{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## slik-wrangler Preprocessing API"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Brief description on how to use the preprocessing module in the slik-wrangler package. This sample notebook explains some very important methods in the preprocessing module"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_path = 'data/titanic.csv'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using Slik-wrangler load file module you only need to specify your data path. \n",
    "\n",
    "Slik-wrangler can infer the file type that was passed and read it as a pandas dataframe.\n",
    "\n",
    "`slik-wrangler.loadfile.read_file` function makes use of the same keyword arguments as pandas read functions. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from slik_wrangler import loadfile as lf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can get a brief summary of the rows and column that was loaded by Slik-wrangler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32m\n",
      "CSV file read sucessfully\n",
      "\u001b[36m\n",
      "Data has 891 rows and 12 columns\n"
     ]
    }
   ],
   "source": [
    "train = lf.read_file(dataset_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Working with a large csv file and you can not load the whole data to Excel or with pandas, with slik-wrangler you can split a csv into multiple csv files. \n",
    "\n",
    "Specify the number of rows that should be present in each csv file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "lf.split_csv_file(dataset_path,row_limit=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>7.2500</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",
       "    </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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before any step in preprocessing your dataset, it is essential to look at the overall state of your dataset. \n",
    "\n",
    "Pandas provide a means to achieve this by its `info()` method, which gives us an insight into the missing values, data type, data size, and data memory usage.\n",
    "\n",
    "While this is useful, with Slik-wrangler you could quickly get an overview of all you need to adjust to make a balanced dataset. By balanced dataset here, we're implying a dataset void of missing values, duplicate values, and inconsistency in the data type of one or more feature columns.\n",
    "\n",
    "Slik-wrangler provides a data quality assessment module (`slik-wrangler.dqa`) for this purpose entirely. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from slik_wrangler.dqa import data_cleanness_assessment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the `slik-wrangler.dqa.data_cleanness_assessment` You could get a general overview of how balanced your dataset is."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[36mChecking for missing values\n",
      "\n",
      "\u001b[33mDataframe contains missing values that you should address. \n",
      "\n",
      "columns=['Age', 'Cabin', 'Embarked']\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<style scoped>\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>missing_counts</th>\n",
       "      <th>missing_percent</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>features</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>177</td>\n",
       "      <td>19.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cabin</th>\n",
       "      <td>687</td>\n",
       "      <td>77.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Embarked</th>\n",
       "      <td>2</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          missing_counts  missing_percent\n",
       "features                                 \n",
       "Age                  177             19.9\n",
       "Cabin                687             77.1\n",
       "Embarked               2              0.2"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[39m\n",
      "\n",
      "\u001b[36mChecking for duplicate variables\n",
      "\n",
      "\u001b[32mNo duplicate values in both rows and columns!!!\n",
      "\u001b[39m\n",
      "\n",
      "\u001b[36mChecking for inconsistent values\n",
      "\n",
      "\u001b[32mNo inconsistent feature columns values!!!\n",
      "\u001b[39m\n",
      "\n"
     ]
    }
   ],
   "source": [
    "data_cleanness_assessment(train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Knowing this, you can proceed to preprocess your dataset using the `slik-wrangler.preprocessing` module."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from slik_wrangler import preprocessing as pp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pp.check_datefield(train,'Ticket')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
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       "    .dataframe thead th {\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",
       "      <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",
       "      <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",
       "    </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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pp.change_case(train,'Name','lower').head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "slik-wrangler will identify the data type of each data point, data points with high cardinality and save it in a file. With slik-wrangler, data integrity can be done efficiently to validate downstream data points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[39m\n",
      "--------------- Identifying columns present in the data ---------------\n",
      "\n",
      "Target column is Survived. Attribute in target column:[0, 1]\n",
