{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"toc": true
},
"source": [
"<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
"<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#字符串函数\" data-toc-modified-id=\"字符串函数-1\"><span class=\"toc-item-num\">1 </span>字符串函数</a></span><ul class=\"toc-item\"><li><span><a href=\"#2个类似字符串元素的函数\" data-toc-modified-id=\"2个类似字符串元素的函数-1.1\"><span class=\"toc-item-num\">1.1 </span>2个类似字符串元素的函数</a></span><ul class=\"toc-item\"><li><span><a href=\"#np.char.add(a,-b)字符串的拼接,类似相对于字符串的+运算\" data-toc-modified-id=\"np.char.add(a,-b)字符串的拼接,类似相对于字符串的+运算-1.1.1\"><span class=\"toc-item-num\">1.1.1 </span>np.char.add(a, b)字符串的拼接,类似相对于字符串的+运算</a></span></li><li><span><a href=\"#np.char.multiply(a,-i)字符串的重复，-类似字符串的*运算\" data-toc-modified-id=\"np.char.multiply(a,-i)字符串的重复，-类似字符串的*运算-1.1.2\"><span class=\"toc-item-num\">1.1.2 </span>np.char.multiply(a, i)字符串的重复， 类似字符串的*运算</a></span></li></ul></li><li><span><a href=\"#3个类似字符串检索的函数\" data-toc-modified-id=\"3个类似字符串检索的函数-1.2\"><span class=\"toc-item-num\">1.2 </span>3个类似字符串检索的函数</a></span><ul class=\"toc-item\"><li><span><a href=\"#计数np.char.count(a,-object,-开始位置，-终止位置)\" data-toc-modified-id=\"计数np.char.count(a,-object,-开始位置，-终止位置)-1.2.1\"><span class=\"toc-item-num\">1.2.1 </span>计数np.char.count(a, object, 开始位置， 终止位置)</a></span></li><li><span><a href=\"#查找np.char.find(a,-object,-开始位置，-终止位置)\" data-toc-modified-id=\"查找np.char.find(a,-object,-开始位置，-终止位置)-1.2.2\"><span class=\"toc-item-num\">1.2.2 </span>查找np.char.find(a, object, 开始位置， 终止位置)</a></span></li><li><span><a href=\"#索引np.char.index(a,-object,-开始位置，-终止位置)\" data-toc-modified-id=\"索引np.char.index(a,-object,-开始位置，-终止位置)-1.2.3\"><span class=\"toc-item-num\">1.2.3 </span>索引np.char.index(a, object, 开始位置， 终止位置)</a></span></li></ul></li><li><span><a href=\"#三个分割的方法\" data-toc-modified-id=\"三个分割的方法-1.3\"><span class=\"toc-item-num\">1.3 </span>三个分割的方法</a></span><ul class=\"toc-item\"><li><span><a href=\"#np.char.split(a,-分割符，-分割次数)\" data-toc-modified-id=\"np.char.split(a,-分割符，-分割次数)-1.3.1\"><span class=\"toc-item-num\">1.3.1 </span>np.char.split(a, 分割符， 分割次数)</a></span></li><li><span><a href=\"#np.char.splitlines(a,-keepends=None)\" data-toc-modified-id=\"np.char.splitlines(a,-keepends=None)-1.3.2\"><span class=\"toc-item-num\">1.3.2 </span>np.char.splitlines(a, keepends=None)</a></span></li><li><span><a href=\"#np.char.partition(a,-分隔符)\" data-toc-modified-id=\"np.char.partition(a,-分隔符)-1.3.3\"><span class=\"toc-item-num\">1.3.3 </span>np.char.partition(a, 分隔符)</a></span></li></ul></li><li><span><a href=\"#一个合并的方法\" data-toc-modified-id=\"一个合并的方法-1.4\"><span class=\"toc-item-num\">1.4 </span>一个合并的方法</a></span><ul class=\"toc-item\"><li><span><a href=\"#np.char.join(a,-b)\" data-toc-modified-id=\"np.char.join(a,-b)-1.4.1\"><span class=\"toc-item-num\">1.4.1 </span>np.char.join(a, b)</a></span></li></ul></li><li><span><a href=\"#1个替换的方法\" data-toc-modified-id=\"1个替换的方法-1.5\"><span class=\"toc-item-num\">1.5 </span>1个替换的方法</a></span><ul class=\"toc-item\"><li><span><a href=\"#np.char.replace(a,-被替换部分，-新的部分,-替换次数)\" data-toc-modified-id=\"np.char.replace(a,-被替换部分，-新的部分,-替换次数)-1.5.1\"><span class=\"toc-item-num\">1.5.1 </span>np.char.replace(a, 被替换部分， 新的部分, 替换次数)</a></span></li></ul></li><li><span><a href=\"#1个中心化的方法\" data-toc-modified-id=\"1个中心化的方法-1.6\"><span class=\"toc-item-num\">1.6 </span>1个中心化的方法</a></span><ul class=\"toc-item\"><li><span><a href=\"#np.char.center()\" data-toc-modified-id=\"np.char.center()-1.6.1\"><span class=\"toc-item-num\">1.6.1 </span>np.char.center()</a></span></li></ul></li><li><span><a href=\"#2种字符串大小写转换\" data-toc-modified-id=\"2种字符串大小写转换-1.7\"><span class=\"toc-item-num\">1.7 </span>2种字符串大小写转换</a></span><ul class=\"toc-item\"><li><span><a href=\"#np.char.lower(a)\" data-toc-modified-id=\"np.char.lower(a)-1.7.1\"><span class=\"toc-item-num\">1.7.1 </span>np.char.lower(a)</a></span></li><li><span><a href=\"#np.char.upper(a)\" data-toc-modified-id=\"np.char.upper(a)-1.7.2\"><span class=\"toc-item-num\">1.7.2 </span>np.char.upper(a)</a></span></li></ul></li><li><span><a href=\"#1中去除字符串两端的方法\" data-toc-modified-id=\"1中去除字符串两端的方法-1.8\"><span class=\"toc-item-num\">1.8 </span>1中去除字符串两端的方法</a></span></li></ul></li></ul></div>"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"#全部行都能输出\n",
