1-Data_Analysis_with_Python/DAwPy_Session10-Postclass.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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>id</th>\n",
       "      <th>staff</th>\n",
       "      <th>department</th>\n",
       "      <th>job</th>\n",
       "      <th>salary</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M0001</td>\n",
       "      <td>Tom BLUE</td>\n",
       "      <td>HR</td>\n",
       "      <td>manager</td>\n",
       "      <td>\"$150,000\"</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M0002</td>\n",
       "      <td>JOHN BLACK</td>\n",
       "      <td>IT</td>\n",
       "      <td>manager</td>\n",
       "      <td>\"$180,000\"</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>E0001</td>\n",
       "      <td>Micheal Brown</td>\n",
       "      <td>IT</td>\n",
       "      <td>data scientist</td>\n",
       "      <td>\"$150,000\"</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>E0002</td>\n",
       "      <td>jason walker</td>\n",
       "      <td>HR</td>\n",
       "      <td>recruiter</td>\n",
       "      <td>130000dolar</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>E0003</td>\n",
       "      <td>Alex Green</td>\n",
       "      <td>IT</td>\n",
       "      <td>backend developer</td>\n",
       "      <td>\"$110,000\"</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>E0004</td>\n",
       "      <td>OSCAR SMİTH</td>\n",
       "      <td>IT</td>\n",
       "      <td>frontend developer</td>\n",
       "      <td>\"$120,000\"</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>E0005</td>\n",
       "      <td>Adrian STAR</td>\n",
       "      <td>IT</td>\n",
       "      <td>data scientist</td>\n",
       "      <td>\"$135,000\"</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>E0006</td>\n",
       "      <td>Albert simon</td>\n",
       "      <td>IT</td>\n",
       "      <td>data scientist</td>\n",
       "      <td>125000dolar</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id          staff department                 job       salary age\n",
       "0  M0001       Tom BLUE         HR             manager   \"$150,000\"  52\n",
       "1  M0002     JOHN BLACK         IT             manager   \"$180,000\"  48\n",
       "2  E0001  Micheal Brown         IT      data scientist   \"$150,000\"  35\n",
       "3  E0002   jason walker         HR           recruiter  130000dolar  38\n",
       "4  E0003     Alex Green         IT   backend developer   \"$110,000\"   -\n",
       "5  E0004    OSCAR SMİTH         IT  frontend developer   \"$120,000\"  32\n",
       "6  E0005    Adrian STAR         IT      data scientist   \"$135,000\"  40\n",
       "7  E0006   Albert simon         IT      data scientist  125000dolar  35"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_excel(\"text_exercise.XLSX\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 8 entries, 0 to 7\n",
      "Data columns (total 6 columns):\n",
      " #   Column      Non-Null Count  Dtype \n",
      "---  ------      --------------  ----- \n",
      " 0   id          8 non-null      object\n",
      " 1   staff       8 non-null      object\n",
      " 2   department  8 non-null      object\n",
      " 3   job         8 non-null      object\n",
      " 4   salary      8 non-null      object\n",
      " 5   age         8 non-null      object\n",
      "dtypes: object(6)\n",
      "memory usage: 512.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         Tom BLUE\n",
       "1       JOHN BLACK\n",
       "2    Micheal Brown\n",
       "3     jason walker\n",
       "4       Alex Green\n",
       "5      OSCAR SMİTH\n",
       "6      Adrian STAR\n",
       "7     Albert simon\n",
       "Name: staff, dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.staff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         tom blue\n",
       "1       john black\n",
       "2    micheal brown\n",
       "3     jason walker\n",
       "4       alex green\n",
       "5     oscar smi̇th\n",
       "6      adrian star\n",
       "7     albert simon\n",
       "Name: staff, dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.staff.str.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         TOM BLUE\n",
       "1       JOHN BLACK\n",
       "2    MICHEAL BROWN\n",
       "3     JASON WALKER\n",
       "4       ALEX GREEN\n",
       "5      OSCAR SMİTH\n",
       "6      ADRIAN STAR\n",
       "7     ALBERT SIMON\n",
       "Name: staff, dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.staff.str.upper()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         Tom Blue\n",
       "1       John Black\n",
       "2    Micheal Brown\n",
       "3     Jason Walker\n",
       "4       Alex Green\n",
       "5     Oscar Smi̇th\n",
       "6      Adrian Star\n",
       "7     Albert Simon\n",
       "Name: staff, dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.staff.str.title()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         Tom blue\n",
