Replacing values greater than a number in pandas dataframe

Very simply : df[df > 9] = 11

You can use apply with list comprehension:

df1['A'] = df1['A'].apply(lambda x: [y if y <= 9 else 11 for y in x])
print (df1)
                                A
2017-01-01 02:00:00  [11, 11, 11]
2017-01-01 03:00:00    [3, 11, 9]

Faster solution is first convert to numpy array and then use numpy.where:

a = np.array(df1['A'].values.tolist())
print (a)
[[33 34 39]
 [ 3 43  9]]

df1['A'] = np.where(a > 9, 11, a).tolist()
print (df1)
                                A
2017-01-01 02:00:00  [11, 11, 11]
2017-01-01 03:00:00    [3, 11, 9]

You can use numpy indexing, accessed through the .values function.

df['col'].values[df['col'].values > x] = y

where you are replacing any value greater than x with the value of y.

So for the example in the question:

df1['A'].values[df1['A'] > 9] = 11

I know this is an old post, but pandas now supports DataFrame.where directly. In your example:

df.where(df <= 9, 11, inplace=True)

Please note that pandas' where is different than numpy.where. In pandas, when the condition == True, the current value in the dataframe is used. When condition == False, the other value is taken.

EDIT:

You can achieve the same for just a column with Series.where:

df['A'].where(df['A'] <= 9, 11, inplace=True)