最近小编认真整理了20+个基于python的实战案例，主要包含：数据分析、可视化、机器学习/深度学习、时序预测等，案例的主要特点：
提供源码：都是基于jupyter notebook，附带一定的注释，运行即可
数据齐全：大部分案例都有提供数据，部分案例使用内置数据集
数据统计分析
基于python和第三方库进行数据处理和分析，主要使用pandas、plotly、matplotlib等库，具体案例：
电子产品（手机）销售分析：
（1）不同内存下的销量(代码片段)
nei_cun = color_size["Number_GB"].value_counts().reset_index()nei_cun.columns = ["Number_of_GB","Count"] # 重命名nei_cun["Number_of_GB"] = nei_cun["Number_of_GB"].apply(lambda x: str(x) + "GB")fig = px.pie(nei_cun, values="Count", names="Number_of_GB")fig.show()nei_cun = color_size["Number_GB"].value_counts().reset_index()nei_cun.columns = ["Number_of_GB","Count"] # 重命名nei_cun["Number_of_GB"] = nei_cun["Number_of_GB"].apply(lambda x: str(x) + "GB")fig = px.pie(nei_cun, values="Count", names="Number_of_GB")fig.show()
（2）不同闪存Ram下的价格分布（代码片段）
fig = px.box(df, y="Sale Price",color="Ram")fig.update_layout(height=600, width=800, showlegend=False)fig.update_layout( title={ "text":'不同<b>闪存</b>下的价格分布', "y":0.96, "x":0.5, "xanchor":"center", "yanchor":"top" }, xaxis_tickfont_size=12, yaxis=dict( title='Distribution', titlefont_size=16, tickfont_size=12, ), legend=dict( x=0, y=1, bgcolor='rgba(255, 255, 255, 0)', bordercolor='rgba(2, 255, 255, 0)' ))fig.show()fig = px.box(df, y="Sale Price",color="Ram")fig.update_layout(height=600, width=800, showlegend=False)fig.update_layout( title={ "text":'不同<b>闪存</b>下的价格分布', "y":0.96, "x":0.5, "xanchor":"center", "yanchor":"top" }, xaxis_tickfont_size=12, yaxis=dict( title='Distribution', titlefont_size=16, tickfont_size=12, ), legend=dict( x=0, y=1, bgcolor='rgba(255, 255, 255, 0)', bordercolor='rgba(2, 255, 255, 0)' ))fig.show()
7万条餐饮数据分析
fig = px.bar(df2_top3,x="行政区",y="店铺数量",color="类别",text="店铺数量")fig.update_layout(title="不同行政区下不同类别的店铺数量对比")fig.show()fig = px.bar(df2_top3,x="行政区",y="店铺数量",color="类别",text="店铺数量")fig.update_layout(title="不同行政区下不同类别的店铺数量对比")fig.show()
不同店铺下的点评数量对比：
4个指标的关系：口味、环境、服务和人均消费
基于python实现RFM模型（用户画像）
RFM模型是客户关系管理（CRM）中的一种重要分析模型，用于衡量客户价值和客户创利能力。该模型通过以下三个指标来评估客户的价值和发展潜力：
近期购买行为（R）：指的是客户最近一次购买的时间间隔。这个指标可以反映客户的活跃程度和购买意向，进而判断客户的质量和潜在价值。
近期购买行为（R）：指的是客户最近一次购买的时间间隔。这个指标可以反映客户的活跃程度和购买意向，进而判断客户的质量和潜在价值。
购买的总体频率（F）：指的是客户在一定时间内购买商品的次数。这个指标可以反映客户对品牌的忠诚度和消费习惯，进而判断客户的潜力和价值。
购买的总体频率（F）：指的是客户在一定时间内购买商品的次数。这个指标可以反映客户对品牌的忠诚度和消费习惯，进而判断客户的潜力和价值。
花了多少钱（M）：指的是客户在一定时间内购买商品的总金额。这个指标可以反映客户的消费能力和对品牌的认可度，进而判断客户的价值和潜力。
花了多少钱（M）：指的是客户在一定时间内购买商品的总金额。这个指标可以反映客户的消费能力和对品牌的认可度，进而判断客户的价值和潜力。
计算R、F、M三个指标值：
data['Recency'] = (datetime.now().date() - data['PurchaseDate'].dt.date).dt.daysfrequency_data = data.groupby('CustomerID')['OrderID'].count().reset_index()# 重命名frequency_data.rename(columns={'OrderID': 'Frequency'}, inplace=True)monetary_data = data.groupby('CustomerID')['TransactionAmount'].sum().reset_index()monetary_data.rename(columns={'TransactionAmount': 'MonetaryValue'}, inplace=True)data['Recency'] = (datetime.now().date() - data['PurchaseDate'].dt.date).dt.daysfrequency_data = data.groupby('CustomerID')['OrderID'].count().reset_index()# 重命名frequency_data.rename(columns={'OrderID': 'Frequency'}, inplace=True)monetary_data = data.groupby('CustomerID')['TransactionAmount'].sum().reset_index()monetary_data.rename(columns={'TransactionAmount': 'MonetaryValue'}, inplace=True)可视化
