CODEWITHSUNDEEP
pythonpandas
armin
armin

Reputation: 651

Pandas get the row value by column name in an apply function in Python?

I have the following dataframe:

>>> d = {'route1': ['a', 'b'], 'route2': ['c', 'd'], 'val1': [1,2]}
>>> df = pd.DataFrame(data=d)
>>> df
  route1 route2  val1
0      a      c     1
1      b      d     2

What I am trying to do is to pass a list that contains some column names and print the row value associated with that columns:

>>> def row_eval(row, list):
>>>    print(row.loc[list])

In the dataframe above, I first find all the columns that contains the name "route" and then apply the row_val func to each row. However I get the following err:

>>> route_cols = [col for col in df.columns if 'route' in col]
>>> route_cols
['route1', 'route2']
>>> df.apply(lambda row: row_eval(row, route_cols)
KeyError: "None of [Index(['route1', 'route2'], dtype='object')] are in the [index]"

Result should look like this:

route1 a
route2 c

route1 b
route2 d

Upvotes: 3

Views: 2254

Answers (3)

AreToo
AreToo

Reputation: 1335

One way to print all of the values in these columns, is to iterate over all of the columns within a loop that iterates through all of the rows. Then to simply print the column name and the value together. The if statement is optional, but it will give a line break between rows, like in your example.

for idx in df.index:
    for column in route_cols:
        print(f'{column} {df.loc[idx, column]}')
        if column == route_cols[-1]:
            print('\n')

Upvotes: 1

SeaBean
SeaBean

Reputation: 23217

Add axis=1 to the apply function:

df.apply(lambda row: row_eval(row, route_cols), axis=1)

Without axis=1, you are iterating over the row index (column-wise). The row index are 0 1 instead of the column index route1 route2 you want to match. Hence, the error message. What you want is to have row-wise operation (i.e. passing row to the apply function), then you need axis=1

Upvotes: 2

dfundako
dfundako

Reputation: 8314

To get you started, you can use either .melt() or .stack() to get real close to your expected output. Not 100% sure if you're looking for 2 dataframes or not.

df[route_cols].stack()

0  route1    a
   route2    c
1  route1    b
   route2    d
dtype: object
    
df[route_cols].melt()

  variable value
0   route1     a
1   route1     b
2   route2     c
3   route2     d

Upvotes: 0

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