How to read Excel with pandas when the last column is blank: avoid usecols pitfalls and select the trailing column safely

Learn how to import Excel in pandas when a trailing column is blank: avoid usecols warnings and ParserError, skip fixed ranges, select the last column via iloc.

Parsing Excel files you don’t control can get tricky when the layout is inconsistent. A common case: several well-formed columns with headers, plus a trailing column that has no header and is frequently blank. Trying to enforce a fixed column range during import may produce warnings and silently drop that last column when it contains no data at the top.

Reproducing the issue

Consider an Excel sheet where columns A–D have headers and data, while column E has no header and may be empty at the top or entirely blank. Reading a fixed range with pandas often looks like the obvious move, but it triggers trouble:

import pandas as pd

xls_path = "input.xlsx"
chopped_df = pd.read_excel(xls_path, usecols="A:E")

This can produce the warning FutureWarning: Defining usecols with out of bounds indices is deprecated and will raise a ParserError in a future version. It also won’t return the fifth column in cases where that column is blank at the top or fully empty. Passing an explicit index list like usecols=[0, 1, 2, 3, 4] leads to the same warning and behavior in this scenario.

Why it happens

When the trailing column is entirely empty, pandas does not load it from Excel at all. Forcing a range that includes such a column makes the selection out of bounds relative to what was actually read, hence the warning and the missing column. If you instruct pandas to treat the first row as plain data (header=None), it will fill missing cells in the top rows with NaN, but a column that is completely empty still won’t be read from the file. In that case, trying to force its inclusion via usecols triggers the same out-of-bounds situation. After the import, you can always inspect the DataFrame and add a new column in pandas if you need that placeholder present.

The practical fix

Instead of constraining columns at read time, import the sheet as-is and select what you need from the resulting DataFrame. This avoids out-of-bounds selections caused by completely blank columns and lets you reliably access the trailing column when it actually contains data.

import pandas as pd

file_uri = "test.xlsx"
full_df = pd.read_excel(file_uri)

# Select the last column regardless of its name or header status
last_col_df = full_df.iloc[0:, -1:]
print(last_col_df)

Here, iloc[0:, -1:] targets all rows and the last column only. If the trailing column has any values in any row, it will be present in the DataFrame and this selection will work. If the column is completely blank and thus not read, the last column will simply be the last one that contains data.

Why this matters

Relying on a hard-coded Excel column range can break whenever a source adds or removes trailing empties, or when a column is temporarily blank. The behavior described will produce deprecation warnings today and can escalate to ParserError in future pandas versions. Removing the dependency on usecols for potentially blank columns makes your import pipeline more resilient to real-world spreadsheets.

Takeaways

When dealing with an optional or often-empty trailing column, avoid forcing the Excel range with usecols. Read the sheet, then pick columns from the resulting DataFrame. If you need to ensure that empty leading cells are represented as NaN, read with header=None to retain top-row empties as data; just remember that a wholly empty column still won’t be loaded and may need to be added afterward in pandas if you require it to exist. This small shift in approach keeps your code future-proof, avoids warnings, and handles the messy edges of uncontrolled Excel inputs gracefully.