Python数据分析教程（三）：实用代码

Python数据分析教程专栏：数据分析 - 标签 - 孤飞 - 博客园 (cnblogs.com)

Python数据分析教程（一）：Numpy - 孤飞 - 博客园 (cnblogs.com)

Python数据分析教程（二）：Pandas - 孤飞 - 博客园 (cnblogs.com)

文件处理

导包

import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns %matplotlib inline 

添加镜像

https://mirrors.tuna.tsinghua.edu.cn/ https://developer.aliyun.com/mirror/ http://mirrors.163.com/ubuntu/ https://mirrors.ustc.edu.cn/ http://mirrors.zju.edu.cn/ http://mirrors.sohu.com/ http://ftp.sjtu.edu.cn/ http://mirror.bjtu.edu.cn/ http://mirror.bjtu.edu.cn/ 

语法

其中http和https是可选的

! pip install xxx -i https://mirrors.tuna.tsinghua.edu.cn/ 

导入文件

excel

data=pd.read_excel(r"C:UsersranxiDesktop附录1 目标客户体验数据.xlsx", sheet_name='data') data.head() 

csv

data=pd.read_csv() 

EDA报告

#生成报告 import pandas_profiling data.profile_report() #输出报告文件 pfr = pandas_profiling.ProfileReport(data) pfr.to_file('report.html') 

dataframe导出excel文件

data.to_excel('data.xlsx') 

数据处理

数据筛选

分类均值展示

cvr_summary = data.groupby("cvr_group_high") cvr_summary.mean().reset_index() 

标签编码

print("client","--" ,data.client.unique())  from sklearn.preprocessing import LabelEncoder data.client = LabelEncoder().fit_transform(data.client) print("client","--" ,data.client.unique())  

交叉比例表

pd.crosstab(data['invited_is'],data["cvr_group_high"],normalize=0) 

计算分布比例

def percent_value_counts(df, feature):     """This function takes in a dataframe and a column and finds the percentage of the value_counts"""     percent = pd.DataFrame(round(df.loc[:,feature].value_counts(dropna=False, normalize=True)*100,2))     ## creating a df with th     total = pd.DataFrame(df.loc[:,feature].value_counts(dropna=False))     ## concating percent and total dataframe      total.columns = ["Total"]     percent.columns = ['Percent']     return pd.concat([total, percent], axis = 1)  percent_value_counts(data, "B7") 

多列apply函数

with_N['B7'] = with_N.apply(lambda x: child_estimator(x['B6'], x['B5']), axis=1) 

卡方检验

#分组间确实是有显著性差异，频数比较的结论才有可信度，故需进行”卡方检验“ from scipy.stats import chi2_contingency   #统计分析 卡方检验 #自定义卡方检验函数 def KF(x):     df1=pd.crosstab(data2['购买意愿'],data2[x])     li1=list(df1.iloc[0,:])     li2=list(df1.iloc[1,:])     kf_data=np.array([li1,li2])     kf=chi2_contingency(kf_data)     if kf[1]<0.05:         print('购买意愿 by {} 的卡方临界值是{:.2f}，小于0.05，表明{}组间有显著性差异,可进行【交叉分析】'.format(x,kf[1],x),'n')     else:         print('购买意愿 by {} 的卡方临界值是{:.2f}，大于0.05，表明{}组间无显著性差异,不可进行交叉分析'.format(x,kf[1],x),'n') #对 kf_var进行卡方检验 print('kf_var的卡方检验结果如下:','n') print(list(map(KF, kf_var))) 

条件筛选

specific=data[(data['a1']>100)|(data['a2']>100)|(data['a3']>100)|(data['a4']>100)|(data['a5']>100)|(data['a6']>100)|(data['a7']>100)|(data['a8']>100)] specific 

specific=data[(data['']>x)|&()] data[data.Cabin=='N'] 

map函数分组

def hour_group_fun(hour):     x = ''     if 0<=hour<8:         x=1     elif 8<=hour<16:          x=2     else:         x=3     return x          ## Applying function to the column. police['hour_group'] =police['hour'].map(hour_group_fun) 

apply多列赋值

with_N['B7'] = with_N.apply(lambda x: child_estimator(x['B6'], x['B5']), axis=1) 

这是一个分布比例函数

def percent_value_counts(df, feature):     """This function takes in a dataframe and a column and finds the percentage of the value_counts"""     percent = pd.DataFrame(round(df.loc[:,feature].value_counts(dropna=False, normalize=True)*100,2))     ## creating a df with th     total = pd.DataFrame(df.loc[:,feature].value_counts(dropna=False))     ## concating percent and total dataframe      total.columns = ["Total"]     percent.columns = ['Percent']     return pd.concat([total, percent], axis = 1) 

