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Python 数据分析基础小结

一、数据读取

1、读写数据库数据

读取函数:

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('数据库产品名+连接工具名：//用户名:密码@数据库IP地址:数据库端口号/数据库名称？charset = 数据库数据编码')

写出函数：

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

2、读写文本文件/csv数据

读取函数：

pandas.read_table(filepath_or_buffer, sep=’ ’, 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)

写出函数：

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

3、读写excel（xls/xlsx）数据

读取函数：

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

写出函数：

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

4、读取剪贴板数据：

pandas.read_clipboard()

二、数据预处理

1、数据清洗

重复数据处理

样本重复：

pandas.DataFrame(Series).drop_duplicates(self, subset=None, keep='first', inplace=False)
特征重复：

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

数值型特征

def drop_features(data,way = 'pearson',assoRate = 1.0):
    '''
    此函数用于求取相似度大于assoRate的两列中的一个，主要目的用于去除数值型特征的重复
    data：数据框，无默认
    assoRate：相似度，默认为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)

缺失值处理

识别缺失值

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

处理缺失值

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

异常值处理

3σ原则

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：此方法只适用于正态分布

箱线图分析

def boxOutRange(Ser):
    '''
    Ser：进行异常值分析的DataFrame的某一列
    '''
    Low = Ser.quantile(0.25)-1.5*(Ser.quantile(0.75)-Ser.quantile(0.25))
    Up = Ser.quantile(0.75)+1.5*(Ser.quantile(0.75)-Ser.quantile(0.25))
    index = (Ser< Low) | (Ser>Up)
    Outlier = Ser.loc[index]
    return(Outlier)

2、合并数据

数据堆叠：pandas.concat(objs, axis=0, join='outer', join_axes=None, ignore_index=False, keys=None, levels=None, names=None, verify_integrity=False, copy=True)
主键合并：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)
重叠合并：pandas.DataFrame.combine_first(self, other)

3、数据变换

哑变量处理：pandas.get_dummies(data, prefix=None, prefix_sep='_', dummy_na=False, columns=None, sparse=False, drop_first=False)
数据离散化：pandas.cut(x, bins, right=True, labels=None, retbins=False, precision=3, include_lowest=False)

4、数据标准化

标准差标准化：sklearn.preprocessing.StandardScaler
离差标准化： sklearn.preprocessing.MinMaxScaler

三、模型构建

1、训练集测试集划分

sklearn.model_selection.train_test_split(*arrays, **options)

2、降维

class sklearn.decomposition.PCA(n_components=None, copy=True, whiten=False, svd_solver=’auto’, tol=0.0, iterated_power=’auto’, random_state=None)

3、交叉验证

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、模型训练与预测

有监督模型

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

5、聚类

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密度聚类：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层次聚类：class sklearn.cluster.Birch(threshold=0.5, branching_factor=50, n_clusters=3, compute_labels=True, copy=True)

评价：

轮廓系数：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：后续含labels_true参数的均需真实值参与

6、分类

常用算法

Adaboost分类：class sklearn.ensemble.AdaBoostClassifier(base_estimator=None, n_estimators=50, learning_rate=1.0, algorithm=’SAMME.R’, random_state=None)
梯度提升树分类：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’)
随机森林分类：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)
高斯过程分类：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)
逻辑回归：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)
多层感知神经网络：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)
决策树：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)

评价：

准确率：sklearn.metrics.accuracy_score(y_true, y_pred, normalize=True, sample_weight=None)
AUC：sklearn.metrics.auc(x, y, reorder=False)
分类报告：sklearn.metrics.classification_report(y_true, y_pred, labels=None, target_names=None, sample_weight=None, digits=2)
混淆矩阵：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)
精确率：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、回归

常用算法：

Adaboost回归：class sklearn.ensemble.AdaBoostRegressor(base_estimator=None, n_estimators=50, learning_rate=1.0, loss=’linear’, random_state=None)
梯度提升树回归：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’)
随机森林回归：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)
高斯过程回归：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)
保序回归：class sklearn.isotonic.IsotonicRegression(y_min=None, y_max=None, increasing=True, out_of_bounds=’nan’)
Lasso回归：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’)
线性回归：class sklearn.linear_model.LinearRegression(fit_intercept=True, normalize=False, copy_X=True, n_jobs=1)
岭回归： 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回归：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)
多层感知神经网络回归：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回归：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)
决策树回归：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)

评价：

可解释方差值：sklearn.metrics.explained_variance_score(y_true, y_pred, sample_weight=None, multioutput=’uniform_average’)
平均绝对误差：sklearn.metrics.mean_absolute_error(y_true, y_pred, sample_weight=None, multioutput=’uniform_average’)[source]
均方误差：sklearn.metrics.mean_squared_error(y_true, y_pred, sample_weight=None, multioutput=’uniform_average’)
均方对数误差：sklearn.metrics.mean_squared_log_error(y_true, y_pred, sample_weight=None, multioutput=’uniform_average’)
中值绝对误差：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, preprocessing
 from sklearn.cross_validation import train_test_split
 from sklearn.metrics import accuracy_score
 iris = datasets.load_iris()
 X, y = iris.data, iris.target
 X_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、绘图

