python: Dictionary key as row index, values as column headers. How do I refer back and select specific values in a df using a dictionary?

I have a dataframe that looks like this:

a=['a','b','c','d']
b=['the','fox','the','then']
c=['quick','jumps','lazy','barks']
d=['brown','over','dog','loudly']
df=pd.DataFrame(zip(a,b,c,d),columns=['indexcol','col1','col2','col3'])

and a dictionary that looks like this:

keys=['a','b','c','d']
vals=[]
vals.append(['col1','col3'])
vals.append(['col1','col2'])
vals.append(['col1','col2','col3'])
vals.append(['col2','col3'])
newdict = {k: v for k, v in zip(keys, vals)}

What I’m trying to do is to create a new column in df which constructs a statement for each row. Taking the first row as an example, the sentence should look like so:

"col1 is ‘the’ | col3 is ‘lazy’ "

another example using the 3rd row just to make the task at hand crystal clear:
"col1 is ‘brown’ | col2 is ‘the’ | col3 is ‘then’ "

essentially, I want to refer to the dictionary values to look up the column in df using the dictionary keys as the row reference matching to indexcol in df.

Thanks in advance.

>Solution :

I’m not sure if I understand you correctly but you can try:

df = df.set_index("indexcol")

for k, v in newdict.items():
    row = df.loc[k]
    df.loc[k, "new_column"] = " | ".join(f"{i} is '{row[i]}'" for i in v)

print(df.reset_index())

Prints:

  indexcol  col1   col2    col3                                      new_column
0        a   the  quick   brown                 col1 is 'the' | col3 is 'brown'
1        b   fox  jumps    over                 col1 is 'fox' | col2 is 'jumps'
2        c   the   lazy     dog  col1 is 'the' | col2 is 'lazy' | col3 is 'dog'
3        d  then  barks  loudly              col2 is 'barks' | col3 is 'loudly'