用python对汽车油耗进行数据分析（anaconda python3.6完全跑通）

编者按：前两天我们微信发了一篇文章《用python对汽车油耗进行数据分析

》，有一网友学习后用python3.6重新跑了一下数据，请大家比较阅读。PPV课致力于为大家提供一个开放、分享、进步的数据科学社区，欢迎小伙伴们给我们分享你的学习心得和博客文章，有你的鼓励和支持，我们会做的更好。投稿请联系QQ149104196。

正文：

1.下载汽车油耗数据集并解压

下载地址：https://www.fueleconomy.gov/feg/download.shtml

vehiclesData.py:

#encoding = utf-8import pandas as pdimport numpy as npfrom ggplot import *import matplotlib.pyplot as plt vehicles = pd.read_csv("../data/vehicles.csv")print(vehicles.head())print(len(vehicles))#- 查看有多少观测点（行）和多少变量（列）

运行结果：

barrels08 barrelsA08 charge120 charge240 city08 city08U cityA08 15.695714 0.0 0.0 0.0 19 0.0 0 1 29.964545 0.0 0.0 0.0 9 0.0 0 2 12.207778 0.0 0.0 0.0 23 0.0 0 3 29.964545 0.0 0.0 0.0 10 0.0 0 4 17.347895 0.0 0.0 0.0 17 0.0 0 cityA08U cityCD cityE … mfrCode c240Dscr charge240b 0.0 0.0 0.0 … NaN NaN 0.0 1 0.0 0.0 0.0 … NaN NaN 0.0 2 0.0 0.0 0.0 … NaN NaN 0.0 3 0.0 0.0 0.0 … NaN NaN 0.0 4 0.0 0.0 0.0 … NaN NaN 0.0 c240bDscr createdOn modifiedOn NaN Tue Jan 01 00:00:00 EST 2013 Tue Jan 01 00:00:00 EST 2013 1 NaN Tue Jan 01 00:00:00 EST 2013 Tue Jan 01 00:00:00 EST 2013 2 NaN Tue Jan 01 00:00:00 EST 2013 Tue Jan 01 00:00:00 EST 2013 3 NaN Tue Jan 01 00:00:00 EST 2013 Tue Jan 01 00:00:00 EST 2013 4 NaN Tue Jan 01 00:00:00 EST 2013 Tue Jan 01 00:00:00 EST 2013 startStop phevCity phevHwy phevComb 0 NaN 0 0 0 1 NaN 0 0 0 2 NaN 0 0 0 3 NaN 0 0 0 4 NaN 0 0 0 [5 rows x 83 columns]39101

其中 pandas中Data Frame类的边界方法head，查看一个很有用的数据框data frame的中，包括每列的非空值数量和各列不同的数据类型的数量。

描述汽车油耗等数据

print(len(vehicles.columns))print(vehicles.columns)

83Index([‘barrels08’, ‘barrelsA08’, ‘charge120’, ‘charge240’, ‘city08’, ‘city08U’, ‘cityA08’, ‘cityA08U’, ‘cityCD’, ‘cityE’, ‘cityUF’, ‘co2’, ‘co2A’, ‘co2TailpipeAGpm’, ‘co2TailpipeGpm’, ‘comb08’, ‘comb08U’, ‘combA08’, ‘combA08U’, ‘combE’, ‘combinedCD’, ‘combinedUF’, ‘cylinders’, ‘displ’, ‘drive’, ‘engId’, ‘eng_dscr’, ‘feScore’, ‘fuelCost08’, ‘fuelCostA08’, ‘fuelType’, ‘fuelType1’, ‘ghgScore’, ‘ghgScoreA’, ‘highway08’, ‘highway08U’, ‘highwayA08’, ‘highwayA08U’, ‘highwayCD’, ‘highwayE’, ‘highwayUF’, ‘hlv’, ‘hpv’, ‘id’, ‘lv2’, ‘lv4’, ‘make’, ‘model’, ‘mpgData’, ‘phevBlended’, ‘pv2’, ‘pv4’, ‘range’, ‘rangeCity’, ‘rangeCityA’, ‘rangeHwy’, ‘rangeHwyA’, ‘trany’, ‘UCity’, ‘UCityA’, ‘UHighway’, ‘UHighwayA’, ‘VClass’, ‘year’, ‘youSaveSpend’, ‘guzzler’, ‘trans_dscr’, ‘tCharger’, ‘sCharger’, ‘atvType’, ‘fuelType2’, ‘rangeA’, ‘evMotor’, ‘mfrCode’, ‘c240Dscr’, ‘charge240b’, ‘c240bDscr’, ‘createdOn’, ‘modifiedOn’, ‘startStop’, ‘phevCity’, ‘phevHwy’, ‘phevComb’], dtype=’object’)

