Apply function to all columns of data frame python

Question

I have two dfs

xx

AVERAGE_CALL_DURATION	AVERAGE_DURATION	CHANGE_OF_DETAILS
267	298 0	0
421	609.33	0.33
330	334 0	0
240.5	666.5	0
628	713 0	0

and

NoC_c

AVERAGE_CALL_DURATION	AVERAGE_DURATION	CHANGE_OF_DETAILS
-5.93	-4.95	0.90
593.50	595.70	1.00

I want to return 1 if the xx column contains the range within NoC_c (where column names are the same

I can do this for one column

def check_between_ranges(xx, NoC_c):
    ranges = NoC_c['AVERAGE_CALL_DURATION']
    
    if (xx['AVERAGE_CALL_DURATION'] >= ranges.iloc[0]) and (xx['AVERAGE_CALL_DURATION'] <= ranges.iloc[1]):
        return 1
    return xx['AVERAGE_CALL_DURATION']

xx['AVERAGE_CALL_DURATION2'] = xx.apply(lambda x: check_between_ranges(x, NoC_c), axis=1)

However, I need remove the element of manually specifying the column name as the actual dfs contain many more columns.

I have tried

a = NoC_c.columns

def check_between_ranges(xx, NoC_c):
    ranges = NoC_c[a]
    
    if (xx[a] >= ranges.iloc[0]) & (xx[a] <= ranges.iloc[1]):
        return 1

xx.apply(lambda x: check_between_ranges(x, NoC_c[a]), axis=1)

However, I get the error ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all() ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all() .

I tried the solutions listed here , although, they were unsuccessful

Also read this to address the specific error but didn't aid in my issue

Any help would be appreciated.

Traceback (most recent call last):

  File "<ipython-input-11-2affca771555>", line 10, in <module>
    xx.apply(lambda x: check_between_ranges(x, NoC_c[a]), axis=1)

  File "C:\Program Files\Anaconda3\lib\site-packages\pandas\core\frame.py", line 7552, in apply
    return op.get_result()

  File "C:\Program Files\Anaconda3\lib\site-packages\pandas\core\apply.py", line 185, in get_result
    return self.apply_standard()

  File "C:\Program Files\Anaconda3\lib\site-packages\pandas\core\apply.py", line 276, in apply_standard
    results, res_index = self.apply_series_generator()

  File "C:\Program Files\Anaconda3\lib\site-packages\pandas\core\apply.py", line 305, in apply_series_generator
    results[i] = self.f(v)

  File "<ipython-input-11-2affca771555>", line 10, in <lambda>
    xx.apply(lambda x: check_between_ranges(x, NoC_c[a]), axis=1)

  File "<ipython-input-11-2affca771555>", line 6, in check_between_ranges
    if (xx[a] >= ranges.iloc[0]) & (xx[a] <= ranges.iloc[1]):

  File "C:\Program Files\Anaconda3\lib\site-packages\pandas\core\generic.py", line 1330, in __nonzero__
    f"The truth value of a {type(self).__name__} is ambiguous. "

ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().

Answer 1

You have almost the solution. Try to add .all() , docs here :

def check_between_ranges(xx, NoC_c):
    ranges = NoC_c[a]
    
    if (xx[a] >= ranges.iloc[0]).all() & (xx[a] <= ranges.iloc[1]).all():
        return 1

Answer 2

Would this work for you?

Comparison Function

def check_between_ranges(x):
    v = []
    
    for c in x.index:
        if (x[c] >= NoC_c.at[0,c]) & (x[c] <= NoC_c.at[1,c]):
            v += [1]
        else:
            v += [x[c]]
            
    return pd.Series(v, index=x.index)

Execution

xx.apply(check_between_ranges, axis=1)

Result

   AVERAGE_CALL_DURATION  AVERAGE_DURATION  CHANGE_OF_DETAILS
0                    1.0              1.00               0.00
1                    1.0            609.33               0.33
2                    1.0              1.00               0.00
3                    1.0            666.50               0.00
4                  628.0            713.00               0.00