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chap7.py
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chap7.py
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#! /usr/bin/env python
#-*- encoding: utf-8 -*-
"""
"""
import pandas as pd
from pandas import DataFrame, Series
import numpy as np
import matplotlib.pylab as plt
MACRODATAPATH = '../pydata-book/ch07/macrodata.csv'
MOVIELENSPATH = '../pydata-book/ch02/movielens/movies.dat'
FOODJSONPATH = '../pydata-book/ch07/foods-2011-10-03.json'
def slide_3():
print "#######Many-to-One#######"
df1 = DataFrame({'key': ['b', 'b', 'a', 'c', 'a', 'a', 'b'],
'data1': range(7)})
df2 = DataFrame({'key': ['a', 'b', 'd'],
'data2': range(3)})
print "***df1***"
print df1
print "***df2***"
print df2
print "***pd.merge df1 and df2***"
print pd.merge(df1, df2)
print pd.merge(df1, df2, on='key')
df3 = DataFrame({'lkey': ['b', 'b', 'a', 'c', 'a', 'a', 'b'],
'data1': range(7)})
df4 = DataFrame({'rkey': ['a', 'b', 'd'],
'data2': range(3)})
print "***pd.merge df3 and df4***"
print pd.merge(df3, df4, left_on='lkey', right_on='rkey')
print "***pd.merge outer join***"
print pd.merge(df1, df2, how='outer')
print "#######Many-to-Many#######"
df1 = DataFrame({'key': ['b', 'b', 'a', 'c', 'a', 'b'],
'data1': range(6)})
df2 = DataFrame({'key': ['a', 'b', 'a', 'b', 'd'],
'data2': range(5)})
print "***df1***"
print df1
print "***df2***"
print df2
print "***pd.merge left join***"
print pd.merge(df1, df2, on='key', how='left')
print "#######Multi Keys#######"
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]})
print "***left***"
print df1
print "***right***"
print df2
print "***pd.merge outer join***"
print pd.merge(left, right, on=['key1', 'key2'], how='outer')
print "***overlapping column***"
print pd.merge(left, right, on='key1')
print pd.merge(left, right, on='key1', suffixes=('_left', '_right'))
def slide_5():
left1 = DataFrame({'key': ['a', 'b', 'a', 'a', 'b', 'c'],
'value': range(6)})
right1 = DataFrame({'group_val': [3.5, 7]}, index=['a', 'b'])
print '***left1***'
print left1
print '***right1***'
print right1
print pd.merge(left1, right1, left_on='key', right_index=True)
print pd.merge(left1, right1, left_on='key', right_index=True, how='outer')
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'])
print '***lefth***'
print lefth
print '***righth***'
print righth
print "***merge lefth and merge by inner***"
print pd.merge(lefth, righth, left_on=['key1', 'key2'], right_index=True)
print "***merge lefth and merge by outer***"
print 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'])
print '***left2***'
print left2
print '***right2***'
print right2
print '***merge left2 and right2***'
print pd.merge(left2, right2,
how='outer',
left_index=True,
right_index=True)
print '***join method***'
print left2.join(right2, how='outer')
print left1.join(right1, on='key')
another = DataFrame([[7., 8.], [9., 10.], [11., 12.], [16., 17.]],
index=['a', 'c', 'e', 'f'],
columns=['New York', 'Oregon'])
print '***another***'
print left2.join([right2, another])
print '***another, outer***'
print left2.join([right2, another], how='outer')
def slide_6():
arr = np.arange(12).reshape((3, 4))
print arr
print '***numpy.concatenate***'
print np.concatenate([arr, arr], axis=1)
s1 = Series([0, 1], index=['a', 'b'])
s2 = Series([2, 3, 4], index=['c', 'd', 'e'])
s3 = Series([5, 6], index=['f', 'g'])
print "***s1***"
print s1
print "***s2***"
print s2
print "***s3***"
print s3
print '***pandas.concat # no index overlap***'
print pd.concat([s1, s2, s3])
print pd.concat([s1, s2, s3], axis=1)
s4 = pd.concat([s1 * 5, s3])
print "***s4***"
print s4
print '***concat s1 and s4 by axis=1***'
print pd.concat([s1, s4], axis=1)
print pd.concat([s1, s4], axis=1, join='inner')
print pd.concat([s1, s4], axis=1, join_axes=[['a', 'c', 'b', 'e']])
result = pd.concat([s1, s1, s3], keys=['one', 'two', 'three'])
print '***result***'
print pd.concat([s1, s1, s3])
print result
print 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'])
print '***df1***'
print df1
print '***df2***'
print df2
print pd.concat([df1, df2], axis=1, keys=['level1', 'level2'])