#查看年份信息print(len(pd.unique(vehicles.year)))#总共的年份print(min(vehicles.year))#最小年份print(max(vehicles.year))#最大的年份

3519842018

#查看变速箱类型print(pd.value_counts(vehicles.trany))#trany变量自动挡是以A开头，手动挡是以M开头；故创建一个新变量trany2：vehicles["trany2"] = vehicles.trany.str[0]print(pd.value_counts(vehicles.trany2))

Automatic 4-spd 11045Manual 5-spd 8337Automatic 3-spd 3151Automatic (S6) 2857Manual 6-spd 2563Automatic 5-spd 2191Automatic 6-spd 1521Manual 4-spd 1483Automatic (S8) 1181Automatic (S5) 830Automatic (variable gear ratios) 730Automatic 7-spd 695Automatic 8-spd 323Automatic (AM-S7) 322Automatic (S7) 288Automatic (S4) 233Automatic (AM7) 194Automatic (AV-S6) 172Automatic 9-spd 170Automatic (A1) 134Automatic (AM6) 122Automatic (AM-S6) 106Automatic (AV-S7) 95Manual 7-spd 93Manual 3-spd 77Automatic (S9) 40Automatic (AV-S8) 31Automatic (S10) 30Automatic (AM-S8) 26Manual 4-spd Doubled 17Automatic (AM5) 14Automatic 10-spd 8Automatic (AM8) 5Automatic (L4) 2Automatic (L3) 2Automatic (AV-S10) 1Automatic (AM-S9) 1Name: trany, dtype: int64A 26520M 12570Name: trany2, dtype: int64

同理可以查看其它特征数据

#分析汽车油耗随时间变化的趋势#- 先按照年份分组grouped = vehicles.groupby('year')#- 再计算其中三列的均值averaged= grouped['comb08', 'highway08', 'city08'].agg([np.mean])#- 为方便分析，对其进行重命名，然后创建一个‘year’的列，包含该数据框data frame的索引averaged.columns = ['comb08_mean', 'highwayo8_mean', 'city08_mean'] averaged['year'] = averaged.indexprint(averaged )

comb08_mean highwayo8_mean city08_mean yearyear 1984 19.881874 23.075356 17.982688 19841985 19.808348 23.042328 17.878307 19851986 19.550413 22.699174 17.665289 19861987 19.228549 22.445068 17.310345 19871988 19.328319 22.702655 17.333628 19881989 19.125759 22.465742 17.143972 19891990 19.000928 22.337662 17.033395 19901991 18.825972 22.253534 16.848940 19911992 18.862623 22.439786 16.805531 19921993 19.104300 22.780421 16.998170 19931994 19.012220 22.725051 16.918534 19941995 18.797311 22.671148 16.569804 19951996 19.584735 23.569211 17.289780 19961997 19.429134 23.451444 17.135171 19971998 19.518473 23.546798 17.113300 19981999 19.611502 23.552817 17.272300 19992000 19.526190 23.414286 17.221429 20002001 19.479693 23.328211 17.275521 20012002 19.168205 23.030769 16.893333 20022003 19.000958 22.836207 16.780651 20032004 19.067736 23.064171 16.740642 20042005 19.193825 23.297599 16.851630 20052006 18.959239 23.048913 16.626812 20062007 18.978686 23.083481 16.605684 20072008 19.276327 23.455771 16.900590 20082009 19.735195 24.017766 17.335025 20092010 20.589883 24.949413 18.106594 20102011 21.011525 25.170213 18.670213 20112012 21.820870 26.106957 19.365217 20122013 23.126164 27.502117 20.663844 20132014 23.518946 27.953871 21.029654 20142015 24.031471 28.568057 21.446105 20152016 25.151878 29.604317 22.597122 20162017 25.089634 29.418550 22.583788 20172018 23.396825 28.056689 20.692744 2018

