38E+05 2.66E+04 1.02E+06 2.85E+04 2.88E+04 2.89E+04 3.26E+04 6.12E+05 2.72E+06 3.21E+05 2.15E+08 3.29E+05 3.39E+05 3.40E+05 4.04E+05
4.51E+04 6.44E+05 3.02E+04 5.24E+05 2.72E+04 1.89E+04 2.42E+04 3.21E+04 5.97E+05 1.10E+08 3.65E+05 1.07E+08 3.17E+05 2.17E+05 2.85E+05 3.80E+05
b.csv:
4.25E+03 1.83E+03 1.09E+03 1.35E+03 1.18E+03 1.24E+03 1.16E+03 1.28E+03 1.08E+08 1.10E+08 2.51E+05 3.13E+05 2.80E+05 2.64E+05 3.23E+05 3.32E+05
4.47E+03 2.20E+03 1.16E+03 1.46E+03 1.28E+03 1.21E+03 1.17E+03 1.36E+03 6.01E+05 2.17E+08 2.92E+05 3.59E+05 3.34E+05 2.84E+05 3.14E+05 3.86E+05
5.12E+03 1.85E+03 1.62E+03 1.59E+03 1.93E+03 1.36E+03 1.36E+03 1.42E+03 7.19E+05 2.16E+08 1.60E+06 7.14E+06 7.10E+05 8.74E+05 8.67E+05 1.37E+06
4.32E+03 1.53E+03 2.03E+03 1.11E+03 1.18E+03 1.18E+03 1.52E+03 1.18E+03 5.81E+05 2.15E+08 2.70E+06 2.84E+05 3.24E+05 3.12E+05 4.25E+05 3.65E+05
4.64E+03 1.53E+03 2.07E+03 1.15E+03 1.15E+03 1.25E+03 1.50E+03 1.13E+03 1.17E+06 1.61E+08 2.74E+06 2.98E+05 2.82E+05 5.38E+07 4.16E+05 3.41E+05
5.03E+03 1.61E+03 1.17E+03 1.15E+03 1.02E+03 1.12E+03 1.40E+03 1.43E+03 2.56E+06 2.16E+08 2.37E+05 2.57E+05 2.43E+05 2.65E+05 4.03E+05 4.43E+05
5.11E+03 1.37E+03 1.24E+03 1.20E+03 1.21E+03 1.10E+03 1.28E+03 1.34E+03 3.09E+06 1.61E+08 2.84E+05 2.93E+05 2.91E+05 2.34E+05 5.40E+07 3.07E+05
5.79E+03 2.51E+03 2.15E+03 2.21E+03 3.57E+03 1.67E+03 2.61E+03 2.28E+03 3.08E+06 4.98E+06 3.60E+06 1.63E+08 7.06E+06 1.95E+06 5.74E+07 3.44E+06
4.49E+03 1.88E+03 1.22E+03 1.47E+03 1.23E+03 1.04E+03 1.42E+03 1.37E+03 6.11E+05 2.67E+06 2.93E+05 2.15E+08 3.31E+05 2.26E+05 4.13E+05 3.53E+05
4.50E+03 2.22E+03 1.40E+03 1.34E+03 1.26E+03 1.22E+03 1.18E+03 1.35E+03 6.43E+05 1.10E+08 3.31E+05 1.07E+08 3.50E+05 3.29E+05 3.69E+05 4.26E+05
c.csv:
1.30E+06 4.34E+05 4.66E+04 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 1.62E+08 5.65E+07 6.02E+06 3.24E+05 3.55E+05 2.83E+05 3.41E+05 4.05E+05
0.00E+00 1.74E+06 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 5.61E+05 2.17E+08 3.12E+05 3.34E+05 2.83E+05 2.83E+05 3.01E+05 3.45E+05
0.00E+00 1.74E+06 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 6.08E+05 2.17E+08 8.92E+05 3.47E+05 3.43E+05 2.22E+05 3.64E+05 2.38E+05
0.00E+00 1.74E+06 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 5.61E+05 2.15E+08 2.90E+06 3.35E+05 3.08E+05 5.85E+05 3.60E+05 3.81E+05
0.00E+00 1.74E+06 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 5.45E+05 2.15E+08 2.90E+06 3.11E+05 3.06E+05 2.88E+05 3.73E+05 3.10E+05
0.00E+00 1.30E+06 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 9.22E+04 4.90E+06 1.65E+08 8.92E+05 3.07E+06 1.37E+06 3.40E+06 1.53E+06 1.52E+07
0.00E+00 1.74E+06 0.00E+00 4.69E+04 0.00E+00 0.00E+00 0.00E+00 0.00E+00 3.09E+06 2.15E+08 3.08E+05 6.15E+06 3.48E+05 3.63E+05 3.85E+05 4.12E+05
0.00E+00 0.00E+00 0.00E+00 1.31E+06 0.00E+00 0.00E+00 4.36E+05 0.00E+00 3.06E+06 1.35E+06 2.31E+05 1.61E+08 2.89E+05 2.05E+05 5.41E+07 1.77E+06
0.00E+00 0.00E+00 0.00E+00 1.74E+06 0.00E+00 0.00E+00 0.00E+00 0.00E+00 6.69E+05 2.27E+06 3.02E+05 2.16E+08 3.27E+05 3.08E+05 3.50E+05 3.75E+05
