Python各种常用语句举例分析

本篇内容主要讲解"Python各种常用语句举例分析"，感兴趣的朋友不妨来看看。本文介绍的方法操作简单快捷，实用性强。下面就让小编来带大家学习"Python各种常用语句举例分析"吧!

Anaconda

 1pip list 2#或者 3conda list 4#其中，pip list 只能查看库，而 conda list 则可以查看库以及库的版本 5 6 7pip install scipy 8pip install scipy --upgrade 9# 或者10conda install scipy11conda update scipy1213# 更新所有库14conda update --all1516# 更新 conda 自身17conda update conda1819# 更新 anaconda 自身20conda update anaconda

jupyter

 1#显示所有列 2pd.set_option('display.max_columns', None) 3 4#显示所有行 5pd.set_option('display.max_rows', None) 6 7#设置value的显示长度为100，默认为50 8pd.set_option('max_colwidth',100) 910#内嵌画图11%matplotlib inline1213#单独画图14%matplotlib qt1516#画图中文乱码、负号17plt.rcParams['font.sans-serif']=['Microsoft YaHei']18plt.rcParams['axes.unicode_minus']=False1920#让一个cell同时有多个输出print21from IPython.core.interactiveshell import InteractiveShell22InteractiveShell.ast_node_interactivity = "all"

主要的数据分析包

 1import numpy as np 2import pandas as pd 3import matplotlib.pyplot as plt 4from matplotlib.figure import SubplotParams   5#我们使用SubplotParams 调整了子图的竖直间距 6#plt.figure(figsize=(12, 6), dpi=200, subplotpars=SubplotParams(hspace=0.3)) 7 8import scipy.stats as stats 9import seaborn as sns10import statsmodels.api as sm

Sklearn

 1from sklearn import datasets    #本地数据 2from sklearn.model_selection import train_test_split    #进行数据分割 3 4from sklearn.feature_extraction import DictVectorizer  #特征抽取和向量化 5from sklearn.preprocessing import PolynomialFeatures   #多项式特征构造 6 7from sklearn.feature_selection import VarianceThreshold  #基于方差特征选择 8from sklearn.feature_selection import SelectKBest,SelectPercentile  #特征选择 9#For classification: chi2, f_classif, mutual_info_classif10#For regression: f_regression, mutual_info_regression11from sklearn.feature_selection import RFE   #递归特征消除 (Recursive Feature Elimination)12from sklearn.feature_selection import SelectFromModel   #基于模型选择特征1314from sklearn.decomposition import PCA  #主成分分析15from sklearn.manifold import MDS  #多维尺度分析16from sklearn.manifold import TSNE  #T分布和随机近邻嵌入1718from sklearn.pipeline import Pipeline       #管道19from sklearn import metrics      #模型评估20from sklearn.model_selection import GridSearchCV  #网格搜索交叉验证21from sklearn.model_selection import KFold  #K折交叉验证22from sklearn.model_selection import cross_val_score  #交叉验证2324from sklearn.linear_model import LinearRegression    #线性回归2526from sklearn.linear_model import LogisticRegression  #逻辑回归2728from sklearn import svm    #支持向量机2930from sklearn.tree import DecisionTreeClassifier  #决策树31from sklearn.ensemble import RandomForestClassifier  #随机森林32from sklearn.ensemble import GradientBoostingClassifier  #梯度提升树3334from sklearn.naive_bayes import MultinomialNB  #多项式朴素贝叶斯35from sklearn.naive_bayes import BernoulliNB  #伯努利朴素贝叶斯36from sklearn.naive_bayes import GaussianNB  #高斯朴素贝叶斯3738from sklearn.neighbors import KNeighborsClassifier  #k紧邻3940from sklearn.cluster import KMeans   #k均值聚类41from sklearn.cluster import DBSCAN  #基于密度的空间聚类42from sklearn.cluster import SpectralClustering  #谱聚类43from sklearn.cluster import Birch  #层次聚类4445from sklearn.externals import joblib  #保存模型

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