How can I normalize the data in a range of columns in my pandas dataframe

Simple way and way more efficient:
Pre-calculate the mean:
dropna() avoid missing data.

mean_age = survey_data.Age.dropna().mean()
max_age = survey_data.Age.dropna().max()
min_age = survey_data.Age.dropna().min()

dataframe['Age'] = dataframe['Age'].apply(lambda x: (x - mean_age ) / (max_age -min_age ))

this way will work...

I think it's better to use 'sklearn.preprocessing' in this case which can give us much more scaling options. The way of doing that in your case when using StandardScaler would be:

from sklearn.preprocessing import StandardScaler
cols_to_norm = ['Age','Height']
surveyData[cols_to_norm] = StandardScaler().fit_transform(surveyData[cols_to_norm])

You can perform operations on a sub set of rows or columns in pandas in a number of ways. One useful way is indexing:

# Assuming same lines from your example
cols_to_norm = ['Age','Height']
survey_data[cols_to_norm] = survey_data[cols_to_norm].apply(lambda x: (x - x.min()) / (x.max() - x.min()))

This will apply it to only the columns you desire and assign the result back to those columns. Alternatively you could set them to new, normalized columns and keep the originals if you want.