pandas working with text data

In [13]: dollars = Series([’12’, ’-$10’, ’$10,000’])
# This does what you’d naively expect:
In [14]: dollars.str.replace(’$’, ’’)
Out[14]:
0 12
1 -10
2 10,000
dtype: object

In [22]: Series([’a1’, ’b2’, ’c3’]).str.extract(’(?P<letter>[ab])(?P<digit>\d)’)
Out[22]:
letter digit
0 a      1
1 b      2
2 NaN    NaN
and optional groups like
In [23]: Series([’a1’, ’b2’, ’3’]).str.extract(’(?P<letter>[ab])?(?P<digit>\d)’)
Out[23]:
letter digit
0 a      1
1 b      2
2 NaN    3

Method Summary

Method Description

cat(): Concatenate strings

split(): Split strings on delimiter

get(): Index into each element (retrieve i-th element)

join(): Join strings in each element of the Series with passed separator

contains(): Return boolean array if each string contains pattern/regex

replace(): Replace occurrences of pattern/regex with some other string

repeat(): Duplicate values (s.str.repeat(3) equivalent to x * 3)

pad(): Add whitespace to left, right, or both sides of strings

center(): Equivalent to pad(side=’both’)

wrap(): Split long strings into lines with length less than a given width

slice(): Slice each string in the Series

slice_replace():Replace slice in each string with passed value

count(): Count occurrences of pattern

startswith(): Equivalent to str.startswith(pat) for each element

endswith(): Equivalent to str.endswith(pat) for each element

findall(): Compute list of all occurrences of pattern/regex for each string

match(): Call re.match on each element, returning matched groups as list

extract(): Call re.match on each element, as match does, but return matched groups as strings for

convenience.

len(): Compute string lengths

strip(): Equivalent to str.strip

rstrip(): Equivalent to str.rstrip

lstrip(): Equivalent to str.lstrip

lower(): Equivalent to str.lower

upper(): Equivalent to str.upper

10.4.