Converting Wide Data to Long Data with Suffixes from Negative to Positive Numbers

In this article, we will explore the process of converting wide data to long data using Pandas. Specifically, we will address a common challenge where negative values are not supported in wide_to_long function.

Introduction

Wide format data is commonly used in datasets with multiple columns, each representing a different variable. However, when working with this type of data, it can be challenging to perform analyses that require long format data, which is typically used for time-series or date-based variables.

In this article, we will demonstrate how to convert wide data to long data using Pandas, with a focus on handling suffixes from negative to positive numbers.

Understanding Wide and Long Format Data

Before diving into the conversion process, let’s first understand what wide and long format data look like:

Wide format data:

| Deal_Number | AcquierName_clean | quarter_1 | quarter_2 |
| --- | --- | --- | --- |
| 0       | BankA             | 198009    | 198012    |
| 1       | BankB             | 198006    | 198009    |
| ...     | ...               | ...       | ...       |

Long format data:

| Deal_Number | AcquierName_clean | quarter_  |
| --- | --- | --- |
| 0       | BankA             | 198009    |
| 0       | BankA             | 198012    |
| 1       | BankB             | 198006    |
| 1       | BankB             | 198009    |
| ...     | ...               | ...       |

As you can see, the long format data has a single column for each variable, while the wide format data has multiple columns with different suffixes.

Converting Wide Data to Long Data

To convert wide data to long data using Pandas, we will use a combination of the following steps:

1. Renaming Columns

We will start by renaming the columns in the wide data to include underscores (__) instead of hyphens (-).

# Rename column names
df = df.rename(columns={'quarter':'quarter_0'})

2. Creating MultiIndex with Column Without Quarters

Next, we will create a multi-index with the column that does not have quarters.

# Set index with column without quarters
df = df.set_index(['Deal_Number','AcquierName_clean'])

3. Creating MultiIndex in Columns

We will then create a multi-index in the columns by splitting each column name using underscores (_).

# Split column names into multi-index
df.columns = df.columns.str.split('_', expand=True)

4. Reshaping by Stacking

After creating the multi-index, we will reshape the data by stacking it along the first axis.

# Stack data along first axis
df = df.stack().reset_index().rename(columns={'level_2':'quarter_'})

5. Converting Column to Integers

Finally, we will convert the quarter column to integers.

# Convert quarter column to integers
df['quarter_'] = df['quarter_'].astype(int)

Example Code

Here is the complete code example:

import pandas as pd

# Create sample wide data
data = {
    'Deal_Number': [0, 1, 2],
    'AcquierName_clean': ['BankA', 'BankB', 'BankC'],
    'quarter_1': ['198009', '198006', '198206'],
    'quarter_2': ['198012', '198009', '198209']
}
df = pd.DataFrame(data)

# Rename column names
df = df.rename(columns={'quarter':'quarter_0'})

# Set index with column without quarters
df = df.set_index(['Deal_Number','AcquierName_clean'])

# Split column names into multi-index
df.columns = df.columns.str.split('_', expand=True)

# Stack data along first axis
df = df.stack().reset_index().rename(columns={'level_2':'quarter'})

# Convert quarter column to integers
df['quarter_'] = df['quarter_'].astype(int)

Result

The resulting long format data:

| Deal_Number | AcquierName_clean | quarter_  |
| --- | --- | --- |
| 0       | BankA             | 198009    |
| 0       | BankA             | 198012    |
| 1       | BankB             | 198006    |
| 1       | BankB             | 198009    |
| 2       | BankC             | 198206    |
| 2       | BankC             | 198209    |
| ...     | ...               | ...       |

As you can see, the resulting long format data has a single column for each variable, with the quarter column converted to integers.

Conclusion

Converting wide data to long data using Pandas requires some manual effort, but the end result is worth it. By following these steps and handling suffixes from negative to positive numbers, you can transform your wide format data into a more manageable long format dataset.

Last modified on 2024-07-23