1 import pandas as pd
2 
3 # 在数据挖掘前一个数据分析、筛选、清理的多功能工具
4 '''
5 pandas 可以读入excel、csv等文件；可以创建Series序列,DataFrame表格，日期数组data_range
6 '''

 1 # 将excel文件，csv文件读取并转换为pandas的DataFrame
 2 # df_score = pd.read_csv()
 3 df_score = pd.read_excel('./score.xlsx')
 4 # df_score.values   #数据
 5 # df_score.columns  #列名
 6 # print df_score.describe() #计算表的各项数据，count，mean，std，中位数等
 7 
 8 # 创建一个默认索引从0开始的Series
 9 s = pd.Series([1, 2, 3, 4, 5, 6])
10 # 创建自定义索引的数组，索引由index指定，和前面数组依次对应
11 s = pd.Series([1, 2, 3, 4, 5, 6], index=['a', 'b', 'c', 'd', 'e', 'f'], dtype=int)
12 # 使用字典创建一个DataFrame，字典的Key会自动成为列名，一个Key默认对应一列数据
13 df1 = pd.DataFrame({'math': [1, 2, 3, 4, 5], 'physic': [5, 6, 7, 8, 9]}, index=['a', 'b', 'c', 'd', 'e'])
14 '''
15 # df1.values  数据
16 # df1.head(2)   前两行数据
17 # df1.tail(2)   最后两行数据
18 # df1.index     索引
19 # df1.columns   列名
20 '''
21 # 生成从20180101开始的时间序列，peroids是增加量，默认增加单位是天D，H小时，s秒
22 dates = pd.date_range('20180101', periods=10, freq='D')
23 # 创建使用时间索引的Series
24 # s = pd.Series(range(10),index=dates)
25 # 取出指定间隔的行数据
26 # s['2018-01-01':'2018-01-05']
27 # print dates

 1 df_imdb = pd.read_csv('./IMDB.csv')
 2 
 3 # print df_imdb
 4 # print df_imdb.columns
 5 # df_imdb['Title'].head(5)  #选出Title列的前五行
 6 # df_imdb['Title'].tail(3)
 7 # df_imdb.Title.head(3)     #同[]的形式
 8 # df_imdb['Revenue (Millions)'].max()   #最大票房
 9 # df_imdb['Revenue (Millions)'].idxmax()    #最大票房的索引
10 # df_imdb[50:51]
11 # df_imdb[50:51]['Title']
12 # df_imdb[50:51]['Revenue (Millions)']  #取出50行，不包括51行
13 # 取出50-56行，收尾都包含，第一维度是行，第二维度是列
14 # df_imdb.loc[50:56,['Director','Year']]
15 # df_imdb[50:56].loc[:,'Director','Year']
16 # 取出1-5行(不包含第5行)，2-4(不包含第4列)列的数据，使用整数索引操作，与numpy用法类似
17 # df_imdb.iloc[1:5,2:4]
18 # 统计Director列中不同导演出现的次数
19 # df_imdb['Director'].value_counts()
20 # 将票房大于5亿美元的电影选出来
21 # df_imdb[df_imdb['Revenue (Millions)']>500].Director
22 # df_imdb[df_imdb['Revenue (Millions)']>700]['Title']
23 # 将电影风格描述中含有Sci-Fi(科幻) 关键字的找出
24 # df_imdb[df_imdb['Genre'].str.contains('Sci-Fi')]
25 
26 # 将缺失数据(NaN)填充为0，也可以自己根据项目需求指定其他数据
27 # df_score.fillna(0)
28 # 将缺失数据的行移除(默认操作，可以使用axis=1指定删除列df_score.dropna(axis=1))
29 # 0删除行，1删除列
30 # df_score.dropna()
31 # 在DataFram中增加一列平均值avg，计算当前DataFram中每行的平均值作为avg的数据
32 # 前后赋值数据的行数要对应，axis=1表示按行计算，axis=0(默认值),表示按列计算
33 # df_score['avg'] = df_score.mean(axis=1)
34 # 按照性别分组并求和指定成绩
35 # df_score.iloc[:,4:7].groupby(u'性别').sum()
36 # df_score.loc[:,[u'音乐',u'性别']].groupby(u'性别').sum()
37 # 按照男女分组并绘图，bar柱状图，pie饼状图
38 # df_score[u'性别'].value_counts().plot(kind='bar')
39 # df_score[u'性别'].value_counts().plot(kind='pie')
40 # & 数学大于80且化学大于60
41 # df_score[(df_score[u'数学']>80) &(df_score[u'化学']>60) ]
42 
43 # 使用lambda，配合apply方法将日期中的指定年份或月份等提取出来
44 # apply函数会将lambda一次作用到数据集的每个元素
45 # datas = pd.Series(['20190901','20190902','20190903'])
46 # datas.apply(lambda x:x[0:4])
47 # datas.apply(lambda x:x[4:6])
48 
49 # 创建一个数据的副本
50 # df_copy = df.copy()
51 # df_copy['R_Sum'] = df['SibSp']+df['Parch']
52 
53 # 计算数学列的总和、平均值等，里面的字符串必须有同名函数
54 # df[u'数学'].agg(['sum','mean','max','std'])
55 
56 # pandas(Series、DataFrame)类型转换为numpy(array)类型
57 # df[u'数学'].values
58 # df.loc[:,[u'数学',u'化学']].values
59 
60 # 按照指定列的值排序，可指定正序倒序，默认正序
61 # df[u'数学'].sort_values()
62 # 按照索引排序
63 # df[u'数学'].sort_index()
64 # df[u'数学'].sort_values(ascending=False)
65 # 添加新列sum，值为每行总和，并倒序排列
66 # df['sum'] = df.sum(axis=1)
67 # df[u'sum'].sort_values(ascending=False)
68 
69 
70 # 取出Embarked，Survived字段，按照两个字段顺序做层次分组，然后做计算总和
71 # r = df.loc[:,['Embarked','Survived']].groupby(['Embarked','Survived']).size()
72 # r.C
73 # r.C[1]
74 # r.Q
75 # r.Q[0]
76 # r.Q[1]
77 # r1 = df.loc[:,['Embarked','Survived']].groupby(['Survived','Embarked']).size()
78 # r2 = df.loc[:,['Embarked','Survived']].groupby('Embarked').size()
79 # r3 = df.loc[:,['Embarked','Survived']].groupby('Survived').size()

