How to iterate over dynamic rows in DataFrame?

Question

I have the following data frame, I want to iterate all the rows dynamically, to find bytes and convert them to float

Example

col1                          col2                        col3
None                          None                        b'R\xb8\x1e%\xda\x16QA'
b'R\xb8\x1e%\xda\x16QA'       b'R\xb8\x1e%\xda\x16QA'     None
None                          None                        None
b'R\xb8\x1e%\xda\x16QA'       None                        None
b'R\xb8\x1e%\xda\x16QA'       None                        b'R\xb8\x1e%\xda\x16QA'

Mi function bytes to float

def bytes2float(byte):
    if byte:
        # do stuff

I want to iterate over all the rows, because the DataFrame is dynamic at the moment I can only do it statically, because I know how many columns the DataFrame has.

Something like that

for index, row in mydf.iterrows():
    # print(row['col1'], row['col2'])
    bytes2float(row['col1'])
    bytes2float(row['col2'])
    bytes2float(row['col3'])
    ... 
    ...

Any ideas or suggestions?

Answer 1

You can iterate over mydf.columns

for index, row in mydf.iterrows():
    for col in mydf.columns:
        bytes2float(row[col])

If you want to run the method bytes2float on every column of every row then you can use applymap

Sample:

import pandas
import numpy as np
df = pd.DataFrame(np.arange(6).reshape(2,3))
print (df.applymap(lambda x: f"*{x}*"))

Output:

     0    1    2
0  *0*  *1*  *2*
1  *3*  *4*  *5*