      "\n",
      "Features with high cardinality:['Name', 'Ticket', 'Cabin']\n",
      "\n",
      "{'cat_feat': ['Name', 'Sex', 'Ticket', 'Cabin', 'Embarked'],\n",
      " 'high_card_feat': ['Name', 'Ticket', 'Cabin'],\n",
      " 'id_column': 'PassengerId',\n",
      " 'input_columns': ['Pclass',\n",
      "                   'Name',\n",
      "                   'Sex',\n",
      "                   'Age',\n",
      "                   'SibSp',\n",
      "                   'Parch',\n",
      "                   'Ticket',\n",
      "                   'Fare',\n",
      "                   'Cabin',\n",
      "                   'Embarked'],\n",
      " 'lower_cat': ['Sex', 'Embarked'],\n",
      " 'num_feat': ['Pclass', 'Age', 'SibSp', 'Parch', 'Fare'],\n",
      " 'parse_dates': [],\n",
      " 'target_column': 'Survived'}\n",
      "\n",
      "Attributes are stored in data\\metadata\n",
      "\n"
     ]
    }
   ],
   "source": [
    "pp.identify_columns(train,'Survived','PassengerId',project_path='./data')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can map your data observations more efficiently with Slik by passing the dictionary of the observation you want to map/rename"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[39m\n",
      "--------------- Mapping passed column ---------------\n",
      "\n",
      "male was mapped to 1\n",
      "\n",
      "female was mapped to 0\n",
      "\n"
     ]
    },
    {
     "data": {
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
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       "      <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",
       "      <td>1</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",
       "      <td>0</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",
       "      <td>0</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",
       "      <td>0</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",
       "      <td>1</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  transformed_Sex  \n",
       "0      0         A/5 21171   7.2500   NaN        S                1  \n",
       "1      0          PC 17599  71.2833   C85        C                0  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S                0  \n",
       "3      0            113803  53.1000  C123        S                0  \n",
       "4      0            373450   8.0500   NaN        S                1  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pp.map_column(train,column_name='Sex',items={'male':1,'female':0}).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "slik-wrangler currently relies on the Interquartile range approach to detect outliers present in a data point. slik-wrangler also fixes the outlier present in the data using different methods like replacing an outlier with the mean of the data point. You can also select the numerical features you want to perform the operation on. You can also display a table identifying at least 'n' outliers in a row."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[39m\n",
      "--------------- Table identifying at least 2 outliers in a row ---------------\n",
      "\n"
     ]
    },
    {
     "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",
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       "    }\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>745</th>\n",
       "      <td>746</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Crosby, Capt. Edward Gifford</td>\n",
       "      <td>male</td>\n",
       "      <td>70.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>WE/P 5735</td>\n",
       "      <td>71.00</td>\n",
       "      <td>B22</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Fortune, Mr. Charles Alexander</td>\n",
       "      <td>male</td>\n",
       "      <td>19.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>19950</td>\n",
       "      <td>263.00</td>\n",
       "      <td>C23 C25 C27</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>89</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Fortune, Miss. Mabel Helen</td>\n",
       "      <td>female</td>\n",
       "      <td>23.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>19950</td>\n",
       "      <td>263.00</td>\n",
       "      <td>C23 C25 C27</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>160</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Sage, Master. Thomas Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>CA. 2343</td>\n",
       "      <td>69.55</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>181</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Sage, Miss. Constance Gladys</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>CA. 2343</td>\n",
       "      <td>69.55</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>202</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Sage, Mr. Frederick</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>CA. 2343</td>\n",
       "      <td>69.55</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>324</th>\n",
       "      <td>325</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Sage, Mr. George John Jr</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>CA. 2343</td>\n",
       "      <td>69.55</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>341</th>\n",
       "      <td>342</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Fortune, Miss. Alice Elizabeth</td>\n",
       "      <td>female</td>\n",
       "      <td>24.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>19950</td>\n",
       "      <td>263.00</td>\n",
       "      <td>C23 C25 C27</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>792</th>\n",
