# -*- 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 = 'd:/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
###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)
pd.merge(df1, df2, on='key')
#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')
pd.merge(df1, df2, how='outer')
#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')
pd.merge(df1, df2, how='inner')
#4
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'))
###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)
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)
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')
left1.join(right1, on='key')
#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')
###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])
pd.concat([s1, s2, s3], axis=1)
s4 = pd.concat([s1 * 5, s3])
pd.concat([s1, s4], axis=1)
pd.concat([s1, s4], axis=1, join='inner')
pd.concat([s1, s4], axis=1, join_axes=[['a', 'c', 'b', 'e']])
#3
result = pd.concat([s1, s1, s3], keys=['one', 'two', 'three'])
result
result.unstack()
#4
pd.concat([s1, s2, s3], axis=1, keys=['one', 'two', 'three'])
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)
pd.concat([df1, df2], axis=1, keys=['level1', 'level2'],
names=['upper', 'lower'])
#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)
###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)
#2
b[:-2].combine_first(a[2:])
#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)
###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()
result
result.unstack()
result.unstack(0)
result.unstack('state')
#2
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)
#3
df = DataFrame({'left': result, 'right': result + 5},
columns=pd.Index(['left', 'right'], name='side'))
df
df.unstack('state')
df.unstack('state').stack('side')
###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]
###移除重复数据
data = DataFrame({'k1': ['one'] * 3 + ['two'] * 4,
'k2': [1, 1, 2, 3, 3, 4, 4]})
data
data.duplicated()
data.drop_duplicates()
data['v1'] = range(7)
data.drop_duplicates(['k1'])
data.drop_duplicates(['k1', 'k2'], take_last=True)
###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'
}
data['animal'] = data['food'].map(str.lower).map(meat_to_animal)
data
data['food'].map(lambda x: meat_to_animal[x.lower()])
# 数据标准化
datafile = 'd:/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
###替换值
data = Series([1., -999., 2., -999., -1000., 3.])
data
data.replace(-999, np.nan)
data.replace([-999, -1000], np.nan)
data.replace([-999, -1000], [np.nan, 0])
data.replace({-999: np.nan, -1000: 0})
###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)
data
data.rename(index=str.title, columns=str.upper)
data.rename(index={'OHIO': 'INDIANA'},
columns={'three': 'peekaboo'})
# 总是返回DataFrame的引用
_ = data.rename(index={'OHIO': 'INDIANA'}, inplace=True)
data
###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]
cats = pd.cut(ages, bins)
cats
cats.labels
cats.levels
pd.value_counts(cats)
pd.cut(ages, [18, 26, 36, 61, 100], right=False)
group_names = ['Youth', 'YoungAdult', 'MiddleAged', 'Senior']
pd.cut(ages, bins, labels=group_names)
data = np.random.rand(20)
pd.cut(data, 4, precision=2)
#2
data = np.random.randn(1000) # Normally distributed
cats = pd.qcut(data, 4) # Cut into quartiles
cats
pd.value_counts(cats)
pd.qcut(data, [0, 0.1, 0.5, 0.9, 1.])
###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()
###排列与随机采样
#1
df = DataFrame(np.arange(5 * 4).reshape((5, 4)))
sampler = np.random.permutation(5)
sampler
df
df.take(sampler)
#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)
sampler
draws = bag.take(sampler)
draws
###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')
df_with_dummy = df[['data1']].join(dummies)
df_with_dummy
#2
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)
genres = sorted(set.union(*genre_iter))
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]
#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))
#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
###字符串对象方法
val = 'a,b, guido'
val.split(',')
pieces = [x.strip() for x in val.split(',')]
pieces
first, second, third = pieces
first + '::' + second + '::' + third
'::'.join(pieces)
'guido' in val
val.index(',')
val.find(':')
val.index(':')
val.count('a')
val.replace(',', '::')
val.replace(',', '')
###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[A-Z0-9._%+-]+)
@
(?P[A-Z0-9.-]+)
\.
(?P[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
data.isnull()
data.str.contains('gmail')
pattern
data.str.findall(pattern, flags=re.IGNORECASE)
matches = data.str.match(pattern, flags=re.IGNORECASE)
matches
matches.str.get(1)
matches.str[0]
data.str[:5]
###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('d:/data/foods-2011-10-03.json'))
len(db)
db[0].keys()
db[0]['nutrients'][0]
nutrients = DataFrame(db[0]['nutrients'])
nutrients[:7]
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']
