一、數(shù)據(jù)讀取

1、讀寫數(shù)據(jù)庫數(shù)據(jù)
讀取函數(shù):

• pandas.read_sql_table(table_name, con, schema=None, index_col=None, coerce_float=True, columns=None)
• pandas.read_sql_query(sql, con, index_col=None, coerce_float=True)
• pandas.read_sql(sql, con, index_col=None, coerce_float=True, columns=None)
• sqlalchemy.creat_engine(‘數(shù)據(jù)庫產(chǎn)品名+連接工具名：//用戶名:密碼@數(shù)據(jù)庫IP地址:數(shù)據(jù)庫端口號/數(shù)據(jù)庫名稱？charset = 數(shù)據(jù)庫數(shù)據(jù)編碼’)

寫出函數(shù)：

DataFrame.to_sql(name, con, schema=None, if_exists=’fail’, index=True, index_label=None, dtype=None)

2、讀寫文本文件/csv數(shù)據(jù)
讀取函數(shù)：

• pandas.read_table(filepath_or_buffer, sep=’t’, header=’infer’, names=None, index_col=None, dtype=None, engine=None, nrows=None)
• pandas.read_csv(filepath_or_buffer, sep=’,’, header=’infer’, names=None, index_col=None, dtype=None, engine=None, nrows=None)

寫出函數(shù)：

• DataFrame.to_csv(path_or_buf=None, sep=’,’, na_rep=”, columns=None, header=True, index=True,index_label=None,mode=’w’,encoding=None)

3、讀寫excel（xls/xlsx）數(shù)據(jù)
讀取函數(shù)：

• pandas.read_excel(io, sheetname=0, header=0, index_col=None, names=None, dtype=None)

寫出函數(shù)：

• DataFrame.to_excel(excel_writer=None, sheetname=None’, na_rep=”, header=True, index=True, index_label=None, mode=’w’, encoding=None)

4、讀取剪貼板數(shù)據(jù)：

pandas.read_clipboard()

二、數(shù)據(jù)預處理1、數(shù)據(jù)清洗
重復數(shù)據(jù)處理

樣本重復：

pandas.DataFrame(Series).drop_duplicates(self, subset=None, keep=’first’, inplace=False)

2. 特征重復：

• 通用

def FeatureEquals(df):dfEquals=pd.DataFrame([],columns=df.columns,index=df.columns)for i in df.columns:for j in df.columns:dfEquals.loc[i,j]=df.loc[:,i].equals(df.loc[:,j])return dfEquals

• 數(shù)值型特征

def drop_features(data,way = 'pearson',assoRate = 1.0):'''此函數(shù)用于求取相似度大于assoRate的兩列中的一個，主要目的用于去除數(shù)值型特征的重復data：數(shù)據(jù)框，無默認assoRate：相似度，默認為1'''assoMat = data.corr(method = way)delCol = []length = len(assoMat)for i in range(length):for j in range(i+1,length):if assoMat.iloc[i,j] >= assoRate:delCol.append(assoMat.columns[j])return(delCol)

缺失值處理
識別缺失值

• DataFrame.isnull()
• DataFrame.notnull()
• DataFrame.isna()
• DataFrame.notna()

處理缺失值

• 刪除：DataFrame.dropna(self, axis=0, how=’any’, thresh=None, subset=None, inplace=False)
• 定值填補： DataFrame.fillna(value=None, method=None, axis=None, inplace=False, limit=None)
• 插補： DataFrame.interpolate(method=’linear’, axis=0, limit=None, inplace=False,limit_direction=’forward’, limit_area=None, downcast=None,**kwargs)

異常值處理

• 3σ原則

def outRange(Ser1): boolInd = (Ser1.mean()-3*Ser1.std()>Ser1) | (Ser1.mean()+3*Ser1.var()< Ser1) index = np.arange(Ser1.shape[0])[boolInd] outrange = Ser1.iloc[index] return outrange
注： 此方法只適用于正態(tài)分布

• 箱線圖分析

def

2、合并數(shù)據(jù)

• 數(shù)據(jù)堆疊：pandas.concat(objs, axis=0, join=’outer’, join_axes=None, ignore_index=False, keys=None, levels=None, names=None, verify_integrity=False, copy=True)
• 主鍵合并：pandas.merge(left, right, how=’inner’, on=None, left_on=None, right_on=None, left_index=False, right_index=False, sort=False,suffixes=(‘_x’, ‘_y’), copy=True, indicator=False)
• 重疊合并：pandas.DataFrame.combine_first(self, other)

