data-analysis/035-pandas-display-opts/pandas-display-options.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 8 Pandas display options\n",
    "\n",
    "This is a notebook for the medium article [8 Commonly used Pandas display options you should know](https://bindichen.medium.com/8-commonly-used-pandas-display-options-you-should-know-a832365efa95)\n",
    "\n",
    "Please check out article for instructions\n",
    "\n",
    "**License**: [BSD 2-Clause](https://opensource.org/licenses/BSD-2-Clause)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('titanic/titanic_train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
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       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Showing more rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
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       "      <th>SibSp</th>\n",
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       "      <td>22.0</td>\n",
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       "      <td>A/5 21171</td>\n",
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       "      <th>1</th>\n",
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       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>C</td>\n",
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       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
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       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C148</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "0              1         0       3   \n",
       "1              2         1       1   \n",
       "2              3         1       3   \n",
       "3              4         1       1   \n",
       "4              5         0       3   \n",
       "..           ...       ...     ...   \n",
       "886          887         0       2   \n",
       "887          888         1       1   \n",
       "888          889         0       3   \n",
       "889          890         1       1   \n",
       "890          891         0       3   \n",
       "\n",
       "                                                  Name     Sex   Age  SibSp  \\\n",
       "0                              Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                               Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                             Allen, Mr. William Henry    male  35.0      0   \n",
       "..                                                 ...     ...   ...    ...   \n",
       "886                              Montvila, Rev. Juozas    male  27.0      0   \n",
       "887                       Graham, Miss. Margaret Edith  female  19.0      0   \n",
       "888           Johnston, Miss. Catherine Helen \"Carrie\"  female   NaN      1   \n",
       "889                              Behr, Mr. Karl Howell    male  26.0      0   \n",
       "890                                Dooley, Mr. Patrick    male  32.0      0   \n",
       "\n",
       "     Parch            Ticket     Fare Cabin Embarked  \n",
       "0        0         A/5 21171   7.2500   NaN        S  \n",
       "1        0          PC 17599  71.2833   C85        C  \n",
       "2        0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3        0            113803  53.1000  C123        S  \n",
       "4        0            373450   8.0500   NaN        S  \n",
       "..     ...               ...      ...   ...      ...  \n",
       "886      0            211536  13.0000   NaN        S  \n",
       "887      0            112053  30.0000   B42        S  \n",
       "888      2        W./C. 6607  23.4500   NaN        S  \n",
       "889      0            111369  30.0000  C148        C  \n",
       "890      0            370376   7.7500   NaN        Q  \n",
       "\n",
       "[891 rows x 12 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# By default, Pandas doesn't want to over run your display\n",
    "# It displays the first 5 rows and last 5 rows elipiss \n",
    "pd.get_option('display.max_rows')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use attribute\n",
    "pd.options.display.max_rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set to a value, for example 200 rows\n",
    "pd.set_option('display.max_rows', 200)\n",
    "\n",
    "# pd.options.display.max_rows = 200"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To show all rows \n",
    "pd.set_option('display.max_rows', None)\n",
    "\n",
    "# Use attribute\n",
    "# pd.options.display.max_rows = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To reset\n",
    "pd.reset_option('display.max_rows')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Showing more columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.get_option('display.max_columns')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.options.display.max_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
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       "      <th>0</th>\n",
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       "      <td>1.186704</td>\n",
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       "      <td>0.849941</td>\n",
       "      <td>-0.510222</td>\n",
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       "      <td>-1.362154</td>\n",
       "      <td>0.525994</td>\n",
       "      <td>-3.049124</td>\n",
       "      <td>-0.302803</td>\n",
       "      <td>-0.718199</td>\n",
       "      <td>0.007243</td>\n",
       "      <td>0.065650</td>\n",
       "      <td>0.818730</td>\n",
       "      <td>-0.451200</td>\n",
       "      <td>-0.105859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.480895</td>\n",
       "      <td>0.071419</td>\n",
