You can use:

np.random.seed(33454)
stepframe = pd.DataFrame({'a': np.random.randint(1, 200, 20), 
                          'b': np.random.randint(1, 200, 20),
                          'c': np.random.randint(1, 200, 20)})

stepframe[stepframe > 150] *= 10
print (stepframe)

Q1 = stepframe.quantile(0.25)
Q3 = stepframe.quantile(0.75)
IQR = Q3 - Q1

df = stepframe[~((stepframe < (Q1 - 1.5 * IQR)) |(stepframe > (Q3 + 1.5 * IQR))).any(axis=1)]

print (df)
      a    b     c
1   109   50   124
3   137   60  1990
4    19  138   100
5    86   83   143
6    55   23    58
7    78  145    18
8   132   39    65
9    37  146  1970
13   67  148  1880
15  124  102    21
16   93   61    56
17   84   21    25
19   34   52   126

Details:

First create boolean DataFrame with chain by |:

print (((stepframe < (Q1 - 1.5 * IQR)) | (stepframe > (Q3 + 1.5 * IQR))))
        a      b      c
0   False   True  False
1   False  False  False
2    True  False  False
3   False  False  False
4   False  False  False
5   False  False  False
6   False  False  False
7   False  False  False
8   False  False  False
9   False  False  False
10   True  False  False
11  False   True  False
12  False   True  False
13  False  False  False
14  False   True  False
15  False  False  False
16  False  False  False
17  False  False  False
18  False   True  False
19  False  False  False

And then use DataFrame.any for check at least one True per row and last invert boolean mask by ~:

print (~((stepframe < (Q1 - 1.5 * IQR)) | (stepframe > (Q3 + 1.5 * IQR))).any(axis=1))
0     False
1      True
2     False
3      True
4      True
5      True
6      True
7      True
8      True
9      True
10    False
11    False
12    False
13     True
14    False
15     True
16     True
17     True
18    False
19     True
dtype: bool

invert solution with changed conditions - < to >= and > to <=, chain by & for AND and last filter by all for check all Trues per rows

print (((stepframe >= (Q1 - 1.5 * IQR)) & (stepframe <= (Q3 + 1.5 * IQR))).all(axis=1))
0     False
1      True
2     False
3      True
4      True
5      True
6      True
7      True
8      True
9      True
10    False
11    False
12    False
13     True
14    False
15     True
16     True
17     True
18    False
19     True
dtype: bool


df = stepframe[((stepframe >= (Q1 - 1.5 * IQR))& (stepframe <= (Q3 + 1.5 * IQR))).all(axis=1)]