pandas three-way joining multiple dataframes on columns
join, merge, pandas, python
Solution
Zero's answer is basically a `reduce` operation. If I had more than a handful of dataframes, I'd put them in a list like this (generated via list comprehensions or loops or whatnot):
dfs = [df0, df1, df2, ..., dfN]
Assuming they have a common column, like `name` in your example, I'd do the following:
import functools as ft
df_final = ft.reduce(lambda left, right: pd.merge(left, right, on='name'), dfs)
That way, your code should work with whatever number of dataframes you want to merge.
Problem
I have 3 CSV files. Each has the first column as the (string) names of people, while all the other columns in each dataframe are attributes of that person. How can I "join" together all three CSV documents to create a single CSV with each row having all the attributes for each unique value of the person's string name? The `join()` function in pandas specifies that I need a multiindex, but I'm confused about what a hierarchical indexing scheme has to do with making a join based on a single index.