       "      <td>0.767205</td>\n",
       "      <td>-0.019002</td>\n",
       "      <td>-0.536622</td>\n",
       "      <td>-0.119269</td>\n",
       "      <td>-0.263658</td>\n",
       "      <td>-0.480815</td>\n",
       "      <td>0.098612</td>\n",
       "      <td>-0.587244</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.471987</td>\n",
       "      <td>0.321259</td>\n",
       "      <td>-1.488728</td>\n",
       "      <td>0.750530</td>\n",
       "      <td>-0.094419</td>\n",
       "      <td>-0.900198</td>\n",
       "      <td>0.610301</td>\n",
       "      <td>0.880217</td>\n",
       "      <td>0.409894</td>\n",
       "      <td>-0.124536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.180861</td>\n",
       "      <td>0.764997</td>\n",
       "      <td>-0.308890</td>\n",
       "      <td>0.428892</td>\n",
       "      <td>-1.136091</td>\n",
       "      <td>-1.437602</td>\n",
       "      <td>-0.614338</td>\n",
       "      <td>-1.512203</td>\n",
       "      <td>1.012743</td>\n",
       "      <td>-1.112001</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.591818</td>\n",
       "      <td>-0.608211</td>\n",
       "      <td>-0.467380</td>\n",
       "      <td>1.248135</td>\n",
       "      <td>-0.664162</td>\n",
       "      <td>0.167317</td>\n",
       "      <td>-0.233845</td>\n",
       "      <td>-1.185163</td>\n",
       "      <td>0.175141</td>\n",
       "      <td>2.526511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.255061</td>\n",
       "      <td>1.313176</td>\n",
       "      <td>0.349234</td>\n",
       "      <td>-0.348577</td>\n",
       "      <td>0.812551</td>\n",
       "      <td>-0.377902</td>\n",
       "      <td>0.095264</td>\n",
       "      <td>1.572975</td>\n",
       "      <td>0.651979</td>\n",
       "      <td>0.037298</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.013205</td>\n",
       "      <td>0.081755</td>\n",
       "      <td>-0.765600</td>\n",
       "      <td>0.196791</td>\n",
       "      <td>-0.723342</td>\n",
       "      <td>-0.824714</td>\n",
       "      <td>-0.593696</td>\n",
       "      <td>-1.485352</td>\n",
       "      <td>-0.273722</td>\n",
       "      <td>0.102742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.221132</td>\n",
       "      <td>1.307598</td>\n",
       "      <td>0.317031</td>\n",
       "      <td>-0.418977</td>\n",
       "      <td>0.672917</td>\n",
       "      <td>1.398562</td>\n",
       "      <td>0.452279</td>\n",
       "      <td>-1.214837</td>\n",
       "      <td>0.396997</td>\n",
       "      <td>1.320128</td>\n",
       "      <td>...</td>\n",
       "      <td>1.569188</td>\n",
       "      <td>1.375768</td>\n",
       "      <td>-0.075782</td>\n",
       "      <td>3.356667</td>\n",
       "      <td>0.769208</td>\n",
       "      <td>-0.471639</td>\n",
       "      <td>-1.223934</td>\n",
       "      <td>-0.797048</td>\n",
       "      <td>-1.027939</td>\n",
       "      <td>-1.339244</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         0         1         2         3         4         5         6   \\\n",
       "0  0.709170  0.708546  1.186704  0.483462 -1.573710  1.246253 -1.218819   \n",
       "1  0.480895  0.071419  0.767205 -0.019002 -0.536622 -0.119269 -0.263658   \n",
       "2 -0.180861  0.764997 -0.308890  0.428892 -1.136091 -1.437602 -0.614338   \n",
       "3 -0.255061  1.313176  0.349234 -0.348577  0.812551 -0.377902  0.095264   \n",
       "4  1.221132  1.307598  0.317031 -0.418977  0.672917  1.398562  0.452279   \n",
       "\n",
       "         7         8         9   ...        11        12        13        14  \\\n",
       "0  0.804348  0.849941 -0.510222  ... -1.362154  0.525994 -3.049124 -0.302803   \n",
       "1 -0.480815  0.098612 -0.587244  ... -1.471987  0.321259 -1.488728  0.750530   \n",
       "2 -1.512203  1.012743 -1.112001  ... -1.591818 -0.608211 -0.467380  1.248135   \n",
       "3  1.572975  0.651979  0.037298  ... -0.013205  0.081755 -0.765600  0.196791   \n",
       "4 -1.214837  0.396997  1.320128  ...  1.569188  1.375768 -0.075782  3.356667   \n",
       "\n",
       "         15        16        17        18        19        20  \n",
       "0 -0.718199  0.007243  0.065650  0.818730 -0.451200 -0.105859  \n",
       "1 -0.094419 -0.900198  0.610301  0.880217  0.409894 -0.124536  \n",
       "2 -0.664162  0.167317 -0.233845 -1.185163  0.175141  2.526511  \n",
       "3 -0.723342 -0.824714 -0.593696 -1.485352 -0.273722  0.102742  \n",
       "4  0.769208 -0.471639 -1.223934 -0.797048 -1.027939 -1.339244  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# \n",
    "pd.DataFrame(np.random.randn(5, 21))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_columns', 30)\n",
    "\n",
    "# pd.options.display.max_columns = 30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "      <th>20</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.110868</td>\n",
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       "      <td>0.064795</td>\n",
       "      <td>-0.879276</td>\n",
       "      <td>-0.174588</td>\n",
       "      <td>-1.292798</td>\n",
       "      <td>0.879012</td>\n",
       "      <td>0.630535</td>\n",
       "      <td>-0.935263</td>\n",
       "      <td>-0.356968</td>\n",
       "      <td>1.214124</td>\n",
       "      <td>-0.794808</td>\n",
       "      <td>-1.932691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.402150</td>\n",
       "      <td>-0.421182</td>\n",
       "      <td>0.626360</td>\n",
       "      <td>-0.663328</td>\n",
       "      <td>0.858142</td>\n",
       "      <td>1.263890</td>\n",
       "      <td>0.724953</td>\n",
       "      <td>0.899655</td>\n",
       "      <td>-1.238758</td>\n",
