Python Pandas style highlight specific cells for each column with different condition
Question:
I’m trying to highlight specific cells for each column with different condition which their value matches the condition for each row.
Below image is what I want to achieve:
The table I attempt to achieve
I searched google and stackoverflow but none of these can meet my requirement. Can anyone who’s familiar with Pandas Style could assist?
Below are the codes I tried and failed:
Ex1
import pandas as pd
df = pd.DataFrame([[10,3,1], [3,7,2], [2,4,4]], columns=list("ABC"))
def highlight(s):
return ['background-color: yellow' if (v>2) else 'background-color: white' for v in s]
df.style.apply(highlight, axis=0)
Ex2
import pandas as pd
df = pd.DataFrame([[10,3,1], [3,7,2], [2,4,4]], columns=list("ABC"))
Column_limit = (df['A'] > 6) | (df['B'] > 2) | (df['C'] < 3)
df[Column_limit].style.applymap(lambda x: 'background-color: yellow', subset=pd.IndexSlice[:, ['A', 'C']])
Ex3
import pandas as pd
df = pd.DataFrame([[10,3,1], [3,7,2], [2,4,4]], columns=list("ABC"))
subsets = pd.IndexSlice[:, 'A']
df.style.applymap(lambda x: 'background-color: yellow', subset = subsets)
Answers:
If there is same number of conditions like some number of columns use:
df = pd.DataFrame([[10,3,1], [3,7,2], [2,4,4]], columns=list("ABC"))
def highlight(x):
c1 = 'background-color: yellow'
# condition
m = pd.concat([(x['A'] > 6), (x['B'] > 2), (x['C'] < 3)], axis=1)
#print (m)
#empty DataFrame of styles
df1 = pd.DataFrame('', index=x.index, columns=x.columns)
#set new columns by condition
return df1.mask(m, c1)
df.style.apply(highlight, axis=None)
If there is a lot of columns and need processing only some of them:
def highlight(x):
c1 = 'background-color: yellow'
#empty DataFrame of styles
df1 = pd.DataFrame('', index=x.index, columns=x.columns)
#set new columns by condition
df1.loc[(x['A'] > 6), 'A'] = c1
df1.loc[(x['B'] > 2), 'B'] = c1
df1.loc[(x['C'] < 3), 'C'] = c1
return df1
df.style.apply(highlight, axis=None)
EDIT:
If need specified all masks but in last step filter only some columns use:
def highlight(x):
c1 = 'background-color: yellow'
#empty DataFrame of styles
df1 = pd.DataFrame('', index=x.index, columns=x.columns)
#set new columns by condition
df1.loc[(x['A'] > 6), 'A'] = c1
df1.loc[(x['B'] > 2), 'B'] = c1
df1.loc[(x['C'] < 3), 'C'] = c1
need = ['A','C']
df1 = df1[need].reindex(x.columns, fill_value='', axis=1)
return df1
Or remove masks which not necessary:
def highlight(x):
c1 = 'background-color: yellow'
#empty DataFrame of styles
df1 = pd.DataFrame('', index=x.index, columns=x.columns)
#set new columns by condition
df1.loc[(x['A'] > 6), 'A'] = c1
df1.loc[(x['C'] < 3), 'C'] = c1
return df1
df.style.apply(highlight, axis=None)
I’m trying to highlight specific cells for each column with different condition which their value matches the condition for each row.
Below image is what I want to achieve:
The table I attempt to achieve
I searched google and stackoverflow but none of these can meet my requirement. Can anyone who’s familiar with Pandas Style could assist?
Below are the codes I tried and failed:
Ex1
import pandas as pd
df = pd.DataFrame([[10,3,1], [3,7,2], [2,4,4]], columns=list("ABC"))
def highlight(s):
return ['background-color: yellow' if (v>2) else 'background-color: white' for v in s]
df.style.apply(highlight, axis=0)
Ex2
import pandas as pd
df = pd.DataFrame([[10,3,1], [3,7,2], [2,4,4]], columns=list("ABC"))
Column_limit = (df['A'] > 6) | (df['B'] > 2) | (df['C'] < 3)
df[Column_limit].style.applymap(lambda x: 'background-color: yellow', subset=pd.IndexSlice[:, ['A', 'C']])
Ex3
import pandas as pd
df = pd.DataFrame([[10,3,1], [3,7,2], [2,4,4]], columns=list("ABC"))
subsets = pd.IndexSlice[:, 'A']
df.style.applymap(lambda x: 'background-color: yellow', subset = subsets)
If there is same number of conditions like some number of columns use:
df = pd.DataFrame([[10,3,1], [3,7,2], [2,4,4]], columns=list("ABC"))
def highlight(x):
c1 = 'background-color: yellow'
# condition
m = pd.concat([(x['A'] > 6), (x['B'] > 2), (x['C'] < 3)], axis=1)
#print (m)
#empty DataFrame of styles
df1 = pd.DataFrame('', index=x.index, columns=x.columns)
#set new columns by condition
return df1.mask(m, c1)
df.style.apply(highlight, axis=None)
If there is a lot of columns and need processing only some of them:
def highlight(x):
c1 = 'background-color: yellow'
#empty DataFrame of styles
df1 = pd.DataFrame('', index=x.index, columns=x.columns)
#set new columns by condition
df1.loc[(x['A'] > 6), 'A'] = c1
df1.loc[(x['B'] > 2), 'B'] = c1
df1.loc[(x['C'] < 3), 'C'] = c1
return df1
df.style.apply(highlight, axis=None)
EDIT:
If need specified all masks but in last step filter only some columns use:
def highlight(x):
c1 = 'background-color: yellow'
#empty DataFrame of styles
df1 = pd.DataFrame('', index=x.index, columns=x.columns)
#set new columns by condition
df1.loc[(x['A'] > 6), 'A'] = c1
df1.loc[(x['B'] > 2), 'B'] = c1
df1.loc[(x['C'] < 3), 'C'] = c1
need = ['A','C']
df1 = df1[need].reindex(x.columns, fill_value='', axis=1)
return df1
Or remove masks which not necessary:
def highlight(x):
c1 = 'background-color: yellow'
#empty DataFrame of styles
df1 = pd.DataFrame('', index=x.index, columns=x.columns)
#set new columns by condition
df1.loc[(x['A'] > 6), 'A'] = c1
df1.loc[(x['C'] < 3), 'C'] = c1
return df1
df.style.apply(highlight, axis=None)