{"id":56900,"date":"2019-05-27T10:56:22","date_gmt":"2019-05-27T05:26:22","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=56900"},"modified":"2019-05-27T10:56:22","modified_gmt":"2019-05-27T05:26:22","slug":"iteration-in-pandas","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/iteration-in-pandas\/","title":{"rendered":"Iteration in Pandas – 3 Unique Ways to Iterate Over DataFrames"},"content":{"rendered":"

Iterating over a dataset allows us to travel and visit all the values present in the dataset<\/em>. 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.<\/p>\n

Iteration in Pandas<\/h2>\n

With Pandas iteration, you can visit each element of the dataset in a sequential manner, you can even apply mathematical operations too while iterating. <\/em>But, b<\/em>efore we start iteration in Pandas, let us import the pandas library-<\/strong><\/p>\n

>>> import pandas as pd<\/pre>\n
Using the .read_csv function<\/strong>, we load a dataset and print the first 5 rows.<\/p>\n
>>> dataflair = pd.read_csv(\"https:\/\/people.sc.fsu.edu\/~jburkardt\/data\/csv\/airtravel.csv\")\r\n>>> dataflair.head()<\/pre>\n
Output-<\/strong><\/p>\n
Month “1958” “1959” “1960”
\n0 JAN 340 360 417
\n1 FEB 318 342 391
\n2 MAR 362 406 419
\n3 APR 348 396 461
\n4 MAY 363 420 472<\/p>\n
Don’t forget to check – 5 Pandas Options to Customize Your Data\u00a0<\/a><\/em><\/strong><\/p>\n
3 Ways for Iteration in Pandas<\/h2>\n
There are 3 ways to iterate over Pandas dataframes are-<\/p>\n
    \n
  1. iteritems():<\/strong> Helps to iterate over each element of the set, column-wise.<\/li>\n
  2. iterrows():<\/strong> Each element of the set, row-wise.<\/li>\n
  3. itertuple():<\/strong> Each row and form a tuple out of them.<\/li>\n<\/ol>\n
    \"Ways<\/a><\/h2>\n
    1. iteritems() in Pandas<\/h3>\n
    The function iteritems() lets us travel and visit each and every value of the dataset.<\/p>\n
    >>> for key,values in dataflair.iteritems():\r\n... print(key, values)\r\n\u2026<\/pre>\n
    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.<\/p>\n
    Output-<\/strong><\/p>\n
    Month 0 JAN
    \n1\u00a0 \u00a0FEB
    \n2\u00a0 \u00a0MAR
    \n3\u00a0 \u00a0APR
    \n4\u00a0 \u00a0MAY
    \n5\u00a0 \u00a0JUN
    \n6\u00a0 \u00a0JUL
    \n7\u00a0 \u00a0AUG
    \n8\u00a0 \u00a0SEP
    \n9\u00a0 \u00a0OCT
    \n10\u00a0 NOV
    \n11\u00a0 \u00a0DEC
    \nName: Month, dtype: object
    \n“1958”\u00a0 0\u00a0 340
    \n1\u00a0 \u00a0318
    \n2\u00a0 \u00a0362
    \n3\u00a0 \u00a0348
    \n4\u00a0 \u00a0363
    \n5\u00a0 \u00a0435
    \n6\u00a0 \u00a0491
    \n7\u00a0 \u00a0505
    \n8\u00a0 \u00a0404
    \n9\u00a0 \u00a0359
    \n10\u00a0 310
    \n11\u00a0 337
    \nName: “1958”, dtype: int64
    \n“1959” 0 360
    \n1\u00a0 \u00a0342
    \n2\u00a0 \u00a0406
    \n3\u00a0 \u00a0396
    \n4\u00a0 \u00a0420
    \n5\u00a0 \u00a0472
    \n6\u00a0 \u00a0548
    \n7\u00a0 \u00a0559
    \n8\u00a0 \u00a0463
    \n9\u00a0 \u00a0407
    \n10\u00a0 362
    \n11\u00a0 405
    \nName: “1959”, dtype: int64
    \n“1960” 0 417
    \n1\u00a0 \u00a0391
    \n2\u00a0 \u00a0419
    \n3\u00a0 \u00a0461
    \n4\u00a0 \u00a0472
    \n5\u00a0 \u00a0535
    \n6\u00a0 \u00a0622
    \n7\u00a0 \u00a0606
    \n8\u00a0 \u00a0508
    \n9\u00a0 \u00a0461
    \n10\u00a0 390
    \n11\u00a0 432
    \nName: “1960”, dtype: int64<\/p>\n
    Do you know how to create pandas panel?<\/a><\/strong><\/em><\/p>\n
    2. iterrows() in Pandas<\/h3>\n