print pd.concat({'level1': df1, 'level2': df2}, axis=1)
print pd.concat([df1, df2],
axis=1,
keys=['level1', 'level2'],
names=['upper', 'lower'])
df1 = DataFrame(np.random.randn(3, 4), columns=['a', 'b', 'c', 'd'])
df2 = DataFrame(np.random.randn(2, 3), columns=['b', 'd', 'a'])
print '***df1***'
print df1
print '***df2***'
print df2
print pd.concat([df1, df2], ignore_index=True)
def slide_7():
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'])
print '***a***'
print a
print '***b***'
print b
b[-1] = np.nan
print '***a***'
print a
print '***b***'
print b
print np.where(pd.isnull(a), b, a)
print '#####combine_first#####'
print '***b[:-2]***'
print b[:-2]
print '***a[2:]***'
print a[2:]
print 'b[:-2].combine_first(a[2:])'
print b[:-2].combine_first(a[2:])
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.]})
print '***df1***'
print df1
print '***df2***'
print df2
print df1.combine_first(df2)
def slide_8():
data = DataFrame(np.arange(6).reshape((2, 3)),
index=pd.Index(['Ohio', 'Colorado'], name='state'),
columns=pd.Index(['one', 'two', 'three'], name='number'))
print data
result = data.stack()
print '***stack()***'
print result
print '***unstack()***'
print result.unstack()
print '***unstack(0)***'
print result.unstack(0)
print "***unstack('state')***"
print result.unstack('state')
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'])
print '***unstack***'
print data2.unstack()
print '***unstack->stack***'
print data2.unstack().stack()
print '***unstack->stack(dropna)***'
print data2.unstack().stack(dropna=False)
df = DataFrame({'left': result, 'right': result + 5},
columns=pd.Index(['left', 'right'],
name='side'))
print 'df'
print df
print "unstack('state')"
print df.unstack('state')
print "unstack('state').stack('side')"
print df.unstack('state').stack('side')
def slide_9():
data = pd.read_csv(MACRODATAPATH)
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')
print ldata[:10]
pivoted = ldata.pivot('date', 'item', 'value')
print pivoted.head()
ldata['value2'] = np.random.randn(len(ldata))
print ldata[:10]
pivoted = ldata.pivot('date', 'item')
print pivoted[:5]
print pivoted['value'][:5]
unstacked = ldata.set_index(['date', 'item']).unstack('item')
print unstacked[:7]
def slide_10():
data = DataFrame({'k1': ['one'] * 3 + ['two'] * 4,
'k2': [1, 1, 2, 3, 3, 4, 4]})
print data
print data.duplicated()
print data.duplicated('k1')
print data.drop_duplicates()
data['v1'] = range(7)
print data
print data.drop_duplicates(['k1'])
print data.drop_duplicates(['k1', 'k2'], take_last=True)
def slide_11():
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]})
print 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)
print data['food']
print data
print data['food'].map(lambda x: meat_to_animal[x.lower()])
def slide_12():
data = Series([1., -999., 2., -999., -1000., 3.])
print data
print data.replace(-999, np.nan)
print data.replace([-999, -1000], np.nan)
print data.replace([-999, -1000], [np.nan, 0])
print data.replace({-999: np.nan, -1000: 0})
def slide_13():
data = DataFrame(np.arange(12).reshape((3, 4)),
index=['Ohio', 'Colorado', 'New York'],
columns=['one', 'two', 'three', 'four'])
print data.index.map(str.upper)
data.index = data.index.map(str.upper)
print data
print data.rename(index=str.title, columns=str.upper)
print data.rename(index={'OHIO': 'INDIANA'},
columns={'three': 'peekaboo'})
_ = data.rename(index={'OHIO': 'INDIANA'}, inplace=True)
print data
def slide_14():
ages = [20, 22, 25, 27, 21, 23, 37, 31, 61, 45, 41, 32]
bins = [18, 25, 35, 60, 100]
cats = pd.cut(ages, bins)
print cats
# labels じゃなくて codes を使え
# print cats.labels
print cats.codes
# print cats.levels
# levels じゃなくて categories を使え
print cats.categories
print pd.value_counts(cats)
print pd.cut(ages, [18, 26, 36, 61, 100], right=False)
group_names = ['Youth', 'YoungAdultl', 'MiddleAged', 'Senior']
print pd.cut(ages, bins, labels=group_names)
data = np.random.rand(20)
print data
print pd.cut(data, 3, precision=2)
data = np.random.randn(1000)
cats = pd.qcut(data, 3)
print cats
print pd.value_counts(cats)
print pd.qcut(data, [0, 0.1, 0.5, 0.9, 1.])
