Classifying pandas columns according to range limits - pandas

Question

I have a dataframe with several numeric columns and their range goes either from 1 to 5 or 1 to 10
I want to create two lists of these columns names this way:
names_1to5 = list of all columns in df with numbers ranging from 1 to 5
names_1to10 = list of all columns in df with numbers from 1 to 10
Example:
IP track batch size type
1 2 3 5 A
9 1 2 8 B
10 5 5 10 C
from the dataframe above:
names_1to5 = ['track', 'batch']
names_1to10 = ['ip', 'size']
I want to use a function that gets a dataframe and perform the above transformation only on columns with numbers within those ranges.
I know that if the column 'max()' is 5 than it's 1to5 same idea when max() is 10
What I already did:
def test(df):
list_1to5 = []
list_1to10 = []
for col in df:
if df[col].max() == 5:
list_1to5.append(col)
else:
list_1to10.append(col)
return list_1to5, list_1to10
I tried the above but it's returning the following error msg:
'>=' not supported between instances of 'float' and 'str'
The type of the columns is 'object' maybe this is the reason. If this is the reason, how can I fix the function without the need to cast these columns to float as there are several, sometimes hundreds of these columns and if I run:
df['column'].max() I get 10 or 5
What's the best way to create this this function?

Answer 1

Use:
string = """alpha IP track batch size
A 1 2 3 5
B 9 1 2 8
C 10 5 5 10"""
temp = [x.split() for x in string.split('\n')]
cols = temp[0]
data = temp[1:]
def test(df):
list_1to5 = []
list_1to10 = []
for col in df.columns:
if df[col].dtype!='O':
if df[col].max() == 5:
list_1to5.append(col)
else:
list_1to10.append(col)
return list_1to5, list_1to10
df = pd.DataFrame(data, columns = cols, dtype=float)
Output:
(['track', 'batch'], ['IP', 'size'])