Pandas高级教程之:处理text数据
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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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