       "      <td>0.236649</td>\n",
       "      <td>-1.551620</td>\n",
       "      <td>-0.288491</td>\n",
       "      <td>0.594793</td>\n",
       "      <td>-0.670040</td>\n",
       "      <td>-0.903135</td>\n",
       "      <td>0.247083</td>\n",
       "      <td>0.528946</td>\n",
       "      <td>-0.075351</td>\n",
       "      <td>-0.740132</td>\n",
       "      <td>0.222681</td>\n",
       "      <td>0.346790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.752562</td>\n",
       "      <td>1.902753</td>\n",
       "      <td>0.445410</td>\n",
       "      <td>0.513394</td>\n",
       "      <td>0.081433</td>\n",
       "      <td>0.113036</td>\n",
       "      <td>0.926231</td>\n",
       "      <td>1.114777</td>\n",
       "      <td>0.921968</td>\n",
       "      <td>-0.117036</td>\n",
       "      <td>-0.298941</td>\n",
       "      <td>-0.521707</td>\n",
       "      <td>-1.387588</td>\n",
       "      <td>0.017271</td>\n",
       "      <td>2.453349</td>\n",
       "      <td>-0.069491</td>\n",
       "      <td>0.995719</td>\n",
       "      <td>-1.110889</td>\n",
       "      <td>0.190637</td>\n",
       "      <td>-0.256021</td>\n",
       "      <td>0.014347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-2.013262</td>\n",
       "      <td>0.894645</td>\n",
       "      <td>1.484421</td>\n",
       "      <td>1.190820</td>\n",
       "      <td>0.128440</td>\n",
       "      <td>-0.864778</td>\n",
       "      <td>-0.410538</td>\n",
       "      <td>0.247292</td>\n",
       "      <td>1.340166</td>\n",
       "      <td>-1.023432</td>\n",
       "      <td>-0.961624</td>\n",
       "      <td>1.159639</td>\n",
       "      <td>0.216179</td>\n",
       "      <td>-0.204903</td>\n",
       "      <td>-0.727183</td>\n",
       "      <td>-0.801503</td>\n",
       "      <td>0.759710</td>\n",
       "      <td>0.547359</td>\n",
       "      <td>1.203605</td>\n",
       "      <td>1.129022</td>\n",
       "      <td>0.120474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.529926</td>\n",
       "      <td>0.513028</td>\n",
       "      <td>-0.654356</td>\n",
       "      <td>-1.201887</td>\n",
       "      <td>-0.374091</td>\n",
       "      <td>0.478381</td>\n",
       "      <td>-1.521653</td>\n",
       "      <td>0.773927</td>\n",
       "      <td>-0.627779</td>\n",
       "      <td>-0.968098</td>\n",
       "      <td>-1.090192</td>\n",
       "      <td>-0.986984</td>\n",
       "      <td>0.860933</td>\n",
       "      <td>0.022260</td>\n",
       "      <td>1.551663</td>\n",
       "      <td>0.248969</td>\n",
       "      <td>-0.598845</td>\n",
       "      <td>-0.141429</td>\n",
       "      <td>-1.277409</td>\n",
       "      <td>-0.479481</td>\n",
       "      <td>-1.642832</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         0         1         2         3         4         5         6   \\\n",
       "0  0.110868  0.383535 -0.033784 -0.492288  0.364078 -1.369811  0.558615   \n",
       "1 -0.402150 -0.421182  0.626360 -0.663328  0.858142  1.263890  0.724953   \n",
       "2  0.752562  1.902753  0.445410  0.513394  0.081433  0.113036  0.926231   \n",
       "3 -2.013262  0.894645  1.484421  1.190820  0.128440 -0.864778 -0.410538   \n",
       "4 -0.529926  0.513028 -0.654356 -1.201887 -0.374091  0.478381 -1.521653   \n",
       "\n",
       "         7         8         9         10        11        12        13  \\\n",
       "0  1.530232 -0.430022 -1.990347  0.064795 -0.879276 -0.174588 -1.292798   \n",
       "1  0.899655 -1.238758  0.236649 -1.551620 -0.288491  0.594793 -0.670040   \n",
       "2  1.114777  0.921968 -0.117036 -0.298941 -0.521707 -1.387588  0.017271   \n",
       "3  0.247292  1.340166 -1.023432 -0.961624  1.159639  0.216179 -0.204903   \n",
       "4  0.773927 -0.627779 -0.968098 -1.090192 -0.986984  0.860933  0.022260   \n",
       "\n",
       "         14        15        16        17        18        19        20  \n",
       "0  0.879012  0.630535 -0.935263 -0.356968  1.214124 -0.794808 -1.932691  \n",
       "1 -0.903135  0.247083  0.528946 -0.075351 -0.740132  0.222681  0.346790  \n",
       "2  2.453349 -0.069491  0.995719 -1.110889  0.190637 -0.256021  0.014347  \n",
       "3 -0.727183 -0.801503  0.759710  0.547359  1.203605  1.129022  0.120474  \n",
       "4  1.551663  0.248969 -0.598845 -0.141429 -1.277409 -0.479481 -1.642832  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(np.random.randn(5, 21))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Show all coloumns\n",
    "pd.set_option('display.max_columns', None)\n",
    "\n",
    "# pd.options.display.max_columns = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To reset\n",
    "pd.reset_option('display.max_columns')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Changing Column max width"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
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       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.get_option('display.max_colwidth')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.options.display.max_colwidth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.get_option('display.max_colwidth')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_colwidth', 500)\n",
    "\n",
    "# pd.options.display.max_colwidth = 500"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
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       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
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       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Thayer)</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>NaN</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                  Name     Sex   Age  SibSp  \\\n",
