return max value from pandas dataframe as a whole, not based on column or rows
dataframe, max, pandas, python
Solution
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()
Problem
I am trying to get the max value from a panda dataframe as a 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.