return max value from pandas dataframe as a whole, not based on column or rows

I am trying to get the max value from a panda dataframe as whole. I am not interested in what row or column it came from. I am just interested in a single max value within the DataFrame.

Here is my DataFrame:

df = pd.DataFrame({'group1': ['a','a','a','b','b','b','c','c','d','d','d','d','d'],
                        'group2': ['c','c','d','d','d','e','f','f','e','d','d','d','e'],
                        'value1': [1.1,2,3,4,5,6,7,8,9,1,2,3,4],
                        'value2': [7.1,8,9,10,11,12,43,12,34,5,6,2,3]})

This is what it looks like:

   group1 group2  value1  value2
0       a      c     1.1     7.1
1       a      c     2.0     8.0
2       a      d     3.0     9.0
3       b      d     4.0    10.0
4       b      d     5.0    11.0
5       b      e     6.0    12.0
6       c      f     7.0    43.0
7       c      f     8.0    12.0
8       d      e     9.0    34.0
9       d      d     1.0     5.0
10      d      d     2.0     6.0
11      d      d     3.0     2.0
12      d      e     4.0     3.0

Expected output:

43.0

I was under the assumption that df.max() would do this job but it returns a max value for each column but I am not interested in that. I need the max from an entire dataframe.

Answers:

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Method 1

The max of all the values in the DataFrame can be obtained using df.to_numpy().max(), or for pandas < 0.24.0 we use df.values.max():

In [10]: df.to_numpy().max()
Out[10]: 'f'

The max is f rather than 43.0 since, in CPython2,

In [11]: 'f' > 43.0
Out[11]: True

In CPython2, Objects of different types … are
ordered by their type names. So any str compares as greater than any int since 'str' > 'int'.

In Python3, comparison of strings and ints raises a TypeError.

To find the max value in the numeric columns only, use

df.select_dtypes(include=[np.number]).max()

Method 2

Hi the simplest answer is the following.
Answer:

df.max().max()

Explanation:
series = df.max() give you a Series containing the maximum values for each column.
Therefore series.max()gives you the maximum for the whole dataframe.

🙂 best answers are usually the simplest

Method 3

An alternative way:

df.melt().value.max()

Essentially melt() transforms the DataFrame into one long column.

Method 4

For the max, check the previous answer…
For the max of the values use e.g.:

val_cols = [c for c in df.columns if c.startswith('val')]
print df[val_cols].max()

Method 5

Max can be found in these two steps:

maxForRow = allData.max(axis=0) #max for each row
globalMax = maxForRow.max(); #max across all rows

All methods was sourced from stackoverflow.com or stackexchange.com, is licensed under cc by-sa 2.5, cc by-sa 3.0 and cc by-sa 4.0