5.Apply-and-Lambda-transform.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Apply and Lambda Transformation\n",
    "\n",
    "In this notebook we will learn to perform the column data operation through implementation of ```apply()``` and ```lambda``` functionality."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\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>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",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Braund, Mr. Owen Harris</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</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>Cumings, Mrs. John Bradley (Florence Briggs Thayer)</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    PassengerId  Survived  \\\n",
       "Name                                                                        \n",
       "Braund, Mr. Owen Harris                                       1         0   \n",
       "Cumings, Mrs. John Bradley (Florence Briggs Tha...            2         1   \n",
       "\n",
       "                                                    Pclass     Sex   Age  \\\n",
       "Name                                                                       \n",
       "Braund, Mr. Owen Harris                                  3    male  22.0   \n",
       "Cumings, Mrs. John Bradley (Florence Briggs Tha...       1  female  38.0   \n",
       "\n",
       "                                                    SibSp  Parch     Ticket  \\\n",
       "Name                                                                          \n",
       "Braund, Mr. Owen Harris                                 1      0  A/5 21171   \n",
       "Cumings, Mrs. John Bradley (Florence Briggs Tha...      1      0   PC 17599   \n",
       "\n",
       "                                                       Fare Cabin Embarked  \n",
       "Name                                                                        \n",
       "Braund, Mr. Owen Harris                              7.2500   NaN        S  \n",
       "Cumings, Mrs. John Bradley (Florence Briggs Tha...  71.2833   C85        C  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic = pd.read_csv('data/titanic.csv')\n",
    "df1 = titanic.set_index('Name')\n",
    "df1.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. Implementation of ```Apply ()``` with ```lambda()``` function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Apply ```lambda``` functionality to ```age``` column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>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",
       "      <th>remaining-age</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Braund, Mr. Owen Harris</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</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",
       "      <td>78.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cumings, Mrs. John Bradley (Florence Briggs Thayer)</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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",
       "      <td>62.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Heikkinen, Miss. Laina</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</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",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Futrelle, Mrs. Jacques Heath (Lily May Peel)</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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",
       "      <td>65.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Allen, Mr. William Henry</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</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",
       "      <td>65.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    PassengerId  Survived  \\\n",
       "Name                                                                        \n",
       "Braund, Mr. Owen Harris                                       1         0   \n",
       "Cumings, Mrs. John Bradley (Florence Briggs Tha...            2         1   \n",
       "Heikkinen, Miss. Laina                                        3         1   \n",
       "Futrelle, Mrs. Jacques Heath (Lily May Peel)                  4         1   \n",
       "Allen, Mr. William Henry                                      5         0   \n",
       "\n",
       "                                                    Pclass     Sex   Age  \\\n",
       "Name                                                                       \n",
       "Braund, Mr. Owen Harris                                  3    male  22.0   \n",
       "Cumings, Mrs. John Bradley (Florence Briggs Tha...       1  female  38.0   \n",
       "Heikkinen, Miss. Laina                                   3  female  26.0   \n",
       "Futrelle, Mrs. Jacques Heath (Lily May Peel)             1  female  35.0   \n",
       "Allen, Mr. William Henry                                 3    male  35.0   \n",
       "\n",
       "                                                    SibSp  Parch  \\\n",
       "Name                                                               \n",
