How to flatten a hierarchical index in columns

📝How to Flatten a Hierarchical Index in Columns: A Complete Guide 📐

Have you ever encountered a data frame with a hierarchical index in the columns and struggled to flatten it? Well, fear not! In this guide, we will walk you through the process of flattening a hierarchical index in columns step by step, with examples and easy-to-understand explanations. So, let's dive in and flatten that index!

🔍 Understanding the Problem

First, let's take a look at the data frame we're dealing with. We have a data frame with a hierarchical index in axis 1 (columns) resulting from a groupby.agg operation. The data frame looks like this:

USAF   WBAN  year  month  day  s_PC  s_CL  s_CD  s_CNT  tempf       
                                 sum   sum   sum    sum   amax   amin
0  702730  26451  1993      1    1     1     0    12     13  30.92  24.98
1  702730  26451  1993      1    2     0     0    13     13  32.00  24.98
2  702730  26451  1993      1    3     1    10     2     13  23.00   6.98
3  702730  26451  1993      1    4     1     0    12     13  10.04   3.92
4  702730  26451  1993      1    5     3     0    10     13  19.94  10.94

As you can see, the column index has multiple levels (e.g., tempf, tempf_amax, tempf_amin). Our goal is to flatten this hierarchical index and make it look like this:

USAF   WBAN  year  month  day  s_PC  s_CL  s_CD  s_CNT  tempf_amax  tempf_amin   
0  702730  26451  1993      1    1     1     0    12     13       30.92       24.98
1  702730  26451  1993      1    2     0     0    13     13       32.00       24.98
2  702730  26451  1993      1    3     1    10     2     13       23.00       6.98
3  702730  26451  1993      1    4     1     0    12     13       10.04       3.92
4  702730  26451  1993      1    5     3     0    10     13       19.94       10.94

This flattened index makes the data frame easier to work with and analyze.

💡 Easy Solutions

Now that we understand the problem at hand, let's explore some easy solutions to flatten the hierarchical index in columns:

1. Using reset_axis and set_axis methods:

   df.columns = df.columns.reset_index().set_axis(['_'.join(col).strip() for col in df.columns.values], axis=1, inplace=False)

   This solution resets the column index, joins the levels with an underscore, and sets the modified axis as the new column index.

2. Using reindex and get_level_values methods:

   df.columns = df.columns.get_level_values(0) + '_' + df.columns.get_level_values(1).astype(str)

   Here, we extract the values of the levels, convert them to strings, concatenate them with an underscore, and assign the result to the column index.

🎯 Putting It into Action

Using the above solutions, we can now flatten the hierarchical index in columns of your data frame. Choose the solution that resonates with you and apply it to your specific scenario. Let's take the first solution as an example:

df.columns = df.columns.reset_index().set_axis(['_'.join(col).strip() for col in df.columns.values], axis=1, inplace=False)

🔀 Conclusion

Flattening a hierarchical index in columns might seem daunting at first, but with the right approach, it can be a breeze. In this guide, we explored the problem, provided easy-to-understand solutions, and even demonstrated the application of one of the solutions. Now it's your turn to give it a try!

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