背景
平时用时知道有相应的设置及相应的原理，具体设置时又不好查找，现特此整理出来供大家收藏
可左右滑动查看代码
Anaconda
pip list
#或者
conda list
#其中，pip list 只能查看库，而 conda list 则可以查看库以及库的版本
pip install scipy
pip install scipy --upgrade
# 或者
conda install scipy
conda update scipy
# 更新所有库
conda update --all
# 更新 conda 自身
conda update conda
# 更新 anaconda 自身
conda update anaconda
jupyter
#显示所有列
pd.set_option('display.max_columns', None)
#显示所有行
pd.set_option('display.max_rows', None)
#设置value的显示长度为100，默认为50
pd.set_option('max_colwidth',100)
#内嵌画图
%matplotlib inline
#单独画图
%matplotlib qt
#画图中文乱码、负号
plt.rcParams['font.sans-serif']=['Microsoft YaHei']
plt.rcParams['axes.unicode_minus']=False
#linux指定字体
from matplotlib.font_manager import FontProperties
zhfont = FontProperties(fname="/home/public/font/SimHei.ttf", size=14)
plt.xlabel('日期',fontproperties = zhfont,fontsize=14)
plt.xticks(fontproperties=zhfont)
#让一个cell同时有多个输出print
from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = "all"
主要的数据分析包
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.figure import SubplotParams
#我们使用SubplotParams 调整了子图的竖直间距
#plt.figure(figsize=(12, 6), dpi=200, subplotpars=SubplotParams(hspace=0.3))
import scipy.stats as stats
import seaborn as sns
import statsmodels.api as sm
Sklearn
from sklearn import datasets #本地数据
from sklearn.model_selection import train_test_split #进行数据分割
from sklearn.feature_extraction import DictVectorizer #特征抽取和向量化
from sklearn.preprocessing import PolynomialFeatures #多项式特征构造
from sklearn.feature_selection import VarianceThreshold #基于方差特征选择
from sklearn.feature_selection import SelectKBest,SelectPercentile #特征选择
#For classification: chi2, f_classif, mutual_info_classif
#For regression: f_regression, mutual_info_regression
from sklearn.feature_selection import RFE #递归特征消除 (Recursive Feature Elimination)
from sklearn.feature_selection import SelectFromModel #基于模型选择特征
from sklearn.decomposition import PCA #主成分分析
from sklearn.manifold import MDS #多维尺度分析
from sklearn.manifold import TSNE #T分布和随机近邻嵌入
from sklearn.pipeline import Pipeline #管道
from sklearn import metrics #模型评估
from sklearn.model_selection import GridSearchCV #网格搜索交叉验证
from sklearn.model_selection import KFold #K折交叉验证
from sklearn.model_selection import cross_val_score #交叉验证
from sklearn.linear_model import LinearRegression #线性回归
from sklearn.linear_model import LogisticRegression #逻辑回归
from sklearn import svm #支持向量机
from sklearn.tree import DecisionTreeClassifier #决策树
from sklearn.ensemble import RandomForestClassifier #随机森林
from sklearn.ensemble import GradientBoostingClassifier #梯度提升树
from sklearn.naive_bayes import MultinomialNB #多项式朴素贝叶斯
from sklearn.naive_bayes import BernoulliNB #伯努利朴素贝叶斯
from sklearn.naive_bayes import GaussianNB #高斯朴素贝叶斯
from sklearn.neighbors import KNeighborsClassifier #k紧邻
from sklearn.cluster import KMeans #k均值聚类
from sklearn.cluster import DBSCAN #基于密度的空间聚类
from sklearn.cluster import SpectralClustering #谱聚类
from sklearn.cluster import Birch #层次聚类
from sklearn.externals import joblib #保存模型
pycharm脚本模板
"""
===========================
@File : ${NAME}
@Author: DataShare
@Date : ${DATE} ${TIME}
===========================
"""
