Set maximum value (upper bound) in pandas DataFrame

I suppose you can do:

maxVal = 15
df['a'].where(df['a'] <= maxVal, maxVal)      # where replace values with other when the 
                                              # condition is not satisfied

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

Or:

df['a'][df['a'] >= maxVal] = maxVal

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.

You can use clip.

Apply to all columns of the data frame:

df.clip(upper=15)

Otherwise apply to selected columns as seen here:

df.clip(upper=pd.Series({'a': 15}), axis=1)