wide to long amid merge

The problem at hand is merging two datasets, df1 and df2, into a single dataset, df_desire. The resulting dataset should have the company name as the index, analyst names as columns, and scores assigned by each analyst.

Background

To understand this problem, we need to know a bit about data manipulation in pandas. When working with datasets that contain multiple variables for each observation (such as analysts), it’s common to convert such data into a “long format”. This process is called pivoting or melting the dataset.

Data Preparation

Let’s begin by defining our datasets:

import pandas as pd
import numpy as np

df1 = pd.DataFrame({
    'company name': ['A', 'B', 'C'],
    'analyst 1 name': ['Tom', 'Mike', np.nan],
    'analyst 2 name': [np.nan, 'Alice', np.nan],
    'analyst 3 name': ['Jane', 'Steve', 'Alex']
})

df2 = pd.DataFrame({
    'company name': ['A', 'B', 'C'],
    'score 1': [3, 5, np.nan],
    'score 2': [np.nan, 1, np.nan],
    'score 3': [6, np.nan, 11]
})

df_desire = pd.DataFrame({
    'company name': ['A', 'A', 'B', 'B', 'B', 'C'],
    'analyst': ['Tom', 'Jane', 'Mike', 'Alice', 'Steve', 'Alex'],
    'score': [3, 6, 5, 1, np.nan, 11]
})

Solution Overview

To achieve the desired outcome, we need to first convert both df1 and df2 into a long format using the melt function. Then, we can concatenate these two datasets along the columns (axis=1) to create our final dataset.

However, as the original poster asked for a more elegant solution, let’s explore an alternative approach that utilizes setting indices and stacking datasets instead.

Alternative Approach

This approach leverages pandas’ ability to work with indices. We’ll set df1 and df2 on the index axis ("company name"), then stack them along the columns using .stack(dropna=False) to preserve NaN values. Finally, we’ll reset the index and drop unnecessary columns.

Here’s how it works:

# Set df1 as index and melt it with id_vars='company name'
x = (
    df1.set_index("company name")
    .stack(dropna=False)
    .reset_index(name="name")  # rename the column to 'analyst'
    .drop(columns=["company name"])  # remove the redundant company_name column
)

# Set df2 as index and melt it with id_vars='company name'
y = df2.set_index("company name").stack(dropna=False).reset_index(name="score")

# Concatenate x and y, select necessary columns, drop rows where all values are NaN
print(
    pd.concat([x, y], axis=1)[["company name", "name", "score"]]
    .dropna(subset=["name", "score"], how="all")
    .reset_index(drop=True)
)

Output

The resulting dataset should match our expected df_desire:

  company name   name  score
0            A    Tom    3.0
1            A   Jane    6.0
2            B   Mike    5.0
3            B  Alice    1.0
4            B  Steve    NaN
5            C   Alex   11.0

Conclusion

The solution presented above provides an elegant way to merge two datasets while preserving the necessary analyst and score information. By using pandas’ powerful set_index and stack functions, we can efficiently convert our data into the desired long format without having to explicitly specify column names or perform intermediate data manipulation steps.

Last modified on 2024-06-27