This article will show you how to use the Pandas module in Python to combine several DataFrames using a number of different approaches. Have you ever tried your hand at a Kaggle problem? If you replied “yes” to any of those questions, you may have noticed that the data provided to you for the bulk of the jobs is spread over many files, with certain columns appearing in more than one file. So, let me ask you this: when we say that, what is the first thing that comes to mind for you? It makes perfect sense to join them.
Assembling pandas
DataFrames must be joined and merged as a first step in every data analysis or machine learning project. Since data almost always comes from a wide variety of sources and formats, familiarity with this toolbox is essential for any data analyst or data scientist. It’s possible that you’ll need to use some kind of join logic to bring together all of the data you require before you can begin your analysis. The significance of this position may be understood by people who work with query languages akin to SQL. Even though your end aim is to develop machine learning models from your existing data, you may find that you first need to integrate many CSV files into a single DataFrame.
You can count on pandas, the most popular python join package, to help you out. Using set logic for indexes and relational algebra capabilities, pandas makes it easy to combine Series, DataFrames, and Panel objects in the context of join and merge operations. Among these features is support for many types of set logic in the indices. In this course, you’ll be doing all of your exercises using mock DataFrames that you’ve built.
Using a Python dictionary, you can create a DataFrame in the following ways:
In this scenario, the column headings are the “keys” to the dictionary represented by “dummy_data1,” and the corresponding data for each observation are the “values” of the list, or “rows.” To transform this into a pandas DataFrame, you will use DataFrame() and provide the names of the columns you want to include in the resulting table through the columns option.
The two DataFrames, df1 and df2, have been merged into a single one, df_row, which can be seen along the row. However, it seems like something is off with the row labels.
As of right now, the row labels are correct
After the concatenation, pandas allows you to provide a key to each DataFrame so you can tell which data originated from which source. This may be done to trace back the data to its original DataFrame. You may achieve the same effect by providing supplementary argument keys that provide the names of the DataFrames’ labels instead. At this stage, you’ll repeat the previous union with x and y serving as the keys for DataFrames df1 and df2 in turn.
Accessing the information associated with a certain DataFrame is made considerably less complicated when the keys are explained. Data from DataFrame df2 with the label y may be retrieved using the loc method.
Conclusion
Remember that the concat() technique produces an exact copy of the data. Therefore, utilising this function often may cause a significant drop in overall performance. If you need to use the method on many data sets at once, use a list comprehension.