       "      <td>793</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Sage, Miss. Stella Anna</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>CA. 2343</td>\n",
       "      <td>69.55</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>846</th>\n",
       "      <td>847</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Sage, Mr. Douglas Bullen</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>CA. 2343</td>\n",
       "      <td>69.55</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>863</th>\n",
       "      <td>864</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Sage, Miss. Dorothy Edith \"Dolly\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>CA. 2343</td>\n",
       "      <td>69.55</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                               Name     Sex  \\\n",
       "745          746         0       1       Crosby, Capt. Edward Gifford    male   \n",
       "27            28         0       1     Fortune, Mr. Charles Alexander    male   \n",
       "88            89         1       1         Fortune, Miss. Mabel Helen  female   \n",
       "159          160         0       3         Sage, Master. Thomas Henry    male   \n",
       "180          181         0       3       Sage, Miss. Constance Gladys  female   \n",
       "201          202         0       3                Sage, Mr. Frederick    male   \n",
       "324          325         0       3           Sage, Mr. George John Jr    male   \n",
       "341          342         1       1     Fortune, Miss. Alice Elizabeth  female   \n",
       "792          793         0       3            Sage, Miss. Stella Anna  female   \n",
       "846          847         0       3           Sage, Mr. Douglas Bullen    male   \n",
       "863          864         0       3  Sage, Miss. Dorothy Edith \"Dolly\"  female   \n",
       "\n",
       "      Age  SibSp  Parch     Ticket    Fare        Cabin Embarked  \n",
       "745  70.0      1      1  WE/P 5735   71.00          B22        S  \n",
       "27   19.0      3      2      19950  263.00  C23 C25 C27        S  \n",
       "88   23.0      3      2      19950  263.00  C23 C25 C27        S  \n",
       "159   NaN      8      2   CA. 2343   69.55          NaN        S  \n",
       "180   NaN      8      2   CA. 2343   69.55          NaN        S  \n",
       "201   NaN      8      2   CA. 2343   69.55          NaN        S  \n",
       "324   NaN      8      2   CA. 2343   69.55          NaN        S  \n",
       "341  24.0      3      2      19950  263.00  C23 C25 C27        S  \n",
       "792   NaN      8      2   CA. 2343   69.55          NaN        S  \n",
       "846   NaN      8      2   CA. 2343   69.55          NaN        S  \n",
       "863   NaN      8      2   CA. 2343   69.55          NaN        S  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "det = pp.detect_fix_outliers(train,target_column='Survived',n=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we have seen with the data quality assessment module, with slik you can also check the mssing values in your data and even plot a percentage distribution to see the top 30 missing values in your dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[39m\n",
      "--------------- Count and Percentage of missing value ---------------\n",
      "\n"
     ]
    },
    {
     "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>features</th>\n",
       "      <th>missing_counts</th>\n",
       "      <th>missing_percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PassengerId</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Survived</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Pclass</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Name</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Sex</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Age</td>\n",
       "      <td>177</td>\n",
       "      <td>19.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>SibSp</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Parch</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Ticket</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Fare</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Cabin</td>\n",
       "      <td>687</td>\n",
       "      <td>77.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Embarked</td>\n",
       "      <td>2</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       features  missing_counts  missing_percent\n",
       "0   PassengerId               0              0.0\n",
       "1      Survived               0              0.0\n",
       "2        Pclass               0              0.0\n",
       "3          Name               0              0.0\n",
       "4           Sex               0              0.0\n",
       "5           Age             177             19.9\n",
       "6         SibSp               0              0.0\n",
       "7         Parch               0              0.0\n",
       "8        Ticket               0              0.0\n",
       "9          Fare               0              0.0\n",
       "10        Cabin             687             77.1\n",
       "11     Embarked               2              0.2"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pp.check_nan(train,plot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "slik-wrangler helps to handle the missing values in your data intelligently and efficiently. You can choose a strategy to handle your numerical features \n",
    "\n",
    "and pass a value for fillna params to handle your categorical features or fill it with the mode by default. \n",
    "\n",
