Use Conditional Formatting in Pandas to Improve Knowledge Visualization

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

The place <methodology> is the particular formatting perform you need to apply, and <arguments> are the parameters required by that perform. 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 Types: To use fast formatting kinds to your dataframe
• Customized Stylization: Create your individual formatting guidelines for the Styler object and go them by 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-Types

Let’s create a dummy inventory value dataset with columns for Date, Price Value, Satisfaction Rating, and Gross sales Quantity to exhibit 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'],
        'Price Value': [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:

Unique Unformatted Dataframe

1. Highlighting Most and Minimal Values

We are able to 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.fashion.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 Colour Gradients

Colour gradients are an efficient approach 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'. This can be a kind 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 Colour Gradients
df.fashion.background_gradient(cmap='viridis', subset=['Satisfaction Score'])

Output:

Colormap - viridis

3. Highlighting Null or Lacking Values

When we've got giant datasets, it turns into troublesome to establish null or lacking values. You need to use conditional formatting utilizing the built-in df.fashion.highlight_null perform for this goal. For instance, on this case, the gross sales quantity of the sixth entry is lacking. You'll be able to spotlight this info like this:

# Highlighting Null or Lacking Values
df.fashion.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 achieved as follows:

import pandas as pd
import numpy as np

# Home costs dataset
df = pd.DataFrame({
   'Neighborhood': ['H1', 'H2', 'H3', 'H4', 'H5', 'H6', 'H7'],
   'Value': [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 perform 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.fashion.applymap(highlight_outliers, subset=['Price'])

Output:

Highlighting Outliers

2. Highlighting Developments

Contemplate that you simply run an organization and are recording your gross sales every day. To research the traits, you need to spotlight the times when your every day gross sales improve by 5% or extra. You'll be able to obtain this utilizing a customized perform 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)

# Every day proportion 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.fashion.apply(highlight_trend, axis=1)

Output:

Spotlight >5% Improve 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 method turns into extraordinarily useful because the dimensionality of your knowledge will increase. Let's discover an instance to higher 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.fashion.applymap(highlight_corr)

Output:

Correlated Columns

Wrapping Up

These are simply among the examples I confirmed as a starter to up your recreation of knowledge visualization. You'll be able to apply related strategies to varied different issues to reinforce the info visualization, comparable to highlighting duplicate rows, grouping into classes and choosing totally different formatting for every class, or highlighting peak values. Moreover, there are numerous different CSS choices you'll be able to discover within the official documentation. You'll be able to 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 when you loved this, there's loads extra to discover. Head over to my writer web page for extra ideas, methods, 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 book "Maximizing Productiveness with ChatGPT". As a Google Era Scholar 2022 for APAC, she champions variety and tutorial excellence. She's additionally acknowledged as a Teradata Variety in Tech Scholar, Mitacs Globalink Analysis Scholar, and Harvard WeCode Scholar. Kanwal is an ardent advocate for change, having based FEMCodes to empower ladies in STEM fields.