Concatenating and Appending dataframes – p.5 Data Analysis with Python and Pandas Tutorial
Welcome to Part 5 of our Data Analysis with Python and Pandas tutorial series. In this tutorial, we’re going to be covering how to combine dataframes in a variety of ways.
In our case with real estate investing, we’re hoping to take the 50 dataframes with housing data and then just combine them all into one dataframe. We do this for multiple reasons. First, it is easier and just makes sense to combine these, but also it will result in less memory being used. Every dataframe has a date and value column. This date column is repeated across all the dataframes, but really they should all just share the one, effectively nearly halving our total column count.
When combining dataframes, you might have quite a few goals in mind. For example, you may want to “append” to them, where you may be adding to the end, basically adding more rows. Or maybe you want to add more columns, like in our case. There are four major ways of combining dataframes, which we’ll begin covering now. The four major ways are: Concatenation, joining, merging, and appending. We’ll begin with Concatenation.
Sample code and text-based version of this tutorial: http://pythonprogramming.net/concatenate-append-data-analysis-python-pandas-tutorial/