       "1       John black\n",
       "2    Micheal brown\n",
       "3     Jason walker\n",
       "4       Alex green\n",
       "5     Oscar smi̇th\n",
       "6      Adrian star\n",
       "7     Albert simon\n",
       "Name: staff, dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.staff.str.capitalize()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         tOM blue\n",
       "1       john black\n",
       "2    mICHEAL bROWN\n",
       "3     JASON WALKER\n",
       "4       aLEX gREEN\n",
       "5     oscar smi̇th\n",
       "6      aDRIAN star\n",
       "7     aLBERT SIMON\n",
       "Name: staff, dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.staff.str.swapcase()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         Tom Blue\n",
       "1       John Black\n",
       "2    Micheal Brown\n",
       "3     Jason Walker\n",
       "4       Alex Green\n",
       "5     Oscar Smi̇th\n",
       "6      Adrian Star\n",
       "7     Albert Simon\n",
       "Name: staff, dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"staff\"] = df.staff.str.title()\n",
    "df.staff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>staff</th>\n",
       "      <th>department</th>\n",
       "      <th>job</th>\n",
       "      <th>salary</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M0001</td>\n",
       "      <td>Tom Blue</td>\n",
       "      <td>HR</td>\n",
       "      <td>manager</td>\n",
       "      <td>\"$150,000\"</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M0002</td>\n",
       "      <td>John Black</td>\n",
       "      <td>IT</td>\n",
       "      <td>manager</td>\n",
       "      <td>\"$180,000\"</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>E0001</td>\n",
       "      <td>Micheal Brown</td>\n",
       "      <td>IT</td>\n",
       "      <td>data scientist</td>\n",
       "      <td>\"$150,000\"</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>E0002</td>\n",
       "      <td>Jason Walker</td>\n",
       "      <td>HR</td>\n",
       "      <td>recruiter</td>\n",
       "      <td>130000dolar</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>E0003</td>\n",
       "      <td>Alex Green</td>\n",
       "      <td>IT</td>\n",
       "      <td>backend developer</td>\n",
       "      <td>\"$110,000\"</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>E0004</td>\n",
       "      <td>Oscar Smi̇th</td>\n",
       "      <td>IT</td>\n",
       "      <td>frontend developer</td>\n",
       "      <td>\"$120,000\"</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>E0005</td>\n",
       "      <td>Adrian Star</td>\n",
       "      <td>IT</td>\n",
       "      <td>data scientist</td>\n",
       "      <td>\"$135,000\"</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>E0006</td>\n",
       "      <td>Albert Simon</td>\n",
       "      <td>IT</td>\n",
       "      <td>data scientist</td>\n",
       "      <td>125000dolar</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id          staff department                 job       salary age\n",
       "0  M0001       Tom Blue         HR             manager   \"$150,000\"  52\n",
       "1  M0002     John Black         IT             manager   \"$180,000\"  48\n",
       "2  E0001  Micheal Brown         IT      data scientist   \"$150,000\"  35\n",
       "3  E0002   Jason Walker         HR           recruiter  130000dolar  38\n",
       "4  E0003     Alex Green         IT   backend developer   \"$110,000\"   -\n",
       "5  E0004   Oscar Smi̇th         IT  frontend developer   \"$120,000\"  32\n",
       "6  E0005    Adrian Star         IT      data scientist   \"$135,000\"  40\n",
       "7  E0006   Albert Simon         IT      data scientist  125000dolar  35"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     True\n",
       "1     True\n",
       "2    False\n",
       "3     True\n",
       "4    False\n",
       "5    False\n",
       "6    False\n",
       "7    False\n",
       "Name: job, dtype: bool"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.job.str.isalpha()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df.job[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      NaN\n",
       "1      NaN\n",
       "2      NaN\n",
       "3      NaN\n",
       "4    False\n",
       "5      NaN\n",
       "6      NaN\n",
       "7      NaN\n",
       "Name: age, dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.age.str.isnumeric()\n",
    "# df içindeki veriler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('O')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.age.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "int"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df.age[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     True\n",
       "1     True\n",
       "2     True\n",
       "3     True\n",
       "4    False\n",
       "5     True\n",
