数据整理与数据预处理

一、数据的清洗

（1）缺失值处理

、

拉格朗日插值发会循环，数据量大的话会很慢

# -*- coding: utf-8 -*-
from __future__ import division
import numpy as np
import os
import matplotlib.pyplot as plt
from scipy.interpolate import lagrange #导入拉格朗日插值函数
np.random.seed(12345)
plt.rc('figure', figsize=(10, 6))
from pandas import Series, DataFrame
import pandas as pd
np.set_printoptions(precision=4, threshold=500)
pd.options.display.max_rows = 100


###缺失值处理——拉格朗日插值法
inputfile = 'E:\week6\data/catering_sale.xls' #销量数据路径
outputfile = 'd:/data/sales.xls' #输出数据路径

data = pd.read_excel(inputfile) #读入数据
data[u'销量'][(data[u'销量'] < 400) | (data[u'销量'] > 5000)] = None #过滤异常值，将其变为空值

#自定义列向量插值函数
#s为列向量，n为被插值的位置，k为取前后的数据个数，默认为5
def ployinterp_column(s, n, k=5):
  y = s[list(range(n-k, n)) + list(range(n+1, n+1+k))] #取数
  y = y[y.notnull()] #剔除空值
  return lagrange(y.index, list(y))(n) #插值并返回插值结果

#逐个元素判断是否需要插值
for i in data.columns:
  for j in range(len(data)):
    if (data[i].isnull())[j]: #如果为空即插值。
      data[i][j] = ployinterp_column(data[i], j)

data.to_excel(outputfile) #输出结果，写入文件

 

牛顿差值需要的话得自己编写

（2）数据合并

###dataframe合并
#1
df1 = DataFrame({'key': ['b', 'b', 'a', 'c', 'a', 'a', 'b'],
                 'data1': range(7)})
df2 = DataFrame({'key': ['a', 'b', 'd'],
                 'data2': range(3)})
df1
df2

pd.merge(df1, df2)#按照相同的key来合并，不是共同拥有的key会省略掉，默认是讲重叠的列明是当做键
pd.merge(df1, df2, on='key')#指定键是哪个键

#2
df3 = DataFrame({'lkey': ['b', 'b', 'a', 'c', 'a', 'a', 'b'],
                 'data1': range(7)})
df4 = DataFrame({'rkey': ['a', 'b', 'd'],
                 'data2': range(3)})
pd.merge(df3, df4, left_on='lkey', right_on='rkey')#分别指定键的名称，两个键都是显示出来

pd.merge(df1, df2, how='outer')#指定连接方法，都显示出来

#3
df1 = DataFrame({'key': ['b', 'b', 'a', 'c', 'a', 'b'],
                 'data1': range(6)})
df2 = DataFrame({'key': ['a', 'b', 'a', 'b', 'd'],
                 'data2': range(5)})
df1
df2

pd.merge(df1, df2, on='key', how='left')#左连接 左边key 有的值会补充进来 
pd.merge(df1, df2, how='inner')#默认方法  只找相同的  笛卡尔集

#4多个键去合并  传入一个由列明组成的列表
left = DataFrame({'key1': ['foo', 'foo', 'bar'],
                  'key2': ['one', 'two', 'one'],
                  'lval': [1, 2, 3]})
right = DataFrame({'key1': ['foo', 'foo', 'bar', 'bar'],
                   'key2': ['one', 'one', 'one', 'two'],
                   'rval': [4, 5, 6, 7]})
pd.merge(left, right, on=['key1', 'key2'], how='outer')

#5
pd.merge(left, right, on='key1')

pd.merge(left, right, on='key1', suffixes=('_left', '_right'))#指定重叠列明的名称

###索引上的合并
#1
left1 = DataFrame({'key': ['a', 'b', 'a', 'a', 'b', 'c'],'value': range(6)})
right1 = DataFrame({'group_val': [3.5, 7]}, index=['a', 'b'])
left1
right1

pd.merge(left1, right1, left_on='key', right_index=True)#右边用index去进行连接 将ab 作为key值去进行连接

pd.merge(left1, right1, left_on='key', right_index=True, how='outer')

