数据分析实例-USA 政府的数据集

痛定思痛。 2022-05-18 01:07 153阅读 0赞

2011年，短链接服务商（URL shortening service）[Bitly][]和美国政府网站[USA.gov][]合作，提供了一份从用户中收集来的匿名数据，这些用户使用了结尾为.gov或.mil的短链接。在2011年，这些数据的动态信息每小时都会保存一次，并可供下载。不过在2017年，这项服务被停掉了。

数据是每小时更新一次，文件中的每一行都用JOSN（JavaScript Object Notation）格式保存。

![70][]

#coding=gbk
    
    path = r'D:\datasets\example.txt'
    line1 = open(path).readline()
    print(line1)        #先读取一行数据看看
    # { "a": "Mozilla\/5.0 (Windows NT 6.1; WOW64) AppleWebKit\/535.11 (KHTML,----
    
    #使用python的内置的JSON模块，将JSON字符串转换成python字典对象
    import json
    records = [json.loads(line) for line in open(path)]
    print(records[0])       #返回的结果对象records是一个有字典组成的数组
    # {'hc': 1331822918, 'u': 'http://www.ncbi.nlm.nih.gov/pubmed/22415991', 'gr': 'MA', 
    print(records[0]['tz']) # America/New_York 现在可以进行键值对查找 tz表示时区

### 1. 先使用纯python对时区进行计数 ###

# time_zones = [rec['tz'] for rec in records ] 此处有些tz 是为空的， 执行会报错
    time_zones = [rec['tz'] for rec in records if 'tz' in rec]
    print(time_zones[5:11]) #可以看到有空数据的存在
    # ['America/New_York', 'Europe/Warsaw', '', '', '', 'America/Los_Angeles']
    
    #遍历时区，统计时区出现的数目，保留到字典当中
    # def get_counts(sequence):
    #     counts = {}
    #     for x in sequence:
    #         if x in counts:
    #             counts[x] += 1
    #         else:
    #             counts[x] = 1
    #     return counts
    #这里可以使用python标准库中defaultdict,与上述的代码得到结果是一样的
    #可以使代码变得更简洁
    from collections import defaultdict
    def get_counts(sequence):
        counts = defaultdict(int)
        for x in sequence:
            counts[x] += 1
        return counts
    counts = get_counts(time_zones)
    print(counts['Europe/Warsaw'])  # 16
      
    # 获取前10 位的时区及其计数值
    # dict = {"a" : "apple", "b" : "banana", "c" : "grape", "d" : "orange"}
    # it = dict.get('a') #此种方法查询，如果找不到的话， 不会报错
    # print(it)   # None
    # l = []
    # for key, value in dict.items():
    #     l.append([key,value])
    # print(l)
    def top_counts(count_dict, n=10):
        value_key_pairs = [(count, tz) for tz, count in count_dict.items()]
        value_key_pairs.sort()  # 依据数量进行排序
        return value_key_pairs[-n:]
    print(top_counts(counts, n=3))  #输出前3 项时区最多的地方及数量
    # [(400, 'America/Chicago'), (521, ''), (1251, 'America/New_York')]
    
    #使用python 标准库里的 collections.Counter 类，
    from collections import Counter 
    counts = Counter(time_zones)
    most3 = counts.most_common(3)
    print(most3)
    # [('America/New_York', 1251), ('', 521), ('America/Chicago', 400)]

### 2. 使用pandas 对时区进行计数及其他可视化操作 ###

import pandas as pd
    import numpy as np
    frame = pd.DataFrame(records)
    print(frame.info())
    #对时区的统计， pandas只需要一行代码即可， pandas可谓强大
    tz_counts = frame['tz'].value_counts()
    print(tz_counts[:5])
    # America/New_York       1251
    #                         521
    # America/Chicago         400
    # America/Los_Angeles     382
    # America/Denver          191
    # Name: tz, dtype: int64
    
    #使用fillna函数替代缺失值，而空字符可以使用 布尔类型数组索引
    clean_tz = frame['tz'].fillna('Missing')
    print(clean_tz[5:11])   # 还是为空值
    clean_tz[clean_tz == ''] ='Unknown'
    tz_counts = clean_tz.value_counts()
    print(tz_counts[:5])
    # America/New_York       1251
    # Unknown                 521
    # America/Chicago         400
    # America/Los_Angeles     382
    # America/Denver          191
    # Name: tz, dtype: int64

%matplotlib inline
    tz_counts[:10].plot(kind='barh', rot=0)

![70 1][]

import seaborn as sns
    subset = tz_counts[:10]
    sns.barplot(y=subset.index, x = subset.values)

![70 2][]

