Let's say I have this data as a starting point:
import pandas as pd
data = [
{"colA": "hello", "colB": 22, "colC": 3.0, "colD": 123476},
{"colA": "there", "colB": 122, "colC": 4.0, "colD": 2384953},
{"colA": "world", "colB": 222, "colC": 5.0, "colD": 39506483},
]
df = pd.DataFrame(data)
with pd.option_context('display.max_rows', None, 'display.max_columns', None, 'display.width', None, 'max_colwidth', 20, 'display.float_format', "{:.2f}".format):
print(df)
It prints:
colA colB colC colD
0 hello 22 3.00 123476
1 there 122 4.00 2384953
2 world 222 5.00 39506483
Now, I would like ONLY the integer column B to be printed as hex - more specifically, as "0x{:02X}" string format.
If it existed, I might have used display.int_format, but that option does not exist as stated in Can you format pandas integers for display, like `pd.options.display.float_format` for floats?
... Then again, such an option would likely not allow me to print ONLY column B in that way.
Another option is doing .apply() as hinted in Converting a string of numbers to hex and back to dec pandas python:
# ...
df = pd.DataFrame(data)
df["colB"] = df["colB"].apply("0x{:02X}".format)
# ...
... which then prints what I want:
colA colB colC colD
0 hello 0x16 3.00 123476
1 there 0x7A 4.00 2384953
2 world 0xDE 5.00 39506483
... however, it also changes the data in my table - and I would like to preserve the original data in the table; I simply want to print some columns as hex.
So - is there a way to specify only some certain columns to be printed as hex in pandas, while keeping the original data in the table (and without explicitly copying the dataframe to a new one just for that kind of printing)?
