Easy methods to Use Conditional Formatting in Pandas to Improve Information Visualization

df.type.<methodology>(<arguments>)

The place <methodology> is the precise formatting operate you need to apply, and <arguments> are the parameters required by that operate. The Styler object returns the formatted dataframe with out altering the unique one. There are two approaches to utilizing conditional formatting with the Styler object:

• Constructed-in Kinds: To use fast formatting kinds to your dataframe
• Customized Stylization: Create your personal formatting guidelines for the Styler object and move them via one of many following strategies (Styler.applymap: element-wise or Styler.apply: column-/row-/table-wise)

Now, we are going to cowl some examples of each approaches that will help you improve the visualization of your knowledge.

Examples: Constructed-in-Kinds

Let’s create a dummy inventory worth dataset with columns for Date, Value Worth, Satisfaction Rating, and Gross sales Quantity to reveal the examples beneath:

import pandas as pd
import numpy as np

knowledge = {'Date': ['2024-03-05', '2024-03-06', '2024-03-07', '2024-03-08', '2024-03-09', '2024-03-10'],
        'Value Worth': [100, 120, 110, 1500, 1600, 1550],
        'Satisfaction Rating': [90, 80, 70, 95, 85, 75],
        'Gross sales Quantity': [1000, 800, 1200, 900, 1100, None]}

df = pd.DataFrame(knowledge)
df

Output:

Authentic Unformatted Dataframe

1. Highlighting Most and Minimal Values

We will use highlight_max and highlight_min features to focus on the utmost and minimal values in a column or row. For column set axis=0 like this:

# Highlighting Most and Minimal Values
df.type.highlight_max(shade="inexperienced", axis=0 , subset=['Cost Price', 'Satisfaction Score', 'Sales Amount']).highlight_min(shade="crimson", axis=0 , subset=['Cost Price', 'Satisfaction Score', 'Sales Amount'])

Output:
 

Max & Min Values

2. Making use of Coloration Gradients

Coloration gradients are an efficient technique to visualize the values in your knowledge. On this case, we are going to apply the gradient to satisfaction scores utilizing the colormap set to 'viridis'. It is a sort of shade coding that ranges from purple (low values) to yellow (excessive values). Right here is how you are able to do this:

# Making use of Coloration Gradients
df.type.background_gradient(cmap='viridis', subset=['Satisfaction Score'])

Output:

Colormap - viridis

3. Highlighting Null or Lacking Values

When we've got massive datasets, it turns into troublesome to establish null or lacking values. You need to use conditional formatting utilizing the built-in df.type.highlight_null operate for this objective. For instance, on this case, the gross sales quantity of the sixth entry is lacking. You may spotlight this info like this:

# Highlighting Null or Lacking Values
df.type.highlight_null('yellow', subset=['Sales Amount'])

Output:
 

Highlighting Lacking Values

Examples: Customized Stylization Utilizing apply() & applymap()

1.  Conditional Formatting for Outliers

Suppose that we've got a housing dataset with their costs, and we need to spotlight the homes with outlier costs (i.e., costs which are considerably increased or decrease than the opposite neighborhoods). This may be performed as follows:

import pandas as pd
import numpy as np

# Home costs dataset
df = pd.DataFrame({
   'Neighborhood': ['H1', 'H2', 'H3', 'H4', 'H5', 'H6', 'H7'],
   'Worth': [50, 300, 360, 390, 420, 450, 1000],
})

# Calculate Q1 (twenty fifth percentile), Q3 (seventy fifth percentile) and Interquartile Vary (IQR)
q1 = df['Price'].quantile(0.25)
q3 = df['Price'].quantile(0.75)
iqr = q3 - q1

# Bounds for outliers
lower_bound = q1 - 1.5 * iqr
upper_bound = q3 + 1.5 * iqr

# Customized operate to focus on outliers
def highlight_outliers(val):
   if val < lower_bound or val > upper_bound:
      return 'background-color: yellow; font-weight: daring; shade: black'
   else:
      return ''

df.type.applymap(highlight_outliers, subset=['Price'])

Output:

Highlighting Outliers

2. Highlighting Traits

Think about that you just run an organization and are recording your gross sales each day. To research the tendencies, you need to spotlight the times when your each day gross sales enhance by 5% or extra. You may obtain this utilizing a customized operate and the apply methodology in pandas. Right here’s how:

import pandas as pd

# Dataset of Firm's Gross sales
knowledge = {'date': ['2024-02-10', '2024-02-11', '2024-02-12', '2024-02-13', '2024-02-14'],
        'gross sales': [100, 105, 110, 115, 125]}

df = pd.DataFrame(knowledge)

# Each day share change
df['pct_change'] = df['sales'].pct_change() * 100

# Spotlight the day if gross sales elevated by greater than 5%
def highlight_trend(row):
    return ['background-color: green; border: 2px solid black; font-weight: bold' if row['pct_change'] > 5 else '' for _ in row]

df.type.apply(highlight_trend, axis=1)

Output:

Spotlight >5% Enhance in Gross sales

3. Highlighting Correlated Columns

Correlated columns are necessary as a result of they present relationships between totally different variables. For instance, if we've got a dataset containing age, earnings, and spending habits and our evaluation reveals a excessive correlation (near 1) between age and earnings, then it means that older folks typically have increased incomes. Highlighting correlated columns helps to visually establish these relationships. This strategy turns into extraordinarily useful because the dimensionality of your knowledge will increase. Let's discover an instance to raised perceive this idea:

import pandas as pd

# Dataset of individuals
knowledge = {
    'age': [30, 35, 40, 45, 50],
    'earnings': [60000, 66000, 70000, 75000, 100000],
    'spending': [10000, 15000, 20000, 18000, 12000]
}

df = pd.DataFrame(knowledge)

# Calculate the correlation matrix
corr_matrix = df.corr()

# Spotlight extremely correlated columns
def highlight_corr(val):
    if val != 1.0 and abs(val) > 0.5:   # Exclude self-correlation
        return 'background-color: blue; text-decoration: underline'
    else:
        return ''

corr_matrix.type.applymap(highlight_corr)

Output:

Correlated Columns

Wrapping Up

These are simply a number of the examples I confirmed as a starter to up your sport of knowledge visualization. You may apply related methods to varied different issues to reinforce the info visualization, reminiscent of highlighting duplicate rows, grouping into classes and deciding on totally different formatting for every class, or highlighting peak values. Moreover, there are various different CSS choices you possibly can discover within the official documentation. You may even outline totally different properties on hover, like magnifying textual content or altering shade. Try the "Enjoyable Stuff" part for extra cool concepts. This text is a part of my Pandas sequence, so should you loved this, there's loads extra to discover. Head over to my creator web page for extra suggestions, tips, and tutorials.

Kanwal Mehreen Kanwal is a machine studying engineer and a technical author with a profound ardour for knowledge science and the intersection of AI with medication. She co-authored the e book "Maximizing Productiveness with ChatGPT". As a Google Technology Scholar 2022 for APAC, she champions variety and tutorial excellence. She's additionally acknowledged as a Teradata Range in Tech Scholar, Mitacs Globalink Analysis Scholar, and Harvard WeCode Scholar. Kanwal is an ardent advocate for change, having based FEMCodes to empower girls in STEM fields.