#2
lefth = DataFrame({'key1': ['Ohio', 'Ohio', 'Ohio', 'Nevada', 'Nevada'],
                   'key2': [2000, 2001, 2002, 2001, 2002],
                   'data': np.arange(5.)})
righth = DataFrame(np.arange(12).reshape((6, 2)),
                   index=[['Nevada', 'Nevada', 'Ohio', 'Ohio', 'Ohio', 'Ohio'],
                          [2001, 2000, 2000, 2000, 2001, 2002]],
                   columns=['event1', 'event2'])
lefth
righth

pd.merge(lefth, righth, left_on=['key1', 'key2'], right_index=True)#需要将两个参数都传进去，不然识别不出来

pd.merge(lefth, righth, left_on=['key1', 'key2'],
         right_index=True, how='outer')

left2 = DataFrame([[1., 2.], [3., 4.], [5., 6.]], index=['a', 'c', 'e'],
                 columns=['Ohio', 'Nevada'])
right2 = DataFrame([[7., 8.], [9., 10.], [11., 12.], [13, 14]],
                   index=['b', 'c', 'd', 'e'], columns=['Missouri', 'Alabama'])
left2
right2
pd.merge(left2, right2, how='outer', left_index=True, right_index=True)

#3
left2.join(right2, how='outer')#加入进去

left1.join(right1, on='key')#左连接

#4
another = DataFrame([[7., 8.], [9., 10.], [11., 12.], [16., 17.]],
                    index=['a', 'c', 'e', 'f'], columns=['New York', 'Oregon'])
left2.join([right2, another])

left2.join([right2, another], how='outer')

 

s1 = Series([0, 1], index=['a', 'b'])
s2 = Series([2, 3, 4], index=['c', 'd', 'e'])
s3 = Series([5, 6], index=['f', 'g'])

pd.concat([s1, s2, s3])#默认concat 在axis=0上工作

pd.concat([s1, s2, s3], axis=1)#

层次索引

###轴向连接
#1
arr = np.arange(12).reshape((3, 4))
arr

np.concatenate([arr, arr], axis=1)

#2
s1 = Series([0, 1], index=['a', 'b'])
s2 = Series([2, 3, 4], index=['c', 'd', 'e'])
s3 = Series([5, 6], index=['f', 'g'])

pd.concat([s1, s2, s3])#默认concat 在axis=0上工作

pd.concat([s1, s2, s3], axis=1)#

s4 = pd.concat([s1 * 5, s3])
pd.concat([s1, s4], axis=1)#显示两列  第二列是s4的值，第一列没有的部分是none

pd.concat([s1, s4], axis=1, join='inner')#只显示a,b 因为ab在两个上面都有

pd.concat([s1, s4], axis=1, join_axes=[['a', 'c', 'b', 'e']])#指定索引

#3
result = pd.concat([s1, s1, s3], keys=['one', 'two', 'three'])#那个数据集的索引
result

result.unstack()#one,two ,three 横坐标，abfg纵坐标，值

#4
pd.concat([s1, s2, s3], axis=1, keys=['one', 'two', 'three'])#数组的列头

df1 = DataFrame(np.arange(6).reshape(3, 2), index=['a', 'b', 'c'],
                columns=['one', 'two'])
df2 = DataFrame(5 + np.arange(4).reshape(2, 2), index=['a', 'c'],
                columns=['three', 'four'])

pd.concat([df1, df2], axis=1, keys=['level1', 'level2'])

pd.concat({'level1': df1, 'level2': df2}, axis=1)#效果与上面相同

pd.concat([df1, df2], axis=1, keys=['level1', 'level2'],
          names=['upper', 'lower'])#层次索引，

#5
df1 = DataFrame(np.random.randn(3, 4), columns=['a', 'b', 'c', 'd'])
df2 = DataFrame(np.random.randn(2, 3), columns=['b', 'd', 'a'])

df1

df2

pd.concat([df1, df2], ignore_index=True)#将无用索引忽视掉

###合并重叠数据
#1
a = Series([np.nan, 2.5, np.nan, 3.5, 4.5, np.nan],
           index=['f', 'e', 'd', 'c', 'b', 'a'])
b = Series(np.arange(len(a), dtype=np.float64),
           index=['f', 'e', 'd', 'c', 'b', 'a'])
b[-1] = np.nan

a

b

np.where(pd.isnull(a), b, a)#替换

#2
b[:-2].combine_first(a[2:])#效果同上

#3
df1 = DataFrame({'a': [1., np.nan, 5., np.nan],
                 'b': [np.nan, 2., np.nan, 6.],
                 'c': range(2, 18, 4)})
df2 = DataFrame({'a': [5., 4., np.nan, 3., 7.],
                 'b': [np.nan, 3., 4., 6., 8.]})
df1.combine_first(df2)#如果两个里面都缺失  还是缺失