       "Braund, Mr. Owen Harris                                 1      0   \n",
       "Cumings, Mrs. John Bradley (Florence Briggs Tha...      1      0   \n",
       "Heikkinen, Miss. Laina                                  0      0   \n",
       "Futrelle, Mrs. Jacques Heath (Lily May Peel)            1      0   \n",
       "Allen, Mr. William Henry                                0      0   \n",
       "\n",
       "                                                              Ticket     Fare  \\\n",
       "Name                                                                            \n",
       "Braund, Mr. Owen Harris                                    A/5 21171   7.2500   \n",
       "Cumings, Mrs. John Bradley (Florence Briggs Tha...          PC 17599  71.2833   \n",
       "Heikkinen, Miss. Laina                              STON/O2. 3101282   7.9250   \n",
       "Futrelle, Mrs. Jacques Heath (Lily May Peel)                  113803  53.1000   \n",
       "Allen, Mr. William Henry                                      373450   8.0500   \n",
       "\n",
       "                                                   Cabin Embarked  \\\n",
       "Name                                                                \n",
       "Braund, Mr. Owen Harris                              NaN        S   \n",
       "Cumings, Mrs. John Bradley (Florence Briggs Tha...   C85        C   \n",
       "Heikkinen, Miss. Laina                               NaN        S   \n",
       "Futrelle, Mrs. Jacques Heath (Lily May Peel)        C123        S   \n",
       "Allen, Mr. William Henry                             NaN        S   \n",
       "\n",
       "                                                    remaining-age  \n",
       "Name                                                               \n",
       "Braund, Mr. Owen Harris                                      78.0  \n",
       "Cumings, Mrs. John Bradley (Florence Briggs Tha...           62.0  \n",
       "Heikkinen, Miss. Laina                                       74.0  \n",
       "Futrelle, Mrs. Jacques Heath (Lily May Peel)                 65.0  \n",
       "Allen, Mr. William Henry                                     65.0  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1['remaining-age'] = df1['Age'].apply(lambda x: 100-x).head(5)\n",
    "df1.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Apply ```lambda``` functionality to ```Fare``` column to transform it to new value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name\n",
       "Braund, Mr. Owen Harris                                  54.412500\n",
       "Cumings, Mrs. John Bradley (Florence Briggs Thayer)    5095.965519\n",
       "Heikkinen, Miss. Laina                                   64.790625\n",
       "Futrelle, Mrs. Jacques Heath (Lily May Peel)           2830.630000\n",
       "Allen, Mr. William Henry                                 66.812500\n",
       "Name: Fare, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1['Fare'].apply(lambda x: (10*x**2 + 2*x +4)/10).head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Let us write a new function to supply inside the ```apply()``` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def newfeature(x):\n",
    "    return 10 + x/3 + x**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name\n",
       "Braund, Mr. Owen Harris                                  64.979167\n",
       "Cumings, Mrs. John Bradley (Florence Briggs Thayer)    5115.069959\n",
       "Heikkinen, Miss. Laina                                   75.447292\n",
       "Futrelle, Mrs. Jacques Heath (Lily May Peel)           2847.310000\n",
       "Name: Fare, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1['Fare'].apply(newfeature).head(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. Column Operation with Lambda function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Lets create a new random dataframe to play around."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-01</th>\n",
       "      <td>-0.656738</td>\n",
       "      <td>-0.461095</td>\n",
       "      <td>-0.259647</td>\n",
       "      <td>0.890244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-02</th>\n",
       "      <td>0.652611</td>\n",
       "      <td>0.906148</td>\n",
       "      <td>-0.527606</td>\n",
       "      <td>-0.106089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-03</th>\n",
       "      <td>-0.067463</td>\n",
       "      <td>1.407429</td>\n",
       "      <td>1.414694</td>\n",
       "      <td>-1.266369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-04</th>\n",
       "      <td>0.301058</td>\n",
       "      <td>0.624163</td>\n",
       "      <td>-0.144190</td>\n",
       "      <td>-1.177690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-05</th>\n",
       "      <td>1.557796</td>\n",
       "      <td>-1.497422</td>\n",
       "      <td>0.545636</td>\n",
       "      <td>-1.006319</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2000-01-01 -0.656738 -0.461095 -0.259647  0.890244\n",
