Data Visualization: Simplifying Complexity at a Glance

A chart can tell a story that rows of numbers can’t. That’s the power of data visualization!

For example below is the screenshot of data in csv format

Raw numbers can feel overwhelming, and spotting patterns is tough.

Now lets see the same data in visualization..

With just one glance, we get the message that Python is on the rise. That’s how visualization transforms data into insight.

Data is crucial for decision-making because it provides insights, evidence, and clarity, helping individuals and organizations make informed choices. And data visualization makes those insights simpler to understand!

How do we turn data into beautiful, insightful charts?

We can transform raw data into stunning visualizations using Python libraries that are designed for plotting and analysis. Here are some of the most popular ones:

Matplotlib – The Foundation of Python Visualization

• Matplotlib is a versatile library for creating static, animated, and interactive plots.
• It gives you full control over every aspect of a figure, from colors to labels.
• Example: Line charts, bar plots, and scatter plots.

Seaborn – Beautiful Statistical Plots with Ease

• Built on top of Matplotlib, Seaborn simplifies creating elegant visualizations.
• It excels at statistical data visualization with built-in themes and color palettes.
• Example: Heatmaps, pair plots, and violin plots.

Pandas Plot – Quick and Easy Plots from DataFrames

• Pandas has built-in plotting capabilities for quick visualizations from datasets.
• It is great for line plots, bar charts, and histograms.
• Example: Plotting trends from CSV data with a single line of code.

How to get source data for visualization?

To create meaningful visualizations, you need quality data. Here are some reliable sources and methods to obtain datasets:

• Public Dataset Repositories : Kaggle, Google Dataset Search, UCI Machine Learning Repository
• APIs for Real-Time Data : GitHub API, OpenWeather API, Twitter(X) API
• Government and Organization Portals
• Web Scraping / CSV Files and Databases

Code for above visualization

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

# Load the dataset
df = pd.read_csv('data_prog_lang.csv')

# Clean column names
df.columns = df.columns.str.strip()

# Convert 'Date' from 'Jan-05' to datetime (month-year)
df['Date'] = pd.to_datetime(df['Date'], format='%b-%y', errors='coerce')

# Print to verify conversion
print(df[['Date']].head())

# Melt dataframe (wide to long format)
df_long = df.melt(id_vars=['Date'],
                  var_name='Language', value_name='Popularity')

# Drop missing values
df_long = df_long.dropna()

# Find top 8 languages by average popularity
top_languages = (
    df_long.groupby('Language')['Popularity']
    .mean()
    .sort_values(ascending=False)
    .head(8)
    .index
)

df_filtered = df_long[df_long['Language'].isin(top_languages)]

# Plot using seaborn
plt.figure(figsize=(14, 7))
sns.lineplot(
    data=df_filtered,
    x='Date',
    y='Popularity',
    hue='Language',
    marker='o',
    palette='tab10'
)

plt.title('Programming Language Trends Over Time', fontsize=16)
plt.xlabel('Date (Month-Year)', fontsize=12)
plt.ylabel('Popularity (%)', fontsize=12)
plt.grid(True)
plt.xticks(rotation=45)
plt.legend(title='Language', bbox_to_anchor=(1.05, 1), loc='upper left')
plt.tight_layout()

# Show the plot
plt.show()