###重塑层次化索引
#1
data = DataFrame(np.arange(6).reshape((2, 3)),
                 index=pd.Index(['Ohio', 'Colorado'], name='state'),
                 columns=pd.Index(['one', 'two', 'three'], name='number'))
data

result = data.stack()#原来列索引不变，把行索引上的值变成列索引，  层次索引
result

result.unstack()#重新转换成DataFrame

result.unstack(0)#沿着这个轴去进行转换

result.unstack('state')

#2 引入缺失值

s1 = Series([0, 1, 2, 3], index=['a', 'b', 'c', 'd'])
s2 = Series([4, 5, 6], index=['c', 'd', 'e'])
data2 = pd.concat([s1, s2], keys=['one', 'two'])
data2.unstack()

data2.unstack().stack()

data2.unstack().stack(dropna=False)#保留缺失值

#3
df = DataFrame({'left': result, 'right': result + 5},
               columns=pd.Index(['left', 'right'], name='side'))
df

df.unstack('state')

df.unstack('state').stack('side')#指定转换的名称

###长宽格式的转换

###长宽格式的转换
#1
data = pd.read_csv('d:data/macrodata.csv')
periods = pd.PeriodIndex(year=data.year, quarter=data.quarter, name='date')
data = DataFrame(data.to_records(),
                 columns=pd.Index(['realgdp', 'infl', 'unemp'], name='item'),
                 index=periods.to_timestamp('D', 'end'))

ldata = data.stack().reset_index().rename(columns={0: 'value'})
wdata = ldata.pivot('date', 'item', 'value')

#2
ldata[:10]

pivoted = ldata.pivot('date', 'item', 'value')#
pivoted.head()

ldata['value2'] = np.random.randn(len(ldata))
ldata[:10]

pivoted = ldata.pivot('date', 'item')
pivoted[:5]

pivoted['value'][:5]

unstacked = ldata.set_index(['date', 'item']).unstack('item')
unstacked[:7]

 1、移除重复数据

###移除重复数据
data = DataFrame({'k1': ['one'] * 3 + ['two'] * 4,
                  'k2': [1, 1, 2, 3, 3, 4, 4]})
data

data.duplicated()#判断是否跟前面的有重复，第二次重复的话是True

data.drop_duplicates()#去掉所有的重复行

data['v1'] = range(7)
data.drop_duplicates(['k1'])#指定去除哪一项的重复列

data.drop_duplicates(['k1', 'k2'], take_last=True)#相同的数据留最后一条

2、利用函数或映射进行数据转换

###利用函数或映射进行数据转换
#1
data = DataFrame({'food': ['bacon', 'pulled pork', 'bacon', 'Pastrami',
                           'corned beef', 'Bacon', 'pastrami', 'honey ham',
                           'nova lox'],
                  'ounces': [4, 3, 12, 6, 7.5, 8, 3, 5, 6]})
data

meat_to_animal = {
  'bacon': 'pig',
  'pulled pork': 'pig',
  'pastrami': 'cow',
  'corned beef': 'cow',
  'honey ham': 'pig',
  'nova lox': 'salmon'
}
#将元数据food 变小写，然后按照meat_to_animal映射生成新的一列
data['animal'] = data['food'].map(str.lower).map(meat_to_animal)# data['food'].map(str.lower)转换成小写
data

data['food'].map(lambda x: meat_to_animal[x.lower()])#同上

3、 数据标准化

# 数据标准化
datafile = 'E:\week6\data/normalization_data.xls' #参数初始化
data = pd.read_excel(datafile, header = None) #读取数据

(data - data.min())/(data.max() - data.min()) #最小-最大规范化
(data - data.mean())/data.std() #零-均值规范化
data/10**np.ceil(np.log10(data.abs().max())) #小数定标规范化

4、替换值

###替换值
data = Series([1., -999., 2., -999., -1000., 3.])
data

data.replace(-999, np.nan)#将-999替换成np的nan

data.replace([-999, -1000], np.nan)#将列表中的值替换成np的nan

data.replace([-999, -1000], [np.nan, 0])#不同的值替换成不同的值

data.replace({-999: np.nan, -1000: 0})#同上

5、重命名轴索引

###重命名轴索引
data = DataFrame(np.arange(12).reshape((3, 4)),
                 index=['Ohio', 'Colorado', 'New York'],
                 columns=['one', 'two', 'three', 'four'])

data.index.map(str.upper)

data.index = data.index.map(str.upper)#转成大写
data

data.rename(index=str.title, columns=str.upper)#复制到新的对象里

data.rename(index={'OHIO': 'INDIANA'},
            columns={'three': 'peekaboo'})#索引和列标签赋值

