102pandas使用.ipynb

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    "# Pandas \n",
    "\n",
    "- 是 Python 语言的一个扩展程序库， 主要应用于数据分析。\n",
    "\n",
    "- 是一个开放源码、BSD 许可的库，提供高性能、易于使用的数据结构和数据分析工具。\n",
    "\n",
    "- 名字衍生自术语 \"panel data\"（面板数据）和 \"Python data analysis\"（Python 数据分析）。\n",
    "\n",
    "- 一个强大的分析结构化数据的工具集，基础是 Numpy（提供高性能的矩阵运算）。\n",
    "\n",
    "- 可以从各种文件格式比如 CSV、JSON、SQL、Microsoft Excel 导入数据。\n",
    "\n",
    "- 可以对各种数据进行运算操作，比如归并、再成形、选择，还有数据清洗和数据加工特征。\n",
    "\n",
    "- 广泛应用在学术、金融、统计学等各个数据分析领域"
   ]
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  {
   "cell_type": "code",
   "execution_count": null,
   "id": "82d9e6b6",
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   "source": [
    "import pandas as pd\n",
    "\n",
    "data = [['jupyter中文集成版',1],['python',2],['jupyter',3]]\n",
    "\n",
    "df = pd.DataFrame(data,columns=['name','order'])\n",
    "\n",
    "print(df)"
   ]
  },
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   "source": [
    "## Pandas 应用\n",
    "\n",
    "Pandas 的主要数据结构是 Series （一维数据）与 DataFrame（二维数据），这两种数据结构足以处理金融、统计、社会科学、工程等领域里的大\n",
    "\n",
    "多数典型用例。\n",
    "\n",
    "## 数据结构\n",
    "\n",
    "Series 是一种类似于一维数组的对象，它由一组数据（各种Numpy数据类型）以及一组与之相关的数据标签（即索引）组成。\n",
    "\n",
    "DataFrame 是一个表格型的数据结构，它含有一组有序的列，每列可以是不同的值类型（数值、字符串、布尔型值）。DataFrame 既有行索引也有\n",
    "\n",
    "列索引，它可以被看做由 Series 组成的字典（共同用一个索引）。\n"
   ]
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   "source": [
    "### Pandas 数据结构 - Series\n",
    "\n",
    "Pandas Series 类似表格中的一个列（column），类似于一维数组，可以保存任何数据类型。\n",
    "\n",
    "Series 由索引（index）和列组成，函数如下：\n",
    "\n",
    "***pandas.Series( data, index, dtype, name, copy)***\n",
    "\n",
    "#### 参数说明：\n",
    "\n",
    "- data：一组数据(ndarray 类型)。\n",
    "\n",
    "- index：数据索引标签，如果不指定，默认从 0 开始。\n",
    "\n",
    "- dtype：数据类型，默认会自己判断。\n",
    "\n",
    "- name：设置名称。\n",
    "\n",
    "- copy：拷贝数据，默认为 False。\n"
   ]
  },
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   "id": "f12b1518",
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   "source": [
    "## Series  与 DataFrame 关系图\n",
    "\n",
    "![avatar][base64str]\n",
    "\n",
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   "id": "689db7bc",
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   "outputs": [],
   "source": [
    "#  第一列为索引列，未指定，pandas会自动分配\n",
    "import pandas as pd\n",
    "\n",
    "a = [1, 2, 3, 4, 5, 6]\n",
    "\n",
    "myser = pd.Series(a)\n",
    "\n",
    "print(myser)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8db2676f",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# 指定索引列\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "data = {1: \"Python\", 2: \"Jave\", 3: \"C++\"}\n",
    "\n",
    "myser = pd.Series(data, index = [1, 2, 3])\n",
    "\n",
    "print(myser)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "051c63e2",
   "metadata": {},
   "source": [
    "#### Pandas 数据结构 - DataFrame\n",
    "\n",
    "DataFrame 是一个表格型的数据结构，它含有一组有序的列，每列可以是不同的值类型（数值、字符串、布尔型值）。DataFrame 既有行索引也有\n",
    "\n",
    "列索引，它可以被看做由 Series 组成的字典（共同用一个索引）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "86119cb5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "data = [['jupyter中文集成版',1],['python',2],['jupyter',3]]\n",
    "\n",
    "df = pd.DataFrame(data,columns=['name','order'])\n",
    "\n",
    "print(df)"
   ]
  }
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