在熊猫数据框中将字符串转换为整数

Question

I have a dataset like below:

                Name       ARowss   TotalRowss        Percentage
                motors        11           11               100 
                trck1         2             2               100 
                trck2         2             2               100 
                hydr1         4             4               100
                gas1          2             2               100

I am doing some cleanup of data for which I have to assign a new number to each value in "Name". All values are unique in "Name" column. So, from the above dataset, "motors" should have 1, "trck1" should have 2, "trck2" should have 3 and so on.

Answer 1

Is this what you want?

In [5]: df['id'] = pd.factorize(df.Name)[0]

In [6]: df
Out[6]:
     Name  ARowss  TotalRowss  Percentage  id
0  motors      11          11         100   0
1   trck1       2           2         100   1
2   trck2       2           2         100   2
3   hydr1       4           4         100   3
4    gas1       2           2         100   4

or this, depending on your goals:

In [10]: df.Name = pd.factorize(df.Name)[0] + 1

In [11]: df
Out[11]:
   Name  ARowss  TotalRowss  Percentage
0     1      11          11         100
1     2       2           2         100
2     3       2           2         100
3     4       4           4         100
4     5       2           2         100

It will also work for non-unique values:

In [15]: df
Out[15]:
     Name  ARowss  TotalRowss  Percentage
0  motors      11          11         100
1   trck1       2           2         100
2   trck2       2           2         100
3   hydr1       4           4         100
4    gas1       2           2         100  # duplicates in `Name`
5    gas1       2           3         111  # 

In [16]: df.Name = pd.factorize(df.Name)[0] + 1

In [17]: df
Out[17]:
   Name  ARowss  TotalRowss  Percentage
0     1      11          11         100
1     2       2           2         100
2     3       2           2         100
3     4       4           4         100
4     5       2           2         100  #
5     5       2           3         111  #