       "0                              Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Thayer)  female  38.0      1   \n",
       "2                               Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                             Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Show all characters\n",
    "pd.set_option('display.max_colwidth', None)\n",
    "\n",
    "# pd.options.display.max_colwidth = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
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       "      <td>22.0</td>\n",
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       "      <td>7.2500</td>\n",
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       "      <td>female</td>\n",
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       "      <th>2</th>\n",
       "      <td>3</td>\n",
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       "      <td>Heikkinen, Miss. Laina</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                  Name     Sex   Age  SibSp  \\\n",
       "0                              Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Thayer)  female  38.0      1   \n",
       "2                               Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                             Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To reset\n",
    "pd.reset_option('display.max_colwidth')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Setting precision for float columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
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       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
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       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                  Name     Sex   Age  SibSp  \\\n",
       "0                              Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Thayer)  female  38.0      1   \n",
       "2                               Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                             Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1        2\n",
       "0  0.574417  0.782574 -1.52673\n",
       "1 -1.973312 -1.517863  1.03958"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(np.random.randn(2, 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.get_option('display.precision')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.options.display.precision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This is not affecting the underlying data, it is only affecting the display\n",
    "pd.set_option('display.precision', 2)\n",
    "\n",
    "# pd.options.display.precision = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Thayer)</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.28</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.92</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.10</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                  Name     Sex   Age  SibSp  \\\n",
       "0                              Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Thayer)  female  38.0      1   \n",
       "2                               Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                             Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket   Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.25   NaN        S  \n",
       "1      0          PC 17599  71.28   C85        C  \n",
       "2      0  STON/O2. 3101282   7.92   NaN        S  \n",
       "3      0            113803  53.10  C123        S  \n",
       "4      0            373450   8.05   NaN        S  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.16</td>\n",
       "      <td>0.17</td>\n",
       "      <td>-2.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.03</td>\n",
       "      <td>-0.24</td>\n",
       "      <td>1.28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      0     1     2\n",
       "0  0.16  0.17 -2.08\n",
       "1  0.03 -0.24  1.28"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(np.random.randn(2, 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.reset_option('display.precision')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Formatting the display for numbers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Formatting big number with comma"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .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>test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.00e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.00e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.45e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.56e+05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       test\n",
       "1  1.00e+05\n",
       "2  2.00e+06\n",
       "3  3.45e+07\n",
       "4  4.56e+05"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test = pd.DataFrame([100000.12345, 2000000.5678, 34500000.6789, 456000.7890],\n",
    "                  index=[1,2,3,4],\n",
    "                  columns=['test'])\n",
    "df_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .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>test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100,000.12345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2,000,000.5678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>34,500,000.6789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>456,000.789</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             test\n",
       "1   100,000.12345\n",
       "2  2,000,000.5678\n",
       "3 34,500,000.6789\n",
       "4     456,000.789"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.float_format',  '{:,}'.format)\n",
