本文是基于《Python数据分析与挖掘实战》的实战部分的第七章的数据——《航空公司客户价值分析》做的分析。

旨在补充原文中的细节代码，并给出文中涉及到的内容的完整代码。

1）在数据预处理部分增加了属性规约、数据变换的代码

2）在模型构建的部分增加了一个画出雷达图的函数代码

1 背景与目标分析

此项目旨在根据航空公司提供的数据，对其客户进行分类，并且比较不同类别客户的价值，为能够更好的为客户提供个性化服务做参考。

2 数据探索性分析

2.1 数据质量分析

#对数据进行基本的探索#返回缺失值个数以及最大最小值import pandas as pddatafile = 'air_data.csv'#航空公司原始数据，第一行是属性名result = 'explore.xlsx'data = pd.read_csv(datafile, encoding='utf-8')explore = data.describe( percentiles = [],include = 'all').Texplore['null'] = len(data)-explore['count']explore1 = explore[['null','max','min']]explore1.columns = [u'空值数',u'最大值',u'最小值']#重命名列名explore1.to_excel(result)

探索结果：

3 数据预处理

3.1 数据清洗

datafile = 'air_data.csv'#航空公司原始数据，第一行是属性名data = pd.read_csv(datafile, encoding='utf-8')# 丢弃掉票价为0的记录；丢弃票价为0、平均折扣不为零、总飞行公里大于0的记录cleanedfile = 'cleaned.xlsx'data1 = data[data['SUM_YR_1'].notnull()*data['SUM_YR_2'].notnull()] #票价非空值才保留,去掉空值#只保留票价非零的，或者平均折扣率与总飞行公里数同时为零的记录index1 = data1['SUM_YR_1'] != 0index2 = data1['SUM_YR_2'] != 0index3 = (data1['SEG_KM_SUM'] == 0) & (data1['avg_discount'] == 0)data1 = data1[index1 | index2 | index3] #或关系data1.to_excel(cleanedfile)data2 = data1[['LOAD_TIME','FFP_DATE','LAST_TO_END','FLIGHT_COUNT','SEG_KM_SUM','avg_discount']]data2.to_excel('datadecrese.xlsx')

3.2 数据规约及属性构造

import numpy as npdata = pd.read_excel('datadecrese.xlsx')data['L1'] = pd.to_datetime(data['LOAD_TIME']) - pd.to_datetime(data['FFP_DATE'])# 以纳秒为单位# data['L3'] = data['L1'].astype('int64')/10**10/8640/30 # 此方法假定每个月是30天，这方法不准确data['L3'] = data['L1']/np.timedelta64(1, 'M') # 将间隔时间转成月份为单位，注意，此处必须加一个中间变量 （****）

# 将表中的浮点类型保留至小数点后四为# f = lambda x:'%.2f' % x# data[['L3']] = data[['L3']].applymap(f) # or data['L3'] = data['L3'].apply(f)# data[['L3']] = data[['L3']].astype('float64')# 注意:使用apply或applymap后，数据类型变成Object,若后续有需要需要在此进行类型转换data["L3"] = data["L3"].round(2) # 等价于上面三句话，数据类型不变data['LAST_TO_END'] = (data['LAST_TO_END']/30).round(2) # 此方法假定每个月是30天，这方法不够准确data['avg_discount'] = data['avg_discount'].round(2)data.drop('L1', axis=1, inplace =True) # 删除中间变量data.drop(data.columns[:3], axis=1, inplace =True) # 去掉不需要的u'LOAD_TIME', u'FFP_DATE'data.rename(columns={'LAST_TO_END':'R','FLIGHT_COUNT':'F','SEG_KM_SUM':'M','avg_discount':'C','L3':'L'},inplace=True)data.to_excel('sxgz.xlsx',index=False)

def f(x):return Series([x.min(),x.max()], index=['min','max'])d = data.apply(f)d.to_excel('summary_data.xlsx')

3.3 数据标准化

# 3> 数据标准化#标准差标准化d1 = pd.read_excel('sxgz.xlsx')d1=d1.astype('float64')d2 = (d1-d1.mean())/d1.std()d1 =d2.iloc[:,[4,0,1,2,3]]d1.columns = ['Z'+i for i in d1.columns]#表头重命名d1.to_excel('sjbzh.xlsx',index=False)

4.模型建立

4.1 k-means

#使用K-means聚类算法分类并分析每类的特征import pandas as pdfrom pandas import DataFrame,Seriesfrom sklearn.cluster import KMeans #导入K均值聚类算法k = 5 # 聚为5类d3 = pd.read_excel('sjbzh.xlsx')#调用k-means算法，进行聚类分析kmodel = KMeans(n_clusters=k, n_jobs=4)# n_job是并行数，一般等于CPU数较好kmodel.fit(d3)labels = kmodel.labels_#查看各样本类别demo = DataFrame(labels,columns=['numbers'])demo1= DataFrame(kmodel.cluster_centers_, columns=d3.columns) # 保存聚类中心demo2= demo['numbers'].value_counts() # 确定各个类的数目demo2

demo4 = pd.concat([demo2,demo1],axis=1)demo4.index.name='labels'demo4.to_excel('kmeansresults.xlsx')