可视化主要是讲解了matplotlib的3D图和统计相关图形的绘制和plotly_express的入门：
(1) matplotlib的3D图形绘制
plt.style.use('fivethirtyeight')fig = plt.figure(figsize=(8,6))ax = fig.gca(projection='3d')z = np.linspace(0, 20, 1000)x = np.sin(z)y = np.cos(z)surf=ax.plot3D(x,y,z)z = 15 * np.random.random(200)x = np.sin(z) + 0.1 * np.random.randn(200)y = np.cos(z) + 0.1 * np.random.randn(200)ax.scatter3D(x, y, z, c=z, cmap='Greens')plt.show()plt.style.use('fivethirtyeight')fig = plt.figure(figsize=(8,6))ax = fig.gca(projection='3d')z = np.linspace(0, 20, 1000)x = np.sin(z)y = np.cos(z)surf=ax.plot3D(x,y,z)z = 15 * np.random.random(200)x = np.sin(z) + 0.1 * np.random.randn(200)y = np.cos(z) + 0.1 * np.random.randn(200)ax.scatter3D(x, y, z, c=z, cmap='Greens')plt.show()
plt.style.use('fivethirtyeight')fig = plt.figure(figsize=(14,8))ax = plt.axes(projection='3d')ax.plot_surface(x, y, z, rstride=1, cstride=1, cmap='viridis', edgecolor='none')ax.set_title('surface')# ax.set(xticklabels=[], # 隐藏刻度# yticklabels=[],# zticklabels=[])plt.show()plt.style.use('fivethirtyeight')fig = plt.figure(figsize=(14,8))ax = plt.axes(projection='3d')ax.plot_surface(x, y, z, rstride=1, cstride=1, cmap='viridis', edgecolor='none')ax.set_title('surface')# ax.set(xticklabels=[], # 隐藏刻度# yticklabels=[],# zticklabels=[])plt.show()
(2) 统计图形绘制
绘制箱型图：
np.random.seed(10)D = np.random.normal((3, 5, 4), (1.25, 1.00, 1.25), (100, 3))fig, ax = plt.subplots(2, 2, figsize=(9,6), constrained_layout=True)ax[0,0].boxplot(D, positions=[1, 2, 3])ax[0,0].set_title('positions=[1, 2, 3]')ax[0,1].boxplot(D, positions=[1, 2, 3], notch=True) # 凹槽显示ax[0,1].set_title('notch=True')ax[1,0].boxplot(D, positions=[1, 2, 3], sym='+') # 设置标记符号ax[1,0].set_title("sym='+'")ax[1,1].boxplot(D, positions=[1, 2, 3], patch_artist=True, showmeans=False, showfliers=False, medianprops={"color": "white", "linewidth": 0.5}, boxprops={"facecolor": "C0", "edgecolor": "white", "linewidth": 0.5}, whiskerprops={"color": "C0", "linewidth": 1.5}, capprops={"color": "C0", "linewidth": 1.5})ax[1,1].set_title("patch_artist=True")# 设置每个子图的x-y轴的刻度范围for i in np.arange(2): for j in np.arange(2): ax[i,j].set(xlim=(0, 4), xticks=[1,2,3], ylim=(0, 8), yticks=np.arange(0, 9))plt.show()np.random.seed(10)D = np.random.normal((3, 5, 4), (1.25, 1.00, 1.25), (100, 3))fig, ax = plt.subplots(2, 2, figsize=(9,6), constrained_layout=True)ax[0,0].boxplot(D, positions=[1, 2, 3])ax[0,0].set_title('positions=[1, 2, 3]')ax[0,1].boxplot(D, positions=[1, 2, 3], notch=True) # 凹槽显示ax[0,1].set_title('notch=True')ax[1,0].boxplot(D, positions=[1, 2, 3], sym='+') # 设置标记符号ax[1,0].set_title("sym='+'")ax[1,1].boxplot(D, positions=[1, 