0.00E+00 8.69E+05 0.00E+00 8.71E+05 0.00E+00 0.00E+00 0.00E+00 0.00E+00 6.68E+05 1.10E+08 3.07E+05 1.08E+08 3.67E+05 2.34E+05 3.71E+05 3.78
Final expected output (after averaging column 8):
5.42E+05 4.25E+03 1.30E+06 125650487
4.64E+04 4.47E+03 0.00E+00 593233.3333
4.36E+04 5.12E+03 0.00E+00 634780
4.32E+04 4.32E+03 0.00E+00 570865
4.44E+04 4.64E+03 0.00E+00 766418
1.47E+05 5.03E+03 0.00E+00 3393342.667
1.81E+05 5.11E+03 0.00E+00 3113608.333
4.94E+05 5.79E+03 0.00E+00 6532673.333
4.79E+04 4.49E+03 0.00E+00 630900.3333
4.51E+04 4.50E+03 0.00E+00 636023
然后我要为所有16列进行循环（将以下集合作为[n，n + 8]，其中n = 0到7。
为冗长的描述道歉，但我似乎无法在python中附加列。 提前谢谢。
+++++++++++++++++++++++++++++++++++++++++++++++ ++++++++++++++++++++++++++++++ 以下是我开始使用的示例代码：
import csv
import numpy as np
import sys
import pandas as pd
import glob
damn = ("a", "b", "c","e","f","g","h","i")
data = []
for fles in range(len(damn)):
core0data = np.genfromtxt('./%s_raw_combine.csv'%damn[fles], dtype=float, delimiter=',',usecols=(0,8))
#core1data = np.genfromtxt('./%s_raw_combine.csv'%damn[fles], dtype=float, delimiter=',',usecols=(1,9))
#core2data = np.genfromtxt('./%s_raw_combine.csv'%damn[fles], dtype=float, delimiter=',',usecols=(2,10))
#core3data = np.genfromtxt('./%s_raw_combine.csv'%damn[fles], dtype=float, delimiter=',',usecols=(3,11))
#core4data = np.genfromtxt('./%s_raw_combine.csv'%damn[fles], dtype=float, delimiter=',',usecols=(4,12))
#core5data = np.genfromtxt('./%s_raw_combine.csv'%damn[fles], dtype=float, delimiter=',',usecols=(5,13))
#core6data = np.genfromtxt('./%s_raw_combine.csv'%damn[fles], dtype=float, delimiter=',',usecols=(6,14))
#core7data = np.genfromtxt('./%s_raw_combine.csv'%damn[fles], dtype=float, delimiter=',',usecols=(7,15))
data.append(core0data)
np.savetxt("writer.csv", data, delimiter= ",")
但是在运行之后，我收到错误：
> python2.7 new.py
Traceback (most recent call last):
File "new.py", line 20, in <module>
np.savetxt("writer.csv", data, delimiter= ",")
File "~/anaconda/lib/python2.7/site-packages/numpy/lib/npyio.py", line 1083, in savetxt
fh.write(asbytes(format % tuple(row) + newline))
TypeError: float argument required, not numpy.ndarray
答案 0 :(得分：2)
这将连接行并将它们写入新的csv：
import csv
from glob import iglob
from itertools import chain
data = []
for file in iglob("*.csv"):
with open(file) as f:
r = csv.reader(f)
data.append(list(chain.from_iterable((float(row[0]), float(row[8])) for row in r)))
with open("new.csv","w") as out:
wr = csv.writer(out)
wr.writerows(zipped)
将数据传递给pandas：
data = []
for file in iglob("*.csv"):
with open(file) as f:
r = csv.reader(f)
data.append(list(chain.from_iterable((float(row[0]), float(row[8])) for row in r)))
zipped = zip(*data)
import pandas as pd
df = pd.DataFrame(zipped)
print(df[0].mean())
print(df[1].mean())
print(df[2].mean())
print(df)
输出：
69610.0
3093.0
103830.0
0 1 2
0 1300000 4250 542000
1 162000000 108000000 108000000
2 0 4470 46400
3 561000 601000 618000
4 0 5120 43600
5 608000 719000 578000
6 0 4320 43200
7 561000 581000 570000
8 0 4640 44400
9 545000 1170000 586000
10 0 5030 147000
11 4900000 2560000 2720000
