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Pandas高级教程之:处理text数据

简介

在1.0之前,只有一种形式来存储text数据,那就是object。在1.0之后,添加了一个新的数据类型叫做StringDtype 。今天将会给大家讲解Pandas中text中的那些事。

创建text的DF

先看下常见的使用text来构建DF的例子:

In [1]: pd.Series(['a', 'b', 'c'])
Out[1]: 
0    a
1    b
2    c
dtype: object

如果要使用新的StringDtype,可以这样:

In [2]: pd.Series(['a', 'b', 'c'], dtype="string")
Out[2]: 
0    a
1    b
2    c
dtype: string

In [3]: pd.Series(['a', 'b', 'c'], dtype=pd.StringDtype())
Out[3]: 
0    a
1    b
2    c
dtype: string

或者使用astype进行转换:

In [4]: s = pd.Series(['a', 'b', 'c'])

In [5]: s
Out[5]: 
0    a
1    b
2    c
dtype: object

In [6]: s.astype("string")
Out[6]: 
0    a
1    b
2    c
dtype: string

String 的方法

String可以转换成大写,小写和统计它的长度:

In [24]: s = pd.Series(['A', 'B', 'C', 'Aaba', 'Baca', np.nan, 'CABA', 'dog', 'cat'],
   ....:               dtype="string")
   ....: 

In [25]: s.str.lower()
Out[25]: 
0       a
1       b
2       c
3    aaba
4    baca
5    <NA>
6    caba
7     dog
8     cat
dtype: string

In [26]: s.str.upper()
Out[26]: 
0       A
1       B
2       C
3    AABA
4    BACA
5    <NA>
6    CABA
7     DOG
8     CAT
dtype: string

In [27]: s.str.len()
Out[27]: 
0       1
1       1
2       1
3       4
4       4
5    <NA>
6       4
7       3
8       3
dtype: Int64

还可以进行trip操作:

In [28]: idx = pd.Index([' jack', 'jill ', ' jesse ', 'frank'])

In [29]: idx.str.strip()
Out[29]: Index(['jack', 'jill', 'jesse', 'frank'], dtype='object')

In [30]: idx.str.lstrip()
Out[30]: Index(['jack', 'jill ', 'jesse ', 'frank'], dtype='object')

In [31]: idx.str.rstrip()
Out[31]: Index([' jack', 'jill', ' jesse', 'frank'], dtype='object')

columns的String操作

因为columns是String表示的,所以可以按照普通的String方式来操作columns:

In [34]: df.columns.str.strip()
Out[34]: Index(['Column A', 'Column B'], dtype='object')

In [35]: df.columns.str.lower()
Out[35]: Index([' column a ', ' column b '], dtype='object')
In [32]: df = pd.DataFrame(np.random.randn(3, 2),
   ....:                   columns=[' Column A ', ' Column B '], index=range(3))
   ....: 

In [33]: df
Out[33]: 
    Column A    Column B 
0    0.469112   -0.282863
1   -1.509059   -1.135632
2    1.212112   -0.173215

分割和替换String

Split可以将一个String切分成一个数组。

In [38]: s2 = pd.Series(['a_b_c', 'c_d_e', np.nan, 'f_g_h'], dtype="string")

In [39]: s2.str.split('_')
Out[39]: 
0    [a, b, c]
1    [c, d, e]
2         <NA>
3    [f, g, h]
dtype: object

要想访问split之后数组中的字符,可以这样:

In [40]: s2.str.split('_').str.get(1)
Out[40]: 
0       b
1       d
2    <NA>
3       g
dtype: object

In [41]: s2.str.split('_').str[1]
Out[41]: 
0       b
1       d
2    <NA>
3       g
dtype: object

使用 expand=True 可以 将split过后的数组 扩展成为多列:

In [42]: s2.str.split('_', expand=True)
Out[42]: 
      0     1     2
0     a     b     c
1     c     d     e
2  <NA>  <NA>  <NA>
3     f     g     h

可以指定分割列的个数:

In [43]: s2.str.split('_', expand=True, n=1)
Out[43]: 
      0     1
0     a   b_c
1     c   d_e
2  <NA>  <NA>
3     f   g_h

replace用来进行字符的替换,在替换过程中还可以使用正则表达式:

s3.str.replace('^.a|dog', 'XX-XX ', case=False)

String的连接

使用cat 可以连接 String:

In [64]: s = pd.Series(['a', 'b', 'c', 'd'], dtype="string")

In [65]: s.str.cat(sep=',')
Out[65]: 'a,b,c,d'

使用 .str来index

pd.Series会返回一个Series,如果Series中是字符串的话,可通过index来访问列的字符,举个例子:

In [99]: s = pd.Series(['A', 'B', 'C', 'Aaba', 'Baca', np.nan,
   ....:                'CABA', 'dog', 'cat'],
   ....:               dtype="string")
   ....: 

In [100]: s.str[0]
Out[100]: 
0       A
1       B
2       C
3       A
4       B
5    <NA>
6       C
7       d
8       c
dtype: string