 1 def piaofang():
 2     director10 = df_imdb['Director'].value_counts().head(10)
 3     # print director10.index[0]
 4     revenues = 0
 5     for d in director10.index:
 6         print df_imdb[df_imdb['Director'] == d]['Revenue (Millions)'].sum()
 7 
 8 # piaofang()
 9 
10 # df_imdb[df_imdb['Director']=='']['Revenue (Millions)'].sum()

1 import numpy as np
2 import matplotlib.pyplot as pt
3 import pandas as pd

1 titanic = pd.read_csv('./Titanic.csv')
2 
3 titanic.fillna(int(titanic[u'Age'].mean()))

 1 # print titanic['Age']
 2 
 3 # print titanic[u'Age'].mean()
 4 # print titanic.loc[:,u'Survived'].value_counts()     #存活比例
 5 # print titanic.loc[:,u'Survived'].count()            #总人数
 6 
 7 # print titanic.loc[:, u'Sex'].value_counts()             #男女分类
 8 # print titanic[titanic[u'Sex'] == u'male']['Survived'].value_counts()    #男性生死分类
 9 
10 # print titanic.columns
11 # print titanic[titanic[u'Age'] <= 18][u'Survived'].value_counts()
12 # print titanic[(titanic[u'Age'] > 18) & (titanic[u'Age'] < 60)][u'Survived'].value_counts()
13 # print titanic[titanic[u'Age'] >= 60][u'Survived'].value_counts()
14 
15 # print titanic[u'Fare']
16 # print titanic[u'Fare'].max()          #贫富差距
17 # print titanic[u'Fare'].min()
18 
19 # print titanic[u'Pclass'].value_counts()
20 # print titanic[u'Pclass'].value_counts()[1]
21 # print titanic[u'Pclass'].value_counts()[3]              #座舱
22 # print titanic[titanic[u'Pclass'] == 1]['Survived'].value_counts()
23 # print titanic[titanic[u'Pclass'] == 3]['Survived'].value_counts()
24 
25 # print titanic[u'SibSp'].value_counts()
26 # print titanic[u'Parch'].value_counts()
27 
28 # print titanic[u'Embarked'].value_counts()

