简介
在1.0之前，只有一种模式来存储text数据，那就是object。在1.0之后，增加了一个新的数据类型叫做StringDtype 。明天将会给大家解说Pandas中text中的那些事。
创立text的DF
先看下常见的应用text来构建DF的例子：
In [1]: pd.Series(['a', 'b', 'c'])Out[1]: 0 a1 b2 cdtype: object如果要应用新的StringDtype，能够这样：
In [2]: pd.Series(['a', 'b', 'c'], dtype="string")Out[2]: 0 a1 b2 cdtype: stringIn [3]: pd.Series(['a', 'b', 'c'], dtype=pd.StringDtype())Out[3]: 0 a1 b2 cdtype: string或者应用astype进行转换：
In [4]: s = pd.Series(['a', 'b', 'c'])In [5]: sOut[5]: 0 a1 b2 cdtype: objectIn [6]: s.astype("string")Out[6]: 0 a1 b2 cdtype: stringString 的办法
String能够转换成大写，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 a1 b2 c3 aaba4 baca5 <NA>6 caba7 dog8 catdtype: stringIn [26]: s.str.upper()Out[26]: 0 A1 B2 C3 AABA4 BACA5 <NA>6 CABA7 DOG8 CATdtype: stringIn [27]: s.str.len()Out[27]: 0 11 12 13 44 45 <NA>6 47 38 3dtype: Int64还能够进行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示意的，所以能够依照一般的String形式来操作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]: dfOut[33]: Column A Column B 0 0.469112 -0.2828631 -1.509059 -1.1356322 1.212112 -0.173215宰割和替换String
Split能够将一个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要想拜访split之后数组中的字符，能够这样：
In [40]: s2.str.split('_').str.get(1)Out[40]: 0 b1 d2 <NA>3 gdtype: objectIn [41]: s2.str.split('_').str[1]Out[41]: 0 b1 d2 <NA>3 gdtype: object应用 expand=True 能够 将split过后的数组 扩大成为多列：
In [42]: s2.str.split('_', expand=True)Out[42]: 0 1 20 a b c1 c d e2 <NA> <NA> <NA>3 f g h能够指定宰割列的个数：
In [43]: s2.str.split('_', expand=True, n=1)Out[43]: 0 10 a b_c1 c d_e2 <NA> <NA>3 f g_hreplace用来进行字符的替换，在替换过程中还能够应用正则表达式：
s3.str.replace('^.a|dog', 'XX-XX ', case=False)String的连贯
应用cat 能够连贯 String：
In [64]: s = pd.Series(['a', 'b', 'c', 'd'], dtype="string")In [65]: s.str.cat(sep=',')Out[65]: 'a,b,c,d'应用 .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来拜访列的字符，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 A1 B2 C3 A4 B5 <NA>6 C7 d8 cdtype: stringIn [101]: s.str[1]Out[101]: 0 <NA>1 <NA>2 <NA>3 a4 a5 <NA>6 A7 o8 adtype: stringextract
Extract用来从String中解压数据，它接管一个 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应用的。
In [102]: pd.Series(['a1', 'b2', 'c3'], .....: dtype="string").str.extract(r'([ab])(\d)', expand=False) .....: Out[102]: 0 10 a 11 b 22 <NA> <NA>下面的例子将Series中的每一字符串都依照正则表达式来进行合成。后面一部分是字符，前面一部分是数字。
留神，只有正则表达式中group的数据才会被extract .
上面的就只会extract数字：
In [106]: pd.Series(['a1', 'b2', 'c3'], .....: dtype="string").str.extract(r'[ab](\d)', expand=False) .....: Out[106]: 0 11 22 <NA>dtype: string还能够指定列的名字如下：
In [103]: pd.Series(['a1', 'b2', 'c3'], .....: dtype="string").str.extract(r'(?P<letter>[ab])(?P<digit>\d)', .....: expand=False) .....: Out[103]: letter digit0 a 11 b 22 <NA> <NA>extractall
和extract类似的还有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]: sOut[113]: A a1a2B b1C c1dtype: stringIn [114]: two_groups = '(?P<letter>[a-z])(?P<digit>[0-9])'In [115]: s.str.extract(two_groups, expand=True)Out[115]: letter digitA a 1B b 1C c 1extract匹配到a1之后就不会持续了。
In [116]: s.str.extractall(two_groups)Out[116]: letter digit match A 0 a 1 1 a 2B 0 b 1C 0 c 1extractall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 False1 False2 True3 True4 True5 Truedtype: booleanIn [128]: pd.Series(['1', '2', '3a', '3b', '03c', '4dx'], .....: dtype="string").str.match(pattern) .....: Out[128]: 0 False1 False2 True3 True4 False5 Truedtype: booleanIn [129]: pd.Series(['1', '2', '3a', '3b', '03c', '4dx'], .....: dtype="string").str.fullmatch(pattern) .....: Out[129]: 0 False1 False2 True3 True4 False5 Falsedtype: booleanString办法总结
最初总结一下String的办法：
| 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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