CODEWITHSUNDEEP
pythonpandasdataframe
Joe
Joe

Reputation: 41

Get column value using column name

I have the following data:

    production_type       type_a       type_b     type_c     type_d     
0             type_a     1.173783    0.714846    0.583621        1
1             type_b     1.418876    0.864110    0.705485        1
2             type_c     1.560452    0.950331    0.775878        1
3             type_d     1.750531    1.066091    0.870388        1
4             type_a     1.797883    1.094929    0.893932        1
5             type_a     1.461784    0.890241    0.726819        1
6             type_b     0.941938    0.573650    0.468344        1
7             type_a     0.507370    0.308994    0.252271        1
8             type_c     0.443565    0.270136    0.220547        1
9             type_d     0.426232    0.259579    0.211928        1
10            type_d     0.425379    0.259060    0.211504        1

I would like to create a new column, list or series to that return the value of the column.

OUTPUT 

    production_type       type_a       type_b     type_c     type_d     Results 
0             type_a     1.173783    0.714846    0.583621        1     1.173783    
1             type_b     1.418876    0.864110    0.705485        1     0.864110    
2             type_c     1.560452    0.950331    0.775878        1     0.775878        
3             type_d     1.750531    1.066091    0.870388        1     1
4             type_a     1.797883    1.094929    0.893932        1     1.797883
5             type_a     1.461784    0.890241    0.726819        1     1.461784
6             type_b     0.941938    0.573650    0.468344        1     0.573650

Basically if its written type_a in the column [production_type] I want to return the results of type_a in a column [Results].

Ive tried the following :

for i in df:
    if i == 'type_a':
        print ('type_a')
    elif i == 'type_b':
        print ('type_b')
    elif i == 'type_c':
        print ('type_c')
    elif i == 'type_d':
        print ('type_d')
    else:
        print('')  
    print('')

using result.append

To generate the dataframe use the following : 

list_cols = ['type_a','type_b','type_c']
df = pd.DataFrame(np.random.rand(10, 3), columns = list_cols )
df['production_type']= ['type_a','type_b','type_c','type_d','type_a','type_a','type_b'
                       ,'type_b','type_c','type_d']
 df['type_d'] = 1
 df['results'] = ''

Any hint on where to search?

Upvotes: 2

Views: 81

Answers (3)

Mihai Alexandru-Ionut
Mihai Alexandru-Ionut

Reputation: 48337

You can use map method by passing a lambda function.

df['Results'] = df.index.map(lambda index : df[df['production_type'][index]][index])

Upvotes: 1

prabhakar
prabhakar

Reputation: 572

You may try

result = list()
index =0
for i in df['production_type']:
    value = df[i][index]
    index = index+1
    result.append(value)

df['Results'] = pd.DataFrame(result)

Upvotes: 1

jpp
jpp

Reputation: 164623

You can use pd.DataFrame.apply for this:

df['Results'] = df.apply(lambda row: row.get(row['production_type']), axis=1)

Explanation

  • pd.DataFrame.apply with axis=1 applies a function to each row and extracts, via an implicit loop, the components of each row.
  • The method allows an anonymous lambda function as an argument.
  • We can define the lambda function to extract the required value from production_type column.

Upvotes: 2

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