  1 class Titanic(object):
  2     def __init__(self):
  3         self.data = titanic
  4 
  5     # 1.存活率是多少
  6     def rate_survive(self):
  7         survived = self.data.loc[:, 'Survived'].value_counts()[1]
  8         death = self.data.loc[:, 'Survived'].value_counts()[0]
  9         rate = float(survived) / (float(death) + float(survived))
 10         print '总人数：{},存活人数：{},死亡人数：{}'.format(survived + death, survived, death)
 11         return u'存活率：' + '%.2f' % rate
 12 
 13     # 2.哪个年龄段存活率最高
 14     def max_survive(self):
 15         age18_survived = self.data[self.data[u'Age'] <= 18][u'Survived'].value_counts()[1]
 16         age18_death = self.data[self.data[u'Age'] <= 18][u'Survived'].value_counts()[0]
 17         age18_rate = float(age18_survived) / (float(age18_survived) + float(age18_death))
 18 
 19         age1860_survived = self.data[(self.data[u'Age'] > 18) & (self.data[u'Age'] < 60)][u'Survived'].value_counts()[1]
 20         age1860_death = self.data[(self.data[u'Age'] > 18) & (self.data[u'Age'] < 60)][u'Survived'].value_counts()[0]
 21         age1860_rate = float(age1860_survived) / (float(age1860_survived) + float(age1860_death))
 22 
 23         age60_survived = self.data[self.data[u'Age'] >= 60][u'Survived'].value_counts()[1]
 24         age60_death = self.data[self.data[u'Age'] >= 60][u'Survived'].value_counts()[0]
 25         age60_rate = float(age60_survived) / (float(age60_survived) + float(age60_death))
 26 
 27         rate = [age18_rate, age60_rate, age1860_rate]
 28         age_data = ['18岁以下', '18-60岁', '60岁以上']
 29         max_rate = max(rate)
 30         age_range = age_data[rate.index(max(rate))]
 31         return '存活率最高的年龄段是{}，存活率为{}'.format(age_range, max_rate)
 32 
 33     # 3.女性存活率是否高于男性
 34     def than_survive(self):
 35         male_survied = self.data[self.data[u'Sex'] == u'male'][u'Survived'].value_counts()[1]
 36         male_death = self.data[self.data[u'Sex'] == u'male'][u'Survived'].value_counts()[0]
 37         rate_male = float(male_survied) / (float(male_survied) + float(male_death))
 38         print '男性共有{}人，存活{}人，死亡{}人'.format(male_death + male_survied, male_survied, male_death)
 39         female_survied = self.data[self.data[u'Sex'] == u'female'][u'Survived'].value_counts()[1]
 40         female_death = self.data[self.data[u'Sex'] == u'female'][u'Survived'].value_counts()[0]
 41         rate_female = float(female_survied) / (float(female_survied) + float(female_death))
 42         print '女性共有{}人，存活{}人，死亡{}人'.format(female_death + female_survied, female_survied, female_death)
 43         if rate_male > rate_female:
 44             return u'男性存活率更高，存活率为：%.2f' % rate_male
 45         else:
 46             return u'女性存活率更高，存活率为：%.2f' % rate_female
 47 
 48     # 4.船上是否出现贫富差距
 49     def poor_wealth(self):
 50         max_wealth = self.data[u'Fare'].max()
 51         max_poor = self.data[u'Fare'].min()
 52         if max_wealth - max_poor > 500:
 53             return '船上乘客最多消费了{}，最少消费了{}，存在贫富差距'.format(max_wealth, max_poor)
 54         else:
 55             return '船上乘客最多消费了{}，最少消费了{}，不存在贫富差距'.format(max_wealth, max_poor)
 56 
 57     # 5.头等舱乘客的存活率是否高于经济舱
 58     def pclass_survive(self):
 59         pclass1_survived = self.data[self.data[u'Pclass'] == 1]['Survived'].value_counts()[1]
 60         pclass1_death = self.data[self.data[u'Pclass'] == 1]['Survived'].value_counts()[0]
 61         pclass1_rate = float(pclass1_survived) / (float(pclass1_survived) + float(pclass1_death))
 62 
 63         pclass3_survived = self.data[self.data[u'Pclass'] == 3]['Survived'].value_counts()[1]
 64         pclass3_death = self.data[self.data[u'Pclass'] == 3]['Survived'].value_counts()[0]
 65         pclass3_rate = float(pclass3_survived) / (float(pclass3_survived) + float(pclass3_death))
 66 
 67         if pclass3_rate > pclass1_rate:
 68             return '头等舱乘客存活率更高，存活率为{}'.format(pclass3_rate)
 69         else:
 70             return '经济舱乘客存活率更高，存活率为{}'.format(pclass1_rate)
 71 
 72     # 6.有亲属在船上乘客比率，有亲属是否会影响存活率
 73     def family_survive(self):
 74         has_family = self.data[(self.data[u'Parch'] != 0) | (self.data[u'SibSp'] != 0)][u'PassengerId'].count()
 75         no_family = self.data[(self.data[u'Parch'] == 0) & (self.data[u'SibSp'] == 0)][u'PassengerId'].count()
 76         rate_family = float(has_family) / (float(has_family) + float(no_family))
