Manipulation of strings

pandas offers the possibility to concisely apply Python’s string methods and regular expressions to whole arrays of data.

See also:

• Strings

• re

Vectorised string functions in pandas

Cleaning up a cluttered dataset for analysis often requires a lot of string manipulation. To make matters worse, a column containing strings sometimes has missing data:

import numpy as np
import pandas as pd


addresses = {
    "Veit": np.nan,
    "Veit Schiele": "veit.schiele@cusy.io",
    "cusy GmbH": "info@cusy.io",
}
addresses = pd.Series(addresses)

addresses

Veit                             NaN
Veit Schiele    veit.schiele@cusy.io
cusy GmbH               info@cusy.io
dtype: object

addresses.isna()

Veit             True
Veit Schiele    False
cusy GmbH       False
dtype: bool

You can apply string and regular expression methods to any value (by passing a lambda or other function) using data.map, but this fails for NA values. To deal with this, Series has array-oriented methods for string operations that skip and pass NA values. These are accessed via Series’ str attribute; for example, we could use str.contains to check whether each email address contains veit:

addresses.str.contains("veit")

Veit              NaN
Veit Schiele     True
cusy GmbH       False
dtype: object

Regular expressions can also be used, along with options such as IGNORECASE:

import re


pattern = r"([A-Z0-9._%+-]+)@([A-Z0-9.-]+)\.([A-Z]{2,4})"
matches = addresses.str.findall(pattern, flags=re.IGNORECASE).str[0]

matches

Veit                                 NaN
Veit Schiele    (veit.schiele, cusy, io)
cusy GmbH               (info, cusy, io)
dtype: object

There are several ways to retrieve a vectorised element. Either use str.get or the index of str:

matches.str.get(1)

Veit             NaN
Veit Schiele    cusy
cusy GmbH       cusy
dtype: object

Similarly, you can also cut strings with this syntax:

addresses.str[:5]

Veit              NaN
Veit Schiele    veit.
cusy GmbH       info@
dtype: object

The pandas.Series.str.extract method returns the captured groups of a regular expression as a DataFrame:

addresses.str.extract(pattern, flags=re.IGNORECASE)

	0	1	2
Veit	NaN	NaN	NaN
Veit Schiele	veit.schiele	cusy	io
cusy GmbH	info	cusy	io

More vectorised pandas string methods:

Method	Description
cat	concatenates strings element by element with optional delimiter
contains	returns a boolean array if each string contains a pattern/gex
count	counts occurrences of the pattern
extract	uses a regular expression with groups to extract one or more strings from a set of strings; the result is a DataFrame with one column per group
endswith	equivalent to x.endswith(pattern) for each element
startswith	equivalent to x.startswith(pattern) for each element
findall	computes list of all occurrences of pattern/regex for each string
get	index in each element (get i-th element)
isalnum	Equivalent to built-in str.alnum
isalpha	Equivalent to built-in str.isalpha
isdecimal	Equivalent to built-in str.isdecimal
isdigit	Equivalent to built-in str.isdigit
islower	Equivalent to built-in str.islower
isnumeric	Equivalent to built-in str.isnumeric
isupper	Equivalent to built-in str.isupper
join	joins strings in each element of the series with the passed separator character
len	calculates the length of each string
lower, upper	converts case; equivalent to x.lower() or x.upper() for each element
match	uses re.match with the passed regular expression for each element, returning True or False if matched.
extract	captures group elements (if any) by index from each string
pad	inserts spaces on the left, right or both sides of strings
centre	Equivalent to pad(side='both')
repeat	Duplicates values (for example s.str.repeat(3) equals x * 3 for each string)
replace	replaces pattern/rulex with another string
slice	splits each string in the series
split	splits strings using delimiters or regular expressions
strip	truncates spaces on both sides, including line breaks
rstrip	truncates spaces on the right side
lstrip	truncates spaces on the left side