In [101]: s.str[1]
Out[101]: 
0    <NA>
1    <NA>
2    <NA>
3       a
4       a
5    <NA>
6       A
7       o
8       a
dtype: string

extract

Extract用来从String中解压数据,它接收一个 expand参数,在0.23版本之前, 这个参数默认是False。如果是false,extract会返回Series,index或者DF 。如果expand=true,那么会返回DF。0.23版本之后,默认是true。

extract通常是和正则表达式一起使用的。

In [102]: pd.Series(['a1', 'b2', 'c3'],
   .....:           dtype="string").str.extract(r'([ab])(\d)', expand=False)
   .....: 
Out[102]: 
      0     1
0     a     1
1     b     2
2  <NA>  <NA>

上面的例子将Series中的每一字符串都按照正则表达式来进行分解。前面一部分是字符,后面一部分是数字。

注意,只有正则表达式中group的数据才会被extract .

下面的就只会extract数字:

In [106]: pd.Series(['a1', 'b2', 'c3'],
   .....:           dtype="string").str.extract(r'[ab](\d)', expand=False)
   .....: 
Out[106]: 
0       1
1       2
2    <NA>
dtype: string

还可以指定列的名字如下:

In [103]: pd.Series(['a1', 'b2', 'c3'],
   .....:           dtype="string").str.extract(r'(?P<letter>[ab])(?P<digit>\d)',
   .....:                                       expand=False)
   .....: 
Out[103]: 
  letter digit
0      a     1
1      b     2
2   <NA>  <NA>

extractall

和extract相似的还有extractall,不同的是extract只会匹配第一次,而extractall会做所有的匹配,举个例子:

In [112]: s = pd.Series(["a1a2", "b1", "c1"], index=["A", "B", "C"],
   .....:               dtype="string")
   .....: 

In [113]: s
Out[113]: 
A    a1a2
B      b1
C      c1
dtype: string

In [114]: two_groups = '(?P<letter>[a-z])(?P<digit>[0-9])'

In [115]: s.str.extract(two_groups, expand=True)
Out[115]: 
  letter digit
A      a     1
B      b     1
C      c     1

extract匹配到a1之后就不会继续了。

In [116]: s.str.extractall(two_groups)
Out[116]: 
        letter digit
  match             
A 0          a     1
  1          a     2
B 0          b     1
C 0          c     1

extractall匹配了a1之后还会匹配a2。

contains 和 match

contains 和 match用来测试DF中是否含有特定的数据:

In [127]: pd.Series(['1', '2', '3a', '3b', '03c', '4dx'],
   .....:           dtype="string").str.contains(pattern)
   .....: 
Out[127]: 
0    False
1    False
2     True
3     True
4     True
5     True
dtype: boolean
In [128]: pd.Series(['1', '2', '3a', '3b', '03c', '4dx'],
   .....:           dtype="string").str.match(pattern)
   .....: 
Out[128]: 
0    False
1    False
2     True
3     True
4    False
5     True
dtype: boolean
In [129]: pd.Series(['1', '2', '3a', '3b', '03c', '4dx'],
   .....:           dtype="string").str.fullmatch(pattern)
   .....: 
Out[129]: 
0    False
1    False
2     True
3     True
4    False
5    False
dtype: boolean

String方法总结

最后总结一下String的方法:

Method Description
cat() Concatenate strings
split() Split strings on delimiter
rsplit() Split strings on delimiter working from the end of the string
get() Index into each element (retrieve i-th element)
join() Join strings in each element of the Series with passed separator
get_dummies() Split strings on the delimiter returning DataFrame of dummy variables
contains() Return boolean array if each string contains pattern/regex
replace() Replace occurrences of pattern/regex/string with some other string or the return value of a callable given the occurrence
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 str.center
ljust() Equivalent to str.ljust
rjust() Equivalent to str.rjust
zfill() Equivalent to str.zfill
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.search on each element, returning DataFrame with one row for each element and one column for each regex capture group
extractall() Call re.findall on each element, returning DataFrame with one row for each match and one column for each regex capture group
len() Compute string lengths
strip() Equivalent to str.strip
rstrip() Equivalent to str.rstrip
lstrip() Equivalent to str.lstrip
partition() Equivalent to str.partition
rpartition() Equivalent to str.rpartition
lower() Equivalent to str.lower
casefold() Equivalent to str.casefold
upper() Equivalent to str.upper
find() Equivalent to str.find
rfind() Equivalent to str.rfind
index() Equivalent to str.index
rindex() Equivalent to str.rindex
capitalize() Equivalent to str.capitalize
swapcase() Equivalent to str.swapcase
normalize() Return Unicode normal form. Equivalent to unicodedata.normalize
translate() Equivalent to str.translate
isalnum() Equivalent to str.isalnum
isalpha() Equivalent to str.isalpha
isdigit() Equivalent to str.isdigit
isspace() Equivalent to str.isspace
islower() Equivalent to str.islower
isupper() Equivalent to str.isupper
istitle() Equivalent to str.istitle
isnumeric() Equivalent to str.isnumeric
isdecimal() Equivalent to str.isdecimal

本文已收录于 http://www.flydean.com/06-python-pandas-text/

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posted @ 2021-06-23 09:27  flydean  阅读(214)  评论(0编辑  收藏  举报