One of the most common tasks in data analysis is to loop through rows of a Pandas DataFrame to perform various operations, like data manipulation and wrangling. In this article, we will discover different ways to iterate over rows of Pandas DataFrames.<\/p>\n\n\n\n
Pandas DataFrames are a two-dimensional array with labeled data structures having different column types. <\/strong>It is a convenient way to work with structured data in Python. DataFrames are a standard and convenient way to store data in a tabular format, with rows to store the information and columns to name the information.<\/p>\n\n\n\n To learn about DataFrames in detail, you can check out how to Convert Pandas Series to DataFrame<\/a>.<\/p>\n\n\n\n Looping through rows in a DataFrame allows us to access and manipulate the values of each row individually. Let us now explore the various methods to iterate over rows of a Pandas DataFrame:<\/p>\n\n\n\n The pandas library in Python provides itterows() Function, which we can use to iterate over rows of a DataFrame.<\/strong> It returns an iterator that yields the index and a Series object containing the values of each row. By using the iterrows() function, we can get the values of every row using the column names as indices of the DataFrame.<\/p>\n\n\n\n The following Python code shows how to use iterrows() function to iterate over rows in Pandas DataFrame:<\/p>\n\n\n\n Output:<\/strong><\/p>\n\n\n\n List comprehension is a brief and efficient way to iterate over rows in a DataFrame while performing operations on the data. We can just simply use a list comprehension to iterate over the values of a particular row. This technique is particularly useful when you need to perform calculations or transformations on the data while iterating.<\/strong><\/p>\n\n\n\n Let us see how to do it in Python:<\/p>\n\n\n\n Output:<\/strong><\/p>\n\n\n\n One more way we can iterate over rows of Pandas DataFrame is by using the apply() function<\/a>. The apply() function in Pandas is very productive as it can allow us to apply a function to each row or column of a DataFrame. This function can be defined using a lambda function or a custom-defined function.<\/p>\n\n\n\n Let us try an example:<\/p>\n\n\n\n Output:<\/strong><\/p>\n\n\n\n In this article, we understood the various techniques and methods we can use to iterate over rows of a Pandas data frame. Now that you have a solid understanding of looping through Pandas, you can confidently apply your learnings from this article to your data analysis projects. If you need help with them, our Data Science Tutors<\/a> are always available!<\/p>\n","protected":false},"excerpt":{"rendered":" Understand how to iterate over rows in pandas dataframe using iterrows(), list comprehension, and apply() functions.<\/p>\n","protected":false},"author":10,"featured_media":686,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"jnews-multi-image_gallery":[],"jnews_single_post":null,"jnews_primary_category":{"id":"","hide":""},"footnotes":""},"categories":[35],"tags":[37],"class_list":["post-684","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-data-science","tag-pandas"],"_links":{"self":[{"href":"https:\/\/favtutor.com\/articles\/wp-json\/wp\/v2\/posts\/684","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/favtutor.com\/articles\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/favtutor.com\/articles\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/favtutor.com\/articles\/wp-json\/wp\/v2\/users\/10"}],"replies":[{"embeddable":true,"href":"https:\/\/favtutor.com\/articles\/wp-json\/wp\/v2\/comments?post=684"}],"version-history":[{"count":5,"href":"https:\/\/favtutor.com\/articles\/wp-json\/wp\/v2\/posts\/684\/revisions"}],"predecessor-version":[{"id":815,"href":"https:\/\/favtutor.com\/articles\/wp-json\/wp\/v2\/posts\/684\/revisions\/815"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/favtutor.com\/articles\/wp-json\/wp\/v2\/media\/686"}],"wp:attachment":[{"href":"https:\/\/favtutor.com\/articles\/wp-json\/wp\/v2\/media?parent=684"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/favtutor.com\/articles\/wp-json\/wp\/v2\/categories?post=684"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/favtutor.com\/articles\/wp-json\/wp\/v2\/tags?post=684"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}3 Ways to Iterate Over Rows in Pandas DataFrame<\/strong><\/h2>\n\n\n\n
1) Using the iterrows() Function<\/strong><\/h3>\n\n\n\n
import pandas as pd\n\ndata = {\n "Name": ["John", "Emma", "Michael"],\n "Age": [45, 30, 55],\n "City": ["New York", "London", "Paris"]\n}\ndf = pd.DataFrame(data)\n\n# Display the original DataFrame\nprint('Original DataFrame:\\n', df)\n\n# Loop through rows using iterrows()\nprint('Iterated Rows:')\nfor index, row in df.iterrows():\n print(row['Name'], row['Age'], row['City'])<\/pre><\/div>\n\n\n\nOriginal DataFrame:\n Name Age City\n0 John 45 New York\n1 Emma 30 London\n2 Michael 55 Paris\n\nIterated Rows:\nJohn 45 New York\nEmma 30 London\nMichael 55 Paris\n<\/code><\/pre>\n\n\n\n2) Using List Comprehension<\/strong><\/h3>\n\n\n\n
import pandas as pd\n\ndata = {\n "Name": ["John", "Emma", "Michael"],\n "Age": [45, 30, 55],\n "City": ["New York", "London", "Paris"]\n}\ndf = pd.DataFrame(data)\n\n# Display the original DataFrame\nprint('Original DataFrame:\\n', df)\n\n# Loop through rows using list comprehension\nprint('Iterated Rows:')\nresult = [row['Name'] for index, row in df.iterrows()]\nprint(result)<\/pre><\/div>\n\n\n\nOriginal DataFrame:\n Name Age City\n0 John 45 New York\n1 Emma 30 London\n2 Michael 55 Paris\n\nIterated Rows:\n['John', 'Emma', 'Michael']\n<\/code><\/pre>\n\n\n\n3) Using the apply() Function<\/strong><\/h3>\n\n\n\n
import pandas as pd\n\ndata = {\n "Name": ["John", "Emma", "Michael"],\n "Age": [45, 30, 55],\n "City": ["New York", "London", "Paris"]\n}\ndf = pd.DataFrame(data)\n\n# Display the original DataFrame\nprint('Original DataFrame:\\n', df)\n\n# Use apply() with a lambda function\ndf['Name'] = df.apply(lambda row: row['Name'].upper(), axis=1)\n\n# Display the new DataFrame. \nprint('Updated DataFrame:\\n',df)<\/pre><\/div>\n\n\n\nOriginal DataFrame:\n Name Age City\n0 John 45 New York\n1 Emma 30 London\n2 Michael 55 Paris\n\nUpdated DataFrame:\n Name Age City\n0 JOHN 45 New York\n1 EMMA 30 London\n2 MICHAEL 55 Paris\n<\/code><\/pre>\n\n\n\nConclusion<\/strong><\/h2>\n\n\n\n