How to iterate over rows in a DataFrame in Pandas

Question

I have a DataFrame from Pandas:

import pandas as pd inp = [{'c1':10, 'c2':100}, {'c1':11,'c2':110}, {'c1':12,'c2':120}] df = pd.DataFrame(inp) print df 

Output:

   c1   c2 0  10  100 1  11  110 2  12  120 

Now I want to iterate over the rows of this frame. For every row I want to be able to access its elements (values in cells) by the name of the columns. For example:

for row in df.rows:    print row['c1'], row['c2'] 

Is it possible to do that in Pandas?

I found this similar question. But it does not give me the answer I need. For example, it is suggested there to use:

for date, row in df.T.iteritems(): 

or

for row in df.iterrows(): 

But I do not understand what the row object is and how I can work with it.

Answer 1

DataFrame.iterrows is a generator which yields both the index and row (as a Series):

import pandas as pd  df = pd.DataFrame({'c1': [10, 11, 12], 'c2': [100, 110, 120]}) df = df.reset_index()  # make sure indexes pair with number of rows for index, row in df.iterrows():     print(row['c1'], row['c2']) 

10 100 11 110 12 120