2, 3], patch_artist=True, showmeans=False, showfliers=False, medianprops={"color": "white", "linewidth": 0.5}, boxprops={"facecolor": "C0", "edgecolor": "white", "linewidth": 0.5}, whiskerprops={"color": "C0", "linewidth": 1.5}, capprops={"color": "C0", "linewidth": 1.5})ax[1,1].set_title("patch_artist=True")# 设置每个子图的x-y轴的刻度范围for i in np.arange(2): for j in np.arange(2): ax[i,j].set(xlim=(0, 4), xticks=[1,2,3], ylim=(0, 8), yticks=np.arange(0, 9))plt.show()
绘制栅格图：
np.random.seed(1)x = [2, 4, 6]D = np.random.gamma(4, size=(3, 50))# plt.style.use('fivethirtyeight')fig, ax = plt.subplots(2, 2, figsize=(9,6), constrained_layout=True)# 默认栅格图-水平方向ax[0,0].eventplot(D)ax[0,0].set_title('default')# 垂直方向ax[0,1].eventplot(D, orientation='vertical', lineoffsets=[1,2,3])ax[0,1].set_title("orientation='vertical', lineoffsets=[1,2,3]")ax[1,0].eventplot(D, orientation='vertical', lineoffsets=[1,2,3], linelengths=0.5) # 线条长度ax[1,0].set_title('linelengths=0.5')ax[1,1].eventplot(D, orientation='vertical', lineoffsets=[1,2,3], linelengths=0.5, colors='orange')ax[1,1].set_title("colors='orange'")plt.show()np.random.seed(1)x = [2, 4, 6]D = np.random.gamma(4, size=(3, 50))# plt.style.use('fivethirtyeight')fig, ax = plt.subplots(2, 2, figsize=(9,6), constrained_layout=True)# 默认栅格图-水平方向ax[0,0].eventplot(D)ax[0,0].set_title('default')# 垂直方向ax[0,1].eventplot(D, orientation='vertical', lineoffsets=[1,2,3])ax[0,1].set_title("orientation='vertical', lineoffsets=[1,2,3]")ax[1,0].eventplot(D, orientation='vertical', lineoffsets=[1,2,3], linelengths=0.5) # 线条长度ax[1,0].set_title('linelengths=0.5')ax[1,1].eventplot(D, orientation='vertical', lineoffsets=[1,2,3], linelengths=0.5, colors='orange')ax[1,1].set_title("colors='orange'")plt.show()
(3) plotly_express入门
使用plotly_express如何快速绘制散点图、散点矩阵图、气泡图、箱型图、小提琴图、经验累积分布图、旭日图等
机器学习
基于机器学习的Titanic生存预测
目标变量分析：
相关性分析：
基于树模型的特征重要性排序代码：
f,ax=plt.subplots(2,2,figsize=(15,12))# 1、模型rf=RandomForestClassifier(n_estimators=500,random_state=0)# 2、训练rf.fit(X,Y)# 3、重要性排序pd.Series(rf.feature_importances_, X.columns).sort_values(ascending=True).plot.barh(width=0.8,ax=ax[0,0])# 4、添加标题ax[0,0].set_title('Feature Importance in Random Forests')ada=AdaBoostClassifier(n_estimators=200,learning_rate=0.05,random_state=0)ada.fit(X,Y)pd.Series(ada.feature_importances_, X.columns).sort_values(ascending=True).plot.barh(width=0.8,ax=ax[0,1],color='#9dff11')ax[0,1].set_title('Feature Importance in AdaBoost')gbc=GradientBoostingClassifier(n_estimators=500,learning_rate=0.1,random_state=0)gbc.fit(X,Y)pd.Series(gbc.feature_importances_, X.columns).sort_values(ascending=True).plot.barh(width=0.8,ax=ax[1,0],cmap='RdYlGn_r')ax[1,0].set_title('Feature Importance in Gradient Boosting')xgbc=xg.XGBClassifier(n_estimators=900,learning_rate=0.1)xgbc.fit(X,Y)pd.Series(xgbc.feature_importances_, X.columns).sort_values(ascending=True).plot.barh(width=0.8,ax=ax[1,1],color='#FD0F00')ax[1,1].set_title('Feature