    "df_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setting decimal format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .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>test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100,000.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2,000,000.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>34,500,000.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>456,000.79</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           test\n",
       "1    100,000.12\n",
       "2  2,000,000.57\n",
       "3 34,500,000.68\n",
       "4    456,000.79"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# note value are rounded\n",
    "pd.set_option('display.float_format',  '{:,.2f}'.format)\n",
    "df_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Formatting with a dollar or percent sign"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .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>test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>234.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>345.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>456.79</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     test\n",
       "1   12.35\n",
       "2  234.57\n",
       "3  345.68\n",
       "4  456.79"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.reset_option('display.float_format')\n",
    "\n",
    "df_test = pd.DataFrame([12.3456, 234.5678, 345.6789, 456.7890],\n",
    "                  index=[1,2,3,4],\n",
    "                  columns=['test'])\n",
    "df_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .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>test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12.35%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>234.57%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>345.68%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>456.79%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     test\n",
       "1  12.35%\n",
       "2 234.57%\n",
       "3 345.68%\n",
       "4 456.79%"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.float_format', '{:.2f}%'.format)\n",
    "df_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .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>test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>$12.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>$234.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>$345.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>$456.79</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     test\n",
       "1  $12.35\n",
       "2 $234.57\n",
       "3 $345.68\n",
       "4 $456.79"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.float_format', '${:.2f}'.format)\n",
    "df_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.reset_option('display.float_format')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Changing plotting backend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x119e221d0>"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "pd.set_option('plotting.backend', 'matplotlib') \n",
    "\n",
    "data = pd.Series(np.random.randn(100).cumsum())\n",
    "data.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.reset_option('plotting.backend')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. Configuring the output of `info()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.options.display.max_info_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1690785"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.options.display.max_info_rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  891 non-null    int64  \n",
      " 1   Survived     891 non-null    int64  \n",
      " 2   Pclass       891 non-null    int64  \n",
      " 3   Name         891 non-null    object \n",
      " 4   Sex          891 non-null    object \n",
      " 5   Age          714 non-null    float64\n",
      " 6   SibSp        891 non-null    int64  \n",
      " 7   Parch        891 non-null    int64  \n",
      " 8   Ticket       891 non-null    object \n",
      " 9   Fare         891 non-null    float64\n",
      " 10  Cabin        204 non-null    object \n",
      " 11  Embarked     889 non-null    object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option(\"max_info_rows\", 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      " #   Column       Dtype  \n",
      "---  ------       -----  \n",
      " 0   PassengerId  int64  \n",
      " 1   Survived     int64  \n",
      " 2   Pclass       int64  \n",
      " 3   Name         object \n",
      " 4   Sex          object \n",
      " 5   Age          float64\n",
      " 6   SibSp        int64  \n",
      " 7   Parch        int64  \n",
      " 8   Ticket       object \n",
      " 9   Fare         float64\n",
      " 10  Cabin        object \n",
      " 11  Embarked     object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option(\"max_info_rows\", 900)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  891 non-null    int64  \n",
      " 1   Survived     891 non-null    int64  \n",
      " 2   Pclass       891 non-null    int64  \n",
      " 3   Name         891 non-null    object \n",
      " 4   Sex          891 non-null    object \n",
      " 5   Age          714 non-null    float64\n",
      " 6   SibSp        891 non-null    int64  \n",
      " 7   Parch        891 non-null    int64  \n",
      " 8   Ticket       891 non-null    object \n",
      " 9   Fare         891 non-null    float64\n",
      " 10  Cabin        204 non-null    object \n",
      " 11  Embarked     889 non-null    object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8. Printing out current settings and reset all options"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "compute.use_bottleneck : bool\n",
