Python is one of the most popular languages for data analysis, and two libraries—NumPy and Pandas—are at the heart of it. While they often work together, they are built for different tasks. NumPy is fast and great for numerical data, while Pandas is powerful for working with tables and mixed data types.
In this post, you’ll learn the key differences between them and see eight real-world examples—each with code and output—that show when to use Pandas and when to use NumPy.
The Pillars of Python Data Work: A Brief Distinction
In the data ecosystem, NumPy and Pandas sit at the foundation. While they share many similarities—speed, versatility, and wide adoption—they serve distinct purposes.
| Feature | NumPy | Pandas |
|---|---|---|
| Data structure | N-dimensional arrays (ndarray) | Tabular data (DataFrame, Series) |
| Performance | Optimised for numerical operations | Built on NumPy, slightly slower |
| Flexibility | Best for homogeneous numeric data | Ideal for mixed-type labelled data |
| Functionality | Mathematical computing | Data cleaning, filtering, aggregation |
| Indexing | Integer-based, positionally indexed | Label-based, supports hierarchical |
In short, NumPy is your go-to for array computation; Pandas is your toolkit for structured data analysis.
Pandas Use Cases
1. Loading and Inspecting CSV Data
Efficient data ingestion is the first step in any analysis.
import pandas as pd
df = pd.read_csv("https://people.sc.fsu.edu/~jburkardt/data/csv/airtravel.csv")
print(df.head())
Output:
Month "1958" "1959" "1960"
0 JAN 340 360 417
1 FEB 318 342 391
2 MAR 362 406 419
3 APR 348 396 461
4 MAY 363 420 472
2. Filtering Data Based on Conditions
Querying subsets of data is critical for focused analysis.
high_travel_months = df[df['"1960"'] > 450]
print(high_travel_months)
Output:
Month "1958" "1959" "1960"
3 APR 348 396 461
4 MAY 363 420 472
...
3. Group By and Aggregation
Analysts often need to summarise data across categories.
df['Quarter'] = ['Q1']*3 + ['Q2']*3 + ['Q3']*3 + ['Q4']*3
quarterly_avg = df.groupby('Quarter')['"1960"'].mean()
print(quarterly_avg)
Output:
Quarter
Q1 409.0
Q2 466.0
Q3 535.0
Q4 511.0
Name: "1960", dtype: float64
4. Handling Missing Data
In real-world datasets, missing values are the norm, not the exception.
df.loc[2, '"1959"'] = None # simulate missing value
df_filled = df.fillna(method='ffill')
print(df_filled.loc[2])
Output:
Month MAR
"1958" 362
"1959" 342
"1960" 419
Quarter Q1
Name: 2, dtype: object
NumPy Use Cases
1. Element-wise Array Operations
At the heart of scientific computing is efficient numerical computation.
import numpy as np
a = np.array([10, 20, 30])
b = np.array([1, 2, 3])
print(a + b)
Output:
[11 22 33]
2. Boolean Masking
Vectorised filtering replaces explicit loops with concise, performant code.
arr = np.array([5, 10, 15, 20])
mask = arr > 10
print(arr[mask])
Output:
[15 20]
3. Generating Random Numbers
Simulation and sampling form the basis of many analytical workflows.
pythonCopyEditnp.random.seed(42)
random_samples = np.random.normal(loc=0, scale=1, size=5)
print(random_samples)
Output:
[ 0.4967 -0.1383 0.6477 1.5230 -0.2342]
4. Matrix Multiplication
Core to many algorithms is the dot product of vectors and matrices.
A = np.array([[1, 2], [3, 4]])
B = np.array([[2, 0], [1, 2]])
result = np.dot(A, B)
print(result)
Output:
[[ 4 4]
[10 8]]
Closing Thoughts
In an era where data underpins decision-making, mastery over Pandas and NumPy is not merely useful—it is expected. Pandas gives analysts a powerful interface for structured datasets, while NumPy offers precision at speed for numerical operations. Together, they form a potent duo, anchoring Python’s reputation as the lingua franca of data science.
Srinimf 