When it comes to choosing a core programming language to power your business goals, Python stands out for its versatility, ease of use, and powerful data handling capabilities.
It's no coincidence that tech giants like Google, Netflix, and Instagram have built crucial parts of their infrastructure using Python.
At Flowmoco we're big fans of the language, and understand well why it's become the backbone of modern business technology. Let's dive in and explore some of the main reasons organisations are choosing Python to help give them a competitive edge.
Why Python Dominates Enterprise Development
Python's philosophy of simplicity and readability has made it the third most popular programming language globally. It's also exceedingly powerful. Most major tech companies have heavy invested in Python to power their services. Some examples:
Google has extensively adopted it ever where they can, and coined their internal motto "Python where we can, C++ where we must." to reflect this.
Google were so convinced of Python's benefits that they completely rewrote their web crawler (originally in Java) using Python. This was a technical and strategic decision that improved maintainability and development speed for one of their most critical systems.
Netflix and Spotify
Both streaming giants use Python extensively for their recommendation engines. Spotify runs over 6,000 Python processes for features including recommendations, top lists, and A/B test analysis. Their data pipelines process millions of data points to deliver personalized experiences to users.
Instagram runs the world's largest deployment of Django (a Python web framework), serving over 500 million daily active users — and since 2017 their total user base has grown to over 1.4 billion in 2024.
They chose Django for a host of reasons — like its speed of feature development, its simplicity, it's Python based data management (more about Django ORM here) — and because Django is built on Python. Python is known for its scalability and maintainability, crucial factors when you're operating at that scale with large teams of developers actively working on the codebase.
Facebook uses Python for approximately 21% of their codebase. It's their go-to language for production engineering, handling everything from hardware imaging to automated maintenance and infrastructure management.
All About The Data: From Raw Data to Business Insights
Python has emerged as the go-to language for working with data, offering a powerful ecosystem of libraries that handle everything from initial data manipulation to complex analysis and visualisation. This integration of tools makes Python particularly valuable for businesses looking to make data-driven decisions.
Two foundational libraries for working with data through Python are NumPy and pandas.
NumPy (Numerical Python) provides support for numerical computations, especially those involving arrays and matrices, and is used under the hood by pandas. NumPy offers high-performance multidimensional array objects called ndarray and a suite of mathematical functions to operate on these arrays. It's well suited for handling large datasets with homogeneous data (e.g., numerical data).
pandas is an essential library that provides high-level tools for working with structured data, using NumPy's ndarray for its core data structures. This means you get NumPy's computational efficiency along with pandas' rich functionality for handling business data.
pandas (like Python) is designed to be very readable and easy to use. To illustrate, here's a toy example of what using pandas to analyse customer purchase patterns could look like:
# pandas is the primary library for data manipulation
import pandas as pd
# seaborn and matplotlib both used for visualization
import seaborn as sns
import matplotlib.pyplot as plt
def analyze_customer_behavior(purchase_data):
# pandas: Create DataFrame from raw data
df = pd.DataFrame(purchase_data)
# pandas: Group and aggregate data
customer_analysis = df.groupby('customer_id').agg({
'purchase_amount': ['sum', 'mean', 'count'],
'product_category': 'nunique'
}).round(2)
# pandas: Calculate derived metrics
customer_analysis['lifetime_value'] = (
customer_analysis['purchase_amount']['sum'] *
customer_analysis['purchase_amount']['count'] / 365
)
# pandas: Calculate segment boundaries
segments = customer_analysis['lifetime_value'].quantile([0.25, 0.5, 0.75])
return customer_analysis, segmentsYou can see from the above how pandas handles both data processing and analysis — loading raw data, cleaning it, performing calculations, and generating business insights — all in a few lines of code. These patterns will be fairly typical regardless of the type of data you're working with.
This data can then be inputed into a visualisation library. Matplotlib is one of most popular and has built-in support with pandas. It's used for creating static, animated, and interactive visualisations in Python. Or for a more higher level library you could use Seaborn (built on top of Matplotlib) for statistical data visualisation.
For example here's how you might analyse marketing campaign performance across channels:
def visualize_campaign_performance(campaign_data):
df = pd.DataFrame(campaign_data)
# Calculate ROI (using NumPy operations internally)
channel_performance = df.groupby('channel').agg({
'spend': 'sum',
'revenue': 'sum'
})
channel_performance['roi'] = (
(channel_performance['revenue'] - channel_performance['spend'])
/ channel_performance['spend'] * 100
)
# Create visualization
fig, ax = plt.subplots(figsize=(10, 6)) # Create figure and axis objects
# seaborn: Create bar plot (draws on the current matplotlib figure)
plot = sns.barplot(data=channel_performance.reset_index(),
x='channel', y='roi',
ax=ax) # Specify which axis to plot on
ax.set_title('ROI by Marketing Channel')
# Return both the figure and the data
return fig, channel_performanceThe point is, Python makes working with and communicating complex data easy.
Making Python Work for Your Business
The key to successfully implementing Python in your organisation lies in understanding where it can provide the most value, and what libraries and frameworks are available to leverage. Python has some incredible tools for a wide variety of use cases:
Application Development
- Fast development cycles — using frameworks like Django (kitchen sink) or Flask (lightweight & modular). Django in particular provides so much out of the box that it hugely speeds up application build & delivery times.
- API development for system integrations — libraries like graphene-python (for GraphQL), or Django REST.
- Content management systems — like Wagtail (built on top of Django)
Data Engineering
- ETL pipeline development
- Data warehouse integration
- Automated reporting systems
Process Automation
- Business process automation
- System monitoring and maintenance
- Automated testing and deployment
Wrapping Up
Python's combination of readability, powerful data handling capabilities, and extensive libraries make it an ideal choice for modern businesses. Whether you're processing complex datasets, building web applications, or automating business processes, Python provides the tools and flexibility to achieve your goals efficiently.
At Flowmoco, we've helped numerous organisations harness Python's capabilities to solve complex business challenges and drive innovation.
Let's discuss how Python can transform your business operations.
Get in touch with us today to start the conversation.