3、數(shù)據(jù)變換

• 啞變量處理：pandas.get_dummies(data, prefix=None, prefix_sep=’_’, dummy_na=False, columns=None, sparse=False, drop_first=False)
• 數(shù)據(jù)離散化：pandas.cut(x, bins, right=True, labels=None, retbins=False, precision=3, include_lowest=False)

4、數(shù)據(jù)標準化

• 標準差標準化：sklearn.preprocessing.StandardScaler
• 離差標準化： sklearn.preprocessing.MinMaxScaler

三、模型構(gòu)建1、訓練集測試集劃分
sklearn.model_selection.train_test_split(*arrays, **options)2、 降維
class sklearn.decomposition.PCA(n_components=None, copy=True, whiten=False, svd_solver=’auto’, tol=0.0, iterated_power=’auto’, random_state=None)3、交叉驗證
sklearn.model_selection.cross_validate(estimator, X, y=None, groups=None, scoring=None, cv=None, n_jobs=1, verbose=0, fit_params=None, pre_dispatch=‘2*n_jobs’, return_train_score=’warn’)

4、模型訓練與預測

• 有監(jiān)督模型

clf = lr.fit(X_train, y_train) clf.predict(X_test)

5、聚類
常用算法：

• K均值：class sklearn.cluster.KMeans(n_clusters=8, init=’k-means++’, n_init=10, max_iter=300, tol=0.0001, precompute_distances=’auto’, verbose=0, random_state=None, copy_x=True, n_jobs=1, algorithm=’auto’)
• DBSCAN密度聚類：class sklearn.cluster.DBSCAN(eps=0.5, min_samples=5, metric=’euclidean’, metric_params=None, algorithm=’auto’, leaf_size=30, p=None, n_jobs=1)
• Birch層次聚類：class sklearn.cluster.Birch(threshold=0.5, branching_factor=50, n_clusters=3, compute_labels=True, copy=True)

評價：

• 輪廓系數(shù)：sklearn.metrics.silhouette_score(X, labels, metric=’euclidean’, sample_size=None, random_state=None, **kwds)
• calinski_harabaz_score：sklearn.metrics.calinski_harabaz_score(X, labels)
• completeness_score：sklearn.metrics.completeness_score(labels_true, labels_pred)
• fowlkes_mallows_score：sklearn.metrics.fowlkes_mallows_score(labels_true, labels_pred, sparse=False)
• homogeneity_completeness_v_measure：sklearn.metrics.homogeneity_completeness_v_measure(labels_true, labels_pred)
• adjusted_rand_score：sklearn.metrics.adjusted_rand_score(labels_true, labels_pred)
• homogeneity_score：sklearn.metrics.homogeneity_score(labels_true, labels_pred)
• mutual_info_score：sklearn.metrics.mutual_info_score(labels_true, labels_pred, contingency=None)
• normalized_mutual_info_score：sklearn.metrics.normalized_mutual_info_score(labels_true, labels_pred)
• v_measure_score：sklearn.metrics.v_measure_score(labels_true, labels_pred)

注：后續(xù)含labels_true參數(shù)的均需真實值參與

6、分類
常用算法

• Adaboost分類：class sklearn.ensemble.AdaBoostClassifier(base_estimator=None, n_estimators=50, learning_rate=1.0, algorithm=’SAMME.R’, random_state=None)
• 梯度提升樹分類：class sklearn.ensemble.GradientBoostingClassifier(loss=’deviance’, learning_rate=0.1, n_estimators=100, subsample=1.0, criterion=’friedman_mse’, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_depth=3, min_impurity_decrease=0.0, min_impurity_split=None, init=None, random_state=None, max_features=None, verbose=0, max_leaf_nodes=None, warm_start=False, presort=’auto’)
• 隨機森林分類：class sklearn.ensemble.RandomForestClassifier(n_estimators=10, criterion=’gini’, max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=’auto’, max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, bootstrap=True, oob_score=False, n_jobs=1, random_state=None, verbose=0, warm_start=False, class_weight=None)
• 高斯過程分類：class sklearn.gaussian_process.GaussianProcessClassifier(kernel=None, optimizer=’fmin_l_bfgs_b’, n_restarts_optimizer=0, max_iter_predict=100, warm_start=False, copy_X_train=True, random_state=None, multi_class=’one_vs_rest’, n_jobs=1)
• 邏輯回歸：class sklearn.linear_model.LogisticRegression(penalty=’l2’, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=None, random_state=None, solver=’liblinear’, max_iter=100, multi_class=’ovr’, verbose=0, warm_start=False, n_jobs=1)
• KNN：class sklearn.neighbors.KNeighborsClassifier(n_neighbors=5, weights=’uniform’, algorithm=’auto’, leaf_size=30, p=2, metric=’minkowski’, metric_params=None, n_jobs=1, **kwargs)
• 多層感知神經(jīng)網(wǎng)絡：class sklearn.neural_network.MLPClassifier(hidden_layer_sizes=(100, ), activation=’relu’, solver=’adam’, alpha=0.0001, batch_size=’auto’, learning_rate=’constant’, learning_rate_init=0.001, power_t=0.5, max_iter=200, shuffle=True, random_state=None, tol=0.0001, verbose=False, warm_start=False, momentum=0.9, nesterovs_momentum=True, early_stopping=False, validation_fraction=0.1, beta_1=0.9, beta_2=0.999, epsilon=1e-08)
• SVM：class sklearn.svm.SVC(C=1.0, kernel=’rbf’, degree=3, gamma=’auto’, coef0=0.0, shrinking=True, probability=False, tol=0.001, cache_size=200, class_weight=None, verbose=False, max_iter=-1, decision_function_shape=’ovr’, random_state=None)
• 決策樹：class sklearn.tree.DecisionTreeClassifier(criterion=’gini’, splitter=’best’, max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=None, random_state=None, max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, class_weight=None, presort=False)