# 总是返回DataFrame的引用
_ = data.rename(index={'OHIO': 'INDIANA'}, inplace=True)#直接在原来数据上修改   不需要传入一个对象  inplace=True

6、离散化与面元划分

###离散化与面元划分
#1
ages = [20, 22, 25, 27, 21, 23, 37, 31, 61, 45, 41, 32]

bins = [18, 25, 35, 60, 100]#切割垫
cats = pd.cut(ages, bins)#ages原来的列表，bins分割点的列表
#返回的是一组面元名称的字符串码   [(18, 25], (18, 25], (18, 25], (25, 35], (18, 25], ..., (25, 35], (60, 100], (35, 60], (35, 60], (25, 35]]

#四个组别的大小关系 (4, object): [(18, 25] < (25, 35] < (35, 60] < (60, 100]]

cats

cats.labels#分组情况 分别属于哪个组 array([0, 0, 0, 1, 0, 0, 2, 1, 3, 2, 2, 1], dtype=int8)

cats.levels#给出了对应分组的名称 Index([u'(18, 25]', u'(25, 35]', u'(35, 60]', u'(60, 100]'], dtype='object')

pd.value_counts(cats)# 不同组别的人数

pd.cut(ages, [18, 26, 36, 61, 100], right=False)#不包括最右边的值

group_names = ['Youth', 'YoungAdult', 'MiddleAged', 'Senior']#
pd.cut(ages, bins, labels=group_names)#设置区间的名字
#如果传入的是面元的数量，就会根据最大值和最小值 进行等长的划分
data = np.random.rand(20)
pd.cut(data, 4, precision=2)#将等长分成四份

#2
data = np.random.randn(1000) # Normally distributed
cats = pd.qcut(data, 4) # Cut into quartiles每个里面的个数尽可能的一样  有余数  从小倒下给值
cats

pd.value_counts(cats)#每组数量

pd.qcut(data, [0, 0.1, 0.5, 0.9, 1.])#指定分位数

7、检测和过滤异常值

###检测和过滤异常值
#1
np.random.seed(12345)
data = DataFrame(np.random.randn(1000, 4))
data.describe()

col = data[3]
col[np.abs(col) > 3]

data[(np.abs(data) > 3).any(1)]

#2
data[np.abs(data) > 3] = np.sign(data) * 3
data.describe()

8、排列与随机采样

###排列与随机采样
#1
df = DataFrame(np.arange(5 * 4).reshape((5, 4)))
sampler = np.random.permutation(5)#重新排序，随机乱排
sampler
df

df.take(sampler)#对原来数组随机乱排

#2
df.take(np.random.permutation(len(df))[:3])

#3
bag = np.array([5, 7, -1, 6, 4])
sampler = np.random.randint(0, len(bag), size=10)#产生一组随机的整数
sampler

draws = bag.take(sampler)
draws

9、计算指标与哑变量

###计算指标与哑变量
#1
df = DataFrame({'key': ['b', 'b', 'a', 'c', 'a', 'b'],
                'data1': range(6)})
pd.get_dummies(df['key'])

dummies = pd.get_dummies(df['key'], prefix='key')#增加前缀
df_with_dummy = df[['data1']].join(dummies)#合并
df_with_dummy#生成哑变量矩阵

#2 某个行 同属于过个分类
mnames = ['movie_id', 'title', 'genres']
movies = pd.read_table('d:/data/movies.dat', sep='::', header=None,
                        names=mnames)
movies[:10]

genre_iter = (set(x.split('|')) for x in movies.genres)#分割最后一行数据
genres = sorted(set.union(*genre_iter))#合并起来

dummies = DataFrame(np.zeros((len(movies), len(genres))), columns=genres)

for i, gen in enumerate(movies.genres):
    dummies.ix[i, gen.split('|')] = 1

movies_windic = movies.join(dummies.add_prefix('Genre_'))
movies_windic.ix[0]#分类的变量

#3
np.random.seed(12345)
values = np.random.rand(10)
values

bins = [0, 0.2, 0.4, 0.6, 0.8, 1]
pd.get_dummies(pd.cut(values, bins))

10、线损率属性构造

#参数初始化
inputfile= 'd:/data/electricity_data.xls' #供入供出电量数据
outputfile = 'd:/data/electricity_data.xls' #属性构造后数据文件

data = pd.read_excel(inputfile) #读入数据
data[u'线损率'] = (data[u'供入电量'] - data[u'供出电量'])/data[u'供入电量']

data.to_excel(outputfile, index = False) #保存结果

1、字符串对象方法（略）

###正则表达式
#1
import re
text = "foo    bar\t baz  \tqux"
re.split('\s+', text)

regex = re.compile('\s+')
regex.split(text)

regex.findall(text)

