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

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