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

Here is a small example that you can build upon:

Basically, lambda x: x.. is the short one-liner of a function. What apply really asks for is a function which you can easily recreate yourself.

import pandas as pd

# Recreate the dataframe
data = dict(Size=[80000,8000000,800000000])
df = pd.DataFrame(data)

# Create a function that returns desired values
# You only need to check upper bound as the next elif-statement will catch the value
def func(x):
    if x < 1e6:
        return "<1m"
    elif x < 1e7:
        return "1-10m"
    elif x < 5e7:
        return "10-50m"
    else:
        return 'N/A'
    # Add elif statements....

df['Classification'] = df['Size'].apply(func)

print(df)

Returns:

        Size Classification
0      80000            <1m
1    8000000          1-10m
2  800000000            N/A