用通过比较另一列的数据获得的数据填充一列

Filling a column with data obtained by comparing data from another column

我有一个table。我正在创建一个新专栏 "Timing"。我需要用数字填充它,具体取决于列 "type" 中的数据。例如,如果 "type" 列的单元格中是 dgv,那么 "timing" 列中应该有一个数字 17,如果是 ds,则为 8,如果是 psp,则为 3,等等。有几个条件。

part of the table 等等

我的代码:

import csv

with open('C:/Notebook/data1.txt','r') as csvinput:
    with open('C:/Notebook/datawr1.txt', 'w') as csvoutput:
        writer = csv.writer(csvoutput, lineterminator='\n')
        reader = csv.reader(csvinput)

        all = []
        row = next(reader)
        row.append('Timing') # Here I create a column "Timing"
        all.append(row)

        for row in reader:  #I think here should be a condition if
            row.append(' ') 
            all.append(row)           


        writer.writerows(all)

我想你可以通过字典d使用map,如果不匹配得到NaN:

df = pd.DataFrame({'type':['dgv','ds','psp', 'a']})
print (df)
  type
0  dgv
1   ds
2  psp
3    a

d = {'dgv':17,'ds':8,'psp':3}
df['Timing'] = df['type'].map(d)
print (df)
  type  Timing
0  dgv    17.0
1   ds     8.0
2  psp     3.0
3    a     NaN

编辑:

在pandas中读取文件是使用read_csv, for writing to_csv(如果是.txt文件没问题):

import pandas as pd
from pandas.compat import StringIO

temp=u"""code,type,date,quantity
0,dgv,07.11.2016,1
0,dgv,08.06.2016,1
0,ds,01.07.2016,1
0,ds,03.08.2016,1
0,ds,03.08.2016,1
0,psp,06.03.2016,1
0,a,07.08.2016,1"""
#after testing replace 'StringIO(temp)' to 'filename.txt'
df = pd.read_csv(StringIO(temp))
print (df)
   code type        date  quantity
0     0  dgv  07.11.2016         1
1     0  dgv  08.06.2016         1
2     0   ds  01.07.2016         1
3     0   ds  03.08.2016         1
4     0   ds  03.08.2016         1
5     0  psp  06.03.2016         1
6     0    a  07.08.2016         1

d = {'dgv':17,'ds':8,'psp':3}
df['Timing'] = df['type'].map(d)
print (df)
   code type        date  quantity  Timing
0     0  dgv  07.11.2016         1    17.0
1     0  dgv  08.06.2016         1    17.0
2     0   ds  01.07.2016         1     8.0
3     0   ds  03.08.2016         1     8.0
4     0   ds  03.08.2016         1     8.0
5     0  psp  06.03.2016         1     3.0
6     0    a  07.08.2016         1     NaN

df.to_csv('myfile.txt', index=False)