3.12. Series Mapping¶
3.12.1. Rationale¶
Series.apply- apply function to data, function can have args and/or kwargsSeries.map- convert data from one to another using function or dict
3.12.2. Apply¶
Signature:
Series.apply(func, convert_dtype=True, args=(), **kwds)Parameters:
func: Callableconvert_dtype: bool; default:Trueargs: tuple**kwds: dict
Returns:
Union[Series, DataFrame]Invoke function on values of Series
Can be ufunc (a NumPy function that applies to the entire Series) or a Python function that only works on single values.
import pandas as pd
import numpy as np
np.random.seed(0)
s = pd.Series(
index = pd.date_range('2000-01-01', periods=4),
data = np.random.randn(4))
s
# 2000-01-01 1.764052
# 2000-01-02 0.400157
# 2000-01-03 0.978738
# 2000-01-04 2.240893
# Freq: D, dtype: float64
s.apply(int)
# 2000-01-01 1
# 2000-01-02 0
# 2000-01-03 0
# 2000-01-04 2
# Freq: D, dtype: int64
s.apply(lambda x: round(x, 2))
# 2000-01-01 1.76
# 2000-01-02 0.40
# 2000-01-03 0.98
# 2000-01-04 2.24
# Freq: D, dtype: float64
s.apply(round, ndigits=2)
# 2000-01-01 1.76
# 2000-01-02 0.40
# 2000-01-03 0.98
# 2000-01-04 2.24
# Freq: D, dtype: float64
s.apply(round, args=(2,))
# 2000-01-01 1.76
# 2000-01-02 0.40
# 2000-01-03 0.98
# 2000-01-04 2.24
# Freq: D, dtype: float64
functools.partial(func, *args, **keywords):
from functools import partial
import pandas as pd
import numpy as np
np.random.seed(0)
s = pd.Series(
index = pd.date_range('2000-01-01', periods=4),
data = np.random.randn(4))
s
# 2000-01-01 1.764052
# 2000-01-02 0.400157
# 2000-01-03 0.978738
# 2000-01-04 2.240893
# Freq: D, dtype: float64
round2 = partial(round, ndigits=2)
square = partial(pow, exp=2)
cube = partial(pow, exp=3)
s.apply(round2)
# 2000-01-01 1.76
# 2000-01-02 0.40
# 2000-01-03 0.98
# 2000-01-04 2.24
# Freq: D, dtype: float64
s.apply(square)
# 2000-01-01 3.111881
# 2000-01-02 0.160126
# 2000-01-03 0.957928
# 2000-01-04 5.021602
# Freq: D, dtype: float64
s.apply(cube)
# 2000-01-01 5.489520
# 2000-01-02 0.064075
# 2000-01-03 0.937561
# 2000-01-04 11.252875
# Freq: D, dtype: float64
3.12.3. Map¶
Signature:
Series.map(arg, na_action=None)Parameters:
arg:
Union[Callable, collections.abc.Mapping, Series]na_action:
Optional[Literal['ignore']]; defaultNone
Returns:
SeriesMap values of Series according to input correspondence.
Used for substituting each value in a Series with another value, that may be derived from a function, a dict or a Series.
When arg is a dictionary, values in Series that are not in the dictionary (as keys) are converted to NaN.
If the dictionary is a dict subclass that defines __missing__ (i.e. provides a method for default values), then this default is used rather than NaN.
import pandas as pd
import numpy as np
np.random.seed(0)
s = pd.Series(
index = pd.date_range('2000-01-01', periods=4),
data = np.random.randn(4))
s
# 2000-01-01 1.764052
# 2000-01-02 0.400157
# 2000-01-03 0.978738
# 2000-01-04 2.240893
# Freq: D, dtype: float64
s.map(int)
# 2000-01-01 1
# 2000-01-02 0
# 2000-01-03 0
# 2000-01-04 2
# Freq: D, dtype: int64
s.map(lambda x: round(x, 2))
# 2000-01-01 1.76
# 2000-01-02 0.40
# 2000-01-03 0.98
# 2000-01-04 2.24
# Freq: D, dtype: float64
import pandas as pd
s = pd.Series(['Watney', 'Twardowski', pd.NA, 'Lewis'])
s
# 0 Watney
# 1 Twardowski
# 2 <NA>
# 3 Lewis
# dtype: object
s.map({'Watney': 'Mark', 'Twardowski': 'Jan'})
# 0 Mark
# 1 Jan
# 2 NaN
# 3 NaN
# dtype: object
s.map('I am a {}'.format)
# 0 My name... Watney
# 1 My name... Twardowski
# 2 My name... <NA>
# 3 My name... Lewis
# dtype: object
s.map('I am a {}'.format, na_action='ignore')
# 0 My name... Watney
# 1 My name... Twardowski
# 2 <NA>
# 3 My name... Lewis
# dtype: object
3.12.4. Cleaning User Input¶
80% of machine learning and data science is cleaning data
3.12.5. Addresses¶
Is This the Same Address?
This is a dump of distinct records of a single address
Which one of the below is a true address?
'ul. Jana III Sobieskiego'
'ul Jana III Sobieskiego'
'ul.Jana III Sobieskiego'
'ulicaJana III Sobieskiego'
'Ul. Jana III Sobieskiego'
'UL. Jana III Sobieskiego'
'ulica Jana III Sobieskiego'
'Ulica. Jana III Sobieskiego'
'os. Jana III Sobieskiego'
'Jana 3 Sobieskiego'
'Jana 3ego Sobieskiego'
'Jana III Sobieskiego'
'Jana Iii Sobieskiego'
'Jana IIi Sobieskiego'
'Jana lll Sobieskiego' # three small letters 'L'
3.12.6. Streets¶
'ul'
'ul.'