特征工程

时间数据处理

police['date'] = pd.to_datetime(police['接警日期'],errors='coerce')  police['year'] =police['date'].dt.year.fillna(0).astype("int")   #转化提取年  police['month'] = police['date'].dt.month.fillna(0).astype("int")  #转化提取月 police['day'] = police['date'].dt.day.fillna(0).astype("int")      #转化提取天  police['dates'] = police['month'].map(str) + '-' + police['day'].map(str) #转化获取月-日  police['time'] = pd.to_datetime(police['接警时间点'],errors='coerce').dt.time  police['hour'] = pd.to_datetime(police['接警时间点'],errors='coerce').dt.hour.fillna(0).astype("int")    #转化提取小时  

SMOTE过抽样

from imblearn.over_sampling import SMOTE model_smote=SMOTE() X,y=model_smote.fit_resample(X,y) X=pd.DataFrame(X,columns=t.columns) #分拆数据集：训练集 和 测试集 X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.3,random_state=0) print('过抽样数据特征：', X.shape,       '训练数据特征：',X_train.shape,       '测试数据特征：',X_test.shape)  print('过抽样后数据标签：', y.shape,       '   训练数据标签：',y_train.shape,       '   测试数据标签：',y_test.shape) 

输出缺失值

print ("Train age missing value: " + str((train.Age.isnull().sum()/len(train))*100)+str("%")) 

影响分析

xgb输出特征重要性

model_xgb= XGBClassifier() model_xgb.fit(X,y) from xgboost import plot_importance plot_importance(model_xgb,height=0.5,color='green',title='') # plt.savefig('imp.png') plt.show() 

计算相关系数并画图

plt.style.use('classic') plt.rcParams['font.sans-serif'] = ['SimHei']  # 黑体 plt.rcParams['axes.unicode_minus'] = False    # 解决无法显示符号的问题 plt.rc("figure", facecolor="white")           #去除灰色边框 plt.figure(figsize=(15,6),dpi=300) df_onehot.corr()['购买意愿'].sort_values(ascending=False).plot(kind='bar',color='dodgerblue') plt.savefig('buyvary1.png', dpi=300) plt.show()  data.corr(method='pearson') data.corr(method='spearman') data.corr(method='kendall') 

Pandas处理

常用操作

为dataframe添加1列

data['age']=list 

合并表格再排序

data = pd.concat([with_N, without_N], axis=0)  data.sort_values(by = '目标客户编号', inplace=True) 

dataframe排序

useful=useful.sort_values(by = ['购买难度'], ascending = [True]) 

选取指定行（以列的值筛选）

first1=data3[(data3['品牌编号']==1)] 

获取列名

kf=list(data2.columns[1:7]) for x in [9,11,12,20,21,24,25,26]:     kf.append(data2.columns[x]) print(kf) 

修改列名

#1、修改列名a，b为A、B。 df.columns = ['A','B'] #2、只修改列名a为A df.rename(columns={'a':'A'}) 

删除一列

data3=data3.drop(1,axis=0) 

列表转dataframe(嵌套列表）

from pandas.core.frame import DataFrame data7=DataFrame(week) data7 

类型转换

Dataframe到Series

Series = Dataframe['column'] 

Series到list

list = Series.to_list() 

list 转 array

array = np.array(list) 

array 转 torch.Tensor

tensor = torch.from_numpy(array) 

torch.Tensor 转 array

array = tensor.numpy() # gpu情况下需要如下的操作 array = tensor.cpu().numpy() 

torch.Tensor 转 list

# 先转numpy，后转list list = tensor.numpy().tolist() 

array 转 list

list = array.tolist() 

list 转 torch.Tensor

tensor=torch.Tensor(list) 

array或者list转Series

series = pd.Series({'a': array}) series2 = pd.Series({'a': list}) 

list转dataframe

data4=DataFrame(li) 

array转dataframe

df = pd.DataFrame(data=data[0:,0:],columns='pregnants','Plasma_glucose_concentration','blood_pressure','Triceps_skin_fold_thickness','serum_insulin','BMI','Diabetes_pedigree_function','Age','Target'] ) 

python需要注意的地方

变量

列表的复制：直接采用a=b的方式会指向同一个内存地址

全局变量：函数内部的变量，外部是无法访问的，在函数内部定义global 后函数运行过才可访问

循环

• continue： 跳出本次循环
• break： 跳出本层循环

运算

矩阵numpy乘法：

• 点乘： np.dot(xy)
• 数乘： np.mat(x,int)