Importance in XgBoost')plt.show()f,ax=plt.subplots(2,2,figsize=(15,12))# 1、模型rf=RandomForestClassifier(n_estimators=500,random_state=0)# 2、训练rf.fit(X,Y)# 3、重要性排序pd.Series(rf.feature_importances_, X.columns).sort_values(ascending=True).plot.barh(width=0.8,ax=ax[0,0])# 4、添加标题ax[0,0].set_title('Feature Importance in Random Forests')ada=AdaBoostClassifier(n_estimators=200,learning_rate=0.05,random_state=0)ada.fit(X,Y)pd.Series(ada.feature_importances_, X.columns).sort_values(ascending=True).plot.barh(width=0.8,ax=ax[0,1],color='#9dff11')ax[0,1].set_title('Feature Importance in AdaBoost')gbc=GradientBoostingClassifier(n_estimators=500,learning_rate=0.1,random_state=0)gbc.fit(X,Y)pd.Series(gbc.feature_importances_, X.columns).sort_values(ascending=True).plot.barh(width=0.8,ax=ax[1,0],cmap='RdYlGn_r')ax[1,0].set_title('Feature Importance in Gradient Boosting')xgbc=xg.XGBClassifier(n_estimators=900,learning_rate=0.1)xgbc.fit(X,Y)pd.Series(xgbc.feature_importances_, X.columns).sort_values(ascending=True).plot.barh(width=0.8,ax=ax[1,1],color='#FD0F00')ax[1,1].set_title('Feature Importance in XgBoost')plt.show()不同模型对比：
基于KNN算法的iris数据集分类
特征分布情况：
pd.plotting.scatter_matrix(X_train, c=y_train, figsize=(15, 15), marker='o', hist_kwds={'bins': 20}, s=60, alpha=.8 )plt.show()pd.plotting.scatter_matrix(X_train, c=y_train, figsize=(15, 15), marker='o', hist_kwds={'bins': 20}, s=60, alpha=.8 )plt.show()
混淆矩阵：
from sklearn.metrics import classification_report,f1_score,accuracy_score,confusion_matrixsns.heatmap(confusion_matrix(y_pred, y_test), annot=True)plt.show()from sklearn.metrics import classification_report,f1_score,accuracy_score,confusion_matrixsns.heatmap(confusion_matrix(y_pred, y_test), annot=True)plt.show()
对新数据预测：
x_new = np.array([[5, 2.9, 1, 0.2]])prediction = knn.predict(x_new)x_new = np.array([[5, 2.9, 1, 0.2]])prediction = knn.predict(x_new)基于随机森林算法的员工流失预测
不同教育背景下的人群对比：
fig = go.Figure(data=[go.Pie( labels=attrition_by['EducationField'], values=attrition_by['Count'], hole=0.4, marker=dict(colors=['#3CAEA3', '#F6D55C']), textposition='inside')])fig.update_layout(title='Attrition by Educational Field', font=dict(size=12), legend=dict( orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1))fig.show()fig = go.Figure(data=[go.Pie( labels=attrition_by['EducationField'], values=attrition_by['Count'], hole=0.4, marker=dict(colors=['#3CAEA3', '#F6D55C']), textposition='inside')])fig.update_layout(title='Attrition by Educational Field', font=dict(size=12), legend=dict( orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1))fig.show()
年龄和月收入关系：
类型编码：