      "    Use the bottleneck library to accelerate if it is installed,\n",
      "    the default is True\n",
      "    Valid values: False,True\n",
      "    [default: True] [currently: True]\n",
      "compute.use_numba : bool\n",
      "    Use the numba engine option for select operations if it is installed,\n",
      "    the default is False\n",
      "    Valid values: False,True\n",
      "    [default: False] [currently: False]\n",
      "compute.use_numexpr : bool\n",
      "    Use the numexpr library to accelerate computation if it is installed,\n",
      "    the default is True\n",
      "    Valid values: False,True\n",
      "    [default: True] [currently: True]\n",
      "display.chop_threshold : float or None\n",
      "    if set to a float value, all float values smaller then the given threshold\n",
      "    will be displayed as exactly 0 by repr and friends.\n",
      "    [default: None] [currently: None]\n",
      "display.colheader_justify : 'left'/'right'\n",
      "    Controls the justification of column headers. used by DataFrameFormatter.\n",
      "    [default: right] [currently: right]\n",
      "display.column_space No description available.\n",
      "    [default: 12] [currently: 12]\n",
      "display.date_dayfirst : boolean\n",
      "    When True, prints and parses dates with the day first, eg 20/01/2005\n",
      "    [default: False] [currently: False]\n",
      "display.date_yearfirst : boolean\n",
      "    When True, prints and parses dates with the year first, eg 2005/01/20\n",
      "    [default: False] [currently: False]\n",
      "display.encoding : str/unicode\n",
      "    Defaults to the detected encoding of the console.\n",
      "    Specifies the encoding to be used for strings returned by to_string,\n",
      "    these are generally strings meant to be displayed on the console.\n",
      "    [default: UTF-8] [currently: gb2312]\n",
      "display.expand_frame_repr : boolean\n",
      "    Whether to print out the full DataFrame repr for wide DataFrames across\n",
      "    multiple lines, `max_columns` is still respected, but the output will\n",
      "    wrap-around across multiple \"pages\" if its width exceeds `display.width`.\n",
      "    [default: True] [currently: True]\n",
      "display.float_format : callable\n",
      "    The callable should accept a floating point number and return\n",
      "    a string with the desired format of the number. This is used\n",
      "    in some places like SeriesFormatter.\n",
      "    See formats.format.EngFormatter for an example.\n",
      "    [default: None] [currently: <built-in method format of str object at 0x1088603b0>]\n",
      "display.html.border : int\n",
      "    A ``border=value`` attribute is inserted in the ``<table>`` tag\n",
      "    for the DataFrame HTML repr.\n",
      "    [default: 1] [currently: 1]\n",
      "display.html.table_schema : boolean\n",
      "    Whether to publish a Table Schema representation for frontends\n",
      "    that support it.\n",
      "    (default: False)\n",
      "    [default: False] [currently: False]\n",
      "display.html.use_mathjax : boolean\n",
      "    When True, Jupyter notebook will process table contents using MathJax,\n",
      "    rendering mathematical expressions enclosed by the dollar symbol.\n",
      "    (default: True)\n",
      "    [default: True] [currently: True]\n",
      "display.large_repr : 'truncate'/'info'\n",
      "    For DataFrames exceeding max_rows/max_cols, the repr (and HTML repr) can\n",
      "    show a truncated table (the default from 0.13), or switch to the view from\n",
      "    df.info() (the behaviour in earlier versions of pandas).\n",
      "    [default: truncate] [currently: truncate]\n",
      "display.latex.escape : bool\n",
      "    This specifies if the to_latex method of a Dataframe uses escapes special\n",
      "    characters.\n",
      "    Valid values: False,True\n",
      "    [default: True] [currently: True]\n",
      "display.latex.longtable :bool\n",
      "    This specifies if the to_latex method of a Dataframe uses the longtable\n",
      "    format.\n",
      "    Valid values: False,True\n",
      "    [default: False] [currently: False]\n",
      "display.latex.multicolumn : bool\n",
      "    This specifies if the to_latex method of a Dataframe uses multicolumns\n",
      "    to pretty-print MultiIndex columns.\n",
      "    Valid values: False,True\n",
      "    [default: True] [currently: True]\n",
      "display.latex.multicolumn_format : bool\n",
      "    This specifies if the to_latex method of a Dataframe uses multicolumns\n",
      "    to pretty-print MultiIndex columns.\n",
      "    Valid values: False,True\n",
      "    [default: l] [currently: l]\n",
      "display.latex.multirow : bool\n",
      "    This specifies if the to_latex method of a Dataframe uses multirows\n",
      "    to pretty-print MultiIndex rows.\n",
      "    Valid values: False,True\n",
      "    [default: False] [currently: False]\n",
      "display.latex.repr : boolean\n",
      "    Whether to produce a latex DataFrame representation for jupyter\n",
      "    environments that support it.\n",
      "    (default: False)\n",
      "    [default: False] [currently: False]\n",
      "display.max_categories : int\n",
      "    This sets the maximum number of categories pandas should output when\n",
      "    printing out a `Categorical` or a Series of dtype \"category\".\n",
      "    [default: 8] [currently: 8]\n",
      "display.max_columns : int\n",
      "    If max_cols is exceeded, switch to truncate view. Depending on\n",
