Let’s say that our DataFrame contains customer data and we have a scoring function for customers that uses multiple customer attributes to give them a score between ‘A’ and ‘F’. 'end_date', 'city', 'state', 'zipcode', 'balance'])ĭf = pd.to_datetime(df) # convert to datetimesĭf = pd.to_datetime(df)ĭtype: object df.head() first_name last_name start_date end_date city state \ġ Sarah Merritt South Maryborough Tennessee from datetime import datetime, timedeltaĭf = pd.DataFrame(Ĭolumns=['first_name', 'last_name', 'start_date', Note that the columns are different data types (we have some strings, an integer, and dates). I’ll do this by making some fake data (using Faker). Now instead of a trivial printing example, let’s look at ways to actually use data for a row in a DataFrame that includes some logic. With a little practice, you can select any combinations of rows or columns to show. If you want to only look at subsets of a DataFrame, instead of using a loop to only display those rows, use the powerful indexing capabilities of pandas. Use head and tail to get a sense of the data. If the DataFrame is large, only some columns and rows may be visible by default. The standard rendering of a DataFrame, whether it is rendered with print or viewed with a Jupyter notebook using display or as an output in a cell will be far better than what would be printed using custom formatting. First off, let’s all agree that this is not a good way to look at the content of a DataFrame. If we look at the original question on Stack Overflow, the question and answer just print the content of the DataFrame. Now what do we want to do with the DataFrame? That’s often the best way to learn, you can think of a first solution as the first draft of your essay, you can improve it with some editing.
However, no one should ever feel bad about writing a first solution that uses iteration instead of other (perhaps better) ways. However, in most cases what beginners are trying to do with iteration is better done with another approach. In some cases, the top voted answer for iteration might be the best choice! But I have heard that iteration is wrong, is that true?įirst, choosing to iterate over the rows of a DataFrame is not automatically the wrong way to solve a problem.
#For loop in r by row how to
Instead of asking how to iterate over DataFrame rows, it makes more sense to understand what the options are that are available, what their advantages and disadvantages are, and then choose the one that makes sense for you. Other answers to the question (especially the second highest rated answer) do a fairly good job of giving other options, but the entire list of 26 (and counting!) answers is extremely confusing. It is also true that there can be serious consequences with iterating over DataFrame rows using the top solution. Obviously people want to iterate over DataFrame rows! The Stack Overflow developers say thousands of people view the answer weekly and copy it to solve their problem.
#For loop in r by row code
It also turns out that question has the most copied answer with a code block on the entire site. Instead of trying to find the one right answer about iteration, it makes better sense to understand the issues involved and know when to choose the best solution.Īs of this writing, the top voted question tagged with ‘pandas’ on Stack Overflow is about how to iterate over DataFrame rows. They may not understand the “correct” way to work with DataFrames yet, but even experienced pandas and NumPy developers will consider iterating over the rows of a DataFrame to solve a problem. The natural way for most programmers to think of what to do next is to build a loop.
Often this question comes up right away for new users who have loaded some data into a DataFrame and now want to do something useful with it. One of the most searched for (and discussed) questions about pandas is how to iterate over rows in a DataFrame.
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