Python Pandas iterate over rows and access column names

Question

I am trying to iterate over the rows of a Python Pandas dataframe. Within each row of the dataframe, I am trying to to refer to each value along a row by its column name.

Here is what I have:

import numpy as np import pandas as pd  df = pd.DataFrame(np.random.rand(10,4),columns=list('ABCD')) print df           A         B         C         D 0  0.351741  0.186022  0.238705  0.081457 1  0.950817  0.665594  0.671151  0.730102 2  0.727996  0.442725  0.658816  0.003515 3  0.155604  0.567044  0.943466  0.666576 4  0.056922  0.751562  0.135624  0.597252 5  0.577770  0.995546  0.984923  0.123392 6  0.121061  0.490894  0.134702  0.358296 7  0.895856  0.617628  0.722529  0.794110 8  0.611006  0.328815  0.395859  0.507364 9  0.616169  0.527488  0.186614  0.278792 

I used this approach to iterate, but it is only giving me part of the solution - after selecting a row in each iteration, how do I access row elements by their column name?

Here is what I am trying to do:

for row in df.iterrows():     print row.loc[0,'A']     print row.A     print row.index() 

My understanding is that the row is a Pandas series. But I have no way to index into the Series.

Is it possible to use column names while simultaneously iterating over rows?

Answer 1

I also like itertuples()

for row in df.itertuples():     print(row.A)     print(row.Index) 

since row is a named tuples, if you meant to access values on each row this should be MUCH faster

speed run :

df = pd.DataFrame([x for x in range(1000*1000)], columns=['A']) st=time.time() for index, row in df.iterrows():     row.A print(time.time()-st) 45.05799984931946  st=time.time() for row in df.itertuples():     row.A print(time.time() - st) 0.48400020599365234 

Answer 2

The item from iterrows() is not a Series, but a tuple of (index, Series), so you can unpack the tuple in the for loop like so:

for (idx, row) in df.iterrows():     print(row.loc['A'])     print(row.A)     print(row.index)  #0.890618586836 #0.890618586836 #Index(['A', 'B', 'C', 'D'], dtype='object')