I have a pandas DataFrame with positive and negative values as a bar chart. I want to plot the positive colors \'green\' and the negative values \'red\' (very original...lol
Define
def bar_color(df,color1,color2):
return np.where(df.values>0,color1,color2).T
then
data.plot.barh(color=bar_color(data,'r','g'))
gives
It also works for multiple bar series
df=pd.DataFrame(np.random.randint(-10,10,(4,6)))
df.plot.barh(color=bar_color(df,'r','g'))
gives
If you want to avoid adding a column, you can do TomAugspurger's solution in one step:
data['values'].plot(kind='barh',
color=(data['values'] > 0).map({True: 'g',
False: 'r'}))
I would create a dummy column for whether the observation is larger than 0.
In [39]: data['positive'] = data['values'] > 0
In [40]: data
Out[40]:
values positive
a -15.0 False
b 10.0 True
c 8.0 True
d -4.5 False
[4 rows x 2 columns]
In [41]: data['values'].plot(kind='barh',
color=data.positive.map({True: 'g', False: 'r'}))
Also, you may want to be careful not to have column names that overlap with DataFrame attributes. DataFrame.values
give the underlying numpy array for a DataFrame. Having overlapping names prevents you from using the df.<column name>
syntax.
Drawing on @Max Ghenis answer (which doesn't work for me but seems to be a minor change in the packages):
tseries = data['values']
color = (tseries > 0).apply(lambda x: 'g' if x else 'r')
splot = tseries.plot.barh(color=color)
gives:
.. what you expect to see.