The Problem (Q-score 4, ranked #291st of 303 in the Excel VBA archive)
The scenario as originally posted in 2014
I have Excel files with multiple sheets, each of which looks a little like this (but much longer):
Sample CD4 CD8
Day 1 8311 17.3 6.44
8312 13.6 3.50
8321 19.8 5.88
8322 13.5 4.09
Day 2 8311 16.0 4.92
8312 5.67 2.28
8321 13.0 4.34
8322 10.6 1.95
The first column is actually four cells merged vertically.
When I read this using pandas.read_excel, I get a DataFrame that looks like this:
Sample CD4 CD8
Day 1 8311 17.30 6.44
NaN 8312 13.60 3.50
NaN 8321 19.80 5.88
NaN 8322 13.50 4.09
Day 2 8311 16.00 4.92
NaN 8312 5.67 2.28
NaN 8321 13.00 4.34
NaN 8322 10.60 1.95
How can I either get Pandas to understand merged cells, or quickly and easily remove the NaN and group by the appropriate value? (One approach would be to reset the index, step through to find the values and replace NaNs with values, pass in the list of days, then set the index to the column. But it seems like there should be a simpler approach.)
Why community consensus is tight on this one
Across 303 Excel VBA entries in the archive, the accepted answer here holds niche answer (below median) status — meaning voters are unusually aligned on the right fix.
The Verified Solution — niche answer (below median) (+7)
Advisory answer — community consensus with reference links
Note: the verified answer below is a reference / advisory response rather than a copy-ready snippet.
You could use the Series.fillna method to forword-fill in the NaN values:
df.index = pd.Series(df.index).fillna(method='ffill')
For example,
In [42]: df
Out[42]:
Sample CD4 CD8
Day 1 8311 17.30 6.44
NaN 8312 13.60 3.50
NaN 8321 19.80 5.88
NaN 8322 13.50 4.09
Day 2 8311 16.00 4.92
NaN 8312 5.67 2.28
NaN 8321 13.00 4.34
NaN 8322 10.60 1.95
[8 rows x 3 columns]
In [43]: df.index = pd.Series(df.index).fillna(method='ffill')
In [44]: df
Out[44]:
Sample CD4 CD8
Day 1 8311 17.30 6.44
Day 1 8312 13.60 3.50
Day 1 8321 19.80 5.88
Day 1 8322 13.50 4.09
Day 2 8311 16.00 4.92
Day 2 8312 5.67 2.28
Day 2 8321 13.00 4.34
Day 2 8322 10.60 1.95
[8 rows x 3 columns]
When to Use It — classic (2013–2016)
Ranked #291st in its category — specialized fit
This pattern sits in the 98% tail relative to the top answer. Reach for it when your scenario closely matches the question title; otherwise browse the Excel VBA archive for a higher-consensus alternative.
What changed between 2014 and 2026
The answer is 12 years old. The Excel VBA object model has been stable across Office 2013, 2016, 2019, 2021, 365, and 2024/2026 LTSC, so the pattern still compiles. Changes that might affect you: 64-bit API declarations (use PtrSafe), blocked macros in downloaded files (Mark-of-the-Web), and the shift toward Office Scripts for web-first workflows.