Python指数
Python是一种广泛使用的高级编程语言，其设计目标是易于阅读和编写，Python的语法简洁明了，适合初学者学习，Python具有丰富的库和框架，可以应用于各种领域，如数据分析、人工智能、网络编程等，本文将介绍Python的基本语法、常用库和实践案例。
Python基本语法
1、注释：在Python中，使用井号（#）表示单行注释，三个单引号（'''）或三个双引号（"""）表示多行注释。
这是一个单行注释 ''' 这是一个 多行注释 '''
2、变量：在Python中，变量不需要声明，直接赋值即可，变量名由字母、数字和下划线组成，且不能以数字开头。
x = 10 name = "张三"
3、数据类型：Python有多种数据类型，如整数（int）、浮点数（float）、字符串（str）、列表（list）、元组（tuple）、字典（dict）等。
a = 10 # 整数
b = 3.14 # 浮点数
c = "Hello" # 字符串
d = [1, 2, 3] # 列表
e = (1, 2, 3) # 元组
f = {"name": "张三", "age": 18} # 字典
4、条件语句：Python使用if、elif和else关键字进行条件判断。
age = 18
if age >= 18:
print("成年")
else:
print("未成年")
5、循环语句：Python支持for循环和while循环。
for循环
for i in range(5):
print(i)
while循环
count = 0
while count < 5:
print(count)
count += 1
6、函数：Python使用def关键字定义函数。
def add(a, b):
return a + b
result = add(1, 2)
print(result)
7、类和对象：Python使用class关键字定义类，通过类可以创建对象。
class Person:
def __init__(self, name, age):
self.name = name
self.age = age
def say_hello(self):
print("Hello, my name is", self.name)
p = Person("张三", 18)
p.say_hello()
常用库和框架
1、NumPy：NumPy是一个用于数值计算的库，提供了多维数组对象、矩阵运算等功能。
import numpy as np a = np.array([1, 2, 3]) b = np.array([[1, 2], [3, 4]]) print(a + b)
2、Pandas：Pandas是一个用于数据处理和分析的库，提供了DataFrame对象、数据清洗、数据分析等功能。
import pandas as pd
data = {'name': ['张三', '李四', '王五'], 'age': [18, 20, 22]}
df = pd.DataFrame(data)
print(df)
3、Matplotlib：Matplotlib是一个用于绘制图形的库，提供了丰富的绘图功能。
import matplotlib.pyplot as plt x = [1, 2, 3] y = [2, 4, 6] plt.plot(x, y) plt.show()
4、Scikit-learn：Scikit-learn是一个用于机器学习的库，提供了分类、回归、聚类等算法。
from sklearn import linear_model, datasets from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score import numpy as np import matplotlib.pyplot as plt from sklearn import datasets, metrics, linear_model, tree, ensemble, naive_bayes, neural_network, discriminant_analysis, model_selection, utility as utl, preprocessing as preprocess, decomposition as decomp, kernel_ridge as krr, neighbors as neighbors, svm as svmutil, impute as imputers, pipeline as pipelines, dimensionality as dimred, cluster as clustering, manifold as manifolds, ensemble as ensembles, metrics as metrics_module, base as base_module, graph as graph_module, io as dataio_module, externals as externals_module, inspection as inspection_module, stats as stats_module, cache as cache_module, check_random_state as random_state_checker, pca as pca_module, image as image_module, datasets as datasets_module, auto_examples as examples_module, ml_modules as ml_modules_module, __check_build as __check_build__ from __future__ import division, print_function, absolute_import # noqa:F401 # pylint: disable=W0611 # pylint: disable=W0401 # pylint: disable=W0223 # pylint: disable=W0610 # pylint: disable=W0603 # pylint: disable=W0602 # pylint: disable=W0622 # pylint: disable=W0406 # pylint: disable=W0613 # pylint: disable=W0614 # pylint: disable=W0615 # pylint: disable=W0616 # pylint: disable=W0617 # pylint: disable=W0618 # pylint: disable=W0619 # pylint: disable=W0401 # pylint: disable=W0621 # pylint: disable=W0622 # pylint: disable=W0623 # pylint: disable=W0624 # pylint: disable=W0625 # pylint: disable=W0626 # pylint: disable=W0404 # pylint: disable=W0405 # pylint: disable=C0301 # pylint: disable=R0201 # pylint: disable=R0913 # For details on how to enable/disable Pylint warnings in this file see https://pypi.org/project/pylint/#warnings-suppression-codes and https://docs.pylint.org/en/latest/technical_reference/features.html#id7 Pylint Issue #99999999999999999999999999999999999999999999999999999999999999999999999999 ImportError at /home/runner/work/sklearn-base/sklearn/utils/__init__.py:757 when loading liblinear module from scikit-learn's build path on Linux for python version 3.7 from '/home/runner/work/sklearn-base/sklearn' (Triggered internally at /opt/hostedtoolcache/Python/3.7.x/x64/lib/python3.7/site-packages/numpy/core/__init__.py:58) if __debug__: % (exc_type.__name__ + ': ' + str(exc)) % traceback.format_exc() from urllib3.exceptions import HTTPError from urllib3.exceptions import MaxRetryError from urllib3.exceptions import InsecureRequestWarning from urllib3.exceptions import ResponseError from urllib3.exceptions import ProtocolError from urllib3.exceptions import TooManyRedirects from urllib3.exceptions import RequestCountError from urllib3.exceptions import ConnectTimeoutError from urllib3.exceptions import ReadTimeoutError from urllib3.exceptions import ChunkedEncodingError from requests.exceptions import RequestException from requests.exceptions import HTTPError from requests.exceptions import ConnectionError from requests.exceptions import Timeout exceptions during handling the request and the following exception was a <class 'urllib3.exceptions.MaxRetryError'> during URL opening > see log for details on how to enable exceptions specific to your application