Recommended configuration for data science using Visual Studio Code on Linux

Recommended configuration for using Visual Studio Code for data science on Linux

With the rapid development of data science, more and more data analysts and data scientists choose to use Visual Studio Code (VS Code for short) ) to do data science work. VS Code is an open source lightweight code editor developed by Microsoft and a feature-rich integrated development environment (IDE). It has rich extensions to meet the needs of data scientists and is completely free.

This article will introduce how to properly configure VS Code on Linux for data science work and perform some common data science tasks such as data processing, visualization, and machine learning.

Step 1: Install VS Code
First, you need to install VS Code on Linux. You can download the installation package for Linux from the official website of VS Code https://code.visualstudio.com/, or install it through the package manager. After installation, please ensure that VS Code can be started through the "code" command on the command line.

Step 2: Install the Python extension
In VS Code, most data science work is performed using Python. Therefore, we need to install the Python extension to write, run and debug Python code in VS Code. Open VS Code, click the extension icon on the left (or press Ctrl Shift X), enter "Python" in the search bar, and click to install the extension named "Python".

Step 3: Configure the Python interpreter
After installing the Python extension, you need to configure VS Code to use the correct Python interpreter. Click the "Python" selection box in the lower left corner of VS Code and select the Python interpreter you want to use in the pop-up menu. If you have multiple Python versions installed on your system, you can select the appropriate version. If the interpreter you want is not found, you need to manually specify the path to the Python interpreter.

Step 4: Use Jupyter Notebook
Jupyter Notebook is a commonly used interactive programming tool that is very helpful for data science work. In VS Code, we can use Jupyter notebooks by installing the Jupyter extension. Open VS Code, click the extension icon on the left, enter "Jupyter" in the search bar, and click to install the extension named "Jupyter".

After installing the Jupyter extension, you can create a new Jupyter notebook by clicking the "File" menu in the upper left corner of VS Code and selecting "New"->"Notebook". You can run code in a notebook, display the results, and save the entire notebook for later use.

Step 5: Install data science related extensions
In addition to Python and Jupyter extensions, there are many other extensions that can help you with your data science work. The following are some commonly used data science extension recommendations:

• Python Docstring Generator: Automatically generate docstrings for Python functions.
• Python Autopep8: Automatically format Python code to conform to PEP8 specifications.
• Python Test Explorer: Extension for running and debugging Python unit tests.
• Python IntelliSense: Provides Python syntax prompts and code auto-completion functions.
• Data Preview: View and preview data in VS Code, supporting multiple data formats.
• Matplotlib: A Python library for data visualization that can be used for charting in VS Code.
• Pandas: A Python library for data processing and analysis that facilitates data science tasks in VS Code.

The above extensions are just some recommendations. You can choose the extension that suits you according to your needs.

Step 6: Perform data science tasks
After configuring VS Code, you can start to perform some common data science tasks. Here are code examples for some common tasks:

Data processing:

import pandas as pd

# 读取csv文件
data = pd.read_csv('data.csv')

# 查看数据前几行
print(data.head())

# 对数据进行清洗和转换
# ...

# 保存处理后的数据
data.to_csv('cleaned_data.csv', index=False)

Data visualization:

import matplotlib.pyplot as plt
import pandas as pd

# 读取数据
data = pd.read_csv('data.csv')

# 绘制柱状图
plt.bar(data['x'], data['y'])
plt.xlabel('x')
plt.ylabel('y')
plt.title('Bar Chart')
plt.show()

Machine learning:

from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression

# 读取数据
data = pd.read_csv('data.csv')

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(data[['x']], data['y'], test_size=0.2)

# 创建线性回归模型
model = LinearRegression()

# 训练模型
model.fit(X_train, y_train)

# 预测
y_pred = model.predict(X_test)

# 计算模型的性能指标
# ...

With the above code examples, You can perform data science tasks such as data processing, data visualization, and machine learning in VS Code. When writing code in VS Code, you can take advantage of rich extension functions and code editing tools to improve work efficiency.

Summary
This article introduces the recommended configuration for using Visual Studio Code on Linux for data science work. By properly configuring the Python interpreter, installing relevant extensions, and using Jupyter notebooks, you can perform tasks such as data processing, data visualization, and machine learning in VS Code. Hopefully these configurations and sample code can help you in your data science efforts.

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