from sklearn.preprocessing import LabelEncoderle = LabelEncoder()df['Attrition'] = le.fit_transform(df['Attrition'])df['BusinessTravel'] = le.fit_transform(df['BusinessTravel'])df['Department'] = le.fit_transform(df['Department'])df['EducationField'] = le.fit_transform(df['EducationField'])df['Gender'] = le.fit_transform(df['Gender'])df['JobRole'] = le.fit_transform(df['JobRole'])df['MaritalStatus'] = le.fit_transform(df['MaritalStatus'])df['Over18'] = le.fit_transform(df['Over18'])df['OverTime'] = le.fit_transform(df['OverTime'])from sklearn.preprocessing import LabelEncoderle = LabelEncoder()df['Attrition'] = le.fit_transform(df['Attrition'])df['BusinessTravel'] = le.fit_transform(df['BusinessTravel'])df['Department'] = le.fit_transform(df['Department'])df['EducationField'] = le.fit_transform(df['EducationField'])df['Gender'] = le.fit_transform(df['Gender'])df['JobRole'] = le.fit_transform(df['JobRole'])df['MaritalStatus'] = le.fit_transform(df['MaritalStatus'])df['Over18'] = le.fit_transform(df['Over18'])df['OverTime'] = le.fit_transform(df['OverTime'])
基于LSTM的股价预测
LSTM网络模型搭建：
from keras.models import Sequentialfrom keras.layers import Dense, LSTMmodel = Sequential()# 输入层model.add(LSTM(128, return_sequences=True, input_shape= (xtrain.shape[1], 1)))# 隐藏层model.add(LSTM(64, return_sequences=False))model.add(Dense(25))# 输出层model.add(Dense(1))# 模型概览model.summary()from keras.models import Sequentialfrom keras.layers import Dense, LSTMmodel = Sequential()# 输入层model.add(LSTM(128, return_sequences=True, input_shape= (xtrain.shape[1], 1)))# 隐藏层model.add(LSTM(64, return_sequences=False))model.add(Dense(25))# 输出层model.add(Dense(1))# 模型概览model.summary()交叉验证实现：
k = 5number_val = len(xtrain) // k # 验证数据集的大小number_epochs = 20all_mae_scores = []all_loss_scores = []for i in range(k): # 只取i到i+1部分作为验证集 vali_X = xtrain[i * number_val: (i+1) * number_val] vali_y = ytrain[i * number_val: (i+1) * number_val] # 训练集 part_X_train = np.concatenate([xtrain[:i * number_val], xtrain[(i+1) * number_val:]], axis=0 ) part_y_train = np.concatenate([ytrain[:i * number_val], ytrain[(i+1) * number_val:]], axis=0 ) print("pxt: \n",part_X_train[:3]) print("pyt: \n",part_y_train[:3]) # 模型训练 history = model.fit(part_X_train, part_y_train, epochs=number_epochs, # 传入验证集的数据 validation_data=(vali_X, vali_y), batch_size=300, verbose=0 # 0-静默模式 1-日志模式 ) mae_history = history.history["mae"] loss_history = history.history["loss"] all_mae_scores.append(mae_history) all_loss_scores.append(loss_history)k = 5number_val = len(xtrain) // k # 验证数据集的大小number_epochs = 20all_mae_scores = []all_loss_scores = []for i in range(k): # 只取i到i+1部分作为验证集 vali_X = xtrain[i * number_val: (i+1) * number_val] vali_y = ytrain[i * number_val: (i+1) * number_val] # 训练集 part_X_train = np.concatenate([xtrain[:i * number_val], xtrain[(i+1) * number_val:]], axis=0 ) part_y_train = np.concatenate([ytrain[:i * number_val], ytrain[(i+1) * number_val:]], axis=0 ) print("pxt: \n",part_X_train[:3]) print("pyt: \n",part_y_train[:3]) # 模型训练 history = model.fit(part_X_train, part_y_train, epochs=number_epochs, # 传入验证集的数据 validation_data=(vali_X, vali_y), batch_size=300, verbose=0 # 0-静默模式 1-日志模式 ) mae_history = history.history["mae"] loss_history = history.history["loss"] all_mae_scores.append(mae_history) all_loss_scores.append(loss_history)时序预测