    With iterrows() we can visit all the elements of a dataset, row-wise.<\/p>\n
    >>> for row_index,row in dataflair.iterrows():\r\n... print(row_index, row)\r\n...<\/pre>\n
    Output-<\/strong><\/p>\n
    0 Month\u00a0 JAN
    \n“1958”\u00a0 \u00a0340
    \n“1959”\u00a0 \u00a0360
    \n“1960”\u00a0 \u00a0417
    \nName: 0, dtype: object
    \n1 Month\u00a0 FEB
    \n“1958”\u00a0 \u00a0318
    \n“1959”\u00a0 \u00a0342
    \n“1960”\u00a0 \u00a0391
    \nName: 1, dtype: object
    \n2 Month\u00a0 MAR
    \n“1958”\u00a0 \u00a0362
    \n“1959”\u00a0 \u00a0406
    \n“1960”\u00a0 \u00a0419
    \nName: 2, dtype: object
    \n3 Month\u00a0 APR
    \n“1958”\u00a0 \u00a0348
    \n“1959”\u00a0 \u00a0396
    \n“1960”\u00a0 \u00a0461
    \nName: 3, dtype: object
    \n4 Month\u00a0 MAY
    \n“1958”\u00a0 \u00a0363
    \n“1959”\u00a0 \u00a0420
    \n“1960”\u00a0 \u00a0472
    \nName: 4, dtype: object
    \n5 Month\u00a0 JUN
    \n“1958”\u00a0 \u00a0435
    \n“1959”\u00a0 \u00a0472
    \n“1960”\u00a0 \u00a0535
    \nName: 5, dtype: object
    \n6 Month\u00a0 JUL
    \n“1958”\u00a0 \u00a0491
    \n“1959”\u00a0 \u00a0548
    \n“1960”\u00a0 \u00a0622
    \nName: 6, dtype: object
    \n7 Month\u00a0 AUG
    \n“1958”\u00a0 \u00a0505
    \n“1959”\u00a0 \u00a0559
    \n“1960”\u00a0 \u00a0606
    \nName: 7, dtype: object
    \n8 Month\u00a0 SEP
    \n“1958”\u00a0 \u00a0404
    \n“1959”\u00a0 \u00a0463
    \n“1960”\u00a0 \u00a0508
    \nName: 8, dtype: object
    \n9 Month\u00a0 OCT
    \n“1958”\u00a0 \u00a0359
    \n“1959”\u00a0 \u00a0407
    \n“1960”\u00a0 \u00a0461
    \nName: 9, dtype: object
    \n10 Month\u00a0 NOV
    \n“1958”\u00a0 \u00a0310
    \n“1959”\u00a0 \u00a0362
    \n“1960”\u00a0 \u00a0390
    \nName: 10, dtype: object
    \n11 Month\u00a0 DEC
    \n“1958”\u00a0 \u00a0337
    \n“1959”\u00a0 \u00a0405
    \n“1960”\u00a0 \u00a0432
    \nName: 11, dtype: object<\/p>\n
    Have you checked – Which Industry Segments are using Python Pandas?<\/a><\/strong><\/em><\/p>\n
    3. itertuples() in Pandas<\/h3>\n
    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.<\/p>\n
    >>> for row in dataflair.itertuples():\r\n... print(row)\r\n...<\/pre>\n
    Output-<\/strong><\/p>\n
    Pandas(Index=0, Month=’JAN’, _2=340, _3=360, _4=417)
    \nPandas(Index=1, Month=’FEB’, _2=318, _3=342, _4=391)
    \nPandas(Index=2, Month=’MAR’, _2=362, _3=406, _4=419)
    \nPandas(Index=3, Month=’APR’, _2=348, _3=396, _4=461)
    \nPandas(Index=4, Month=’MAY’, _2=363, _3=420, _4=472)
    \nPandas(Index=5, Month=’JUN’, _2=435, _3=472, _4=535)
    \nPandas(Index=6, Month=’JUL’, _2=491, _3=548, _4=622)
    \nPandas(Index=7, Month=’AUG’, _2=505, _3=559, _4=606)
    \nPandas(Index=8, Month=’SEP’, _2=404, _3=463, _4=508)
    \nPandas(Index=9, Month=’OCT’, _2=359, _3=407, _4=461)
    \nPandas(Index=10, Month=’NOV’, _2=310, _3=362, _4=390)
    \nPandas(Index=11, Month=’DEC’, _2=337, _3=405, _4=432)<\/p>\n
    Summary<\/h2>\n
    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()\u00a0for row-wise, and itertuple() for each row and form a tuple out of them.\u00a0This simplifies the process of operating on your dataset.<\/p>\n
    Let’s dive into Pandas more deeper with Pandas Function Applications<\/a><\/strong><\/em><\/p>\n
    All queries in your mind related to “Iteration in Pandas” should be redirected into the comments below.<\/p>\n","protected":false},"excerpt":{"rendered":"
    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. 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