"from IPython.core.interactiveshell import InteractiveShell\n",
"InteractiveShell.ast_node_interactivity = \"all\""
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 字符串函数"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"基本和python内置的字符串方法一样"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2个类似字符串元素的函数"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### np.char.add(a, b)字符串的拼接,类似相对于字符串的+运算\n",
"a b 是两个ndarray对象， 这两个对象的元素都必须全是字符串，其作用是把对应位置的字符串进行加法运算。 \n",
"要求是a b的形状相同， 或者满足广播规则！"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['123', '456', '789'], dtype='<U3')"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"array(['asd', 'fgh', 'jkl'], dtype='<U3')"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a = np.array(['123', '456', '789'])\n",
"b = np.array(['asd', 'fgh', 'jkl'])\n",
"a\n",
"b"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['123asd', '456fgh', '789jkl'], dtype='<U6')"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.char.add(a, b)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"但是由于如果只含有bool型， 数值型， 字符串作为元素的时候， 会自动全部转化为字符串，所以也是可以的。"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['True', '123', '12.45', '999'], dtype='<U32')"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"array(['A', 'B', 'C', 'D'], dtype='<U1')"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a = np.array([True, 123, 12.45, '999']) # 会全部转化为字符串\n",
"b = np.array(['A', 'B', 'C', 'D'])\n",
"a\n",
"b"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['TrueA', '123B', '12.45C', '999D'], dtype='<U33')"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.char.add(a, b)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### np.char.multiply(a, i)字符串的重复， 类似字符串的*运算\n",
"a是ndarray对象， 并且，a中的元素全部是字符串，i是一个整数。"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['123', '456', '789', '12'], dtype='<U3')"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a = np.array(['123', '456', '789', 12])\n",
"a"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['123123123123123123', '456456456456456456', '789789789789789789',\n",
" '121212121212'], dtype='<U18')"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.char.multiply(a, 6)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3个类似字符串检索的函数"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 计数np.char.count(a, object, 开始位置， 终止位置)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['112,1', 'asda1', 'asd', '232'], dtype='<U5')"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a = np.array(['112,1', 'asda1', 'asd', 232])\n",
"a"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([3, 1, 0, 0])"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.char.count(a, '1')"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([2, 1, 0, 0])"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.char.count(a, '1', 1) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 查找np.char.find(a, object, 开始位置， 终止位置)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['112,1', 'asda1', 'asd', '232'], dtype='<U5')"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a = np.array(['112,1', 'asda1', 'asd', 232])\n",
"a"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0, 4, -1, -1])"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.char.find(a, '1') # 返回第一次出出现的索引 ，如果找不到返回-1"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 4, 4, -1, -1])"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.char.find(a, '1', 2) # 在指定范围内查找， 如果找到就返回索引， 找不到返回-1"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 索引np.char.index(a, object, 开始位置， 终止位置)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['112,a1', '1asda1', '11asd', 'a232'], dtype='<U6')"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a = np.array(['112,a1', '1asda1', '11asd', 'a232'])\n",
"a"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([4, 1, 2, 0])"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.char.index(a, 'a') # 返回第一次出现的索引"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"ename": "ValueError",