def slide_15():
np.random.seed(12345)
data = DataFrame(np.random.randn(1000, 4))
print data.describe()
col = data[3]
print col[np.abs(col) > 3]
print data[(np.abs(data) > 3).any(1)]
data[np.abs(data) > 3] = np.sign(data) * 3
print data.describe()
def slide_16():
df = DataFrame(np.arange(5 * 4).reshape(5, 4))
sampler = np.random.permutation(5)
print sampler
print df
print df.take(sampler)
print df.take(np.random.permutation(len(df))[:3])
bag = np.array([5, 7, -1, 6, 4])
sampler = np.random.randint(0, len(bag), size=10)
print sampler
draws = bag.take(sampler)
print draws
def slide_17():
df = DataFrame({'key': ['b', 'b', 'a', 'c', 'a', 'b'],
'data1': range(6)})
print pd.get_dummies(df['key'])
dummies = pd.get_dummies(df['key'], prefix='key')
print dummies
df_with_dummy = df[['data1']].join(dummies)
print df_with_dummy
mnames = ['movie_id', 'title', 'genres']
movies = pd.read_table(MOVIELENSPATH,
sep='::',
header=None,
engine='python',
names=mnames)
print movies[:10]
genre_iter = (set(x.split('|')) for x in movies.genres)
genres = sorted(set.union(*genre_iter))
print genres
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_'))
print movies_windic.ix[0]
values = np.random.rand(10)
print values
bins = [0, 0.2, 0.4, 0.6, 0.8, 1]
print pd.get_dummies(pd.cut(values, bins))
def slide_18():
val = 'a, b, guido'
print val.split(',')
pieces = [x.strip() for x in val.split(',')]
print pieces
first, second, third = pieces
print first + '::' + second + '::' + third
print '::'.join(pieces)
print "'guido' in val is " + ('guido' in val)
print "val.index(','): %d" % val.index(',')
print "val.find(':'): %d" % val.find(':')
print "val.count(','): %d" % val.count(',')
print val.replace(',', '::')
print val.replace(',', '')
def slide_19():
import re
text = "foo bar\t baz \tqux"
print text
print "1つ以上の空白と消す"
print re.split('\s+', text)
regex = re.compile('\s+')
print "コンパイルしてから消す"
print regex.split(text)
print regex.findall(text)
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}'
regex = re.compile(pattern, flags=re.IGNORECASE)
print text
print regex.findall(text)
m = regex.search(text)
print text[m.start():m.end()]
print regex.match(text)
print regex.sub('REDACTED', text)
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')
print m.groups()
print regex.findall(text)
print regex.sub(r'Username: \1, Domain: \2, Suffix: \3', text)
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')
print m.groupdict()
def slide_20():
import re
data = {'Dave': 'dave@google.com',
'Steve': 'steve@gmail.com',
'Rob': 'rob@gmail.com',
'Wes': np.nan}
data = Series(data)
print data
print data.isnull()
print data.str.contains('gmail')
pattern = r'([A-Z0-9._%+-]+)@([A-Z0-9.-]+)\.([A-Z]{2,4})'
print data.str.findall(pattern, flags=re.IGNORECASE)
matches = data.str.match(pattern, flags=re.IGNORECASE)
print matches
print matches.str.get(1)
print matches.str[0]
print data
print data.str[:5]
def slide_21():
import json
db = json.load(open(FOODJSONPATH))
print len(db)
print db[0].keys()
print db[0]['nutrients'][0]
nutrients = DataFrame(db[0]['nutrients'])
print nutrients[:7]
info_keys = ['description', 'group', 'id', 'manufacturer']
info = DataFrame(db, columns=info_keys)
print info[:5]
print pd.value_counts(info.group)[:10]
print "今から全部のnutrientsを扱うよ"
nutrients = []
for rec in db:
fnuts = DataFrame(rec['nutrients'])
fnuts['id'] = rec['id']
nutrients.append(fnuts)
nutrients = pd.concat(nutrients, ignore_index=True)
print "なんか重複多い"
print nutrients.duplicated().sum()
nutrients = nutrients.drop_duplicates()
print "infoとnutrients両方にdescriptionとgroupがあるから変えよう"
col_mapping = {'description': 'food', 'group': 'fgroup'}
info = info.rename(columns=col_mapping, copy=False)
col_mapping = {'description': 'nutrient', 'group': 'nutgroup'}
nutrients = nutrients.rename(columns=col_mapping, copy=False)
ndata = pd.merge(nutrients, info, on='id', how='outer')
print ndata.ix[30000]
result = ndata.groupby(['nutrient', 'fgroup'])['value'].quantile(0.5)
result['Zinc, Zn'].order().plot(kind='barh')
plt.show()
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']]
max_foods.food = max_foods.food.str[:50]
print max_foods.ix['Amino Acids']['food']
if __name__ == '__main__':
# slide_3()
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slide_7()
slide_8()
slide_9()
slide_10()
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slide_12()
slide_13()
slide_14()
slide_15()
slide_16()
slide_17()
slide_18()
slide_19()
slide_20()
slide_21()
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