    "You can also drop missing values across the rows and columns using threshold parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Dropping rows with 75% missing value: Number of records dropped is 0\n",
      "\n",
      "Dropping Columns with 50% missing value: ['Cabin']\n",
      "\n",
      "New data shape is (891, 11)\n"
     ]
    }
   ],
   "source": [
    "data = pp.handle_nan(dataframe=train,target_name='Survived',strategy='mean',fillna='mode',\n",
    "                     drop_outliers=True,thresh_x=75,thresh_y=50,display_inline=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Beyond slik-wrangler preprocessing abilities, you can also engineer new features intelligently. \n",
    "slik-wrangler can help you bin/discretize your age column intelligently and creating new data points with the transformations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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",
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       "      <td>Mid-Age</td>\n",
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       "</table>\n",
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      ],
      "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  \\\n",
       "0                              Braund, Mr. Owen Harris    male  22.000000   \n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.000000   \n",
       "2                               Heikkinen, Miss. Laina  female  26.000000   \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.000000   \n",
       "4                             Allen, Mr. William Henry    male  35.000000   \n",
       "..                                                 ...     ...        ...   \n",
       "886                              Montvila, Rev. Juozas    male  27.000000   \n",
       "887                       Graham, Miss. Margaret Edith  female  19.000000   \n",
       "888           Johnston, Miss. Catherine Helen \"Carrie\"  female  29.081737   \n",
       "889                              Behr, Mr. Karl Howell    male  26.000000   \n",
       "890                                Dooley, Mr. Patrick    male  32.000000   \n",
       "\n",
       "     SibSp     Parch            Ticket       Fare Embarked   binned_Age  \n",
       "0      1.0  0.000000         A/5 21171   7.250000        S  Young Adult  \n",
       "1      1.0  0.000000          PC 17599  32.204208        C      Mid-Age  \n",
       "2      0.0  0.000000  STON/O2. 3101282   7.925000        S  Young Adult  \n",
       "3      1.0  0.000000            113803  53.100000        S      Mid-Age  \n",
       "4      0.0  0.000000            373450   8.050000        S      Mid-Age  \n",
       "..     ...       ...               ...        ...      ...          ...  \n",
       "886    0.0  0.000000            211536  13.000000        S  Young Adult  \n",
       "887    0.0  0.000000            112053  30.000000        S  Young Adult  \n",
       "888    1.0  0.381594        W./C. 6607  23.450000        S  Young Adult  \n",
       "889    0.0  0.000000            111369  30.000000        C  Young Adult  \n",
       "890    0.0  0.000000            370376   7.750000        Q      Mid-Age  \n",
       "\n",
       "[891 rows x 12 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pp.bin_age(data,'Age')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "with Slik you can infer the schema of your pandas dataframe and save the schema file in a project path you define"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[39m\n",
      "--------------- Creating Schema file ---------------\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'dtype': {'PassengerId': 'int64',\n",
       "  'Survived': 'int64',\n",
       "  'Pclass': 'int64',\n",
       "  'Name': 'object',\n",
       "  'Sex': 'object',\n",
       "  'Age': 'float64',\n",
       "  'SibSp': 'int64',\n",
       "  'Parch': 'int64',\n",
       "  'Ticket': 'object',\n",
       "  'Fare': 'float64',\n",
       "  'Cabin': 'object',\n",
       "  'Embarked': 'object'}}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# import ,yaml\n",
    "pp.create_schema_file(train,target_column='Survived',id_column='PassengerId',save=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "with Slik you can also drop uninformative field in your pandas dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[39m\n",
      "--------------- Dropping uninformative fields ---------------\n",
      "\n",
      "uninformative fields dropped: []\n"
     ]
    },
    {
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       "      <td>0</td>\n",
       "      <td>3</td>\n",
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       "      <td>male</td>\n",
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       "  </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": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pp.drop_uninformative_fields(train,exclude='Parch')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Slik helps you manage your data points better by handling different management operations techniques on pandas dataframe based on columns. \n",
    "\n",
    "Operations include selecting of columns, dropping column and dropping duplicates. By selecting the list data points that you need to perform the transformation on and choosing the particular transformation you want "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[39m\n",
      "--------------- Dropping duplicates across the columns ---------------\n",
      "\n",
      "New datashape is (891, 12)\n"
     ]
    },
    {
     "data": {
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       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
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       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