12 0 5110 181000
13 3090000 3090000 3160000
14 0 5790 494000
15 3060000 3080000 13500000
16 0 4490 47900
17 669000 611000 612000
18 0 4500 45100
19 668000 643000 597000
得到每行的平均值：
print(df.mean(1))
输出：
0 615416.666667
1 11660.000000
2 16956.666667
3 9953.333333
4 16240.000000
5 24973.333333
6 15840.000000
7 8560.000000
8 16346.666667
9 9876.666667
10 50676.666667
11 41210.000000
12 62036.666667
13 9980.000000
14 166596.666667
15 44093.333333
16 17463.333333
17 11323.333333
18 16533.333333
19 11150.000000
dtype: float64
添加该列：
df[3] = df.mean(1)
print(df)
输出：
0 1 2 3
0 1300000 4250 542000 615416.666667
1 0 1280 33700 11660.000000
2 0 4470 46400 16956.666667
3 0 1360 28500 9953.333333
4 0 5120 43600 16240.000000
5 0 1420 73500 24973.333333
6 0 4320 43200 15840.000000
7 0 1180 24500 8560.000000
8 0 4640 44400 16346.666667
9 0 1130 28500 9876.666667
10 0 5030 147000 50676.666667
11 92200 1430 30000 41210.000000
12 0 5110 181000 62036.666667
13 0 1340 28600 9980.000000
14 0 5790 494000 166596.666667
15 0 2280 130000 44093.333333
16 0 4490 47900 17463.333333
17 0 1370 32600 11323.333333
18 0 4500 45100 16533.333333
19 0 1350 32100 11150.000000
保存到csv：
df.to_("new.csv",sep=" ")
输出：
0 1 2 3
0 1300000.0 4250.0 542000.0 615416.6666666666
1 0.0 1280.0 33700.0 11660.0
2 0.0 4470.0 46400.0 16956.666666666668
3 0.0 1360.0 28500.0 9953.333333333334
4 0.0 5120.0 43600.0 16240.0
5 0.0 1420.0 73500.0 24973.333333333332
6 0.0 4320.0 43200.0 15840.0
7 0.0 1180.0 24500.0 8560.0
8 0.0 4640.0 44400.0 16346.666666666666
9 0.0 1130.0 28500.0 9876.666666666666
10 0.0 5030.0 147000.0 50676.666666666664
11 92200.0 1430.0 30000.0 41210.0
12 0.0 5110.0 181000.0 62036.666666666664
13 0.0 1340.0 28600.0 9980.0
14 0.0 5790.0 494000.0 166596.66666666666
15 0.0 2280.0 130000.0 44093.333333333336
16 0.0 4490.0 47900.0 17463.333333333332
17 0.0 1370.0 32600.0 11323.333333333334
18 0.0 4500.0 45100.0 16533.333333333332
19 0.0 1350.0 32100.0 11150.0
如果您不想要名称和行索引：
df.to_csv("new.csv",sep=" ",index=False,header=False)
输出：
1300000.0 4250.0 542000.0 615416.6666666666
0.0 1280.0 33700.0 11660.0
0.0 4470.0 46400.0 16956.666666666668
0.0 1360.0 28500.0 9953.333333333334
0.0 5120.0 43600.0 16240.0
0.0 1420.0 73500.0 24973.333333333332
0.0 4320.0 43200.0 15840.0
0.0 1180.0 24500.0 8560.0
0.0 4640.0 44400.0 16346.666666666666
0.0 1130.0 28500.0 9876.666666666666
0.0 5030.0 147000.0 50676.666666666664
92200.0 1430.0 30000.0 41210.0
0.0 5110.0 181000.0 62036.666666666664
0.0 1340.0 28600.0 9980.0
0.0 5790.0 494000.0 166596.66666666666
0.0 2280.0 130000.0 44093.333333333336
0.0 4490.0 47900.0 17463.333333333332
0.0 1370.0 32600.0 11323.333333333334
0.0 4500.0 45100.0 16533.333333333332
0.0 1350.0 32100.0 11150.0
答案 1 :(得分：0)
尝试大熊猫。 在许多好处中，将是明确的列标记和列切片。
没有完全复制，但也许我们可以努力雕刻完整的熊猫解决方案。
import pandas as pd
filenames = ['a.csv','b.csv','c.csv']
for i,filename in enumerate(filenames):
df = pd.read_csv(filename,header=None)
df.columns = df.columns + 1 #
filenames[i] = df
dfs = pd.concat(filenames,axis=1)
print dfs.loc[:,[1,8]].head()
cols = [1,3]
print dfs[cols].mean()