Visualizing Nifty 50 Market Capitalization utilizing Plotly Treemap – Python Tutorial

Rajandran R
Telecom Engineer turned Full-time Derivative Trader. Mostly Trading Nifty, Banknifty, USDINR and High Liquid Stock Derivatives. Trading the Markets Since 2006 onwards. Using Market Profile and Orderflow for greater than a decade. Designed and printed 100+ open supply buying and selling programs on varied buying and selling instruments. Strongly consider that market understanding and strong buying and selling frameworks are the important thing to the buying and selling success. Writing about Markets, Trading System Design, Market Sentiment, Trading Softwares & Trading Nuances since 2007 onwards. Author of Marketcalls.in)

December 31, 2023

2 minutes learn

In the area of information science, visualization is not only an aesthetic selection however a vital facet of study. It’s the method that transforms numbers and datasets into visible context, bringing readability and perception to advanced data. For monetary markets, the place understanding traits, patterns, and anomalies might be the distinction between revenue and loss, the worth of visualization is unmatched. It interprets intricate market information into comprehensible and actionable visuals, enabling merchants, analysts, and strategists to make knowledgeable choices swiftly.

Visualizing Nifty 50 Market Capitalization utilizing Plotly Treemap – Python Tutorial
Visualizing Nifty 50 Market Capitalization utilizing Plotly Treemap – Python Tutorial 3

What is Treemap Visualization?

tree map is a strong visualization software that portrays hierarchical information by way of nested rectangles. In the context of the inventory market, every rectangle represents a inventory entity, and its measurement displays a quantitative variable, resembling market capitalization. This technique is especially efficient in displaying the proportionate market share of firms inside an index like Nifty 50.

Plotly Library for Treemap Visualization

Plotly, a number one library in Python for interactive information visualization gives an intensive vary of plotting choices, together with the treemap. It excels in creating refined plots that aren’t simply visually interesting however interactive, permitting customers to hover over, zoom, and click on for extra detailed data. For inventory market information, this interactivity is invaluable, offering a dynamic option to discover and analyze the market’s composition and traits.

Below is a step-by-step information explaining the offered Python code for making a treemap visualization of Nifty 50 firms primarily based on their market capitalization utilizing Plotly.

Importing Libraries and Loading Data:

  • The script begins by importing vital libraries: pandas for information manipulation and plotly.categorical for visualization.
pip set up pandas
pip set up plotly
  • It masses the market capitalization information of Nifty 50 firms from a CSV file right into a pandas DataBody.
import pandas as pd
import plotly.categorical as px

# Load the info from a CSV file
information = pd.read_csv('https://uncooked.githubusercontent.com/marketcalls/information/foremost/marketcap.csv')

Data Cleaning and Preparation:

  • The ‘Marketcap’ column is cleaned to take away any forex symbols and commas, after which transformed into float values ​​for numerical operations.
  • A ‘Company’ column is created from the ‘Symbol’ to symbolize firm names.
  • A customized ‘Label’ is generated by combining the corporate image with its market cap, formatted for readability.
information['Marketcap'] = information['Marketcap'].change('[$,]', '', regex=True).astype(float)
information['Company'] = information['Symbol']
information['Label'] = information['Symbol'] + "<br>" + information['Marketcap'].apply(lambda x: f"₹{x:,.2f}")

Creating the Treemap:

  • Using Plotly Express, a treemap is created with the ‘Label’ as the trail and ‘Marketcap’ because the values. This designates the scale of every rectangle.
  • The title supplies context, indicating these are free float market cap values ​​of Nifty 50 firms.
  • The determine’s structure is up to date to specify the scale, guaranteeing the treemap is spacious and clear.
  • Finally, fig.present() renders the plot. In an interactive Python atmosphere, it will show a dynamic treemap the place customers can hover over particular person shares to see detailed data.
fig = px.treemap(information, path=['Label'], values='Marketcap', title='Market Capitalization - Nifty50 Companies - (Free float marketcap values in ₹ thousand cr.)')
fig.update_layout(width=1200, top=700)
fig.present()

Here is the Complete Python Code to Visualze the Nifty 50 Marketcap

import pandas as pd
import plotly.categorical as px

# Load the info from a CSV file
information = pd.read_csv('https://uncooked.githubusercontent.com/marketcalls/information/foremost/marketcap.csv')

# Clean the 'MarketCap' column and convert it to floats
information['Marketcap'] = information['Marketcap'].change('[$,]', '', regex=True).astype(float)

# Now that we've got cleaned the 'MarketCap' column, we will proceed.
information['Company'] = information['Symbol']  # Create a column for firm names if not already current


# Create a customized label that mixes the corporate image with the market cap
# Format the market cap as you favor, right here it is formatted as a float with two decimal locations
information['Label'] = information['Symbol'] + "<br>" + information['Marketcap'].apply(lambda x: f"₹{x:,.2f}")

# Create the treemap utilizing Plotly Express
fig = px.treemap(information, path=['Label'], values='Marketcap',
                 title='Market Capitalization - Nifty50 Companies - (Free float marketcap values in ₹ thousand cr.)')


# Set the determine measurement
fig.update_layout(width=1200, top=700)

# Show the plot
fig.present()

This information confirmed you construct a cool treemap with Plotly and Python, providing you with a dwell image of the Nifty 50’s market cap. These visuals are tremendous useful – they are not simply fairly photos, however real-deal insights that assist analysts, merchants, and anybody into shares get the hold of the market’s twists and turns.

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