The World of Python Programming

Master NumPy np.array: Basics to Advanced Data Analysis

Data Handling & Analysis
目次

1. Introduction

Python is a very popular programming language for data analysis, machine learning, and scientific computing. Among them, the numpy (NumPy) library is a powerful tool for streamlining numerical computations. This article provides a detailed explanation of Python’s “np.array“, from fundamentals to practical applications, and introduces concrete techniques useful for processing and analyzing numerical data.

Official page

NumPy quickstart — NumPy v2.3 Manual

…

2. What is numpy?

numpy is a numerical computing library that offers multi-dimensional arrays (ndarray), matrix operations, and statistical analysis features, serving as a foundation for data analysis. By working together with other Python libraries, it makes complex data processing simple and fast.

How to install numpy

Installing numpy is easy. Just run the command below.
pip install numpy

Why choose numpy

The fast and efficient data-processing capabilities of numpy are indispensable for scientific computing and data science. For more details on numpy, see the official documentation.
NumPy

Why NumPy? Powerful n-dimensional arrays. Numerical computin…

3. Basics of np.array

Python’s np.array is the core data structure of numpy and includes functionality for efficiently handling numerical data. For example, here’s how to create a one-dimensional array.
import numpy as np
array_1d = np.array([1, 2, 3, 4, 5])
print(array_1d)
Tip: Specifying the data type of np.array can also improve memory efficiency. Use the dtype argument to specify integer types (such as int32) or floating-point types.

4. Creating Multidimensional Arrays

np.array also supports multidimensional arrays. For example, the following code creates a two-dimensional array.
array_2d = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
print(array_2d)
FAQ Q: How do I create arrays with three or more dimensions? A: You can create them by passing a further nested list to np.array.

5. Array Operations

np.array allows you to retrieve elements and perform partial operations using indexing and slicing. Below is a basic example of indexing.
array_2d = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
print(array_2d[1, 2])  # Output: 6
Using slicing, you can easily extract specific rows or columns.

6. Reshaping Arrays

In data analysis, you often need to change the shape of arrays; using the reshape method lets you reshape them efficiently.
array_1d = np.array([1, 2, 3, 4, 5, 6])
array_2d = array_1d.reshape(2, 3)
print(array_2d)
Tip: You can also use the resize method for reshaping, but be careful because it modifies the original array directly.

7. Array Operations

Using numpy‘s arithmetic capabilities makes operations between arrays simple. The example below performs element-wise addition of two arrays.
array1 = np.array([1, 2, 3])
array2 = np.array([4, 5, 6])
print(array1 + array2)  # Output: [5 7 9]
FAQ Q: Can I perform operations when the arrays have different shapes? A: By using numpy‘s broadcasting feature, you can correctly perform operations on arrays with different shapes.

8. Using Statistical Functions

In data analysis, checking statistical measures such as an array’s mean, maximum and minimum values, and variance is important. For example, below is how to calculate the mean of an array.
array = np.array([1, 2, 3, 4, 5])
print(array.mean())  # Output: 3.0

9. Practical Example: Application to Data Analysis

Here, using fictional sales data, we’ll analyze sales growth rates and examine monthly sales trends.
import numpy as np
import matplotlib.pyplot as plt

sales_data = np.array([120, 150, 170, 200, 220, 180, 190, 210, 230, 250, 240, 260])

# Monthly sales growth rate
growth_rate = (sales_data[1:] - sales_data[:-1]) / sales_data[:-1] * 100
print("Monthly growth rate:", growth_rate)

# Plotting the sales data
plt.plot(range(1, 13), sales_data, marker='o')
plt.title("Monthly Sales Trend")
plt.xlabel("Month")
plt.ylabel("Sales (thousands)")
plt.grid(True)
plt.show()
Tip: Visualizing the data makes it easier to intuitively identify growth peaks and trends, which is helpful for business analysis.

10. Summary

This article explained the basics of using np.array in Python and its applications to data analysis. The key points are as follows.
  1. Basic usage: Creating np.array and specifying data types.
  2. Multidimensional arrays: Creating and manipulating two-dimensional and three-dimensional arrays.
  3. Reshaping: Methods for reshaping using reshape and flatten.
  4. Operations and statistics: Element-wise operations and calculating statistical measures.
  5. Practical examples: Calculating growth rates and visualizing sales data.
These concepts form the foundation for data analysis and machine learning projects, so be sure to make use of them. If you want to deepen your understanding, also consult the official numpy documentation and other related topics.