#可以把字符串的第一节分离出来， 大致是对应其浏览器
    results = pd.Series([x.split()[0] for x in frame.a.dropna()])  #[0] 是获得第一项数据
    print(results[:5])
    # 0               Mozilla/5.0    #火狐浏览器
    # 1    GoogleMaps/RochesterNY
    # 2               Mozilla/4.0
    # 3               Mozilla/5.0
    # 4               Mozilla/5.0
    # dtype: object
    print(results.value_counts()[:5])
    # Mozilla/5.0               2594
    # Mozilla/4.0                601
    # GoogleMaps/RochesterNY     121
    # Opera/9.80                  34
    # TEST_INTERNET_AGENT         24
    # dtype: int64
    
    #接下来， 按Windows 和非Windows 用户对时区进行分解，由于有agent的缺失，先将其移除
    cframe = frame[frame.a.notnull()]
    # print(cframe.head())
    
    #新增一行特征属性,将有Windows字符的数据认为是 微软的操作系统
    cframe['os'] = np.where(cframe.a.str.contains('Windows'), 'Windows', 'Not Windows')
    print(cframe.os[:5])
    # 0        Windows
    # 1    Not Windows
    # 2        Windows
    # 3    Not Windows
    # 4        Windows
    # Name: os, dtype: object
    
    #根据时区和操作系统进行分组
    by_tz_os = cframe.groupby(['tz', 'os'])
    print(by_tz_os.size()[:5])  #打印出前5 行, size()对分组结果进行计数， 类似于value_counts函数
    # tz                 os         
    #                    Not Windows    245
    #                    Windows        276
    # Africa/Cairo       Windows          3
    # Africa/Casablanca  Windows          1
    # Africa/Ceuta       Windows          2
    # dtype: int64
    agg_counts = by_tz_os.size().unstack().fillna(0)    #使用unstack 对计数结果重塑成一个表格
    print(agg_counts[:5])
    # os                   Not Windows  Windows
    # tz                                       
    #                            245.0    276.0
    # Africa/Cairo                 0.0      3.0
    # Africa/Casablanca            0.0      1.0
    # Africa/Ceuta                 0.0      2.0
    # Africa/Johannesburg          0.0      1.0
    
    #根据agg_counts中的行数构造一个简洁的索引数组
    indexer = agg_counts.sum(1).argsort()
    print(indexer[:5])
    # tz
    #                        24
    # Africa/Cairo           20
    # Africa/Casablanca      21
    # Africa/Ceuta           92
    # Africa/Johannesburg    87
    # dtype: int64
    #使用take 按照这个顺序截取最后10 行
    count_subset = agg_counts.take(indexer)[-10:]
    print(count_subset)
    # os                   Not Windows  Windows
    # tz                                       
    # America/Denver             132.0     59.0
    # America/Los_Angeles        130.0    252.0
    # America/Chicago            115.0    285.0
    #                            245.0    276.0
    # America/New_York           339.0    912.0
    count_subset = count_subset.stack()
    count_subset.name = 'total'
    count_subset = count_subset.reset_index()   # 重新建立索引，不把地址 作为索引
    print(count_subset[:5])
    #                   tz           os  total
    # 0  America/Sao_Paulo  Not Windows   13.0
    # 1  America/Sao_Paulo      Windows   20.0
    # 2      Europe/Madrid  Not Windows   16.0
    # 3      Europe/Madrid      Windows   19.0
    # 4   Pacific/Honolulu  Not Windows    0.0

sns.barplot(x = 'total',y='tz', hue='os',data= count_subset)

![70 3][]

由于不能很好地看出Windows用户与其他用户的区别， 所以对各行进行归一化处理

def norm_total(group):
        group['normed_total'] = group.total / group.total.sum()
        return group
    def norm_total(group):
        group['normed_total'] = group.total / group.total.sum()
        return group
    In [57]:
    
    results = count_subset.groupby('tz').apply(norm_total)
    sns.barplot(x='normed_total', y= 'tz', hue='os', data =results)

![70 4][]

count_subset = agg_counts.take(indexer)[-10:]
    count_subset
    
    Out[58]:
    os	Not Windows	Windows
    tz		
    America/Sao_Paulo	13.0	20.0
    Europe/Madrid	16.0	19.0
    Pacific/Honolulu	0.0	36.0
    Asia/Tokyo	2.0	35.0
    Europe/London	43.0	31.0
    America/Denver	132.0	59.0
    America/Los_Angeles	130.0	252.0
    America/Chicago	115.0	285.0
    245.0	276.0
    America/New_York	339.0	912.0

count\_subset.plot(kind='barh', stacked=True)\#stacked 为堆积条形图

![70 5][]

normed_subset = count_subset.div(count_subset.sum(1), axis=0)
    normed_subset.plot(kind='barh', stacked = True)

![70 6][]

参考：《利用Python进行数据分析》

[Bitly]: https://bitly.com/
[USA.gov]: https://www.usa.gov/
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