随机数

import random print( random.randint(1,10) )        # 产生 1 到 10 的一个整数型随机数   print( random.random() )             # 产生 0 到 1 之间的随机浮点数 print( random.uniform(1.1,5.4) )     # 产生  1.1 到 5.4 之间的随机浮点数，区间可以不是整数 print( random.choice('tomorrow') )   # 从序列中随机选取一个元素 print( random.randrange(1,100,2) )   # 生成从1到100的间隔为2的随机整数 a=[1,3,5,6,7]                # 将序列a中的元素顺序打乱 random.shuffle(a) print(a) 

import random import string # 随机整数： print random.randint(1,50) # 随机选取0到100间的偶数： print random.randrange(0, 101, 2) # 随机浮点数： print random.random() print random.uniform(1, 10) # 随机字符： print random.choice('abcdefghijklmnopqrstuvwxyz!@#$%^&*()') # 多个字符中生成指定数量的随机字符： print random.sample('zyxwvutsrqponmlkjihgfedcba',5) # 从a-zA-Z0-9生成指定数量的随机字符： ran_str = ''.join(random.sample(string.ascii_letters + string.digits, 8)) print ran_str # 多个字符中选取指定数量的字符组成新字符串： print ''.join(random.sample(['z','y','x','w','v','u','t','s','r','q','p','o','n','m','l','k','j','i','h','g','f','e','d','c','b','a'], 5)) # 随机选取字符串： print random.choice(['剪刀', '石头', '布']) # 打乱排序 items = [1, 2, 3, 4, 5, 6, 7, 8, 9, 0] print random.shuffle(items) 

画图

画图准备

解决中文符号显示问题

plt.rcParams['font.sans-serif'] = ['SimHei']  # 黑体 plt.rcParams['axes.unicode_minus'] = False    # 解决无法显示符号的问题  sns.set(font='SimHei', font_scale=0.8)        # 解决Seaborn中文显示问题 

设置背景样式

plt.style.use('classic') plt.rc("figure", facecolor="white")           #去除灰色边框 

绘图

这是一个画箱线图代码

import matplotlib.pyplot as plt sns.set_style('darkgrid') fig, ax = plt.subplots(figsize=(16,12),ncols=2) ax1 = sns.boxplot(x="Embarked", y="Fare", hue="Pclass", data=train, ax = ax[0]); ax2 = sns.boxplot(x="Embarked", y="Fare", hue="Pclass", data=test, ax = ax[1]); ax1.set_title("Training Set", fontsize = 18) ax2.set_title('Test Set',  fontsize = 18) fig.show() 

画缺口饼图

churn_value=data['cvr_group_high'].value_counts() labels=data['cvr_group_high'].value_counts().index plt.figure(figsize=(7,7)) plt.pie(churn_value,labels=['一般客户', '高价值客户'],colors=["#75bbfd","#00ffff"], explode=(0.05,0),autopct='%1.1f%%', shadow=False) plt.rcParams['font.sans-serif']=['SimHei'] plt.rcParams['axes.unicode_minus'] = False plt.title("高价值客户占比23.4%") #plt.savefig('pie.png', dpi=300)  

画相关性系数图

mask = np.zeros_like(data.corr(), dtype=np.bool) #mask[np.triu_indices_from(mask)] = True  plt.subplots(figsize = (15,12)) sns.heatmap(data.corr(),              annot=True, #             mask = mask,             cmap = 'RdBu', ## in order to reverse the bar replace "RdBu" with "RdBu_r"             linewidths=.9,              linecolor='gray',             fmt='.2g',             center = 0,             square=True) plt.title("Correlations Among Features", y = 1.03,fontsize = 20, pad = 40) #相关性矩阵 plt.savefig('cor.png', dpi=300)  plt.show()   

画核密度估计

fig = plt.figure(figsize=(15,8),) ## I have included to different ways to code a plot behigh, choose the one that suites you.  ax=sns.kdeplot(data.client[data.cvr_group_high == 0] ,                 color='gray',                shade=True,                label='high') ax=sns.kdeplot(data.loc[(data['cvr_group_high'] == 1),'client'] ,                 color='g',                shade=True,                 label='high',                ) plt.title('client - high vs high', fontsize = 25, pad = 40) plt.ylabel("Frequency of cvr", fontsize = 15, labelpad = 20) plt.xlabel("Client", fontsize = 15,labelpad =20) ## Converting xticks into words for better understanding labels = ['H5', 'android', 'ios','pc','wap'] plt.xticks(sorted(data.client.unique()), labels) plt.legend() 