"evalue": "substring not found",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-19-6e54c96fdcbf>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mchar\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'1'\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# 找不到就报错\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;32mD:\\Anaconda3\\lib\\site-packages\\numpy\\core\\defchararray.py\u001b[0m in \u001b[0;36mindex\u001b[1;34m(a, sub, start, end)\u001b[0m\n\u001b[0;32m 752\u001b[0m \"\"\"\n\u001b[0;32m 753\u001b[0m return _vec_string(\n\u001b[1;32m--> 754\u001b[1;33m a, integer, 'index', [sub, start] + _clean_args(end))\n\u001b[0m\u001b[0;32m 755\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 756\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mValueError\u001b[0m: substring not found"
]
}
],
"source": [
"np.char.index(a, '1') # 找不到就报错"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 三个分割的方法"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### np.char.split(a, 分割符， 分割次数)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['112,a1', '1asda1', '11asd', '1a232'], dtype='<U6')"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a = np.array(['112,a1', '1asda1', '11asd', '1a232'])\n",
"a"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([list(['112,', '1']), list(['1', 'sd', '1']), list(['11', 'sd']),\n",
" list(['1', '232'])], dtype=object)"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.char.split(a, 'a') # 第三个参数不写默认全部分割， 返回的结果是以列表的形式存储"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([list(['112,', '1']), list(['1', 'sda1']), list(['11', 'sd']),\n",
" list(['1', '232'])], dtype=object)"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.char.split(a, 'a', 1) # 只分割一次"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### np.char.splitlines(a, keepends=None)\n",
"分隔符只能是\\r \\n \\r\\n"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['112\\n,a1', '1as\\tda1', '11a\\rsd', '1a2\\r\\n3 2'], dtype='<U8')"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a = np.array(['112\\n,a1', '1as\\tda1', '11a\\rsd', '1a2\\r\\n3 2'])\n",
"a"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([list(['112', ',a1']), list(['1as\\tda1']), list(['11a', 'sd']),\n",
" list(['1a2', '3 2'])], dtype=object)"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.char.splitlines(a, keepends=None)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([list(['112\\n', ',a1']), list(['1as\\tda1']), list(['11a\\r', 'sd']),\n",
" list(['1a2\\r\\n', '3 2'])], dtype=object)"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.char.splitlines(a, keepends=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### np.char.partition(a, 分隔符)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['122aa12', '232as', '1a1asas'], dtype='<U7')"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a = np.array(['122aa12', '232as', '1a1asas'])\n",
"a"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([['122', 'a', 'a12'],\n",
" ['232', 'a', 's'],\n",
" ['1', 'a', '1asas']], dtype='<U5')"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.char.partition(a, 'a') # 返回的是一个二维数组， 保留分隔符"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 一个合并的方法"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### np.char.join(a, b)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"相当于对应位置上的字符串进行join方法"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['+', '-'], dtype='<U1')"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a = np.array(['+', '-'])\n",
"a"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['1234', '5678'], dtype='<U4')"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"b = np.array(['1234', '5678'])\n",
"b"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['1+2+3+4', '5-6-7-8'], dtype='<U7')"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.char.join(a, b)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1个替换的方法"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### np.char.replace(a, 被替换部分， 新的部分, 替换次数)"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['asdasd', 'asdqweasd', 'asddsfasdaaa'], dtype='<U12')"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a = np.array(['asdasd', 'asdqweasd', 'asddsfasdaaa'])\n",
