![]() The theory is exactly the same for pandas merge. ![]() In SQL, we learned that there are different JOIN types. One of the most important questions is how you want to merge these tables. Sometimes you have to add a few extra parameters though. (Just try it!) Pandas Merge… But how? Inner, outer, left or right?Īs you can see, the basic merge method is pretty simple. The only difference between the two is the order of the columns in the output table. I could have done this the other way around: merge() pandas method on it and as a parameter I specified the second dataframe ( zoo_eats). (Oh, hey, where are all the lions? We will get back to that soon, I promise!)īamm! Simple, right? Just in case, let’s see what’s happening here:įirst, I specified the first dataframe ( zoo), then I applied the. Okay, now let’s see the pandas merge method: Zoo_eats = pd.DataFrame(,, ,, ], columns=) □ Just copy-paste this (really long) one line into the pandas_tutorial_1 Jupyter Notebook we made in the first Pandas tutorial: If I were you, to put this into a proper pandas dataframe, I’d follow the process from the Pandas Tutorial 1 article, but if you want to do this the lazy way, here’s a shortcut. For your convenience, here’s the raw data of the zoo_eats dataframe: animal food In this table, it’s finally possible to analyze, for instance, how many animals in our zoo eat meat or vegetables.įirst of all, you have the zoo dataframe already, but for this exercise you will have to create a zoo_eats dataframe, too. We want to merge these two pandas dataframes into one big dataframe. ![]() Let me show you an example! Let’s take our zoo dataframe (from our previous tutorials) in which we have all our animals… and let’s say that we have another dataframe, zoo_eats, that contains information about the food requirements for each species. Note: Although it’s called merge in pandas, it’s almost the same as SQL’s JOIN method. The point is that it’s quite usual that during your analysis you have to pull your data from two or more different tables. There are many reasons behind this by using multiple data tables, it’s easier to manage your data, it’s easier to avoid redundancy, you can save some disk space, you can query the smaller tables faster, etc. We store it in a few smaller ones instead. In real life data projects, we usually don’t store all the data in one big data table.
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