評價：

• 準確率：sklearn.metrics.accuracy_score(y_true, y_pred, normalize=True, sample_weight=None)
• AUC：sklearn.metrics.auc(x, y, reorder=False)
• 分類報告：sklearn.metrics.classification_report(y_true, y_pred, labels=None, target_names=None, sample_weight=None, digits=2)
• 混淆矩陣：sklearn.metrics.confusion_matrix(y_true, y_pred, labels=None, sample_weight=None)
• kappa：sklearn.metrics.cohen_kappa_score(y1, y2, labels=None, weights=None, sample_weight=None)
• F1值：sklearn.metrics.f1_score(y_true, y_pred, labels=None, pos_label=1, average=’binary’, sample_weight=None)
• 精確率：sklearn.metrics.precision_score(y_true, y_pred, labels=None, pos_label=1, average=’binary’, sample_weight=None)
• 召回率：sklearn.metrics.recall_score(y_true, y_pred, labels=None, pos_label=1, average=’binary’, sample_weight=None)
• ROC：sklearn.metrics.roc_curve(y_true, y_score, pos_label=None, sample_weight=None, drop_intermediate=True)

7、回歸
常用算法：

• Adaboost回歸：class sklearn.ensemble.AdaBoostRegressor(base_estimator=None, n_estimators=50, learning_rate=1.0, loss=’linear’, random_state=None)
• 梯度提升樹回歸：class sklearn.ensemble.GradientBoostingRegressor(loss=’ls’, learning_rate=0.1, n_estimators=100, subsample=1.0, criterion=’friedman_mse’, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_depth=3, min_impurity_decrease=0.0, min_impurity_split=None, init=None, random_state=None, max_features=None, alpha=0.9, verbose=0, max_leaf_nodes=None, warm_start=False, presort=’auto’)
• 隨機森林回歸：class sklearn.ensemble.RandomForestRegressor(n_estimators=10, criterion=’mse’, max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=’auto’, max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, bootstrap=True, oob_score=False, n_jobs=1, random_state=None, verbose=0, warm_start=False)
• 高斯過程回歸：class sklearn.gaussian_process.GaussianProcessRegressor(kernel=None, alpha=1e-10, optimizer=’fmin_l_bfgs_b’, n_restarts_optimizer=0, normalize_y=False, copy_X_train=True, random_state=None)
• 保序回歸：class sklearn.isotonic.IsotonicRegression(y_min=None, y_max=None, increasing=True, out_of_bounds=’nan’)
• Lasso回歸：class sklearn.linear_model.Lasso(alpha=1.0, fit_intercept=True, normalize=False, precompute=False, copy_X=True, max_iter=1000, tol=0.0001, warm_start=False, positive=False, random_state=None, selection=’cyclic’)
• 線性回歸：class sklearn.linear_model.LinearRegression(fit_intercept=True, normalize=False, copy_X=True, n_jobs=1)
• 嶺回歸： class sklearn.linear_model.Ridge(alpha=1.0, fit_intercept=True, normalize=False, copy_X=True, max_iter=None, tol=0.001, solver=’auto’, random_state=None)
• KNN回歸：class sklearn.neighbors.KNeighborsRegressor(n_neighbors=5, weights=’uniform’, algorithm=’auto’, leaf_size=30, p=2, metric=’minkowski’, metric_params=None, n_jobs=1, **kwargs)
• 多層感知神經(jīng)網(wǎng)絡回歸：class sklearn.neural_network.MLPRegressor(hidden_layer_sizes=(100, ), activation=’relu’, solver=’adam’, alpha=0.0001, batch_size=’auto’, learning_rate=’constant’, learning_rate_init=0.001, power_t=0.5, max_iter=200, shuffle=True, random_state=None, tol=0.0001, verbose=False, warm_start=False, momentum=0.9, nesterovs_momentum=True, early_stopping=False, validation_fraction=0.1, beta_1=0.9, beta_2=0.999, epsilon=1e-08)
• SVM回歸：class sklearn.svm.SVR(kernel=’rbf’, degree=3, gamma=’auto’, coef0=0.0, tol=0.001, C=1.0, epsilon=0.1, shrinking=True, cache_size=200, verbose=False, max_iter=-1)
• 決策樹回歸：class sklearn.tree.DecisionTreeRegressor(criterion=’mse’, splitter=’best’, max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=None, random_state=None, max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, presort=False)