      "    `large_repr`, objects are either centrally truncated or printed as\n",
      "    a summary view. 'None' value means unlimited.\n",
      "\n",
      "    In case python/IPython is running in a terminal and `large_repr`\n",
      "    equals 'truncate' this can be set to 0 and pandas will auto-detect\n",
      "    the width of the terminal and print a truncated object which fits\n",
      "    the screen width. The IPython notebook, IPython qtconsole, or IDLE\n",
      "    do not run in a terminal and hence it is not possible to do\n",
      "    correct auto-detection.\n",
      "    [default: 20] [currently: 20]\n",
      "display.max_colwidth : int or None\n",
      "    The maximum width in characters of a column in the repr of\n",
      "    a pandas data structure. When the column overflows, a \"...\"\n",
      "    placeholder is embedded in the output. A 'None' value means unlimited.\n",
      "    [default: 50] [currently: 50]\n",
      "display.max_info_columns : int\n",
      "    max_info_columns is used in DataFrame.info method to decide if\n",
      "    per column information will be printed.\n",
      "    [default: 100] [currently: 100]\n",
      "display.max_info_rows : int or None\n",
      "    df.info() will usually show null-counts for each column.\n",
      "    For large frames this can be quite slow. max_info_rows and max_info_cols\n",
      "    limit this null check only to frames with smaller dimensions than\n",
      "    specified.\n",
      "    [default: 1690785] [currently: 900]\n",
      "display.max_rows : int\n",
      "    If max_rows is exceeded, switch to truncate view. Depending on\n",
      "    `large_repr`, objects are either centrally truncated or printed as\n",
      "    a summary view. 'None' value means unlimited.\n",
      "\n",
      "    In case python/IPython is running in a terminal and `large_repr`\n",
      "    equals 'truncate' this can be set to 0 and pandas will auto-detect\n",
      "    the height of the terminal and print a truncated object which fits\n",
      "    the screen height. The IPython notebook, IPython qtconsole, or\n",
      "    IDLE do not run in a terminal and hence it is not possible to do\n",
      "    correct auto-detection.\n",
      "    [default: 60] [currently: 60]\n",
      "display.max_seq_items : int or None\n",
      "    when pretty-printing a long sequence, no more then `max_seq_items`\n",
      "    will be printed. If items are omitted, they will be denoted by the\n",
      "    addition of \"...\" to the resulting string.\n",
      "\n",
      "    If set to None, the number of items to be printed is unlimited.\n",
      "    [default: 100] [currently: 100]\n",
      "display.memory_usage : bool, string or None\n",
      "    This specifies if the memory usage of a DataFrame should be displayed when\n",
      "    df.info() is called. Valid values True,False,'deep'\n",
      "    [default: True] [currently: True]\n",
      "display.min_rows : int\n",
      "    The numbers of rows to show in a truncated view (when `max_rows` is\n",
      "    exceeded). Ignored when `max_rows` is set to None or 0. When set to\n",
      "    None, follows the value of `max_rows`.\n",
      "    [default: 10] [currently: 10]\n",
      "display.multi_sparse : boolean\n",
      "    \"sparsify\" MultiIndex display (don't display repeated\n",
      "    elements in outer levels within groups)\n",
      "    [default: True] [currently: True]\n",
      "display.notebook_repr_html : boolean\n",
      "    When True, IPython notebook will use html representation for\n",
      "    pandas objects (if it is available).\n",
      "    [default: True] [currently: True]\n",
      "display.pprint_nest_depth : int\n",
      "    Controls the number of nested levels to process when pretty-printing\n",
      "    [default: 3] [currently: 3]\n",
      "display.precision : int\n",
      "    Floating point output precision (number of significant digits). This is\n",
      "    only a suggestion\n",
      "    [default: 6] [currently: 6]\n",
      "display.show_dimensions : boolean or 'truncate'\n",
      "    Whether to print out dimensions at the end of DataFrame repr.\n",
      "    If 'truncate' is specified, only print out the dimensions if the\n",
      "    frame is truncated (e.g. not display all rows and/or columns)\n",
      "    [default: truncate] [currently: truncate]\n",
      "display.unicode.ambiguous_as_wide : boolean\n",
      "    Whether to use the Unicode East Asian Width to calculate the display text\n",
      "    width.\n",
      "    Enabling this may affect to the performance (default: False)\n",
      "    [default: False] [currently: False]\n",
      "display.unicode.east_asian_width : boolean\n",
      "    Whether to use the Unicode East Asian Width to calculate the display text\n",
      "    width.\n",
      "    Enabling this may affect to the performance (default: False)\n",
      "    [default: False] [currently: False]\n",
      "display.width : int\n",
      "    Width of the display in characters. In case python/IPython is running in\n",
      "    a terminal this can be set to None and pandas will correctly auto-detect\n",
      "    the width.\n",
      "    Note that the IPython notebook, IPython qtconsole, or IDLE do not run in a\n",
      "    terminal and hence it is not possible to correctly detect the width.\n",
      "    [default: 80] [currently: 80]\n",
      "io.excel.ods.reader : string\n",
      "    The default Excel reader engine for 'ods' files. Available options:\n",
      "    auto, odf.\n",
      "    [default: auto] [currently: auto]\n",
      "io.excel.ods.writer : string\n",
      "    The default Excel writer engine for 'ods' files. Available options:\n",
