Pandas序列.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\n系列(Series)是能够保存任何类型的数据(整数，字符串，浮点数，Python对象等)的一维标记数组。轴标签统称为索引\\nPandas系列可以使用以下构造函数创建\\npandas.Series( data, index, dtype, copy)\\ndata 数据采取各种形式，如：ndarray，list，constants\\nindex 索引值必须是唯一的和散列的，与数据的长度相同。 默认np.arange(n)如果没有索引被传递\\n'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "系列(Series)是能够保存任何类型的数据(整数，字符串，浮点数，Python对象等)的一维标记数组。轴标签统称为索引\n",
    "Pandas系列可以使用以下构造函数创建\n",
    "    pandas.Series( data, index, dtype, copy)\n",
    "    data 数据采取各种形式，如：ndarray，list，constants\n",
    "    index 索引值必须是唯一的和散列的，与数据的长度相同。 默认np.arange(n)如果没有索引被传递\n",
    "    dtype dtype用于数据类型。如果没有，将推断数据类型\n",
    "    copy 复制数据，默认为false\n",
    "可以使用各种输入创建一个序列，比如:数组，字典，标量值或常数\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Series([], dtype: float64)\n"
     ]
    }
   ],
   "source": [
    "# 创建一个空的序列\n",
    "import pandas as pd\n",
    "s = pd.Series()\n",
    "print(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    a\n",
      "1    b\n",
      "2    c\n",
      "3    d\n",
      "dtype: object\n",
      "----------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
      "100    a\n",
      "101    b\n",
      "102    c\n",
      "103    d\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "# ndarray()创建一个序列\n",
    "# 如果数据是ndarray，则传递的索引的数值大小必须具有与数组相同的长度。 如果没有传递索引值，那么默认的索引将是范围(n)，其中n是数组长度，即[0,1,2,3…. range(len(array))-1] - 1]\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "data = np.array(['a','b','c','d'])\n",
    "s = pd.Series(data)\n",
    "print(s)\n",
    "print('----------------'*10)\n",
    "# 自定义索引值。\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "data = np.array(['a','b','c','d'])\n",
    "s = pd.Series(data,index=[100,101,102,103])\n",
    "print(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a    0.0\n",
      "b    1.0\n",
      "c    2.0\n",
      "dtype: float64\n",
      "------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
      "b    1.0\n",
      "c    2.0\n",
      "d    NaN\n",
      "a    0.0\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# 使用字典创建一个系列\n",
    "# 字典(dict)可以作为输入传递，如果没有指定索引，则按排序顺序取得字典键以构造索引。 如果传递了索引，索引中与标签对应的数据中的值将被拉出。\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "data = {'a' : 0., 'b' : 1., 'c' : 2.}\n",
    "s = pd.Series(data)\n",
    "print(s)\n",
    "# 字典键用于构建索引\n",
    "print('------------'*20)\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "data = {'a' : 0., 'b' : 1., 'c' : 2.}\n",
    "s = pd.Series(data,index=['b','c','d','a'])\n",
    "print(s)\n",
    "# 索引顺序保持不变，缺少的元素使用NaN(不是数字)填充"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    5\n",
      "1    5\n",
      "2    5\n",
      "3    5\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# 使用标量创建一个序列\n",
    "# 如果数据是标量值，则必须提供索引。将重复该值以匹配索引的长度。\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "s = pd.Series(5, index=[0, 1, 2, 3])\n",
    "print(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "检索第一个元素\n",
      "1\n",
      "检索系列中的前三个元素\n",
      "a    1\n",
      "b    2\n",
      "c    3\n",
      "dtype: int64\n",
      "检索最后三个元素\n",
      "c    3\n",
      "d    4\n",
      "e    5\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# 从具有位置的系列中访问数据\n",
    "# 系列中的数据可以使用类似于访问ndarray中的数据来访问\n",
    "import pandas as pd\n",
    "s = pd.Series([1,2,3,4,5],index = ['a','b','c','d','e'])\n",
    "# 检索第一个元素\n",
    "print('检索第一个元素')\n",
    "print (s[0])\n",
    "print('检索系列中的前三个元素')\n",
    "# 检索系列中的前三个元素\n",
    "print (s[:3])\n",
    "# 检索最后三个元素\n",
    "print(\"检索最后三个元素\")\n",
    "print(s[-3:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "使用索引标签值检索单个元素\n",
      "1\n",
      "使用索引标签值列表检索多个元素\n",
      "a    1\n",
      "c    3\n",
      "d    4\n",
      "dtype: int64\n"
     ]
    },
    {
     "ename": "KeyError",
     "evalue": "'f'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32md:\\programs\\python\\python36\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_value\u001b[1;34m(self, series, key)\u001b[0m\n\u001b[0;32m   3108\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3109\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mlibindex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_value_box\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3110\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.get_value_box\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.get_value_box\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: 'str' object cannot be interpreted as an integer",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-21-c900001bc2bf>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      9\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'a'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'c'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'd'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     10\u001b[0m \u001b[1;31m# 如果data中不包含的标签，则会出现异常\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 11\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'f'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32md:\\programs\\python\\python36\\lib\\site-packages\\pandas\\core\\series.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m    764\u001b[0m         \u001b[0mkey\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_apply_if_callable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    765\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 766\u001b[1;33m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_value\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    767\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    768\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mis_scalar\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\programs\\python\\python36\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_value\u001b[1;34m(self, series, key)\u001b[0m\n\u001b[0;32m   3115\u001b[0m                     \u001b[1;32mraise\u001b[0m \u001b[0mInvalidIndexError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3116\u001b[0m                 \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3117\u001b[1;33m                     \u001b[1;32mraise\u001b[0m \u001b[0me1\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3118\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[1;33m:\u001b[0m  \u001b[1;31m# pragma: no cover\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3119\u001b[0m                 \u001b[1;32mraise\u001b[0m \u001b[0me1\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\programs\\python\\python36\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_value\u001b[1;34m(self, series, key)\u001b[0m\n\u001b[0;32m   3101\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3102\u001b[0m             return self._engine.get_value(s, k,\n\u001b[1;32m-> 3103\u001b[1;33m                                           tz=getattr(series.dtype, 'tz', None))\n\u001b[0m\u001b[0;32m   3104\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3105\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m0\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minferred_type\u001b[0m \u001b[1;32min\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m'integer'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'boolean'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_value\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_value\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'f'"
     ]
    }
   ],
   "source": [
    "# 使用标签检索数据(索引)\n",
    "# 一个系列就像一个固定大小的字典，可以通过索引标签获取和设置值。\n",
    "import pandas as pd\n",
    "s = pd.Series([1,2,3,4,5],index = ['a','b','c','d','e'])\n",
    "# 使用索引标签值检索单个元素\n",
    "print('使用索引标签值检索单个元素')\n",
    "print (s['a'])\n",
    "print('使用索引标签值列表检索多个元素')\n",
    "print(s[['a','c','d']])\n",
    "# 如果data中不包含的标签，则会出现异常\n",
    "print(s['f'])"
   ]
  },
  {
   "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}