Set maximum value (upper bound) in pandas DataFrame

Question

I\'m trying to set a maximum value of a pandas DataFrame column. For example:

my_dict = {\'a\':[10,12,15,17,19,20]}
df = pd.DataFrame(my_dict)

df[\'a\'].se         


        

Answer 1

numpy.clip is a good, fast alternative.

df

    a
0  10
1  12
2  15
3  17
4  19
5  20

np.clip(df['a'], a_max=15, a_min=None)

0    10
1    12
2    15
3    15
4    15
5    15
Name: a, dtype: int64

# Or,
np.clip(df['a'].to_numpy(), a_max=15, a_min=None)
# array([10, 12, 15, 15, 15, 15])

---

From v0.21 onwards, you can also use DataFrame.clip_upper.

Note
This method (along with clip_lower) has been deprecated from v0.24 and will be removed in a future version.

df.clip_upper(15)
# Or, for a specific column,
df['a'].clip_upper(15)

    a
0  10
1  12
2  15
3  15
4  15
5  15

In similar vein, if you only want to set the lower bound, use DataFrame.clip_lower. These methods are also avaliable on Series objects.