4.2 雷达图

#画雷达图 客户群特征分析图subset = demo1.copy()subset = subset.round(3)subset.to_excel('testradar.xlsx')data = subset.as_matrix() # 将表格数据转成数组from radar1 import drawRader # 从已经编写好的画雷达图的函数中导入title = 'RadarPicture'rgrids = [0.5, 1, 1.5, 2, 2.5]itemnames = ['ZL','ZR','ZF','ZM','ZC']labels = list('abcde')drawRader(itemnames=itemnames,data=data,title=title,labels=labels, saveas = '2.jpg',rgrids=rgrids)

radar1：

import numpy as npimport matplotlib.pyplot as pltfrom matplotlib.path import Pathfrom matplotlib.spines import Spinefrom matplotlib.projections.polar import PolarAxesfrom matplotlib.projections import register_projectiondef radar_factory(num_vars, frame='circle'):"""Create a radar chart with `num_vars` axes.This function creates a RadarAxes projection and registers it.Parameters----------num_vars : intNumber of variables for radar chart.frame : {'circle' | 'polygon'}Shape of frame surrounding axes."""# calculate evenly-spaced axis anglestheta = np.linspace(0, 2*np.pi, num_vars, endpoint=False)# rotate theta such that the first axis is at the toptheta += np.pi/2def draw_poly_patch(self):verts = unit_poly_verts(theta)return plt.Polygon(verts, closed=True, edgecolor='k')def draw_circle_patch(self):# unit circle centered on (0.5, 0.5)return plt.Circle((0.5, 0.5), 0.5)patch_dict = {'polygon': draw_poly_patch, 'circle': draw_circle_patch}if frame not in patch_dict:raise ValueError('unknown value for `frame`: %s' % frame)class RadarAxes(PolarAxes):name = 'radar'# use 1 line segment to connect specified pointsRESOLUTION = 1# define draw_frame methoddraw_patch = patch_dict[frame]def fill(self, *args, **kwargs):"""Override fill so that line is closed by default"""closed = kwargs.pop('closed', True)return super(RadarAxes, self).fill(closed=closed, *args, **kwargs)def plot(self, *args, **kwargs):"""Override plot so that line is closed by default"""lines = super(RadarAxes, self).plot(*args, **kwargs)for line in lines:self._close_line(line)def _close_line(self, line):x, y = line.get_data()# FIXME: markers at x[0], y[0] get doubled-upif x[0] != x[-1]:x = np.concatenate((x, [x[0]]))y = np.concatenate((y, [y[0]]))line.set_data(x, y)def set_varlabels(self, labels):self.set_thetagrids(np.degrees(theta), labels)def _gen_axes_patch(self):return self.draw_patch()def _gen_axes_spines(self):if frame == 'circle':return PolarAxes._gen_axes_spines(self)# The following is a hack to get the spines (i.e. the axes frame)# to draw correctly for a polygon frame.# spine_type must be 'left', 'right', 'top', 'bottom', or `circle`.spine_type = 'circle'verts = unit_poly_verts(theta)# close off polygon by repeating first vertexverts.append(verts[0])path = Path(verts)spine = Spine(self, spine_type, path)spine.set_transform(self.transAxes)return {'polar': spine}register_projection(RadarAxes)return thetadef unit_poly_verts(theta):"""Return vertices of polygon for subplot axes.This polygon is circumscribed by a unit circle centered at (0.5, 0.5)"""x0, y0, r = [0.5] * 3verts = [(r*np.cos(t) + x0, r*np.sin(t) + y0) for t in theta]return vertsdef example_data():# The following data is from the Denver Aerodata1 = [['ZL','ZR','ZF','ZM','ZC'],('R',[[0.063,-0.0040000000000000001, -0.22600000000000001,-0.22900000000000001,2.1949999999999998],[1.161, -0.377, -0.086999999999999994, -0.095000000000000001, -0.159],[0.48299999999999998,-0.79900000000000004,2.4830000000000001,2.4249999999999998,0.308],[-0.314,1.6859999999999999,-0.57399999999999995,-0.53700000000000003,-0.17299999999999999],[-0.69999999999999996, -0.41499999999999998, -0.161, -0.161, -0.253]])]return data1if __name__ == '__main__':N = 5theta = radar_factory(N, frame='polygon')data = example_data()spoke_labels = data.pop(0)fig, axes = plt.subplots(figsize=(9, 9), nrows=2, ncols=2,subplot_kw=dict(projection='radar'))fig.subplots_adjust(wspace=0.25, hspace=0.20, top=0.85, bottom=0.05)colors = ['b', 'r', 'g', 'm', 'y']# Plot the four cases from the example data on separate axesfor ax, (title, case_data) in zip(axes.flatten(), data):ax.set_rgrids([0.5, 1, 1.5,2,2.5])ax.set_title(title, weight='bold', size='medium', position=(0.5, 1.1),horizontalalignment='center', verticalalignment='center')for d, color in zip(case_data, colors):ax.plot(theta, d, color=color)ax.fill(theta, d, facecolor=color, alpha=0.25)ax.set_varlabels(spoke_labels)# add legend relative to top-left plotax = axes[0, 0]plt.rcParams['font.sans-serif'] = ['SimHei']plt.rcParams['axes.unicode_minus'] = Falselabels = (list('abcde'))legend = ax.legend(labels, loc=(0.9, .95),labelspacing=0.1, fontsize='small')fig.text(0.5, 0.965, '5-Factor Solution Profiles Across Four Scenarios',horizontalalignment='center', color='black', weight='bold',size='large')plt.show()

虽然以前刚开始接触数据分析做过一遍这个项目，第二遍浏览下来感觉比第一次轻松许多。

参考大佬的文章：/u012063773/article/details/79297670

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