How one can Use Conditional Formatting in Pandas to Improve Knowledge Visualization

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

The place <technique> is the precise formatting perform you wish 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 personal formatting guidelines for the Styler object and go them by way of one of many following strategies (Styler.applymap: element-wise or Styler.apply: column-/row-/table-wise)

Now, we'll cowl some examples of each approaches that will help you improve the visualization of your information.

Examples: Constructed-in-Types

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

import pandas as pd
import numpy as np

information = {'Date': ['2024-03-05', '2024-03-06', '2024-03-07', '2024-03-08', '2024-03-09', '2024-03-10'],
        'Value 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(information)
df

Output:

Unique Unformatted Dataframe

1. Highlighting Most and Minimal Values

We will use highlight_max and highlight_min capabilities 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(colour="green", axis=0 , subset=['Cost Price', 'Satisfaction Score', 'Sales Amount']).highlight_min(colour="red", axis=0 , subset=['Cost Price', 'Satisfaction Score', 'Sales Amount'])

Output:
 

Max & Min Values

2. Making use of Colour Gradients

Colour gradients are an efficient method to visualize the values in your information. On this case, we'll apply the gradient to satisfaction scores utilizing the colormap set to 'viridis'. It is a sort of colour 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 now have giant datasets, it turns into tough to determine 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 now have a housing dataset with their costs, and we wish to spotlight the homes with outlier costs (i.e., costs which might be considerably greater 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  upper_bound:
      return 'background-color: yellow; font-weight: daring; colour: black'
   else:
      return ''

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

Output:

Highlighting Outliers

2. Highlighting Traits

Contemplate that you just run an organization and are recording your gross sales every day. To investigate the developments, you wish 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 technique in pandas. Right here’s how:

import pandas as pd

# Dataset of Firm's Gross sales
information = {'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(information)

# Day by 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.fashion.apply(highlight_trend, axis=1)

Output:

3. Highlighting Correlated Columns

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

import pandas as pd

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

df = pd.DataFrame(information)

# 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 comparable strategies to varied different issues to reinforce the information visualization, corresponding to highlighting duplicate rows, grouping into classes and choosing completely different formatting for every class, or highlighting peak values. Moreover, there are numerous different CSS choices you possibly can discover within the official documentation. You'll be able to even outline completely different properties on hover, like magnifying textual content or altering colour. Try the "Enjoyable Stuff" part for extra cool concepts. This text is a part of my Pandas sequence, so in case you loved this, there's a lot extra to discover. Head over to my creator 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 information science and the intersection of AI with drugs. She co-authored the book "Maximizing Productivity with ChatGPT". As a Google Technology Scholar 2022 for APAC, she champions range and educational 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.