Pandas Passing Variable Names Into Column Name

I have a dataframe that contains 13 different column names, I have separated these headings into two lists. I now want to perform different operations on each of these lists. Is i

Solution 1:

I think you can use subset created from listCONT:

printdf
  age fnlwgt  capital-gain
0   a    9th             5
1   b    9th             6
2   c    8th             3

CONT = ['age','fnlwgt']

printdf[CONT]
  age fnlwgt
0   a    9th
1   b    9th
2   c    8th

printdf[CONT].count()
age       3
fnlwgt    3
dtype: int64

printdf[['capital-gain']]
   capital-gain
0             5
1             6
2             3

Maybe better as list is dictionary, which is created by to_dict:

d = df[CONT].count().to_dict()
print d
{'age': 3, 'fnlwgt': 3}
print d['age']
3
print d['fnlwgt']
3

Solution 2:

try:

for column_name, column in df.transpose().iterrows(): 
    if column_name in CONT:
        print(df[column_name].count()) 
    else: 
        print('')

edit:

To answer your question more precisely: You can use variables to select cols in 2 ways: df[list_of_columns] will return a DataFrame with the subset of cols in list_of_columns. df[column_name] will return the Series for column_name

Solution 3:

The following will print the count of each column in the dataframe if it is a subset of your CONT list.

CONT = ['age', 'fnlwgt', 'capital-gain', 'capital-loss']
df = pd.DataFrame(np.random.rand(5, 2), columns=CONT[:2])

>>> df
        age    fnlwgt
0  0.079796  0.736956
1  0.120187  0.778335
2  0.698782  0.691850
3  0.421074  0.369500
4  0.125983  0.454247

Select the subset of columns and perform a transform.

>>> df[[c for c in CONT if c in df]].count()
age       5
fnlwgt    5
dtype: int64