![]() The first thing to know about the drop_duplicates syntax is that this technique is a method, not a function. I’ll show some examples later in the example section, but here, I just want to break down the syntax piece by piece. Here, I’ll explain how the syntax of the Pandas drop_duplicates() method. Having said that, let’s take a look at the syntax of Pandas drop duplicates, so we can better understand how it works. There’s actually a few different ways to remove duplicate rows, and it really depends on several parameters in the syntax. Here, we’re going to discuss the drop duplicates technique, which enables you to remove duplicate rows from a dataframe, once you’ve found them. We’re not going to cover those tools in this tutorial. There are also some ways to identify duplicates by using aggregation and summation. In Python, you can use the Pandas unique method. There are several ways to identify duplicate rows. With that said, as an analyst or data scientist, you need ways to identify and remove duplicate rows of data. In fact, before you do a join, you almost always need to check for duplicate records! In particular, duplicated data often causes problems when data scientists aggregate data.ĭuplicate rows can also be a really big problem when you merge or join multiple datasets together. If a data system accidentally recorded a duplicate record for a particular salesperson, it might over-report that person’s actual sales performance.įrankly, there are many possible examples in fields like marketing, finance, accounting, and other areas, where having duplicate data could be an issue.Īdditionally, there are certain types of data wrangling operations where duplicates can cause big problems. Sometimes, duplicate rows can cause problems with an analysis or a specific data science technique.įor example, imagine you’re working with sales data. When you’re working with some types of data, it might be normal – even expected – to have duplicate rows of data.īut there are some instances where having duplicate rows of data is bad. ![]() There’s also a different type of duplicate, where two rows have the same value for one or more important columns (but not necessarily every column). So for example, if two rows had the same value for every column, we’d consider those to be duplicate rows. Importantly, it’s possible for a dataframe to have duplicate rows of data. It’s Possible to Have Rows with the Exact Same Data If you’ve ever used Excel, a Pandas dataframe is really a lot like an Excel spreadsheet, in the sense that they both have this row-and-column structure. For example, if you had a dataframe with sales data, the individual rows might record the sales information for individual people. Typically, the columns of a dataframe are variables, and the rows typically record individual data records. Pandas dataframes store data in a row-and-column format. Let’s look more carefully at dataframe structure. We use dataframes to store certain types of data, and we use Pandas techniques to manipulate dataframe data. (Remember: the drop duplicates method operates on Pandas dataframes.)Ī dataframe is a data structure in Python that’s available in the Pandas package. Dataframes Store Python Dataįirst, let’s quickly review what a dataframe is. This will give you some context, and help you understand exactly what this technique does, and why we might use it. I’ll show you some examples of this in the examples section, but first, I want to quickly review some fundamentals about Pandas and Pandas dataframes. Stated simply, the Pandas drop duplicates method removes duplicate rows from a Pandas dataframe. ![]() Having said that, if you really want to know how this technique works, you should probably read the whole tutorial.Īn Introduction to Pandas Drop Duplicates If you need something specific, you can click on one of the links above. Examples: How to drop duplicate rows from a dataframe.An Introduction to Pandas Drop Duplicates.You can click on any of the following links, and it will take you to the appropriate section in the tutorial. The tutorial will explain what the technique does, explain the syntax, and it will also show you clear examples. This tutorial will show you how to use Pandas drop duplicates to remove duplicate rows from a dataframe.
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