'Ul.'
'UL.'
'ulica'
'Ulica'
'os'
'os.'
'Os.'
'osiedle'
'oś'
'oś.'
'Oś.'
'ośedle'
'pl'
'pl.'
'Pl.'
'plac'
'al'
'al.'
'Al.'
'aleja'
'aleia'
'alei'
'aleii'
'aleji'
3.12.7. House and Apartment Number¶
'Ćwiartki 3/4'
'Ćwiartki 3 / 4'
'Ćwiartki 3 m. 4'
'Ćwiartki 3 m 4'
'Brighton Beach 1st apt 2'
'Brighton Beach 1st apt. 2'
'Myśliwiecka 3/5/7'
'180f/8f'
'180f/8'
'180/8f'
'Jana Twardowskiego III 3 m. 3'
'Jana Twardowskiego 13d bud. A piętro II sala 3'
3.12.8. Phone Numbers¶
+48 (12) 355 5678
+48 123 555 678
123 555 678
+48 12 355 5678
+48 123-555-678
+48 123 555 6789
+1 (123) 555-6789
+1 (123).555.6789
+1 800-python
+48123555678
+48 123 555 678 wew. 1337
+48 123555678,1
+48 123555678,1,,2
3.12.9. Example¶
String cleaning:
expected = 'Jana Twardowskiego III'
text = 'UL. jana \tTWArdoWskIEGO 3'
# Convert to common format
text = text.upper()
# Remove unwanted whitespaces
text = text.replace('\t', '')
# Remove unwanted special characters
text = text.replace('.', '')
# Remove unwanted text
text = text.replace('UL', '')
text = text.replace('3', 'III')
# Formatting
text = text.title()
text = text.replace('Iii', 'III')
text = text.strip()
print('Matched:', text == expected)
# Matched: True
print(text)
# Jana Twardowskiego III
Remove Polish diacritics:
def pl_to_latin(text):
PL = {'ą': 'a', 'ć': 'c', 'ę': 'e',
'ł': 'l', 'ń': 'n', 'ó': 'o',
'ś': 's', 'ż': 'z', 'ź': 'z'}
result = ''.join(PL.get(x,x) for x in text.lower())
return result.capitalize()
s = pd.Series(['Poznań', 'Swarzędz', 'Kraków',
'Łódź', 'Gdańsk', 'Koło', 'Dęblin'])
s
# 0 Poznań
# 1 Swarzędz
# 2 Kraków
# 3 Łódź
# 4 Gdańsk
# 5 Koło
# 6 Dęblin
# dtype: object
s.map(pl_to_latin)
# 0 Poznan
# 1 Swarzedz
# 2 Krakow
# 3 Lodz
# 4 Gdansk
# 5 Kolo
# 6 Deblin
# dtype: object
s.apply(pl_to_latin)
# 0 Poznan
# 1 Swarzedz
# 2 Krakow
# 3 Lodz
# 4 Gdansk
# 5 Kolo
# 6 Deblin
# dtype: object
3.12.10. Assignments¶
"""
* Assignment: Series Mapping Clean
* Complexity: medium
* Lines of code: 15 lines
* Time: 21 min
English:
1. Use data from "Given" section (see below)
2. Convert `DATA` (see input section) to `pd.Series`
3. Write function to clean up data
4. Function takes one `str` argument
5. Function returns cleaned text
6. Apply function to all elements of `pd.Series`
7. Compare result with "Tests" section (see below)
Polish:
1. Użyj danych z sekcji "Given" (patrz poniżej)
2. Przekonwertuj `DATA` (patrz sekcja input) do `pd.Series`
3. Napisz funkcję czyszczącą dane
4. Funkcja przyjmuje jeden argument typu `str`
5. Funkcja zwraca oczyszczony tekst
6. Zaaplikuj funkcję na wszystkich elementach `pd.Series`
7. Porównaj wyniki z sekcją "Tests" (patrz poniżej)
Tests:
>>> type(result) is pd.Series
True
>>> pd.set_option('display.width', 500)
>>> pd.set_option('display.max_columns', 10)
>>> pd.set_option('display.max_rows', 10)
>>> result # doctest: +NORMALIZE_WHITESPACE
0 Mieszka II
1 Zygmunta III Wazy
2 Bolesława Chrobrego
3 Jana III Sobieskiego
4 Jana III Sobieskiego
...
6 Jana III Sobieskiego
7 Jana III Sobieskiego
8 Jana III Sobieskiego
9 Jana III Sobieskiego
10 Jana III Sobieskiego
Length: 11, dtype: object
TODO: Translate input data to English
"""
# Given
import pandas as pd
DATA = ['ul.Mieszka II',
'UL. Zygmunta III WaZY',
' bolesława chrobrego ',
'ul Jana III SobIESkiego',
'\tul. Jana trzeciego Sobieskiego',
'ulicaJana III Sobieskiego',
'UL. JA NA 3 SOBIES KIEGO',
'ULICA JANA III SOBIESKIEGO ',
'ULICA. JANA III SOBIeskieGO',
' Jana 3 Sobieskiego ',
'Jana III Sobi eskiego ']
def clean(text: str) -> str:
pass