This is my script ...
from termcolor import colored
import pandas as pd
import numpy as np
data = {'Col1': [0, 15, 10, 5, 20, 17, 20], 'Col2': [11, 6, -3, 16, 21, 8, 26], 'STKS': [20, 30, 40, 50, 60, 70, 80], 'Col4': [3, 8, -13, -18, 3, 23, 42], 'Col5': [4, 19, 8, 11, 6, 20, 32]}
df = pd.DataFrame(data)
ATM = 50
def hightlight(series, excluded, spcl_col, spcl_val, extra_col= [] ):
def excl_formatter(series):
return [colored(str(x), 'white') for x in series]
def norm_formatter(series, extra=False):
tmp = series.sort_values()
if extra:
tmp.iloc[:-2] = tmp.iloc[:-2].map(lambda x: colored(x, *[('white' ,), ('green' ,),('light_red' ,),][np.sign(x)]))
else:
tmp.iloc[:-2] = tmp.iloc[:-2].map(lambda x: colored(str(x), 'white'))
tmp.iloc[-2] = colored(tmp.iloc[-2], None, 'on_magenta')
tmp.iloc[-1] = colored(tmp.iloc[-1], None, 'on_blue')
return tmp
def spcl_formatter(series):
return [colored(x, None, 'on_blue') if x == spcl_val else colored(x, None, 'on_red') for x in series]
if series.name == spcl_col:
return spcl_formatter(series)
if series.name in excluded:
return excl_formatter(series)
return norm_formatter(series, extra=series.name in extra_col)
col_to_exclude = ['STKS']
df_f1 = df.apply(hightlight, excluded=col_to_exclude, spcl_col='STKS',spcl_val=ATM, extra_col=['Col2', 'Col4']).rename(columns=lambda x: colored(x, 'white', None))
print(df_f1.to_string(index= False))
It was working well but after updating my python IDE it started to give future warning as ...
<string>:22: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['\x1b[97m0\x1b[0m' '\x1b[97m5\x1b[0m' '\x1b[97m10\x1b[0m'
'\x1b[97m15\x1b[0m' '\x1b[97m17\x1b[0m']' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.
<string>:19: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['\x1b[91m-3\x1b[0m' '\x1b[32m6\x1b[0m' '\x1b[32m8\x1b[0m'
'\x1b[32m11\x1b[0m' '\x1b[32m16\x1b[0m']' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.
<string>:19: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['\x1b[91m-18\x1b[0m' '\x1b[91m-13\x1b[0m' '\x1b[32m3\x1b[0m'
'\x1b[32m3\x1b[0m' '\x1b[32m8\x1b[0m']' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.
<string>:22: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['\x1b[97m4\x1b[0m' '\x1b[97m6\x1b[0m' '\x1b[97m8\x1b[0m'
'\x1b[97m11\x1b[0m' '\x1b[97m19\x1b[0m']' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.
I got a solution to hide this warning by following....
import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)
Is there any way to handle this dtype caused warning plz.
The issue is due to tmp being of int64 dtype and trying to assign strings to it.
Define tmp as an object Series in norm_formatter:
def norm_formatter(series, extra=False):
tmp = series.sort_values().astype(object)
# ... rest of code ...
Which will avoid the warning.