Making matplotlib boxplot include columns with NaN values

Question

I've been trying to plot a data frame as a box plot using matplotlib. My data frame looks something like this:

   9-1   9-2   9-3   9-4  9-5
0   23  16.0  18.0  18.0   26
1   27  18.0  20.0  17.0   33
2   10   9.0  15.0   8.0   30
3   23  30.0  19.0   5.0   15
4   10  10.0  23.0  29.0   12
5   50  13.0   8.0  20.0   23
6   12  24.0  31.0  27.0   35
7   10  29.0   NaN   7.0   22
8   34   NaN   NaN  16.0   31
9   28   NaN   NaN   NaN   24

Where every column is a day of a measurement. Below is what my plot looks like. I'd like to be able to plot every day in the box plot, including ones with NaN values. I know that matplotlib only plots the columns where there aren't any NaN values. Would this be possible to do using matplotlib or would I have to convert the data frame into a list of lists or Numpy array? Any help would be appreciated!

I know that there is a function in Pandas where we can put make a box plot using df.boxplot() or df.plot.box(), however, I strongly prefer to use matplotlib's boxplot() function instead. Also, I want to be able to plot the all of the values for days with complete measurements (like 9-1 and 9-5) instead of cutting them off to correspond to all the valid rows in the days with NaN values.

For images of the boxplot, https://i.sstatic.net/pBh6pEdf.png is what I produced and https://imgur.com/a/vvDFNR1 is what I'm trying to create with matplotlib's boxplot() function.

mozway · Accepted Answer · 2025-09-18 06:40:59Z

3

You could convert the DataFrame to a list of lists (or list of arrays) without NaNs (with dropna) and pass the names separately. I'll do this here using a dictionary:

data = {c: df[c].dropna().tolist() for c in df}
# {'9-1': [23, 27, 10, 23, 10, 50, 12, 10, 34, 28],
#  '9-2': [16.0, 18.0, 9.0, 30.0, 10.0, 13.0, 24.0, 29.0],
#  '9-3': [18.0, 20.0, 15.0, 19.0, 23.0, 8.0, 31.0],
#  '9-4': [18.0, 17.0, 8.0, 5.0, 29.0, 20.0, 27.0, 7.0, 16.0],
#  '9-5': [26, 33, 30, 15, 12, 23, 35, 22, 31, 24]}

plt.boxplot(data.values(), tick_labels=data.keys())

Or, as a one-liner:

plt.boxplot([df[c].dropna() for c in df], tick_labels=list(df))

And for the sake of completeness, this can of course be obtained easily with the pandas API:

df.plot.box()

Output:

edited Sep 18 at 6:40

answered Sep 18 at 6:34

mozway

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