Question

我正在尝试编写一个简单的脚本，将csv输出文件从Fortran代码转换为Pandas DataFrame对象，以便我可以进行更多分析。 csv有两列，但由多个附加的数据块组成，形状为[n，2]（每个样本名称的格式为RN_x）。我得到了以下代码，但生成的DataFrame对象不允许分析。我在下面附上了一个示例文件（比原件大很多）。顺便说一下，数据文件中的第一列是日期，但是输出中的数字对应于si = imulation中的一天。任何建议都将不胜感激。

import numpy as np
import pandas as pd
import csv as csv
readdata = csv.reader(open('C:/data/Test.csv', 'r'))
data = []
for row in readdata:
    data.append(row)
a = np.array(data).reshape(11,-1, order = 'F')
col = a[0,:4].reshape(4)
row = pd.Index(a[4:,0:1].reshape(7))
b = a[4:,5:]
df = pd.DataFrame(b, index = row, columns = col)

样品：

RN_48865,
1,Observed
1,0
259,Computed
1,0.000014
91,0.000014
182,0.000014
274,0.000014
366,0.000014
457,0.000014
548,0.000014
RN_7445,
1,Observed
1,0
259,Computed
1,0.000013
91,0.000013
182,0.000013
274,0.000013
366,0.000013
457,0.000013
548,0.000013
RN_9288,
1,Observed
1,0
259,Computed
1,0.000011
91,0.000011
182,0.000011
274,0.000011
366,0.000011
457,0.000011
548,0.000011
RN_10955,
1,Observed
1,0
259,Computed
1,0.000014
91,0.000014
182,0.000014
274,0.000014
366,0.000014
457,0.000014
548,0.000014

示例输出：

Index,RN_48865,RN_7445,RN_9288,RN_10955
1,0.000014,0.000013,0.000011,0.000014
91,0.000014,0.000013,0.000011,0.000014
182,0.000014,0.000013,0.000011,0.000014
274,0.000014,0.000013,0.000011,0.000014
366,0.000014,0.000013,0.000011,0.000014
457,0.000014,0.000013,0.000011,0.000014
548,0.000014,0.000013,0.000011,0.000014

Answer 1

你其实是在问几个问题。这是我从所需输出中可以理解的：

source="""RN_48865,
    1,Observed
    1,0
    259,Computed
    1,0.000014
    91,0.000014
    182,0.000014
    274,0.000014
    366,0.000014
    457,0.000014
    548,0.000014
    RN_7445,
    1,Observed
    1,0
    259,Computed
    1,0.000013
    91,0.000013
    182,0.000013
    274,0.000013
    366,0.000013
    457,0.000013
    548,0.000013
    RN_9288,
    1,Observed
    1,0
    259,Computed
    1,0.000011
    91,0.000011
    182,0.000011
    274,0.000011
    366,0.000011
    457,0.000011
    548,0.000011
    RN_10955,
    1,Observed
    1,0
    259,Computed
    1,0.000014
    91,0.000014
    182,0.000014
    274,0.000014
    366,0.000014
    457,0.000014
    548,0.000014
"""
import pandas as pd
import numpy as np
import StringIO
df = pd.read_csv(StringIO.StringIO(source), header=None)
rns = np.where(df[0].apply(lambda x: x.lstrip().startswith('RN_')))[0]
length = rns[1] - rns[0]
index = df[0].iloc[4:length]
cols = df[0][::length].apply(lambda x: x.lstrip()).values
result_df = pd.DataFrame(index=index)
for col_num, col_start in enumerate(range(0, len(df), length)):
    result_df[cols[col_num]] = df[1][col_num*length+4 : (col_num+1)*length].values
print result_df

输出：

     RN_48865   RN_7445   RN_9288  RN_10955
1    0.000014  0.000013  0.000011  0.000014
91   0.000014  0.000013  0.000011  0.000014
182  0.000014  0.000013  0.000011  0.000014
274  0.000014  0.000013  0.000011  0.000014
366  0.000014  0.000013  0.000011  0.000014
457  0.000014  0.000013  0.000011  0.000014
548  0.000014  0.000013  0.000011  0.000014

日期使用：

pandas.read_csv('file',
  parse_date=0,  # 0th column
  date_parser=lambda x: pandas.Timestamp('1995-1-1')+timedelta(x))

使用numpy或pandas从长格式操作csv文件

1 个答案: