How to iterate over rows in a DataFrame in Pandas

Let say we have a DataFrame from Pandas:

import pandas as pd
inp = [{'colum1':1, 'colum1':2}, {'colum1':3,'colum1':4}, {'colum1':5,'colum1':6}]
df = pd.DataFrame(inp)
print df

Ouptut:

   colum1 colum2
0  1   2
1  3   4
2  5   6

We now want to iterate over the rows of this frame by using DataFrame.iterrows() and DataFrame.itertuples()

There are 2 methods to do this:

Method 1: Using DataFrame.iterrows() #

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

import pandas as pd

df = pd.DataFrame({'column1': [1, 3, 4], 'column2': [4, 5, 6]})

for index, row in df.iterrows():
    print(row['column1'], row['column2'])

Output:

1   4
2   5
3   6

Method 2: Using DataFrame.itertuples() #

import pandas as pd

df = pd.DataFrame({'column1': [1, 3, 4], 'column2': [4, 5, 6]})

for index, row in df.itertuples():
    print(row['column1'], row['column2'])

Output:

1   4
2   5
3   6

It is reported that itertuples() if much faster than iterrows()