Python Pandas - Highlighting maximum value in column
Variation highlighting max value column-wise (axis=1) using two colors. One color highlights duplicate max values. The other color highlights only the last column containing the max value.
def highlight_last_max(data, colormax='antiquewhite', colormaxlast='lightgreen'):
colormax_attr = f'background-color: {colormax}'
colormaxlast_attr = f'background-color: {colormaxlast}'
max_value = data.max()
is_max = [colormax_attr if v == max_value else '' for v in data]
is_max[len(data) - list(reversed(data)).index(max_value) - 1] = colormaxlast_attr
return is_max
df.style.apply(highlight_last_max,axis=1)
Easy way to color max, min, or null values in pandas.DataFrame is to uses style of pandas.DataFrame.style, which Contains methods for building a styled HTML representation of the DataFrame. Here is an example:
- Color Max Values:
your_df.style.highlight_max(color = 'green')
- Color Min Values:
your_df.style.highlight_min(color = 'red')
- Color Null values:
your_df.highlight_null(color = 'yellow')
- If you want to apply all in the same output:
your_df.style.highlight_max(color='green').highlight_min(color='red').highlight_null(null_color='yellow')
If you are using Python 3 this should easily do the trick
dfPercent.style.highlight_max(color = 'yellow', axis = 0)
There is problem you need convert values to floats for correct max
, because get max value of strings - 9
is more as 1
:
def highlight_max(data, color='yellow'):
'''
highlight the maximum in a Series or DataFrame
'''
attr = 'background-color: {}'.format(color)
#remove % and cast to float
data = data.replace('%','', regex=True).astype(float)
if data.ndim == 1: # Series from .apply(axis=0) or axis=1
is_max = data == data.max()
return [attr if v else '' for v in is_max]
else: # from .apply(axis=None)
is_max = data == data.max().max()
return pd.DataFrame(np.where(is_max, attr, ''),
index=data.index, columns=data.columns)
Sample:
dfPercent = pd.DataFrame({'2014/2015':['10.3%','9.7%','9.2%'],
'2015/2016':['4.8%','100.8%','9.7%']})
print (dfPercent)
2014/2015 2015/2016
0 10.3% 4.8%
1 9.7% 100.8%
2 9.2% 9.7%
Command:
dfPercent.style.apply(highlight_max)