Pandas DataFrame won’t reindex and transpose, returns NaN

When working with Pandas DataFrames, it’s common to encounter scenarios where the data needs to be transformed or rearranged. However, sometimes the expected outcome doesn’t materialize as anticipated. In this article, we’ll explore a specific scenario where attempting to reindex and transpose a DataFrame results in NaN values.

The Problem

Suppose you have a Pandas DataFrame invoice_desc containing information about invoices, including columns for invoice description, billing ID, issue date, due date, currency, invoice subtotal, VAT (value-added tax), and amount due. You want to select specific lines from this DataFrame, reindex the remaining lines, and then transpose the resulting DataFrame.

Here’s an example code snippet that attempts to accomplish this:

invoice_desc = pd.read_csv('path', sep=',', nrows=9, header=None)
i = ['invoiceNum', 'issueDate', 'dueDate', 'invoiceSubtotal']
invoice_desc2 = invoice_desc.loc[[2, 3, 4, 8], :]
invoice_desc2 = invoice_desc2.T
invoice_desc2.columns = i
print(invoice_desc2)

However, when you run this code, the resulting DataFrame invoice_desc2 contains NaN values instead of the expected data.

The Explanation

To understand why this is happening, let’s break down each step of the process:

1. Locating rows: When we use invoice_desc.loc[[2, 3, 4, 8], :], we’re selecting specific rows from the original DataFrame invoice_desc. The [2, 3, 4, 8] list specifies which rows to include in the new DataFrame.
2. Transposing: After selecting the desired rows, we transpose the resulting DataFrame using invoice_desc2 = invoice_desc2.T. This operation swaps the row and column indices of the DataFrame.
3. Renaming columns: Finally, we assign new column names to the transposed DataFrame using invoice_desc2.columns = i.

However, when we run this code, something unexpected happens:

• The rows selected from invoice_desc contain NaN values in certain positions.
• When transposing invoice_desc2, these NaN values are preserved and become part of the new DataFrame.

What’s Going On?

To understand why this is happening, let’s take a closer look at how Pandas handles missing data:

• Pandas stores missing data as NaN (Not a Number) by default.
• When you select rows from a DataFrame using loc, any NaN values in those positions are included in the selection.

In our example code snippet, we’re selecting specific rows from invoice_desc and then transposing invoice_desc2. However, since some of these rows contain NaN values, those NaN values become part of the new DataFrame when we transpose it.

Solutions

Now that we understand what’s going on, let’s explore a few ways to fix this issue:

1. Remove NaN Values Before Transposing

Before transposing invoice_desc2, you can remove any rows that contain NaN values using dropna:

invoice_desc = pd.read_csv('path', sep=',', nrows=9, header=None)
i = ['invoiceNum', 'issueDate', 'dueDate', 'invoiceSubtotal']
invoice_desc2 = invoice_desc.loc[[2, 3, 4, 8], :]
invoice_desc2 = invoice_desc2.dropna()  # Remove rows with NaN values
invoice_desc2 = invoice_desc2.T
invoice_desc2.columns = i
print(invoice_desc2)

This approach ensures that the new DataFrame invoice_desc2 doesn’t contain any NaN values.

2. Fill NaN Values Before Transposing

Alternatively, you can fill any missing data with a specific value using fillna. This way, when you transpose invoice_desc2, the NaN values will be replaced:

invoice_desc = pd.read_csv('path', sep=',', nrows=9, header=None)
i = ['invoiceNum', 'issueDate', 'dueDate', 'invoiceSubtotal']
invoice_desc2 = invoice_desc.loc[[2, 3, 4, 8], :]
invoice_desc2['invoiceNum'] = invoice_desc2['invoiceNum'].fillna('Unknown')
invoice_desc2['issueDate'] = invoice_desc2['issueDate'].fillna('01-Jan-2016')
invoice_desc2['dueDate'] = invoice_desc2['dueDate'].fillna('31-Dec-2015')
invoice_desc2['invoiceSubtotal'] = invoice_desc2['invoiceSubtotal'].fillna(0)
invoice_desc2 = invoice_desc2.T
invoice_desc2.columns = i
print(invoice_desc2)

In this example, we’re filling the NaN values in each column with a specific default value. This way, when we transpose invoice_desc2, the resulting DataFrame will contain only valid data.

3. Use Pandas’ Built-in Features

Pandas provides built-in features for handling missing data and data transformation. For instance, you can use pivot_table to reshape your data:

invoice_desc = pd.read_csv('path', sep=',', nrows=9, header=None)
i = ['invoiceNum', 'issueDate', 'dueDate', 'invoiceSubtotal']
invoice_desc2 = invoice_desc.pivot_table(values='Amount due', index='invoiceNum', columns=['issueDate', 'dueDate'], aggfunc='first')
print(invoice_desc2)

In this example, we’re using pivot_table to reshape the data. The resulting DataFrame will contain only valid data.

Conclusion

When working with Pandas DataFrames, it’s essential to understand how missing data is handled and how to transform your data effectively. In this article, we explored a scenario where attempting to reindex and transpose a DataFrame resulted in NaN values. We examined the underlying reasons for this behavior and presented several solutions for addressing these issues.

By applying the techniques outlined in this article, you can overcome common challenges when working with Pandas DataFrames and produce high-quality data transformations that meet your requirements.

Last modified on 2025-04-18