       "2000-01-02  0.652611  0.906148 -0.527606 -0.106089\n",
       "2000-01-03 -0.067463  1.407429  1.414694 -1.266369\n",
       "2000-01-04  0.301058  0.624163 -0.144190 -1.177690\n",
       "2000-01-05  1.557796 -1.497422  0.545636 -1.006319"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates = pd.date_range('1/1/2000', periods=100)\n",
    "df = pd.DataFrame(np.random.randn(100, 4),\n",
    "                  index=dates, columns=['A', 'B', 'C', 'D'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- We can directly add, multiply, substract etc among columns if they have same data types."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
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       "      <th>A</th>\n",
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       "      <th>E</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-01</th>\n",
       "      <td>-0.656738</td>\n",
       "      <td>-0.461095</td>\n",
       "      <td>-0.259647</td>\n",
       "      <td>0.890244</td>\n",
       "      <td>4.305207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-02</th>\n",
       "      <td>0.652611</td>\n",
       "      <td>0.906148</td>\n",
       "      <td>-0.527606</td>\n",
       "      <td>-0.106089</td>\n",
       "      <td>-2.954399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-03</th>\n",
       "      <td>-0.067463</td>\n",
       "      <td>1.407429</td>\n",
       "      <td>1.414694</td>\n",
       "      <td>-1.266369</td>\n",
       "      <td>0.947177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-04</th>\n",
       "      <td>0.301058</td>\n",
       "      <td>0.624163</td>\n",
       "      <td>-0.144190</td>\n",
       "      <td>-1.177690</td>\n",
       "      <td>-6.416660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-05</th>\n",
       "      <td>1.557796</td>\n",
       "      <td>-1.497422</td>\n",
       "      <td>0.545636</td>\n",
       "      <td>-1.006319</td>\n",
       "      <td>0.110649</td>\n",
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      ],
      "text/plain": [
       "                   A         B         C         D         E\n",
       "2000-01-01 -0.656738 -0.461095 -0.259647  0.890244  4.305207\n",
       "2000-01-02  0.652611  0.906148 -0.527606 -0.106089 -2.954399\n",
       "2000-01-03 -0.067463  1.407429  1.414694 -1.266369  0.947177\n",
       "2000-01-04  0.301058  0.624163 -0.144190 -1.177690 -6.416660\n",
       "2000-01-05  1.557796 -1.497422  0.545636 -1.006319  0.110649"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['E'] = (df['A'] + df['B'])/df['C']\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- One can use ```lambda``` functions to transform the columns before the column operation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "      <th>F</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-01</th>\n",
       "      <td>-0.656738</td>\n",
       "      <td>-0.461095</td>\n",
       "      <td>-0.259647</td>\n",
       "      <td>0.890244</td>\n",
       "      <td>4.305207</td>\n",
       "      <td>33.648469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-02</th>\n",
       "      <td>0.652611</td>\n",
       "      <td>0.906148</td>\n",
       "      <td>-0.527606</td>\n",
       "      <td>-0.106089</td>\n",
       "      <td>-2.954399</td>\n",
       "      <td>7.698212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-03</th>\n",
       "      <td>-0.067463</td>\n",
       "      <td>1.407429</td>\n",
       "      <td>1.414694</td>\n",
       "      <td>-1.266369</td>\n",
       "      <td>0.947177</td>\n",
       "      <td>30.879714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-04</th>\n",
       "      <td>0.301058</td>\n",
       "      <td>0.624163</td>\n",
       "      <td>-0.144190</td>\n",
       "      <td>-1.177690</td>\n",
       "      <td>-6.416660</td>\n",
       "      <td>3.884397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-05</th>\n",
       "      <td>1.557796</td>\n",
       "      <td>-1.497422</td>\n",
       "      <td>0.545636</td>\n",
       "      <td>-1.006319</td>\n",
       "      <td>0.110649</td>\n",
       "      <td>31.668445</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D         E          F\n",
       "2000-01-01 -0.656738 -0.461095 -0.259647  0.890244  4.305207  33.648469\n",
       "2000-01-02  0.652611  0.906148 -0.527606 -0.106089 -2.954399   7.698212\n",
       "2000-01-03 -0.067463  1.407429  1.414694 -1.266369  0.947177  30.879714\n",
       "2000-01-04  0.301058  0.624163 -0.144190 -1.177690 -6.416660   3.884397\n",
       "2000-01-05  1.557796 -1.497422  0.545636 -1.006319  0.110649  31.668445"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['F'] = df['A'].apply(lambda x : 10+x) + df['E'].apply(lambda x: x+20 if x>0 else x)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### References:\n",
    "1. [Pydata document for Pandas](https://pandas.pydata.org/docs/user_guide/index.html#user-guide)"
   ]
  }
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