#- 使用ggplot包将结果绘成散点图allCarPlt = ggplot(averaged, aes('year', 'comb08_mean')) + geom_point(colour='steelblue') + xlab("Year") + ylab("Average MPG") + ggtitle("All cars")print(allCarPlt)

#- 去除混合动力汽车criteria1 = vehicles.fuelType1.isin(['Regular Gasoline', 'Premium Gasoline', 'Midgrade Gasoline']) criteria2 = vehicles.fuelType2.isnull() criteria3 = vehicles.atvType != 'Hybrid'vehicles_non_hybrid = vehicles[criteria1 & criteria2 & criteria3]#print(vehicles_non_hybrid)#- 将得到的数据框data frame按年份分组，并计算平均油耗grouped = vehicles_non_hybrid.groupby(['year']) averaged = grouped['comb08'].agg([np.mean]) averaged['hahhahah'] = averaged.indexprint(averaged)

mean hahhahahyear 1984 19.121622 19841985 19.394686 19851986 19.320457 19861987 19.164568 19871988 19.367607 19881989 19.141964 19891990 19.031459 19901991 18.838060 19911992 18.861566 19921993 19.137383 19931994 19.092632 19941995 18.872591 19951996 19.530962 19961997 19.368000 19971998 19.329545 19981999 19.239759 19992000 19.169345 20002001 19.075058 20012002 18.950270 20022003 18.761711 20032004 18.967339 20042005 19.005510 20052006 18.786398 20062007 18.987512 20072008 19.191781 20082009 19.738095 20092010 20.466736 20102011 20.838219 20112012 21.407328 20122013 22.228877 20132014 22.279835 20142015 22.418539 20152016 22.742509 20162017 22.817854 20172018 22.911504 2018

#- 查看是否大引擎的汽车越来越少print(pd.unique(vehicles_non_hybrid.displ))

[ 2. 4.9 2.2 5.2 1.8 1.6 2.3 2.8 4. 5. 3.3 3.1 3.8 4.6 3.4 3. 5.9 2.5 4.5 6.8 2.4 2.9 5.7 4.3 3.5 5.8 3.2 4.2 1.9 2.6 7.4 3.9 1.5 1.3 4.1 8. 6. 3.6 5.4 5.6 1. 2.1 1.2 6.5 2.7 4.7 5.5 1.1 5.3 4.4 3.7 6.7 4.8 1.7 6.2 8.3 1.4 6.1 7. 8.4 6.3 nan 6.6 6.4 0.9]

#- 去掉nan值，并用astype方法保证各个值都是float型的criteria = vehicles_non_hybrid.displ.notnull() vehicles_non_hybrid = vehicles_non_hybrid[criteria] vehicles_non_hybrid.loc[:,'displ'] = vehicles_non_hybrid.displ.astype('float') criteria = vehicles_non_hybrid.comb08.notnull() vehicles_non_hybrid = vehicles_non_hybrid[criteria] vehicles_non_hybrid.loc[:,'comb08'] = vehicles_non_hybrid.comb08.astype('float')#- 最后用ggplot包来绘图gasOnlineCarsPlt = ggplot(vehicles_non_hybrid, aes('displ', 'comb08')) + geom_point(color='steelblue') +xlab('Engine Displacement') + ylab('Average MPG') + ggtitle('Gasoline cars')print(gasOnlineCarsPlt)

