How to efficiently merge multiple DataFrames with different column names in pandas?
I'm trying to implement I'm trying to merge three DataFrames with different sets of columns into one single DataFrame. Each DataFrame represents different data related to users while some columns are common, and others are unique to each DataFrame. For instance, I have the following DataFrames: ```python import pandas as pd df1 = pd.DataFrame({ 'user_id': [1, 2, 3], 'name': ['Alice', 'Bob', 'Charlie'] }) df2 = pd.DataFrame({ 'user_id': [2, 3, 4], 'age': [28, 22, 30] }) df3 = pd.DataFrame({ 'user_id': [1, 3, 5], 'country': ['US', 'UK', 'Canada'] }) ``` I want to merge these three DataFrames on the `user_id` column, but I'm running into issues when I try to use `pd.concat()` with `axis=0`. The resultant DataFrame has a lot of `NaN` values because of the mismatches in column names. Here’s what I tried: ```python result = pd.concat([df1, df2, df3], axis=0) ``` This gives me a DataFrame with many missing values and it’s not the way I want to visualize the user data. I also tried merging them using `pd.merge()` multiple times, but I’m confused about how to handle the `how` parameter correctly for different scenarios. Can someone suggest the best approach to merge these DataFrames in such a way that I can keep all unique columns and fill missing values appropriately? I’m using pandas version 1.4.3. Any insights would be appreciated! I'm working on a application that needs to handle this. Thanks in advance! I'm open to any suggestions.