7. 显示控制.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 显示控制\n",
    "讲完怎么构造基本数据结构，不忙讲复杂函数和操作，先讲讲怎么让数据呈现的更符合心意更重要。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "__auther__ = 'zhenhang.sun@gmail.com'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'D:\\\\github\\\\pandas-tutorial'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. 函数说明\n",
    "- get/set/reset_opthon其实是从本地配置文件去查询和设置这个关键字；\n",
    "- 这些关键字都是以字符串给定的，可以使用任何正则表达式，但如果匹配到多个则报错，所以最好是精确表示。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.1 查询默认参数\n",
    "##### `pd.get_option(key)`\n",
    "- key：也即上面列出的关键字"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.2 自定义参数\n",
    "##### `pd.set_option(key, value)`\n",
    "- key：要设置的关键字\n",
    "- value：int，要设置的值"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.3 恢复自定义参数为默认参数。\n",
    "##### `pandas.reset_option(key)`\n",
    "- key：上面提到的关键字"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. 重要参数\n",
    "一般来说，对我们有用的get_option和set_option操作不多，主要有以下三种关键字：\n",
    "- 'display.max_rows'\n",
    "- 'display.max_columns'\n",
    "- 'display.max_colwidth'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.1 max_rows\n",
    "控制可以显示的最大行数。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "pd.get_option('display.max_rows')# 60的意思就是最多显示60行，如果行数超过60，那么将省略显示一部分。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    NaN\n",
       "1    NaN\n",
       "2    NaN\n",
       "3    NaN\n",
       "4    NaN\n",
       "5    NaN\n",
       "6    NaN\n",
       "7    NaN\n",
       "8    NaN\n",
       "9    NaN\n",
       "10   NaN\n",
       "11   NaN\n",
       "12   NaN\n",
       "13   NaN\n",
       "14   NaN\n",
       "15   NaN\n",
       "16   NaN\n",
       "17   NaN\n",
       "18   NaN\n",
       "19   NaN\n",
       "20   NaN\n",
       "21   NaN\n",
       "22   NaN\n",
       "23   NaN\n",
       "24   NaN\n",
       "25   NaN\n",
       "26   NaN\n",
       "27   NaN\n",
       "28   NaN\n",
       "29   NaN\n",
       "30   NaN\n",
       "31   NaN\n",
       "32   NaN\n",
       "33   NaN\n",
       "34   NaN\n",
       "35   NaN\n",
       "36   NaN\n",
       "37   NaN\n",
       "38   NaN\n",
       "39   NaN\n",
       "40   NaN\n",
       "41   NaN\n",
       "42   NaN\n",
       "43   NaN\n",
       "44   NaN\n",
       "45   NaN\n",
       "46   NaN\n",
       "47   NaN\n",
       "48   NaN\n",
       "49   NaN\n",
       "50   NaN\n",
       "51   NaN\n",
       "52   NaN\n",
       "53   NaN\n",
       "54   NaN\n",
       "55   NaN\n",
       "56   NaN\n",
       "57   NaN\n",
       "58   NaN\n",
       "59   NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pd.set_option('display.max_rows', 61)\n",
    "pd.Series( index = range(0, 60) )  # 把60改成61试试，然后将上面注释取消，再试试。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.2 max_columns\n",
    "控制可以显示的最大列数，这个参数对DataFrame更有价值。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.get_option('display.max_columns')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
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       "  <tbody>\n",
       "  </tbody>\n",
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      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19]\n",
       "Index: []"
      ]
     },
     "execution_count": 7,
     "metadata": {},
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   "source": [
    "# pd.set_option('display.max_columns', 21)\n",
    "pd.DataFrame( columns = range(0,20)) # 分别设置20和21试试，然后将上面注释取消，再试试。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.3 max_colwidth\n",
    "控制每个网格点能够显示的最大字符数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.get_option('display.max_colwidth')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    1111111111111111111111111111111111111111111111...\n",
       "dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pd.set_option('display.max_colwidth', 51)\n",
    "s = '1'*50    # 把50改成49试试，把上面的注释取消，再试试\n",
    "pd.Series( data=[s], index = ['1'])  #实际应用中把max_colwidth大体设得大一点就可以"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.4 precision\n",
    "控制浮点类型显示的小数位数，不影响实际精度。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.get_option('display.precision')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.010101\n",
       "dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pd.set_option('display.precision', 10)\n",
    "a = 1.01010101\n",
    "s = pd.Series( data= [a])  # \n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.01010101"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[0] # 在Series数据被截断显示，但实际上精度并没变， 把上面的注释取消，再试试"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.5 colheader_justify\n",
    "控制DataFrame的列名对齐位置，靠左或者靠右"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'right'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.get_option('display.colheader_justify')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>000000000000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 a\n",
       "0  000000000000000"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pd.set_option('display.colheader_justify','left')  # 我的这个设置好像无效，不知道咋回事\n",
    "pd.DataFrame( data = ['000000000000000'], columns = ['a'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.6. 更多设置参数查看官方说明\n",
    "https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.set_option.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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