Pandas Merge - How to Merge Pandas Data Frames

To merge data frames in pandas means to combine multiple data frames together. This is the first video you should watch on how to perform merges with Pandas.

What is Pandas? A Python data analysis library

You will eventually come to a point where you have data in two different dataframes. There may even be a time where you have data in three different dataframes. Even though you have been avoiding learning SQL joins and using Excel to combine data, you will eventually have to merge multiple data frames in pandas.

Fear not, Pandas makes is really easy and painless.

The video starts by creating two tables in Excel and then moving them over to Pandas. These are the tables we are going to merge. For those who are familiar with SQL, this is equivalent to a join.

Merge on Column:
Notice that the two dataframes share a common column. This is the column that we will use to merge the dataframes. The two data sets need a common column in order to be able to be merged. Without this common column, pandas will have no idea how to combine the two dataframes. At this point we use the .merge() function and tell Pandas what column the two dataframes will be joining or merging on.

Note that by default the merges will be "inner joins". This means that only the values they share in common will be shown. There are also different types of merges we can perform. Left and right joins are explain with examples.

The reader is not expected to have SQL experience, but it will certainly help you concentrate on Pandas merges and not on SQL.