Iteration in Pandas – 3 Unique Ways to Iterate Over DataFrames

Iterating over a dataset allows us to travel and visit all the values present in the dataset. This facilitates our grasp on the data and allows us to carry out more complex operations. There are various ways for Iteration in Pandas over a dataframe. We can go, row-wise, column-wise or iterate over each in the form of a tuple.

Iteration in Pandas

With Pandas iteration, you can visit each element of the dataset in a sequential manner, you can even apply mathematical operations too while iterating. But, before we start iteration in Pandas, let us import the pandas library-

>>> import pandas as pd

Using the .read_csv function, we load a dataset and print the first 5 rows.

>>> dataflair = pd.read_csv("https://people.sc.fsu.edu/~jburkardt/data/csv/airtravel.csv")
>>> dataflair.head()

Output-

Month “1958” “1959” “1960”
0 JAN 340 360 417
1 FEB 318 342 391
2 MAR 362 406 419
3 APR 348 396 461
4 MAY 363 420 472

Don’t forget to check – 5 Pandas Options to Customize Your Data 

3 Ways for Iteration in Pandas

There are 3 ways to iterate over Pandas dataframes are-

1. iteritems(): Helps to iterate over each element of the set, column-wise.
2. iterrows(): Each element of the set, row-wise.
3. itertuple(): Each row and form a tuple out of them.

1. iteritems() in Pandas

The function iteritems() lets us travel and visit each and every value of the dataset.

>>> for key,values in dataflair.iteritems():
... print(key, values)
…

We see in the output that because of iteritems(), our code snippet runs through each and every element in all the columns of the dataset.

Output-

Month 0 JAN
1   FEB
2   MAR
3   APR
4   MAY
5   JUN
6   JUL
7   AUG
8   SEP
9   OCT
10  NOV
11   DEC
Name: Month, dtype: object
“1958”  0  340
1   318
2   362
3   348
4   363
5   435
6   491
7   505
8   404
9   359
10  310
11  337
Name: “1958”, dtype: int64
“1959” 0 360
1   342
2   406
3   396
4   420
5   472
6   548
7   559
8   463
9   407
10  362
11  405
Name: “1959”, dtype: int64
“1960” 0 417
1   391
2   419
3   461
4   472
5   535
6   622
7   606
8   508
9   461
10  390
11  432
Name: “1960”, dtype: int64

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2. iterrows() in Pandas

With iterrows() we can visit all the elements of a dataset, row-wise.

>>> for row_index,row in dataflair.iterrows():
... print(row_index, row)
...

Output-

0 Month  JAN
“1958”   340
“1959”   360
“1960”   417
Name: 0, dtype: object
1 Month  FEB
“1958”   318
“1959”   342
“1960”   391
Name: 1, dtype: object
2 Month  MAR
“1958”   362
“1959”   406
“1960”   419
Name: 2, dtype: object
3 Month  APR
“1958”   348
“1959”   396
“1960”   461
Name: 3, dtype: object
4 Month  MAY
“1958”   363
“1959”   420
“1960”   472
Name: 4, dtype: object
5 Month  JUN
“1958”   435
“1959”   472
“1960”   535
Name: 5, dtype: object
6 Month  JUL
“1958”   491
“1959”   548
“1960”   622
Name: 6, dtype: object
7 Month  AUG
“1958”   505
“1959”   559
“1960”   606
Name: 7, dtype: object
8 Month  SEP
“1958”   404
“1959”   463
“1960”   508
Name: 8, dtype: object
9 Month  OCT
“1958”   359
“1959”   407
“1960”   461
Name: 9, dtype: object
10 Month  NOV
“1958”   310
“1959”   362
“1960”   390
Name: 10, dtype: object
11 Month  DEC
“1958”   337
“1959”   405
“1960”   432
Name: 11, dtype: object

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3. itertuples() in Pandas

The function itertuples() creates a tuple for every row in the dataset. Thus iterating over it would give us a tuple of the rows present.

>>> for row in dataflair.itertuples():
... print(row)
...

Output-

Pandas(Index=0, Month=’JAN’, _2=340, _3=360, _4=417)
Pandas(Index=1, Month=’FEB’, _2=318, _3=342, _4=391)
Pandas(Index=2, Month=’MAR’, _2=362, _3=406, _4=419)
Pandas(Index=3, Month=’APR’, _2=348, _3=396, _4=461)
Pandas(Index=4, Month=’MAY’, _2=363, _3=420, _4=472)
Pandas(Index=5, Month=’JUN’, _2=435, _3=472, _4=535)
Pandas(Index=6, Month=’JUL’, _2=491, _3=548, _4=622)
Pandas(Index=7, Month=’AUG’, _2=505, _3=559, _4=606)
Pandas(Index=8, Month=’SEP’, _2=404, _3=463, _4=508)
Pandas(Index=9, Month=’OCT’, _2=359, _3=407, _4=461)
Pandas(Index=10, Month=’NOV’, _2=310, _3=362, _4=390)
Pandas(Index=11, Month=’DEC’, _2=337, _3=405, _4=432)

Summary

Hopefully, the above-given Pandas tutorial helped you understand the various methods of accessing and iterating over your dataset. We used iteritems() for column-wise, iterrows() for row-wise, and itertuple() for each row and form a tuple out of them. This simplifies the process of operating on your dataset.

Let’s dive into Pandas more deeper with Pandas Function Applications

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