評價：

• 可解釋方差值：sklearn.metrics.explained_variance_score(y_true, y_pred, sample_weight=None, multioutput=’uniform_average’)
• 平均絕對誤差：sklearn.metrics.mean_absolute_error(y_true, y_pred, sample_weight=None, multioutput=’uniform_average’)[source]
• 均方誤差：sklearn.metrics.mean_squared_error(y_true, y_pred, sample_weight=None, multioutput=’uniform_average’)
• 均方對數(shù)誤差：sklearn.metrics.mean_squared_log_error(y_true, y_pred, sample_weight=None, multioutput=’uniform_average’)
• 中值絕對誤差：sklearn.metrics.median_absolute_error(y_true, y_pred)
• R2值：sklearn.metrics.r2_score(y_true, y_pred, sample_weight=None, multioutput=’uniform_average’)

八、demo

from sklearn import neighbors, datasets, preprocessingfrom sklearn.cross_validation import train_test_splitfrom sklearn.metrics import accuracy_scoreiris = datasets.load_iris()X, y = iris.data, iris.targetX_train, X_test, y_train, y_test = train_test_split(X, y, random_state=33)scaler = preprocessing.StandardScaler().fit(X_train)X_train = scaler.transform(X_train)X_test = scaler.transform(X_test)knn = neighbors.KNeighborsClassifier(n_neighbors=5)knn.fit(X_train, y_train)y_pred = knn.predict(X_test)accuracy_score(y_test, y_pred)

四、繪圖

3、中文

plt.rcParams['font.sans-serif'] = 'SimHei' ##設置字體為SimHei顯示中文 plt.rcParams['axes.unicode_minus'] = False ##設置正常顯示符號

4、不同圖形

• 散點圖：matplotlib.pyplot.scatter(x, y, s=None, c=None, marker=None, cmap=None, norm=None, vmin=None, vmax=None, alpha=None, linewidths=None, verts=None, edgecolors=None, hold=None, data=None,**kwargs)
• 折線圖： matplotlib.pyplot.plot(*args, **kwargs)
• 直方圖：matplotlib.pyplot.bar（left，height，width = 0.8，bottom = None，hold = None，data = None，** kwargs ）
• 餅圖：matplotlib.pyplot.pie(x, explode=None, labels=None, colors=None, autopct=None, pctdistance=0.6, shadow=False, labeldistance=1.1, startangle=None, radius=None, counterclock=True, wedgeprops=None, textprops=None, center=(0, 0), frame=False, hold=None, data=None)
• 箱線圖：matplotlib.pyplot.boxplot(x, notch=None, sym=None, vert=None, whis=None, positions=None, widths=None, patch_artist=None, bootstrap=None, usermedians=None, conf_intervals=None, meanline=None, showmeans=None, showcaps=None, showbox=None, showfliers=None, boxprops=None, labels=None, flierprops=None, medianprops=None, meanprops=None, capprops=None, whiskerprops=None, manage_xticks=True, autorange=False, zorder=None, hold=None, data=None)

