from __future__ import division
from numpy.random import randn
import numpy as np
import os
import matplotlib.pyplot as plt
np.random.seed(12345)
plt.rc('figure', figsize=(10, 6))
from pandas import Series, DataFrame
import pandas
import pandas as pd
np.set_printoptions(precision=4, threshold=500)
pd.options.display.max_rows = 100

%matplotlib inline

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')

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')

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')

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')

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

pd.merge(left, right, on='key1', suffixes=('_left', '_right'))

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')

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)

left2.join(right2, how='outer')

left1.join(right1, on='key')

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')

arr = np.arange(12).reshape((3, 4))

arr

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'])

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']])

result = pd.concat([s1, s1, s3], keys=['one', 'two', 'three'])

result

# Much more on the unstack function later
result.unstack()

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'])

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)

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)

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.]})
df1.combine_first(df2)

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')

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)

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

df.unstack('state')

df.unstack('state').stack('side')

data = pd.read_csv('ch07/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')

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]

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)

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()])

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})

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'})

# Always returns a reference to a DataFrame
_ = data.rename(index={'OHIO': 'INDIANA'}, inplace=True)
data

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)

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.])

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)]

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

df = DataFrame(np.arange(5 * 4).reshape((5, 4)))
sampler = np.random.permutation(5)
sampler

df

df.take(sampler)

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)

sampler

draws = bag.take(sampler)
draws

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

mnames = ['movie_id', 'title', 'genres']
movies = pd.read_table('ch02/movielens/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]

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))

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(',')

val.replace(',', '::')

val.replace(',', '')

import re
text = "foo    bar\t baz  \tqux"
re.split('\s+', text)

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

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}'

# re.IGNORECASE makes the regex case-insensitive
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))

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))

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()

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]

import json
db = json.load(open('ch07/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']