       "6     True\n",
       "7     True\n",
       "Name: age, dtype: bool"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.age.astype(\"str\").str.isnumeric()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.age = df.age.astype(\"str\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df.age[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.age.replace(\"-\", np.nan, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>id</th>\n",
       "      <th>staff</th>\n",
       "      <th>department</th>\n",
       "      <th>job</th>\n",
       "      <th>salary</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M0001</td>\n",
       "      <td>Tom Blue</td>\n",
       "      <td>HR</td>\n",
       "      <td>manager</td>\n",
       "      <td>\"$150,000\"</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M0002</td>\n",
       "      <td>John Black</td>\n",
       "      <td>IT</td>\n",
       "      <td>manager</td>\n",
       "      <td>\"$180,000\"</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>E0001</td>\n",
       "      <td>Micheal Brown</td>\n",
       "      <td>IT</td>\n",
       "      <td>data scientist</td>\n",
       "      <td>\"$150,000\"</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>E0002</td>\n",
       "      <td>Jason Walker</td>\n",
       "      <td>HR</td>\n",
       "      <td>recruiter</td>\n",
       "      <td>130000dolar</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>E0003</td>\n",
       "      <td>Alex Green</td>\n",
       "      <td>IT</td>\n",
       "      <td>backend developer</td>\n",
       "      <td>\"$110,000\"</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>E0004</td>\n",
       "      <td>Oscar Smi̇th</td>\n",
       "      <td>IT</td>\n",
       "      <td>frontend developer</td>\n",
       "      <td>\"$120,000\"</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>E0005</td>\n",
       "      <td>Adrian Star</td>\n",
       "      <td>IT</td>\n",
       "      <td>data scientist</td>\n",
       "      <td>\"$135,000\"</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>E0006</td>\n",
       "      <td>Albert Simon</td>\n",
       "      <td>IT</td>\n",
       "      <td>data scientist</td>\n",
       "      <td>125000dolar</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id          staff department                 job       salary  age\n",
       "0  M0001       Tom Blue         HR             manager   \"$150,000\"   52\n",
       "1  M0002     John Black         IT             manager   \"$180,000\"   48\n",
       "2  E0001  Micheal Brown         IT      data scientist   \"$150,000\"   35\n",
       "3  E0002   Jason Walker         HR           recruiter  130000dolar   38\n",
       "4  E0003     Alex Green         IT   backend developer   \"$110,000\"  NaN\n",
       "5  E0004   Oscar Smi̇th         IT  frontend developer   \"$120,000\"   32\n",
       "6  E0005    Adrian Star         IT      data scientist   \"$135,000\"   40\n",
       "7  E0006   Albert Simon         IT      data scientist  125000dolar   35"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.age = df.age.astype(\"float\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>M0001</td>\n",
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       "      <td>\"$150,000\"</td>\n",
       "      <td>52.0</td>\n",
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       "      <th>1</th>\n",
       "      <td>M0002</td>\n",
       "      <td>John Black</td>\n",
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       "      <td>manager</td>\n",
       "      <td>\"$180,000\"</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>E0001</td>\n",
       "      <td>Micheal Brown</td>\n",
       "      <td>IT</td>\n",
       "      <td>data scientist</td>\n",
       "      <td>\"$150,000\"</td>\n",
       "      <td>35.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>E0002</td>\n",
       "      <td>Jason Walker</td>\n",
       "      <td>HR</td>\n",
       "      <td>recruiter</td>\n",
       "      <td>130000dolar</td>\n",
       "      <td>38.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>E0003</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
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       "      <td>E0004</td>\n",
       "      <td>Oscar Smi̇th</td>\n",
       "      <td>IT</td>\n",
       "      <td>frontend developer</td>\n",
       "      <td>\"$120,000\"</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>E0005</td>\n",
       "      <td>Adrian Star</td>\n",
       "      <td>IT</td>\n",
       "      <td>data scientist</td>\n",
       "      <td>\"$135,000\"</td>\n",
       "      <td>40.0</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>E0006</td>\n",
       "      <td>Albert Simon</td>\n",
       "      <td>IT</td>\n",
       "      <td>data scientist</td>\n",
       "      <td>125000dolar</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "      id          staff department                 job       salary   age\n",
