Iterating over rows and columns in Pandas DataFrame

First let's create a data frame with values.

 
import pandas as pd
import numpy as np
df = pd.DataFrame()
df['Name'] = ['John', 'Doe', 'Bill','Jim','Harry','Ben']
df['TotalMarks'] = [82, 38, 63,22,55,40]
df['Grade'] = ['A', 'E', 'B','E','C','D']
df['Promoted'] = [True, False,True,False,True,True]
df

 
    Name  TotalMarks Grade  Promoted
0   John          82     A      True
1    Doe          38     E     False
2   Bill          63     B      True
3    Jim          22     E     False
4  Harry          55     C      True
5    Ben          40     D      True

A simple for loop through Pandas DataFrame using index

 
for index in df.index:
    print(df['Name'][index], " , " ,  df['Promoted'][index])

 
John  ,  True
Doe  ,  False
Bill  ,  True
Jim  ,  False
Harry  ,  True
Ben  ,  True

Important!!

Iterating through pandas dataFrame objects is generally slow. Iteration beats the whole purpose of using DataFrame. It is an anti-pattern and is something you should only do when you have exhausted every other option. It is better look for a List Comprehensions , vectorized solution or DataFrame.apply() method.

Pandas DataFrame loop using list comprehension

 
result = [(x, y,z) for x, y,z in zip(df['Name'], df['Promoted'],df['Grade'])]
result

 
[('John', True, 'A'),
 ('Doe', False, 'E'),
 ('Bill', True, 'B'),
 ('Jim', False, 'E'),
 ('Harry', True, 'C'),
 ('Ben', True, 'D')]

Pandas DataFrame loop using DataFrame.apply()

 
result = df.apply(lambda row: row["Name"] + " , " + str(row["TotalMarks"]) + " , " +  row["Grade"], axis = 1)
result

 
0     John , 82 , A
1      Doe , 38 , E
2     Bill , 63 , B
3      Jim , 22 , E
4    Harry , 55 , C
5      Ben , 40 , D

**Other Pandas DataFrame looping methods (DON'T*!)

Using loc()

 
for i in range(len(df)) :
  print(df.loc[i, "Name"], ", " , df.loc[i, "Promoted"])

 
John ,  True
Doe ,  False
Bill ,  True
Jim ,  False
Harry ,  True
Ben ,  True

Using iloc[]

 
for i in range(len(df)) :
  print(df.iloc[i, 0], " , " ,df.iloc[i, 1], " , " , df.iloc[i, 3])

 
John  ,  82  ,  True
Doe  ,  38  ,  False
Bill  ,  63  ,  True
Jim  ,  22  ,  False
Harry  ,  55  ,  True
Ben  ,  40  ,  True

Using iterrows()

 
for index, row in df.iterrows():
    print (row["Name"], " , " ,  row["TotalMarks"] , " , " ,  row["Grade"])

 
John  ,  82  ,  A
Doe  ,  38  ,  E
Bill  ,  63  ,  B
Jim  ,  22  ,  E
Harry  ,  55  ,  C
Ben  ,  40  ,  D

Using itertuples()

 
for row in df.itertuples(index = True, name ='Pandas'):
    print (getattr(row, "Name"), " , " , getattr(row, "TotalMarks"))

 
John  ,  82
Doe  ,  38
Bill  ,  63
Jim  ,  22
Harry  ,  55
Ben  ,  40

Using iteritems()

 
for key, value in df.iteritems():
    print(key, value)
    print()

 
Name 0     John
1      Doe
2     Bill
3      Jim
4    Harry
5      Ben
Name: Name, dtype: object
TotalMarks 0    82
1    38
2    63
3    22
4    55
5    40
Name: TotalMarks, dtype: int64
Grade 0    A
1    E
2    B
3    E
4    C
5    D
Name: Grade, dtype: object
Promoted 0     True
1    False
2     True
3    False
4     True
5     True
Name: Promoted, dtype: bool

Next : How to drop rows/columns of Pandas DataFrame whose value is NaN