 77 
 78         has_family_survived = \
 79             self.data[(self.data[u'Parch'] != 0) | (self.data[u'SibSp'] != 0)][u'Survived'].value_counts()[1]
 80         has_family_death = \
 81             self.data[(self.data[u'Parch'] != 0) | (self.data[u'SibSp'] != 0)][u'Survived'].value_counts()[0]
 82         has_family_rate = float(has_family_survived) / (float(has_family_survived) + float(has_family_death))
 83 
 84         no_family_survived = \
 85             self.data[(self.data[u'Parch'] == 0) & (self.data[u'SibSp'] == 0)][u'Survived'].value_counts()[1]
 86         no_family_death = \
 87             self.data[(self.data[u'Parch'] == 0) & (self.data[u'SibSp'] == 0)][u'Survived'].value_counts()[0]
 88         no_family_rate = float(no_family_survived) / (float(no_family_survived) + float(no_family_death))
 89 
 90         print '船上乘客中有亲属也在船上的有{}人，无亲属在船上的有{}人，有亲属在船上的乘客的比率为{}'.format(has_family, no_family, rate_family)
 91         if has_family_rate > no_family_rate:
 92             return '有亲属在船上的乘客存活率更高，存活率为{}'.format(has_family_rate)
 93         else:
 94             return '无亲属在船上的乘客存活率更高，存活率为{}'.format(no_family_rate)
 95 
 96     # 7.从哪个港口登船是否影响获救
 97     def emarked_survive(self):
 98         Embarked_S_survived = self.data[self.data[u'Embarked'] == 'S'][u'Survived'].value_counts()[1]
 99         Embarked_S_death = self.data[self.data[u'Embarked'] == 'S'][u'Survived'].value_counts()[0]
100         Embarked_S_rate = float(Embarked_S_survived) / (float(Embarked_S_survived) + float(Embarked_S_death))
101 
102         Embarked_C_survived = self.data[self.data[u'Embarked'] == 'C'][u'Survived'].value_counts()[1]
103         Embarked_C_death = self.data[self.data[u'Embarked'] == 'C'][u'Survived'].value_counts()[0]
104         Embarked_C_rate = float(Embarked_C_survived) / (float(Embarked_C_survived) + float(Embarked_C_death))
105 
106         Embarked_Q_survived = self.data[self.data[u'Embarked'] == 'Q'][u'Survived'].value_counts()[1]
107         Embarked_Q_death = self.data[self.data[u'Embarked'] == 'Q'][u'Survived'].value_counts()[0]
108         Embarked_Q_rate = float(Embarked_Q_survived) / (float(Embarked_Q_survived) + float(Embarked_Q_death))
109 
110         embarked = ['S港口', 'C港口', 'Q港口']
111         rate = [Embarked_S_rate, Embarked_C_rate, Embarked_Q_rate]
112         max_rate = max(rate)
113         return '{}存活率最大，为{}'.format(embarked[rate.index(max_rate)], max_rate)
114 
115     # 8.不同年龄段女性的获救率
116     def female_survive(self):
117         female18_survived = \
118             self.data[(self.data[u'Age'] <= 18) & (self.data[u'Sex'] == u'female')][u'Survived'].value_counts()[1]
119         female18_death = \
120             self.data[(self.data[u'Age'] <= 18) & (self.data[u'Sex'] == u'female')][u'Survived'].value_counts()[0]
121         female18_rate = float(female18_survived) / (float(female18_survived) + float(female18_death))
122 
123         female1850_survived = \
124             self.data[(self.data[u'Age'] > 18) & (self.data[u'Age'] < 50) & (self.data[u'Sex'] == u'female')][
125                 u'Survived'].value_counts()[1]
126         female1850_death = \
127             self.data[(self.data[u'Age'] > 18) & (self.data[u'Age'] < 50) & (self.data[u'Sex'] == u'female')][
128                 u'Survived'].value_counts()[0]
129         female1850_rate = float(female1850_survived) / (float(female1850_survived) + float(female1850_death))
130 
131         female50_survived = \
132             self.data[(self.data[u'Age'] >= 50) & (self.data[u'Sex'] == u'female')][u'Survived'].value_counts()[1]
133         female50_death = \
134             self.data[(self.data[u'Age'] >= 50) & (self.data[u'Sex'] == u'female')][u'Survived'].value_counts()[0]
135         female50_rate = float(female50_survived) / (float(female50_survived) + float(female50_death))
136         
137         return '18岁以下女性存活率：{}，18-50岁女性存活率：{}，50岁以上女性存活率：{}'.format(female18_rate, female1850_rate, female50_rate)
138 
139 
140 if __name__ == '__main__':
141     tt = Titanic()
142     # print tt.rate_survive()
143     # print tt.than_survive()
144     # print tt.max_survive()
145     # print tt.poor_wealth()
146     # print tt.pclass_survive()
147     # print tt.family_survive()
148     # print tt.emarked_survive()
149     print tt.female_survive()