#2
text = """Dave dave@google.com
Steve steve@gmail.com
Rob rob@gmail.com
Ryan ryan@yahoo.com
"""
pattern = r'[A-Z0-9._%+-]+@[A-Z0-9.-]+\.[A-Z]{2,4}'

# re.IGNORECASE 的作用是使正则表达式对大小写不敏感
regex = re.compile(pattern, flags=re.IGNORECASE)

regex.findall(text)

m = regex.search(text)
m

text[m.start():m.end()]

print(regex.match(text))

print(regex.sub('REDACTED', text))

#3
pattern = r'([A-Z0-9._%+-]+)@([A-Z0-9.-]+)\.([A-Z]{2,4})'
regex = re.compile(pattern, flags=re.IGNORECASE)

m = regex.match('wesm@bright.net')
m.groups()

regex.findall(text)

print(regex.sub(r'Username: \1, Domain: \2, Suffix: \3', text))

#4
regex = re.compile(r"""
    (?P<username>[A-Z0-9._%+-]+)
    @
    (?P<domain>[A-Z0-9.-]+)
    \.
    (?P<suffix>[A-Z]{2,4})""", flags=re.IGNORECASE|re.VERBOSE)

m = regex.match('wesm@bright.net')
m.groupdict()

###pandas中矢量化的字符串函数
data = {'Dave': 'dave@google.com', 'Steve': 'steve@gmail.com',
        'Rob': 'rob@gmail.com', 'Wes': np.nan}
data = Series(data)

data
#如果直接调用字符串的处理方式会报错
data.isnull()

data.str.contains('gmail')#判断有没有gmail  不会报错 直接跳过

pattern#pattern = r'([A-Z0-9._%+-]+)@([A-Z0-9.-]+)\.([A-Z]{2,4})' 正则

data.str.findall(pattern, flags=re.IGNORECASE)#跳过nan

matches = data.str.match(pattern, flags=re.IGNORECASE)
matches

matches.str.get(1)#第二例

matches.str[0]

data.str[:5]#字符串子串的截取

应用

###示例：USDA食品数据库
'''
{
  "id": 21441,
  "description": "KENTUCKY FRIED CHICKEN, Fried Chicken, EXTRA CRISPY,
Wing, meat and skin with breading",
  "tags": ["KFC"],
  "manufacturer": "Kentucky Fried Chicken",
  "group": "Fast Foods",
  "portions": [
    {
      "amount": 1,
      "unit": "wing, with skin",
      "grams": 68.0
    },

    ...
  ],
  "nutrients": [
    {
      "value": 20.8,
      "units": "g",
      "description": "Protein",
      "group": "Composition"
    },

    ...
  ]
}
'''

import json
db = json.load(open('E:\week6\data/foods-2011-10-03.json'))
len(db)

db[0].keys()

db[0]['nutrients'][0]

nutrients = DataFrame(db[0]['nutrients'])#转换成DataFrame格式 
nutrients[:7]#查看前7行

info_keys = ['description', 'group', 'id', 'manufacturer']
info = DataFrame(db, columns=info_keys)

info[:5]
info

pd.value_counts(info.group)[:10]

nutrients = []

for rec in db:
    fnuts = DataFrame(rec['nutrients'])
    fnuts['id'] = rec['id']
    nutrients.append(fnuts)

nutrients = pd.concat(nutrients, ignore_index=True)

nutrients

nutrients.duplicated().sum()

nutrients = nutrients.drop_duplicates()

col_mapping = {'description' : 'food',
               'group'       : 'fgroup'}
info = info.rename(columns=col_mapping, copy=False)
info

col_mapping = {'description' : 'nutrient',
               'group' : 'nutgroup'}
nutrients = nutrients.rename(columns=col_mapping, copy=False)
nutrients

ndata = pd.merge(nutrients, info, on='id', how='outer')

ndata

ndata.ix[30000]

result = ndata.groupby(['nutrient', 'fgroup'])['value'].quantile(0.5)
result['Zinc, Zn'].order().plot(kind='barh')

by_nutrient = ndata.groupby(['nutgroup', 'nutrient'])

get_maximum = lambda x: x.xs(x.value.idxmax())
get_minimum = lambda x: x.xs(x.value.idxmin())

max_foods = by_nutrient.apply(get_maximum)[['value', 'food']]

# make the food a little smaller
max_foods.food = max_foods.food.str[:50]

max_foods.ix['Amino Acids']['food']