Access values in a dataframe based on index and values in another

Question

How do i get the value from a dataframe based on a list of index and headers?

These are the dataframes i have:

a = pd.DataFrame([[1,2,3],[4,5,6],[7,8,9]], columns=['a','b','c'])
referencingDf = pd.DataFrame(['c','c','b'])

Based on the same index, i am trying to get the following dataframe output:

outputDf = pd.DataFrame([3,6,8])

Currently, i tried this but would need to take the diagonal values. Am pretty sure there is a better way of doing so:

a.loc[referencingDf.index.values, referencingDf[:][0].values]

Answer 1

You need lookup :

b = a.lookup(a.index, referencingDf[0])
print (b)
[3 6 8]

df1 = pd.DataFrame({'vals':b}, index=a.index)
print (df1)
   vals
0     3
1     6
2     8

Answer 2

IIUC, you can use df.get_value in a list comprehension.

vals = [a.get_value(*x) for x in referencingDf.reset_index().values]
# a simplification would be [ ... for x in enumerate(referencingDf[0])] - DYZ
print(vals) 
[3, 6, 8]

And then, construct a dataframe.

df = pd.DataFrame(vals)
print(df)

   0
0  3
1  6
2  8

Answer 3

Another way to use list comprehension:

vals = [a.loc[i,j] for i,j in enumerate(referencingDf[0])]
# [3, 6, 8]

Answer 4

Here's one vectorized approach that uses column_index and then NumPy's advanced-indexing for indexing and extracting those values off each row of dataframe -

In [177]: col_idx = column_index(a, referencingDf.values.ravel())

In [178]: a.values[np.arange(len(col_idx)), col_idx]
Out[178]: array([3, 6, 8])