Python：Pandas：将行值与列名/键值匹配

Question

Problem
"How to match a row value to a column name and take that intersecting value in pandas"

Context
We have a pandas df like this:

df = pd.DataFrame([{'name': 'john', 'john': 1, 'mac': 10}, {'name': 'mac', 'john': 2, 'mac': 20}], columns=["name", "john", "mac"])

Looking like this:

name | john | mac
john |  1   | 10
mac  |  2   | 20

Desired output

name | john | mac  | value
john |  1   | 10   | 1
mac  |  2   | 20   | 20

In words, the column "value" should take the number from the corresponding column where name intersects.

So, if name == 'john' , then take the value from column 'john'
So, if name == 'mac' , then take the value from column 'mac'

Tried so far
Bunch of lambdas (none successful).

Specifications
Python: 3.5.2
Pandas: 0.18.1

Answer 1

You could use DataFrame.lookup , which accepts the row and column labels to use:

In [66]: df
Out[66]: 
   name  john  mac
0  john     1   10
1   mac     2   20

In [67]: df["value"] = df.lookup(df.index, df.name)

In [68]: df
Out[68]: 
   name  john  mac  value
0  john     1   10      1
1   mac     2   20     20

Note that this will have problems with duplicate row labels (which could be trivially worked around by adding a reset_index). It should be faster than calling apply , which can be pretty slow, but if your frames aren't too large both should work well enough.

Answer 2

well imo lambda is the way to go, but you can make it very short such has:

df = pd.DataFrame([{'name': 'john', 'john': 5, 'mac': 10}, {'name': 'mac', 'john': 10, 'mac': 15}], columns=["name", "john", "mac"])
df = df.set_index('name')
df
Out[64]: 
      john  mac
name           
john     5   10
mac     10   15

df['values'] = df.apply(lambda x: x[x.name], axis=1)
In[68]: df
Out[68]: 
      john  mac  values
name                   
john     5   10       5
mac     10   15      15

I did set the index to name for convinience but you could do it without it such has:

df = pd.DataFrame([{'name': 'john', 'john': 5, 'mac': 10}, {'name': 'mac', 'john': 10, 'mac': 15}], columns=["name", "john", "mac"])
df['values'] = df.apply(lambda x: x[x['name']], axis=1)
df
Out[71]: 
   name  john  mac  values
0  john     5   10       5
1   mac    10   15      15