I currently have a string of values which I retrieved after filtering through data from a csv file. ultimately I had to do some filtering of the data but I have the same numbers as a list, dataframe, or array. I just need to take the numbers in the string and convert them to hex and then take the first 8 numbers of the hex and convert that to dec for each element in the string. Lastly I also need to convert the last 8 of the same hex and then to dec as well for each value in the string.
I cannot provide a snippet because it is sensitive data, but here is an example.
I basically have something like this
>>> list_A
[52894036, 78893201, 45790373]
If I convert it to a dataframe and call df.dtypes, it says dtype: object and I can convert the values of Column A to bool, int, or string, but the dtype is always an object.
It does not matter whether it is a function, or just a simple loop. I have been trying many methods and am unable to attain the results I need. But ultimately the data is taken from different csv files and will never be the same values or list size.
Pandas is designed to work primarily with integers and floats, with no particular facilities for hexadecimal that I know of, but you can use apply to access standard python conversion functions like hex and int:
df=pd.DataFrame({ 'a':[52894036999, 78893201999, 45790373999] })
df['b'] = df['a'].apply( hex )
df['c'] = df['b'].apply( int, base=0 )
Results:
a b c
0 52894036999 0xc50baf407 52894036999
1 78893201999 0x125e66ba4f 78893201999
2 45790373999 0xaa951a86f 45790373999
Note that this answer is for Python 3. For Python 2 you may need to strip off the trailing "L" in column "b" with str[:-1].