How to get the max/min value in Pandas DataFrame when nan value in it

Question

Since one column of my pandas dataframe has nan value, so when I want to get the max value of that column, it just return error.

>>> df.iloc[:, 1].max()
'error:512'

How can I skip that nan value and get the max value of that column?

Please post the DataFrame. The max function skips nan by default - see docs. — Alex
– Alex, Commented Jul 21, 2016 at 15:33
What is "error 512"? Is that something written by you? python/pandas does not throw that error AFAIK. — Merlin
– Merlin, Commented Jul 21, 2016 at 16:20

Divakar · Accepted Answer · 2016-07-21 15:34:03Z

23

You can use NumPy's help with np.nanmax, np.nanmin :

In [28]: df
Out[28]: 
   A   B  C
0  7 NaN  8
1  3   3  5
2  8   1  7
3  3   0  3
4  8   2  7

In [29]: np.nanmax(df.iloc[:, 1].values)
Out[29]: 3.0

In [30]: np.nanmin(df.iloc[:, 1].values)
Out[30]: 0.0

answered Jul 21, 2016 at 15:34

Divakar

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Alex · Accepted Answer · 2016-07-21 15:27:46Z

18

You can use Series.dropna.

res = df.iloc[:, 1].dropna().max()

answered Jul 21, 2016 at 15:27

Alex

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1 Comment

Christov Over a year ago

This is quite nice. np.nanmin and np.nanmax are sensitive to warnings as discussed here, but this approach solves that issue.

Gilco · Accepted Answer · 2019-04-14 11:09:48Z

4

if you dont use iloc or loc, it is simple as:

df['column'].max()

or

df['column'][df.index.min():df.index.max()]

or any kind of range in this second square brackets

answered Apr 14, 2019 at 11:09

Gilco

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Rene · Accepted Answer · 2022-05-25 11:21:33Z

4

You can set numeric_only = True when calling max:

df.iloc[:, 1].max(numeric_only = True)

Attention:

For everyone trying to use it with pandas.series This is not working nevertheless it is mentioned in the docs

See post on github

edited May 25, 2022 at 11:21

Rene

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answered Aug 27, 2019 at 3:45

user5054

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YoongKang Lim · Accepted Answer · 2018-01-24 09:01:24Z

2

Dataframe aggregate function.agg() will automatically ignore NaN value. df.agg({'income':'max'})

Besides, it can also be use together with .groupby

df.groupby('column').agg({'income':['max','mean']})

edited Jan 24, 2018 at 9:01

answered Aug 2, 2017 at 3:04

YoongKang Lim

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pizza lover · Accepted Answer · 2016-07-21 22:02:54Z

1

When the df contains NaN values it reports NaN values, Using np.nanmax(df.values) gave the desired answer.

answered Jul 21, 2016 at 22:02

pizza lover

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