Pandas, Merging DataFrames Concatenating DataFrames DataFrames can be stacked and combined into one new DataFrame using pandas' concat method. The DataFrame named left is being concatenated with itself in this instance. Row indexes from the original tables will be kept in the index for the DataFrame that results. The final DataFrame will include the columns from both frames if the two DataFrames passed to the concat function have different columns. If so, some of the cells for the columns that weren't there in the original DataFrames will eventually have NaN or missing values because they weren't present in the first DataFrame we merged into this larger DataFrame. Inner Join We could also try an inner join in place of having additional rows with missing numbers. As it merges the column values of two DataFrames into a new DataFrame, the inner join operation is helpful for integrating data as seen here. Observe that the correct method to achieve this is to tell concat that the join type is inner. The concatenated DataFrames in the previous slide were stacked vertically. The indices zero, one, two, and three are used to arrange them horizontally next to one another. Unfortunately, because the key columns were duplicated when they were merged into the new DataFrame separately, this isn't the appropriate merge for our data in the horizontal stacking. Appending DataFrames Concatenation can be replaced with add. Also, we may attach the DataFrame to any other DataFrame by using the append function. Although it is a function of the DataFrame itself, it functions in a manner similar to the concat function. We will therefore say left add and then provide it with a new DataFrame. We have the same number of empty cells here that we had in our initial application of concat. Merge Operation Merge is the name of the operation that will truly combine these two frames for us. Using the merge procedure has the advantage of removing redundant columns from the DataFrames it joins. So, it behaves very similarly to concat utilizing an inner join, removing only the duplicate columns we had. Any of these techniques can be useful depending on the circumstance, but I like to use merge a lot because I frequently need to mix data from various sources that use the same keys. Conclusion In conclusion, joining and merging operations that are clear and straightforward, like those used in relational databases, are advantageous for data in the form of tables. Via its native DataFrame capabilities, Pandas supports these database-like processes, making it simpler to integrate data from many sources for analytics on the combined datasets. To sum up, any data analyst must be able to combine data from various DataFrames. You may simply combine data sets and evaluate data from many sources by employing the facilities given by pandas. We have looked at a few of the most popular methods for combining data sets.