       "0  M0001       Tom Blue         HR             manager   \"$150,000\"  52.0\n",
       "1  M0002     John Black         IT             manager   \"$180,000\"  48.0\n",
       "2  E0001  Micheal Brown         IT      data scientist   \"$150,000\"  35.0\n",
       "3  E0002   Jason Walker         HR           recruiter  130000dolar  38.0\n",
       "4  E0003     Alex Green         IT   backend developer   \"$110,000\"   NaN\n",
       "5  E0004   Oscar Smi̇th         IT  frontend developer   \"$120,000\"  32.0\n",
       "6  E0005    Adrian Star         IT      data scientist   \"$135,000\"  40.0\n",
       "7  E0006   Albert Simon         IT      data scientist  125000dolar  35.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     \"$150,000\"\n",
       "1     \"$180,000\"\n",
       "2     \"$150,000\"\n",
       "3    130000dolar\n",
       "4     \"$110,000\"\n",
       "5     \"$120,000\"\n",
       "6     \"$135,000\"\n",
       "7    125000dolar\n",
       "Name: salary, dtype: object"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.salary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1    False\n",
       "2    False\n",
       "3     True\n",
       "4    False\n",
       "5    False\n",
       "6    False\n",
       "7     True\n",
       "Name: salary, dtype: bool"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.salary.str.isalnum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0               manager\n",
       "1               manager\n",
       "2        data scientist\n",
       "3             recruiter\n",
       "4     backend developer\n",
       "5    frontend developer\n",
       "6        data scientist\n",
       "7        data scientist\n",
       "Name: job, dtype: object"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.job"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1    False\n",
       "2     True\n",
       "3    False\n",
       "4    False\n",
       "5    False\n",
       "6     True\n",
       "7     True\n",
       "Name: job, dtype: bool"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.job.str.startswith(\"data\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>\"$150,000\"</td>\n",
       "      <td>35.0</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>E0005</td>\n",
       "      <td>Adrian Star</td>\n",
       "      <td>IT</td>\n",
       "      <td>data scientist</td>\n",
       "      <td>\"$135,000\"</td>\n",
       "      <td>40.0</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>E0006</td>\n",
       "      <td>Albert Simon</td>\n",
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       "      <td>35.0</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "      id          staff department             job       salary   age\n",
       "2  E0001  Micheal Brown         IT  data scientist   \"$150,000\"  35.0\n",
       "6  E0005    Adrian Star         IT  data scientist   \"$135,000\"  40.0\n",
       "7  E0006   Albert Simon         IT  data scientist  125000dolar  35.0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df.job.str.startswith(\"data\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1    False\n",
       "2    False\n",
       "3    False\n",
       "4     True\n",
       "5     True\n",
       "6    False\n",
       "7    False\n",
       "Name: job, dtype: bool"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.job.str.endswith(\"per\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      "text/plain": [
       "      id         staff department                 job      salary   age\n",
       "4  E0003    Alex Green         IT   backend developer  \"$110,000\"   NaN\n",
       "5  E0004  Oscar Smi̇th         IT  frontend developer  \"$120,000\"  32.0"
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     "execution_count": 23,
     "metadata": {},
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    "\n",
    "df.loc[df.job.str.endswith(\"per\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0               manager\n",
       "1               manager\n",
       "2        data scientist\n",
       "3             recruiter\n",
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       "7        data scientist\n",
       "Name: job, dtype: object"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.job"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1    False\n",
       "2    False\n",
       "3    False\n",
       "4     True\n",
       "5     True\n",
       "6    False\n",
       "7    False\n",
       "Name: job, dtype: bool"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.job.str.contains(\"end\")"
   ]
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   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>32.0</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "      id         staff department                 job      salary   age\n",
