The pandas style option to add a background gradient is great for quickly inspecting my output table. However, it is applied either row-wise or columns-wise. Would it be possible to apply it to the whole dataframe at once?
EDIT: A minimum working example:
df = pd.DataFrame([[3,2,10,4],[20,1,3,2],[5,4,6,1]])
df.style.background_gradient()
Currently you can't set the background_gradient for both the rows/columns simultaneously as pointed by Nickil Maveli. The trick is to customize the pandas function background_gradient:
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib import colors
def background_gradient(s, m, M, cmap='PuBu', low=0, high=0):
rng = M - m
norm = colors.Normalize(m - (rng * low),
M + (rng * high))
normed = norm(s.values)
c = [colors.rgb2hex(x) for x in plt.cm.get_cmap(cmap)(normed)]
return ['background-color: %s' % color for color in c]
df = pd.DataFrame([[3,2,10,4],[20,1,3,2],[5,4,6,1]])
df.style.apply(background_gradient,
cmap='PuBu',
m=df.min().min(),
M=df.max().max(),
low=0,
high=0.2)
dict with pandas.DataFrame is like this dict_dfs = {'df1': pd.DataFrame(...), 'df2': pd.DataFrame(...), ...}, you can do use from IPython.display import display and put display(df.style.apply(...)) inside a for-loop (e.g., for df in dict_dfs.values()) (obs.: since it's not possible to use block-formatting for code inside the comments, I had to simplify the examples, but I hope it's clear).Setting axis=None is working for me in 1.0.5
You can use axis=None to get rid of the min and max computations in the call:
def background_gradient(s, m=None, M=None, cmap='PuBu', low=0, high=0):
print(s.shape)
if m is None:
m = s.min().min()
if M is None:
M = s.max().max()
rng = M - m
norm = colors.Normalize(m - (rng * low),
M + (rng * high))
normed = s.apply(norm)
cm = plt.cm.get_cmap(cmap)
c = normed.applymap(lambda x: colors.rgb2hex(cm(x)))
ret = c.applymap(lambda x: 'background-color: %s' % x)
return ret
df.style.apply(background_gradient, axis=None)
Edit: You may need to use normed = s.apply(lambda x: norm(x.values)) for this to work on matplotlib 2.2
You need to set axis to None. For me best solution using seaborn:
import seaborn as sns
import pandas as pd
cm = sns.color_palette("blend:white,green", as_cmap=True)
df.style.background_gradient(cmap = cm,axis=None)
axis=None?axis=Noneis:The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().background_gradientfor both the rows/columns simultaneously. But however you could create a custom function and apply colors to a particular threshold of the values you want to distinguish them by.