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       "</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": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pp.manage_columns(train,['PassengerId'],drop_duplicates='columns').head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Trim whitespaces from ends of each value across all data points in a pandas dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<p>891 rows × 12 columns</p>\n",
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      ],
      "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": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pp.trim_all_columns(train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Slik can clean your data in one line of code. The `slik.preprocessing.preprocess` function cleans your data\n",
    "by removing outliers present in the data, handing missing values, featurizing datetime columns, and mapping relevant columns.\n",
    "The functions saves the preprocessed file in a project path that you specify."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The task for preprocessing is classification\n",
      "\n",
      "Dropping rows with 75% missing value: Number of records dropped is 0\n",
      "\n",
      "Dropping Columns with 75% missing value: ['Cabin']\n",
      "\n",
      "New data shape is (891, 11)\n",
      "\u001b[39m\n",
      "--------------- Mapping target columns ---------------\n",
      "\n",
      "0 was mapped to 0\n",
      "\n",
      "1 was mapped to 1\n",
      "\n",
      "\u001b[39m\n",
      "--------------- Bucketize Age columns ---------------\n",
      "\n",
      " Inferred age column: [Age]\n",
      "\u001b[39m\n",
      "--------------- Mapping passed column ---------------\n",
      "\n",
      "male was mapped to 0\n",
      "\n",
      "female was mapped to 1\n",
      "\n",
      "\u001b[39m\n",
      "--------------- Dropping uninformative fields ---------------\n",
      "\n",
      "uninformative fields dropped: []\n",
      "\u001b[39m\n",
      "--------------- Creating Schema file ---------------\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'dtype': {'PassengerId': 'int64',\n",
       "  'Pclass': 'int64',\n",
       "  'Name': 'object',\n",
       "  'Age': 'float64',\n",
       "  'SibSp': 'float64',\n",
       "  'Parch': 'float64',\n",
       "  'Ticket': 'object',\n",
       "  'Fare': 'float64',\n",
       "  'Embarked': 'object',\n",
       "  'transformed_Survived': 'int64',\n",
       "  'binned_Age': 'object',\n",
       "  'transformed_Sex': 'int64'}}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Schema file stored in Titanic\\data\\metadata\n",
      "\u001b[39m\n",
      "--------------- Preview the preprocessed data ---------------\n",
      "\n"
     ]
    },
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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>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>transformed_Survived</th>\n",
       "      <th>binned_Age</th>\n",
       "      <th>transformed_Sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.250000</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>Young Adult</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>32.204208</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "      <td>Mid-Age</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.925000</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>Young Adult</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.100000</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>Mid-Age</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.050000</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>Mid-Age</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass                                               Name  \\\n",
       "0            1       3                            Braund, Mr. Owen Harris   \n",
       "1            2       1  Cumings, Mrs. John Bradley (Florence Briggs Th...   \n",
       "2            3       3                             Heikkinen, Miss. Laina   \n",
       "3            4       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)   \n",
       "4            5       3                           Allen, Mr. William Henry   \n",
       "\n",
       "    Age  SibSp  Parch            Ticket       Fare Embarked  \\\n",
       "0  22.0    1.0    0.0         A/5 21171   7.250000        S   \n",
       "1  38.0    1.0    0.0          PC 17599  32.204208        C   \n",
       "2  26.0    0.0    0.0  STON/O2. 3101282   7.925000        S   \n",
       "3  35.0    1.0    0.0            113803  53.100000        S   \n",
       "4  35.0    0.0    0.0            373450   8.050000        S   \n",
       "\n",
       "   transformed_Survived   binned_Age  transformed_Sex  \n",
       "0                     0  Young Adult                0  \n",
       "1                     1      Mid-Age                1  \n",
       "2                     1  Young Adult                1  \n",
       "3                     1      Mid-Age                1  \n",
       "4                     0      Mid-Age                0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[39m\n",
      "--------------- Preprocessed data saved ---------------\n",
      "\n",
      "\n",
      " Input data preprocessed successfully and stored in ./Titanic\\data\\train_data.pkl\n",
      "\n"
     ]
    }
   ],
   "source": [
    "pp.preprocess(data=train,target_column='Survived',train=True,display_inline=True,project_path='./Titanic',logging='display')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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