5、Demo

import numpy as np import matplotlib.pyplot as pltbox = dict(facecolor='yellow', pad=5, alpha=0.2)fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2) fig.subplots_adjust(left=0.2, wspace=0.6)# Fixing random state for reproducibility np.random.seed(19680801)ax1.plot(2000*np.random.rand(10)) ax1.set_title('ylabels not aligned') ax1.set_ylabel('misaligned 1', bbox=box) ax1.set_ylim(0, 2000)ax3.set_ylabel('misaligned 2',bbox=box) ax3.plot(np.random.rand(10))labelx = -0.3 # axes coordsax2.set_title('ylabels aligned') ax2.plot(2000*np.random.rand(10)) ax2.set_ylabel('aligned 1', bbox=box) ax2.yaxis.set_label_coords(labelx, 0.5) ax2.set_ylim(0, 2000)ax4.plot(np.random.rand(10)) ax4.set_ylabel('aligned 2', bbox=box) ax4.yaxis.set_label_coords(labelx, 0.5)plt.show()

五、完整Demo

import numpy as np import pandas as pd airline_data = pd.read_csv("../data/air_data.csv",encoding="gb18030") #導入航空數(shù)據(jù) print('原始數(shù)據(jù)的形狀為：',airline_data.shape)## 去除票價為空的記錄 exp1 = airline_data["SUM_YR_1"].notnull() exp2 = airline_data["SUM_YR_2"].notnull() exp = exp1 & exp2 airline_notnull = airline_data.loc[exp,:] print('刪除缺失記錄后數(shù)據(jù)的形狀為：',airline_notnull.shape)#只保留票價非零的，或者平均折扣率不為0且總飛行公里數(shù)大于0的記錄。 index1 = airline_notnull['SUM_YR_1'] != 0 index2 = airline_notnull['SUM_YR_2'] != 0 index3 = (airline_notnull['SEG_KM_SUM']> 0) & (airline_notnull['avg_discount'] != 0) airline = airline_notnull[(index1 | index2) & index3] print('刪除異常記錄后數(shù)據(jù)的形狀為：',airline.shape)airline_selection = airline[["FFP_DATE","LOAD_TIME","FLIGHT_COUNT","LAST_TO_END","avg_discount","SEG_KM_SUM"]] ## 構(gòu)建L特征 L = pd.to_datetime(airline_selection["LOAD_TIME"]) - pd.to_datetime(airline_selection["FFP_DATE"]) L = L.astype("str").str.split().str[0] L = L.astype("int")/30 ## 合并特征 airline_features = pd.concat([L,airline_selection.iloc[:,2:]],axis = 1) print('構(gòu)建的LRFMC特征前5行為：n',airline_features.head())from sklearn.preprocessing import StandardScaler data = StandardScaler().fit_transform(airline_features) np.savez('../tmp/airline_scale.npz',data) print('標準化后LRFMC五個特征為：n',data[:5,:])from sklearn.cluster import KMeans #導入kmeans算法 airline_scale = np.load('../tmp/airline_scale.npz')['arr_0'] k = 5 ## 確定聚類中心數(shù)#構(gòu)建模型 kmeans_model = KMeans(n_clusters = k,n_jobs=4,random_state=123) fit_kmeans = kmeans_model.fit(airline_scale) #模型訓練 kmeans_model.cluster_centers_ #查看聚類中心kmeans_model.labels_ #查看樣本的類別標簽#統(tǒng)計不同類別樣本的數(shù)目 r1 = pd.Series(kmeans_model.labels_).value_counts() print('最終每個類別的數(shù)目為：n',r1)#繪制直方圖矩陣 center = kmeans_model.cluster_centers_ names = ['入會時長','最近乘坐過本公司航班','乘坐次數(shù)','里程','平均折扣率'] import matplotlib.pyplot as plt %matplotlib inline ax = plt.figure(figsize=(8,8)) for i in range(k):ax1 = ax.add_subplot(k,1,i+1)plt.bar(range(5),center[:,i],width = 0.5)plt.xlabel('類別')plt.ylabel(names[i]) plt.savefig('聚類分析柱形圖.png') plt.show()#繪制雷達圖 fig = plt.figure(figsize=(8,8)) ax = fig.add_subplot(111, polar=True)# polar參數(shù) angles = np.linspace(0, 2*np.pi, k, endpoint=False) angles = np.concatenate((angles, [angles[0]])) # 閉合 Linecolor = ['bo-','r+:','gD--','yv-.','kp-'] #點線顏色 Fillcolor = ['b','r','g','y','k'] for i in range(k):data = np.concatenate((center[i], [center[i][0]])) # 閉合ax.plot(angles,data,Linecolor[i], linewidth=2)# 畫線ax.fill(angles, data, facecolor=Fillcolor[i], alpha=0.25)# 填充 ax.set_thetagrids(angles * 180/np.pi, names) ax.set_title("客戶分群雷達圖", va='bottom')## 設定標題 ax.set_rlim(-1,3)## 設置各指標的最終范圍 ax.grid(True)

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