In [1]: df
Out[1]:
   data
0     1
1     2
2     3
3     4

You want to apply a function that conditionally returns a value based on the selected dataframe column.

In [2]: df['data'].apply(lambda x: 'true' if x <= 2.5 else 'false')
Out[2]:
0     true
1     true
2    false
3    false
Name: data

You can then assign that returned column to a new column in your dataframe:

In [3]: df['desired_output'] = df['data'].apply(lambda x: 'true' if x <= 2.5 else 'false')

In [4]: df
Out[4]:
   data desired_output
0     1           true
1     2           true
2     3          false
3     4          false

is that what you want?

In [300]: frame[['b','c']].apply(lambda x: x['c'] if x['c']>0 else x['b'], axis=1)
Out[300]:
0   -1.099891
1    0.582815
2    0.901591
3    0.900856
dtype: float64

The following lambda expression should work:

lambda x: float(x.replace(",","") if not math.isnan(x) else x)

Note the mandatory else-part. This assumes that you want the nan's unchanged. See the docs on Conditional Expressions.

Alternatively, you can use loc:

import pandas as pd

df = pd.DataFrame({"age": [-100, 300, 400, 500, 600, 700]})
df["age"].loc[(df["age"] < 500) & (df["age"] >= 0)] = 0

Now your df looks like this:

    age
0   -100
1   0
2   0
3   500
4   600
5   700