[英]Append to a new csv from other csv columns in python
我有8個.csv文件,其中包含16列和沒有標題的“ n”行。 我想解析每個.csv並獲取列[0,8] [#,其中列0是x,y,z等的值,列8始終是a#的值],然后將數據放入新的列。 CSV。 完成此操作后,new.csv應該具有16列(每個input.csv中有2列)和“ n”行。
現在,我只想從new.csv中獲取Column [1,3,5,7,9,11,13,15]的平均值,並將其附加到另一個文件或該文件中。 基本上,在新的csv中,我希望形成輸入文件的colum [8]和輸入文件中的每一column [0]的平均值。 因此,期望的最終輸出應具有9列和n行的形狀。 樣本輸入文件:
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
然后我要對所有16列進行循環(將以下集合設為[n,n + 8],其中n = 0到7。
抱歉,冗長的說明,但是我似乎無法在python中追加這些列。 提前致謝。
++++++++++++++++++++++++++++++++++++++++++++++++++ +++++++++++++++++++++++++++++這是我開始的示例代碼:
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= ",")
但是運行此命令后,出現錯誤:
> 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
這將合並行並將它們寫入新的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)
將數據傳遞給熊貓:
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)
輸出:
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
得到每一行的平均值:
print(df.mean(1))
輸出:
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
添加該列:
df[3] = df.mean(1)
print(df)
輸出:
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=" ")
輸出:
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
如果您不希望名稱和行索引:
df.to_csv("new.csv",sep=" ",index=False,header=False)
輸出:
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
嘗試熊貓。 在眾多好處中,將包括顯式的列標簽和列切片。
尚未完全復制,但也許我們可以努力開發出完整的熊貓解決方案。
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()
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