模型训练

导入模块

#加载模块 from sklearn.preprocessing import StandardScaler import warnings warnings.filterwarnings("ignore") #过滤掉警告的意思 from pyforest import * import pandas as pd import numpy as np  from sklearn.ensemble import RandomForestClassifier     #随机森林 from sklearn.svm import SVC,LinearSVC                   #支持向量机 from sklearn.linear_model import LogisticRegression     #逻辑回归 from sklearn.neighbors import KNeighborsClassifier      #KNN算法 from sklearn.cluster import KMeans                     #K-Means 聚类算法 from sklearn.naive_bayes import GaussianNB              #朴素贝叶斯 from sklearn.tree import DecisionTreeClassifier         #决策树  import xgboost as xgb from xgboost import XGBClassifier                       from catboost import CatBoostClassifier                 from sklearn.ensemble import AdaBoostClassifier from sklearn.ensemble import GradientBoostingClassifier   from sklearn.metrics import classification_report,precision_score,recall_score,f1_score,accuracy_score #分类报告 from sklearn.metrics import confusion_matrix           #混淆矩阵 from sklearn.metrics import silhouette_score           #轮廓系数（评价k-mean聚类效果） from sklearn.model_selection import GridSearchCV       #交叉验证 from sklearn.metrics import make_scorer from sklearn.ensemble import VotingClassifier          #投票  def plot_predictions(test,predicted):     #整体平移     x=np.arange(0,len(test))+1 #     x[0]=1 #     my_x_ticks = np.arange(1, 14, 1) #     plt.xticks(my_x_ticks)     plt.plot(x,test,label='Real')     plt.plot(x,predicted,color='darkOrange',linestyle='--',label='Predicted') #     plt.xlabel('month')     plt.ylabel('count')     plt.legend() import math def mse_loss(y_true, y_pred):     return np.sum(np.power(y_true - y_pred, 2)) / y_true.shape[0] / 2 def return_rmse(test,predicted):     rmse = math.sqrt(mse_loss(test, predicted))     return rmse #     print("The mean squared error is {}.".format(rmse))  Classifiers=[             ["Random Forest",RandomForestClassifier()],              ["Support Vector Machine",SVC()],              ["LogisticRegression",LogisticRegression()],              ["KNN",KNeighborsClassifier(n_neighbors=5)],              ["Naive Bayes",GaussianNB()],              ["Decision Tree",DecisionTreeClassifier()],              ["AdaBoostClassifier",AdaBoostClassifier()],              ["GradientBoostingClassifier", GradientBoostingClassifier()],              ["XGB", XGBClassifier()], ] 

设置训练集

X=train.drop(['目标客户编号','品牌类型','购买意愿'], axis = 1) # X=train.drop(['目标客户编号','品牌类型'], axis = 1) t=X headers = X.columns X= X.astype(float) y = train["购买意愿"] 

训练模型

import warnings warnings.filterwarnings('ignore') Classify_result=[] names=[] prediction=[] for name,classifier in Classifiers:     classifier=classifier     classifier.fit(X_train,y_train)     y_pred=classifier.predict(X_test)     recall=recall_score(y_test,y_pred,average='macro')     precision=precision_score(y_test,y_pred,average='macro')     f1score = f1_score(y_test, y_pred,average='macro')     mse = return_rmse(y_test,y_pred)     class_eva=pd.DataFrame([recall,precision,f1score,mse])     Classify_result.append(class_eva)     name=pd.Series(name)     names.append(name)     y_pred=pd.Series(y_pred)     prediction.append(y_pred)     plot_predictions(y_test,y_pred) #     # plt.savefig('seven1.png', dpi=300)     plt.show() 

模型评估

names=pd.DataFrame(names) names=names[0].tolist() result=pd.concat(Classify_result,axis=1) result.columns=names result.index=["recall","precision","f1score","mse"] result 

小工具

tqdm显示进度条

from tqdm import tqdm  for I in tqdm(): 

记录时间

Import time time_begin = time.time() #code,你的程序 time_end = time.time() time = time_end - time_begin print('time:', time) 

jupyter操作

• Shift+上下键 # 按住Shift进行上下键操作可复选多个cell
• Shift-M # 合并所选cell或合并当前cell和下方的cell
• Ctrl + Shift + - # 从光标所在的位置拆分cell

原创作者：孤飞-博客园
原文链接：https://www.cnblogs.com/ranxi169/p/16838967.html