#- 查看是否平均起来汽车越来越少了grouped_by_year = vehicles_non_hybrid.groupby(['year']) avg_grouped_by_year = grouped_by_year['displ', 'comb08'].agg([np.mean])#- 计算displ和conm08的均值，并改造数据框data frameavg_grouped_by_year['year'] = avg_grouped_by_year.index melted_avg_grouped_by_year = pd.melt(avg_grouped_by_year, id_vars='year')#- 创建分屏绘图p = ggplot(aes(x='year', y='value', color = 'variable_0'), data=melted_avg_grouped_by_year) p + geom_point() + facet_grid("variable_0",scales="free") #scales参数fixed表示固定坐标轴刻度，free表示反馈坐标轴刻度print(p)

调查汽车的制造商和型号

#- 首先查看cylinders变量有哪些可能的值print(pd.unique(vehicles_non_hybrid.cylinders))

[ 4. 12. 8. 6. 5. 10. 2. 3. 16. nan]

#- 再将cylinders变量转换为float类型，这样可以轻松方便地找到data frame的子集vehicles_non_hybrid.cylinders = vehicles_non_hybrid.cylinders.astype('float') pd.unique(vehicles_non_hybrid.cylinders)#- 现在，我们可以查看各个时间段有四缸引擎汽车的品牌数量vehicles_non_hybrid_4 = vehicles_non_hybrid[(vehicles_non_hybrid.cylinders==4.0)]  grouped_by_year_4_cylinder =vehicles_non_hybrid_4.groupby(['year']).make.nunique()#fig = grouped_by_year_4_cylinder.plot()plt.plot(grouped_by_year_4_cylinder) plt.xlabel("Year") plt.ylabel("Number of 4-Cylinder Maker") plt.show()

分析：

我们可以从上图中看到，从1985年以来四缸引擎汽车的品牌数量呈下降趋势。然而，需要注意的是，这张图可能会造成误导，因为我们并不知道汽车品牌总数是否在同期也发生了变化。为了一探究竟，我们继续一下操作。

# - 查看各年有四缸引擎汽车的品牌的列表，找出每年的品牌列表grouped_by_year_4_cylinder = vehicles_non_hybrid_4.groupby(['year']) unique_makes = []from functools import reducefor name, group in grouped_by_year_4_cylinder:  # list中存入set(),set里包含每年中的不同品牌  unique_makes.append(set(pd.unique(group['make']))) unique_makes = reduce(set.intersection, unique_makes)print(unique_makes)

{‘Nissan’, ‘Toyota’, ‘Chevrolet’, ‘Honda’, ‘Mazda’, ‘Volkswagen’, ‘Dodge’, ‘Subaru’, ‘Ford’, ‘Jeep’}

由上可知：在此期间只有10家制造商每年都制造四缸引擎汽车。

接下来，我们去发现这些汽车生产商的型号随时间的油耗表现。

#创建一个空列表，最终用来产生布尔值Booleansboolean_mask = []#用iterrows生成器generator遍历data frame中的各行来产生每行及索引for index, row in vehicles_non_hybrid_4.iterrows():  #判断每行的品牌是否在此前计算的unique_makes集合中,在将此布尔值Blooeans添加在Booleans_mask集合后面  make = row['make']     boolean_mask.append(make in unique_makes) df_common_makes = vehicles_non_hybrid_4[boolean_mask]#- 先将数据框data frame按year和make分组，然后计算各组的均值df_common_makes_grouped = df_common_makes.groupby(['year', 'make']).agg(np.mean).reset_index()#- 最后利用ggplot提供的分屏图来显示结果oilWithTime = ggplot(aes(x='year', y='comb08'), data = df_common_makes_grouped) + geom_line() + facet_wrap('make')print(oilWithTime)

来源：http://blog.csdn.net/xiaoql520/article/details/78059723

本文已经作者授权，未经许可，严禁二次转载

原文始发于微信公众号（PPV课数据科学社区）：用python对汽车油耗进行数据分析（anaconda python3.6完全跑通）