Welcome to the Python Data Science Handbook! This guide will help you get started with Python and data science, covering topics from basic data manipulation to advanced machine learning algorithms.

安装 Python 和依赖库

Before you start, make sure you have Python installed on your system. You can download it from the official Python website. Once Python is installed, you will also need to install some libraries for data science, such as NumPy, Pandas, and Matplotlib.

pip install numpy pandas matplotlib

数据导入与处理

Data manipulation is a key part of data science. Pandas is a powerful library for data manipulation in Python. It provides data structures like DataFrames and Series, which make it easy to work with structured data.

读取数据

import pandas as pd

data = pd.read_csv('data.csv')
print(data.head())

数据清洗

Data cleaning involves handling missing values, removing duplicates, and dealing with outliers. Pandas provides functions to help with these tasks.

data = data.dropna()
data = data.drop_duplicates()
data = data[data['column'] <= threshold]

统计分析

Statistical analysis is a fundamental part of data science. Python provides libraries like SciPy and Statsmodels for statistical computations.

描述性统计

import numpy as np

mean = np.mean(data['column'])
median = np.median(data['column'])
std_dev = np.std(data['column'])

回归分析

import statsmodels.api as sm

X = data[['column1', 'column2']]
y = data['column3']

model = sm.OLS(y, X).fit()
print(model.summary())

可视化

Data visualization is a critical tool for understanding and communicating data. Matplotlib and Seaborn are popular libraries for creating plots in Python.

绘制散点图

import matplotlib.pyplot as plt

plt.scatter(data['column1'], data['column2'])
plt.xlabel('Column 1')
plt.ylabel('Column 2')
plt.show()

机器学习

Machine learning is a key component of data science. Scikit-learn is a powerful library for machine learning in Python.

决策树

from sklearn.tree import DecisionTreeClassifier

model = DecisionTreeClassifier()
model.fit(X_train, y_train)

扩展阅读

For more information on Python and data science, check out the following resources:

Python Data Science Handbook