1、创建画布或子图

函数名称	函数作用
plt.figure	创建一个空白画布，可以指定画布大小，像素。
figure.add_subplot	创建并选中子图，可以指定子图的行数，列数，与选中图片编号。

2、绘制

函数名称	函数作用
plt.title	在当前图形中添加标题，可以指定标题的名称，位置，颜色，字体大小等参数。
plt.xlabel	在当前图形中添加x轴名称，可以指定位置，颜色，字体大小等参数。
plt.ylabel	在当前图形中添加y轴名称，可以指定位置，颜色，字体大小等参数。
plt.xlim	指定当前图形x轴的范围，只能确定一个数值区间，而无法使用字符串标识。
plt.ylim	指定当前图形y轴的范围，只能确定一个数值区间，而无法使用字符串标识。
plt.xticks	指定x轴刻度的数目与取值
plt.yticks	指定y轴刻度的数目与取值
plt.legend	指定当前图形的图例，可以指定图例的大小，位置，标签。

3、中文

plt.rcParams['font.sans-serif'] = 'SimHei' ##设置字体为SimHei显示中文
plt.rcParams['axes.unicode_minus'] = False ##设置正常显示符号

4、不同图形

散点图：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)
折线图： matplotlib.pyplot.plot(*args, **kwargs)
直方图：matplotlib.pyplot.bar（left，height，width = 0.8，bottom = None，hold = None，data = None，** kwargs ）
饼图：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)
箱线图：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 plt

box = 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 coords

ax2.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") #导入航空数据
print('原始数据的形状为：',airline_data.shape)

## 去除票价为空的记录
exp1 = airline_data["SUM_YR_1"].notnull()
exp2 = airline_data["SUM_YR_2"].notnull()
exp = exp1 & exp2
airline_notnull = airline_data.loc[exp,:]
print('删除缺失记录后数据的形状为：',airline_notnull.shape)

#只保留票价非零的，或者平均折扣率不为0且总飞行公里数大于0的记录。
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('删除异常记录后数据的形状为：',airline.shape)

airline_selection = airline[["FFP_DATE","LOAD_TIME",
    "FLIGHT_COUNT","LAST_TO_END",
    "avg_discount","SEG_KM_SUM"]]
## 构建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('构建的LRFMC特征前5行为：
',airline_features.head())

from sklearn.preprocessing import StandardScaler
data = StandardScaler().fit_transform(airline_features)
np.savez('../tmp/airline_scale.npz',data)
print('标准化后LRFMC五个特征为：
',data[:5,:])

from sklearn.cluster import KMeans #导入kmeans算法
airline_scale = np.load('../tmp/airline_scale.npz')['arr_0']
k = 5 ## 确定聚类中心数

#构建模型
kmeans_model = KMeans(n_clusters = k,n_jobs=4,random_state=123)
fit_kmeans = kmeans_model.fit(airline_scale)   #模型训练
kmeans_model.cluster_centers_ #查看聚类中心

kmeans_model.labels_ #查看样本的类别标签

#统计不同类别样本的数目
r1 = pd.Series(kmeans_model.labels_).value_counts()
print('最终每个类别的数目为：
',r1)

#绘制直方图矩阵
center = kmeans_model.cluster_centers_ 
names = ['入会时长','最近乘坐过本公司航班','乘坐次数','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('类别')
    plt.ylabel(names[i])
plt.savefig('聚类分析柱形图.png')
plt.show()

#绘制雷达图
fig = plt.figure(figsize=(8,8))
ax = fig.add_subplot(111, polar=True)# polar参数
angles = np.linspace(0, 2*np.pi, k, endpoint=False)
angles = np.concatenate((angles, [angles[0]])) # 闭合
Linecolor = ['bo-','r+:','gD--','yv-.','kp-'] #点线颜色
Fillcolor = ['b','r','g','y','k']
for i in range(k):
    data = np.concatenate((center[i], [center[i][0]])) # 闭合
    ax.plot(angles,data,Linecolor[i], linewidth=2)# 画线
    ax.fill(angles, data, facecolor=Fillcolor[i], alpha=0.25)# 填充
ax.set_thetagrids(angles * 180/np.pi, names)
ax.set_title("客户分群雷达图", va='bottom')## 设定标题
ax.set_rlim(-1,3)## 设置各指标的最终范围
ax.grid(True)

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原文地址：https://www.cnblogs.com/WoLykos/p/9582920.html

Python 数据分析基础小结

一、数据读取

1、读写数据库数据

读取函数:

写出函数：

2、读写文本文件/csv数据

读取函数：

写出函数：

3、读写excel（xls/xlsx）数据

读取函数：

写出函数：

4、读取剪贴板数据：

二、数据预处理

1、数据清洗

重复数据处理

缺失值处理

异常值处理

2、合并数据

3、数据变换

4、数据标准化

三、模型构建

1、训练集测试集划分

2、 降维

3、交叉验证

4、模型训练与预测

5、聚类

常用算法：

评价：

6、分类

常用算法

评价：

7、回归

常用算法：

评价：

八、demo

四、绘图

1、创建画布或子图

2、绘制

3、中文

4、不同图形

5、Demo

五、完整Demo

2、降维