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‘)
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
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)
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‘
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>
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 matchcontains 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
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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