How to convert mulitple columns of a df from hexadecimal to decimal

Question

There are multiple columns in the df, out of which only selected columns has to be converted from hexa decimal to decimal

Selected column names are stored in a list A = ["Type 2", "Type 4"]

{'Type 1': {0: 1, 1: 3, 2: 5, 3: 7, 4: 9, 5: 11, 6: 13, 7: 15, 8: 17},
 'Type 2': {0: 'AA',
  1: 'BB',
  2: 'CC',
  3: '55',
  4: '88',
  5: '96',
  6: 'FF',
  7: 'FFFFFF',
  8: 'FEEE'},
 'Type 3': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0, 5: 0, 6: 0, 7: 0, 8: 0},
 'Type 4': {0: '23',
  1: 'fefe',
  2: 'abcd',
  3: 'dddd',
  4: 'dad',
  5: 'cfe',
  6: 'cf42',
  7: '321',
  8: '0'},
 'Type 5': {0: -120,
  1: -120,
  2: -120,
  3: -120,
  4: -120,
  5: -120,
  6: -120,
  7: -120,
  8: -120}}

You're asking [how to convert from hex to decimal](https://stackoverflow.com/questions/9210525/how-do-i-convert-hex-to-decimal-in-python), and then you just want to use the pandas apply function. Note that it's easier to get an answer if you provide the test data: there's an unnecessary hurdle in your question where we have to convert from PNG to python. You should supply the data as a dictionary (`df.to_dict()`) or in some other form that can easily be converted to a dataframe. — Kraigolas, Mar 14 '21 at 19:23

score 0 · Answer 1 · answered Mar 14 '21 at 19:41

0

Say, you have the string "AA" in hex.

You can convert hex to decimal like this:

str(int("AA", 16))

Similarly, for a dataframe column that has hexadecimal values, you can use a lambda function.

df['Type2'] = df['Type2'].apply(lambda x: str(int(str(x), 16)))

Assuming, df is the name of the imported dataframe.

answered Mar 14 '21 at 19:41

Ujjayanta Bhaumik

73
2
8

It works, but is there a way to ignore NaN and floating values or invalid string if present in the particular column? – Alice Mar 17 '21 at 08:20
For nans, you could try: df['column_name'] = df['column_name'].apply(lambda x: str(int(str(x), 16)) if(np.all(pd.notnull(x))) else x) – Ujjayanta Bhaumik Mar 18 '21 at 02:40
For me, I had wanted octal -> decimal, but values were saved as float. Above didn't work me, I used the similar: .apply(lambda x: int(str(int(x)), 8)) in case it helps anyone else. – Civil Dec 09 '21 at 04:57

score 0 · Answer 2 · answered Mar 15 '21 at 00:48

You can use pandas.DataFrame.applymap to cast element-wise:

>>> df[["Type 2", "Type 4"]].applymap(lambda n: int(n, 16))
     Type 2  Type 4
0       170      35
1       187   65278
2       204   43981
3        85   56797
4       136    3501
5       150    3326
6       255   53058
7  16777215     801
8     65262       0

How to convert mulitple columns of a df from hexadecimal to decimal

2 Answers2