基于AMIRA的销量预测
自相关性图：
偏自相关性：
预测未来10天
p,d,q = 5,1,2model = sm.tsa.statespace.SARIMAX(df['Revenue'], order=(p, d, q), seasonal_order=(p, d, q, 12))model = model.fit()model.summary()ten_predictions = model.predict(len(df), len(df) + 10) # 预测10天p,d,q = 5,1,2model = sm.tsa.statespace.SARIMAX(df['Revenue'], order=(p, d, q), seasonal_order=(p, d, q, 12))model = model.fit()model.summary()ten_predictions = model.predict(len(df), len(df) + 10) # 预测10天
基于prophet的天气预测
特征间的关系：
预测效果：
其他案例
python的6种实现99乘法表
提供2种：
for i in range(1, 10): for j in range(1, i+1): # 例如3*3、4*4的情况，必须保证j能取到i值，所以i+1；range函数本身是不包含尾部数据 print(f'{j}x{i}={i*j} ', end="") # end默认是换行；需要改成空格 print("\n") # 末尾自动换空行for i in range(1, 10): for j in range(1, i+1): # 例如3*3、4*4的情况，必须保证j能取到i值，所以i+1；range函数本身是不包含尾部数据 print(f'{j}x{i}={i*j} ', end="") # end默认是换行；需要改成空格 print("\n") # 末尾自动换空行for i in range(1, 10): # 外层循环 j = 1 # 内层循环初始值 while j <= i: # 内层循环条件：从1开始循环 print("{}x{}={}".format(i,j,(i*j)), end=' ') # 输出格式 j += 1 # j每循环一次加1，进入下次，直到j<=i的条件不满足，再进入下个i的循环中 print("\n")for i in range(1, 10): # 外层循环 j = 1 # 内层循环初始值 while j <= i: # 内层循环条件：从1开始循环 print("{}x{}={}".format(i,j,(i*j)), end=' ') # 输出格式 j += 1 # j每循环一次加1，进入下次，直到j<=i的条件不满足，再进入下个i的循环中 print("\n")i = 1 # i初始值while i <= 9: # 循环终止条件 j = 1 # j初始值 while j <= i: # j的大小由i来控制 print(f'{i}x{j}={i*j} ', end='') j += 1 # j每循环一次都+1，直到j<=i不再满足，跳出这个while循环 i += 1 # 跳出上面的while循环后i+1，只要i<9就换行进入下一轮的循环；否则结束整个循环 print('\n')i = 1 # i初始值while i <= 9: # 循环终止条件 j = 1 # j初始值 while j <= i: # j的大小由i来控制 print(f'{i}x{j}={i*j} ', end='') j += 1 # j每循环一次都+1，直到j<=i不再满足，跳出这个while循环 i += 1 # 跳出上面的while循环后i+1，只要i<9就换行进入下一轮的循环；否则结束整个循环 print('\n')python实现简易计算器（GUI界面）
提供部分代码：
import tkinter as tkroot = tk.Tk() root.title("Standard Calculator") root.resizable(0, 0) e = tk.Entry(root, width=35, bg='#f0ffff', fg='black', borderwidth=5, justify='right', font='Calibri 15')e.grid(row=0, column=0, columnspan=3, padx=12, pady=12)# 点击按钮def buttonClick(num): temp = e.get( ) e.delete(0, tk.END) e.insert(0, temp + num) # 清除按钮def buttonClear(): e.delete(0, tk.END)def buttonGet(oper): global num1, math num1 = e.get() math = oper e.insert(tk.END, math) try: num1 = float(num1) except ValueError: buttonClear()import tkinter as tkroot = tk.Tk() root.title("Standard Calculator") root.resizable(0, 0) e = tk.Entry(root, width=35, bg='#f0ffff', fg='black', borderwidth=5, justify='right', font='Calibri 15')e.grid(row=0, column=0, columnspan=3, padx=12, pady=12)# 点击按钮def buttonClick(num): temp = e.get( ) e.delete(0, tk.END) e.insert(0, temp + num) # 清除按钮def buttonClear(): e.delete(0, tk.END)def buttonGet(oper): global num1, math num1 = e.get() math = oper e.insert(tk.END, math) try: num1 = float(num1) except ValueError: buttonClear()如果你对python、对数据分析感兴趣，且掌握jupyter notebook的使用，更多详细内容，欢迎咨询小编，公众号后台回复：联系小编