      "    auto, odf.\n",
      "    [default: auto] [currently: auto]\n",
      "io.excel.xls.reader : string\n",
      "    The default Excel reader engine for 'xls' files. Available options:\n",
      "    auto, xlrd.\n",
      "    [default: auto] [currently: auto]\n",
      "io.excel.xls.writer : string\n",
      "    The default Excel writer engine for 'xls' files. Available options:\n",
      "    auto, xlwt.\n",
      "    [default: auto] [currently: auto]\n",
      "io.excel.xlsb.reader : string\n",
      "    The default Excel reader engine for 'xlsb' files. Available options:\n",
      "    auto, pyxlsb.\n",
      "    [default: auto] [currently: auto]\n",
      "io.excel.xlsm.reader : string\n",
      "    The default Excel reader engine for 'xlsm' files. Available options:\n",
      "    auto, xlrd, openpyxl.\n",
      "    [default: auto] [currently: auto]\n",
      "io.excel.xlsm.writer : string\n",
      "    The default Excel writer engine for 'xlsm' files. Available options:\n",
      "    auto, openpyxl.\n",
      "    [default: auto] [currently: auto]\n",
      "io.excel.xlsx.reader : string\n",
      "    The default Excel reader engine for 'xlsx' files. Available options:\n",
      "    auto, xlrd, openpyxl.\n",
      "    [default: auto] [currently: auto]\n",
      "io.excel.xlsx.writer : string\n",
      "    The default Excel writer engine for 'xlsx' files. Available options:\n",
      "    auto, openpyxl, xlsxwriter.\n",
      "    [default: auto] [currently: auto]\n",
      "io.hdf.default_format : format\n",
      "    default format writing format, if None, then\n",
      "    put will default to 'fixed' and append will default to 'table'\n",
      "    [default: None] [currently: None]\n",
      "io.hdf.dropna_table : boolean\n",
      "    drop ALL nan rows when appending to a table\n",
      "    [default: False] [currently: False]\n",
      "io.parquet.engine : string\n",
      "    The default parquet reader/writer engine. Available options:\n",
      "    'auto', 'pyarrow', 'fastparquet', the default is 'auto'\n",
      "    [default: auto] [currently: auto]\n",
      "mode.chained_assignment : string\n",
      "    Raise an exception, warn, or no action if trying to use chained assignment,\n",
      "    The default is warn\n",
      "    [default: warn] [currently: warn]\n",
      "mode.sim_interactive : boolean\n",
      "    Whether to simulate interactive mode for purposes of testing\n",
      "    [default: False] [currently: False]\n",
      "mode.use_inf_as_na : boolean\n",
      "    True means treat None, NaN, INF, -INF as NA (old way),\n",
      "    False means None and NaN are null, but INF, -INF are not NA\n",
      "    (new way).\n",
      "    [default: False] [currently: False]\n",
      "mode.use_inf_as_null : boolean\n",
      "    use_inf_as_null had been deprecated and will be removed in a future\n",
      "    version. Use `use_inf_as_na` instead.\n",
      "    [default: False] [currently: False]\n",
      "    (Deprecated, use `mode.use_inf_as_na` instead.)\n",
      "plotting.backend : str\n",
      "    The plotting backend to use. The default value is \"matplotlib\", the\n",
      "    backend provided with pandas. Other backends can be specified by\n",
      "    providing the name of the module that implements the backend.\n",
      "    [default: matplotlib] [currently: matplotlib]\n",
      "plotting.matplotlib.register_converters : bool or 'auto'.\n",
      "    Whether to register converters with matplotlib's units registry for\n",
      "    dates, times, datetimes, and Periods. Toggling to False will remove\n",
      "    the converters, restoring any converters that pandas overwrote.\n",
      "    [default: auto] [currently: auto]\n"
     ]
    }
   ],
   "source": [
    "pd.describe_option()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "display.max_info_rows : int or None\n",
      "    df.info() will usually show null-counts for each column.\n",
      "    For large frames this can be quite slow. max_info_rows and max_info_cols\n",
      "    limit this null check only to frames with smaller dimensions than\n",
      "    specified.\n",
      "    [default: 1690785] [currently: 1690785]\n",
      "display.max_rows : int\n",
      "    If max_rows is exceeded, switch to truncate view. Depending on\n",
      "    `large_repr`, objects are either centrally truncated or printed as\n",
      "    a summary view. 'None' value means unlimited.\n",
      "\n",
      "    In case python/IPython is running in a terminal and `large_repr`\n",
      "    equals 'truncate' this can be set to 0 and pandas will auto-detect\n",
      "    the height of the terminal and print a truncated object which fits\n",
      "    the screen height. The IPython notebook, IPython qtconsole, or\n",
      "    IDLE do not run in a terminal and hence it is not possible to do\n",
      "    correct auto-detection.\n",
      "    [default: 60] [currently: 200]\n",
      "display.min_rows : int\n",
      "    The numbers of rows to show in a truncated view (when `max_rows` is\n",
      "    exceeded). Ignored when `max_rows` is set to None or 0. When set to\n",
      "    None, follows the value of `max_rows`.\n",
      "    [default: 10] [currently: 10]\n"
     ]
    }
   ],
   "source": [
    "# specific searching, rows display options\n",
    "pd.describe_option('rows')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      ": boolean\n",
      "    use_inf_as_null had been deprecated and will be removed in a future\n",
      "    version. Use `use_inf_as_na` instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# reset all options\n",
    "pd.reset_option('all')"
   ]
  }
 ],
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