"a"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['AsdAsd', 'AsdqweAsd', 'AsddsfAsdAAA'], dtype='<U12')"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.char.replace(a, 'a', 'A') # 默认全部替换"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['Asdasd', 'Asdqweasd', 'Asddsfasdaaa'], dtype='<U12')"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.char.replace(a, 'a', 'A', 1) # 只替换一次"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1个中心化的方法 \n",
"### np.char.center()\n",
"将每一个字符串放中间，其余用特定的符号补齐"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['a', 'b', 'c', 'python'], dtype='<U6')"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a = np.array(['a', 'b', 'c', 'python'])\n",
"a"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['*************************************************a**************************************************',\n",
" '*************************************************b**************************************************',\n",
" '*************************************************c**************************************************',\n",
" '***********************************************python***********************************************'],\n",
" dtype='<U100')"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.char.center(a, 100, '*')"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([' a ',\n",
" ' b ',\n",
" ' c ',\n",
" ' python '],\n",
" dtype='<U100')"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.char.center(a, 100) # 不指定符号就默认用空格补齐"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2种字符串大小写转换"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### np.char.lower(a) \n",
"将a中的每一个大写字符转化为小写"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['asASd', 'fSDAgh', 'jSADkl'], dtype='<U6')"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a = np.array(['asASd', 'fSDAgh', 'jSADkl'])\n",
"a"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['asasd', 'fsdagh', 'jsadkl'], dtype='<U6')"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.char.lower(a)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### np.char.upper(a) \n",
"将a中的每一个每一个小写字符转化为大写"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['asdaASDAS', 'asdaSADAASDads', 'asd'], dtype='<U14')"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a = np.array(['asdaASDAS', 'asdaSADAASDads', 'asd'])\n",
"a"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['ASDAASDAS', 'ASDASADAASDADS', 'ASD'], dtype='<U14')"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.char.upper(a)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1中去除字符串两端的方法"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['1232311', 'qweqwe1', 'qweq123'], dtype='<U7')"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a = np.array(['1232311', 'qweqwe1', 'qweq123'])\n",
"a"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['2323', 'qweqwe', 'qweq123'], dtype='<U7')"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.char.strip(a, '1') "
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([' as\\t', '\\nasxa '], dtype='<U7')"
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a = np.array([' as\\t', '\\nasxa '])\n",
"a"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['as', 'asxa'], dtype='<U7')"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.char.strip(a) # 第二个参数不填默认去除两端的空格 \\t \\n等特殊字符"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['1232311', 'qweqwe1', 'qweq123'], dtype='<U7')"
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a = np.array(['1232311', 'qweqwe1', 'qweq123'])\n",
"a"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.4"
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"toc": {
"base_numbering": 1,
"nav_menu": {},
"number_sections": true,
"sideBar": true,
"skip_h1_title": false,
"title_cell": "Table of Contents",
"title_sidebar": "Contents",
"toc_cell": true,
"toc_position": {
"height": "calc(100% - 180px)",
"left": "10px",
"top": "150px",
"width": "349.167px"
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