import numpy as np
import pandas as pd
PREVIOUS_MAX_ROWS = pd.options.display.max_rows
pd.options.display.max_rows = 20
np.random.seed(12345)
import matplotlib.pyplot as plt
plt.rc('figure', figsize=(10, 6))
np.set_printoptions(precision=4, suppress=True)
string_data = pd.Series(['aardvark', 'artichoke', np.nan, 'avocado'])
string_data
string_data.isnull()
string_data[0] = None
string_data.isnull()
from numpy import nan as NA
data = pd.Series([1, NA, 3.5, NA, 7])
data.dropna()
data[data.notnull()]
data = pd.DataFrame([[1., 6.5, 3.], [1., NA, NA],
[NA, NA, NA], [NA, 6.5, 3.]])
cleaned = data.dropna()
data
cleaned
data.dropna(how='all')
data[4] = NA
data
data.dropna(axis=1, how='all')
df = pd.DataFrame(np.random.randn(7, 3))
df.iloc[:4, 1] = NA
df.iloc[:2, 2] = NA
df
df.dropna()
df.dropna(thresh=2)
df.fillna(0)
df.fillna({1: 0.5, 2: 0})
_ = df.fillna(0, inplace=True)
df
df = pd.DataFrame(np.random.randn(6, 3))
df.iloc[2:, 1] = NA
df.iloc[4:, 2] = NA
df
df.fillna(method='ffill')
df.fillna(method='ffill', limit=2)
data = pd.Series([1., NA, 3.5, NA, 7])
data.fillna(data.mean())
data = pd.DataFrame({'k1': ['one', 'two'] * 3 + ['two'],
'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'], keep='last')
data = pd.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'
}
lowercased = data['food'].str.lower()
lowercased
data['animal'] = lowercased.map(meat_to_animal)
data
data['food'].map(lambda x: meat_to_animal[x.lower()])
data = pd.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 = pd.DataFrame(np.arange(12).reshape((3, 4)),
index=['Ohio', 'Colorado', 'New York'],
columns=['one', 'two', 'three', 'four'])
transform = lambda x: x[:4].upper()
data.index.map(transform)
data.index = data.index.map(transform)
data
data.rename(index=str.title, columns=str.upper)
data.rename(index={'OHIO': 'INDIANA'},
columns={'three': 'peekaboo'})
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.codes
cats.categories
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.])
data = pd.DataFrame(np.random.randn(1000, 4))
data.describe()
col = data[2]
col[np.abs(col) > 3]
data[(np.abs(data) > 3).any(1)]
data[np.abs(data) > 3] = np.sign(data) * 3
data.describe()
np.sign(data).head()
df = pd.DataFrame(np.arange(5 * 4).reshape((5, 4)))
sampler = np.random.permutation(5)
sampler
df
df.take(sampler)
df.sample(n=3)
choices = pd.Series([5, 7, -1, 6, 4])
draws = choices.sample(n=10, replace=True)
draws
df = pd.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('datasets/movielens/movies.dat', sep='::',
header=None, names=mnames)
movies[:10]
all_genres = []
for x in movies.genres:
all_genres.extend(x.split('|'))
genres = pd.unique(all_genres)
genres
zero_matrix = np.zeros((len(movies), len(genres)))
dummies = pd.DataFrame(zero_matrix, columns=genres)
gen = movies.genres[0]
gen.split('|')
dummies.columns.get_indexer(gen.split('|'))
for i, gen in enumerate(movies.genres):
indices = dummies.columns.get_indexer(gen.split('|'))
dummies.iloc[i, indices] = 1
movies_windic = movies.join(dummies.add_prefix('Genre_'))
movies_windic.iloc[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))
data = {'Dave': 'dave@google.com', 'Steve': 'steve@gmail.com',
'Rob': 'rob@gmail.com', 'Wes': np.nan}
data = pd.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]
pd.options.display.max_rows = PREVIOUS_MAX_ROWS