Pandas Dataframe Examples: Styling Cells and Conditional Formatting

Pandas Dataframe Examples: Styling Cells and Conditional Formatting

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Style cell if condition

Here we apply elementwise formatting, because the logic only depends on the single value itself.

Use df.applymap(styler_function) where styler_function takes a cell value and returns a CSS style

Example: Change background color for even numbers

import pandas as pd

df = pd.DataFrame({
    "name":         ["alan","beth","charlie","david", "edward"],
    "age" :         [34,    12,     43,      32,      77],
    "num_children": [1,     0,      2,       1,       6],
    "num_pets":     [1,     0,      1,       2,       0],
    "bank_balance": [100.0, 10.0,   -10.0,   30.0,    30.0]})

def even_number_background(cell_value):

    highlight = 'background-color: darkorange;'
    default = ''

    if type(cell_value) in [float, int]:
        if cell_value % 2 == 0:
            return highlight
    return default

df.style.applymap(is_even_background)

base-dataframe Base dataframe
  
pandas-dataframe-with-elementwise-styling All even numbers had their
background changed

Row-wise style

In other words: for row, take all values and decide which ones to style.

Use df.style.apply(func, axis=1). Use subset=[cols] to limit application to some columns only.

Example: Highlight whether each person has more children or more pets

import pandas as pd

df = pd.DataFrame({
    "name":         ["alan","beth","charlie","david", "edward"],
    "age" :         [34,    12,     43,      32,      77],
    "num_children": [1,     0,      2,       1,       6],
    "num_pets":     [1,     0,      1,       2,       0],
    "bank_balance": [100.0, 10.0,   -10.0,   30.0,    30.0]})

def more_children_or_more_pets_background(row):    

    highlight = 'background-color: lightcoral;'
    default = ''

    # must return one string per cell in this row
    if row['num_children'] > row['num_pets']:
        return [highlight, default]
    elif row['num_pets'] > row['num_children']:
        return [default, highlight]
    else:
        return [default, default]

df.style.apply(more_children_or_more_pets_background, subset=['num_children', 'num_pets'], axis=1)

base-dataframe-unstyled Base dataframe, unstyled.
  
styled-dataframe-with-rowwise-styling Charlie and Edward have more children
than pets, while David has more
pets than children.

Highlight cell if largest in column

That is, column-wise styling.

Use df.style.apply(func, axis=0)

Example: Change background colour in the maximum values for num_children and num_pets:

import pandas as pd

df = pd.DataFrame({
    "name":         ["alan","beth","charlie","david", "edward"],
    "age" :         [34,    12,     43,      32,      77],
    "num_children": [1,     0,      2,       1,       6],
    "num_pets":     [1,     0,      1,       2,       0],
    "bank_balance": [100.0, 10.0,   -10.0,   30.0,    30.0]})

def maximum_value_in_column(column):    

    highlight = 'background-color: palegreen;'
    default = ''

    maximum_in_column = column.max()

    # must return one string per cell in this column
    return [highlight if v == maximum_in_column else default for v in column]

df.style.apply(maximum_value_in_column, subset=['num_children', 'num_pets'], axis=0)

base-unstyled-dataframe Base dataframe
  
styled-pandas-dataframe The maximum values for num_children and
and num_pets are 6 and 2,
respectively.

Apply style to column only

In order to restrict application of some style to some columns, use subset=[cols]:

  • df.style.apply(func, subset=['col1'], axis=1) to apply func rowwise to column 'col1' only

  • df.style.apply(func, subset=['col2'], axis=0) to apply func columnwise to column 'col2' only

Chain styles

In other words, apply multiple styles one after the other:

In this example:

  • use red font for negative values in bank_balance
  • write the highest age in bold
  • use light red background for zero values in num_children and num_pets
import pandas as pd

df = pd.DataFrame({
    "name":         ["alan","beth","charlie","david", "edward"],
    "age" :         [34,    12,     43,      32,      77],
    "num_children": [1,     0,      2,       1,       6],
    "num_pets":     [1,     0,      1,       2,       0],
    "bank_balance": [100.0, 10.0,   -10.0,   30.0,    30.0]})

def red_font_negatives(series):
    highlight = 'color: red;'
    default = ''
    return [highlight if e < 0 else default for e in series]  

def bold_max_value_in_series(series):
    highlight = 'font-weight: bold;'
    default = ''

    return [highlight if e == series.max() else default for e in series]  

def red_background_zero_values(cell_value):
    highlight = 'background-color: tomato;'
    default = ''
    if cell_value == 0:
        return highlight
    else:
        return default     

(df
 .style
 .apply(red_font_negatives, axis=0, subset=['bank_balance'])
 .apply(bold_max_value_in_series, axis=0, subset=['age'])
 .applymap(red_background_zero_values))

base-unstyled Base dataset, unstyled
  
dataframe-with-multiple-chained-applications Multiple stylings applied together:
largest age in bold, zero values in
pets or children and negative
bank balances.

Multiple styles in same function

You can use multiple else clauses in a ternary if-else condition too:

import pandas as pd

df = pd.DataFrame({
    "name":         ["alan","beth","charlie","david", "edward"],
    "bank_balance": [100.0, 10.0,   -10.0,   30.0,    30.0]})

def colour_numbers(series):
    red    = 'background-color: red;'
    orange = 'background-color: orange;'     
    default = ''

    # note multiple else ..if conditions
    return [red if e < 0 else orange if e < 15 else default for e in series]  

(df
 .style
 .apply(colour_numbers, axis=0, subset=['bank_balance']))

before-pandas-dataframe-no-styling BEFORE: no styling
  
after-applied-pandas-styling-to-individual-cells AFTER: our logic now checks
two conditions and depending
on which one matched, the cell
gets a different colour

python pandas

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