       "4  E0003    Alex Green         IT   backend developer  \"$110,000\"   NaN\n",
       "5  E0004  Oscar Smi̇th         IT  frontend developer  \"$120,000\"  32.0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df.job.str.contains(\"end\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     \"$150,000\"\n",
       "1     \"$180,000\"\n",
       "2     \"$150,000\"\n",
       "3    130000dolar\n",
       "4     \"$110,000\"\n",
       "5     \"$120,000\"\n",
       "6     \"$135,000\"\n",
       "7    125000dolar\n",
       "Name: salary, dtype: object"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.salary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1    False\n",
       "2    False\n",
       "3     True\n",
       "4    False\n",
       "5    False\n",
       "6    False\n",
       "7     True\n",
       "Name: salary, dtype: bool"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.salary.str.contains(\"[a-z]\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      id         staff department             job       salary   age\n",
       "3  E0002  Jason Walker         HR       recruiter  130000dolar  38.0\n",
       "7  E0006  Albert Simon         IT  data scientist  125000dolar  35.0"
      ]
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     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df.salary.str.contains(\"[a-z]\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>E0002</td>\n",
       "      <td>Jason Walker</td>\n",
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       "      <td>recruiter</td>\n",
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       "      <td>38.0</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>E0006</td>\n",
       "      <td>Albert Simon</td>\n",
       "      <td>IT</td>\n",
       "      <td>data scientist</td>\n",
       "      <td>125000dolar</td>\n",
       "      <td>35.0</td>\n",
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      "text/plain": [
       "      id         staff department             job       salary   age\n",
       "3  E0002  Jason Walker         HR       recruiter  130000dolar  38.0\n",
       "7  E0006  Albert Simon         IT  data scientist  125000dolar  35.0"
      ]
     },
     "execution_count": 30,
     "metadata": {},
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   ],
   "source": [
    "df.loc[df.salary.str.contains(\"[a-z]+\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>\"$180,000\"</td>\n",
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       "      <th>4</th>\n",
       "      <td>E0003</td>\n",
       "      <td>Alex Green</td>\n",
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       "      <td>\"$110,000\"</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>E0004</td>\n",
       "      <td>Oscar Smi̇th</td>\n",
       "      <td>IT</td>\n",
       "      <td>frontend developer</td>\n",
       "      <td>\"$120,000\"</td>\n",
       "      <td>32.0</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>E0005</td>\n",
       "      <td>Adrian Star</td>\n",
       "      <td>IT</td>\n",
       "      <td>data scientist</td>\n",
       "      <td>\"$135,000\"</td>\n",
       "      <td>40.0</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>E0006</td>\n",
       "      <td>Albert Simon</td>\n",
       "      <td>IT</td>\n",
       "      <td>data scientist</td>\n",
       "      <td>125000dolar</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id          staff department                 job       salary   age\n",
       "0  M0001       Tom Blue         HR             manager   \"$150,000\"  52.0\n",
       "1  M0002     John Black         IT             manager   \"$180,000\"  48.0\n",
       "2  E0001  Micheal Brown         IT      data scientist   \"$150,000\"  35.0\n",
       "3  E0002   Jason Walker         HR           recruiter  130000dolar  38.0\n",
       "4  E0003     Alex Green         IT   backend developer   \"$110,000\"   NaN\n",
       "5  E0004   Oscar Smi̇th         IT  frontend developer   \"$120,000\"  32.0\n",
       "6  E0005    Adrian Star         IT      data scientist   \"$135,000\"  40.0\n",
       "7  E0006   Albert Simon         IT      data scientist  125000dolar  35.0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    IT\n",
       "6    IT\n",
       "7    IT\n",
       "Name: department, dtype: object"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df.job.str.startswith(\"data\"), \"department\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[df.job.str.startswith(\"data\"), \"department\"] = \"DS\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>48.0</td>\n",
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       "      <td>DS</td>\n",
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       "      <td>\"$110,000\"</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>E0004</td>\n",
       "      <td>Oscar Smi̇th</td>\n",
       "      <td>IT</td>\n",
       "      <td>frontend developer</td>\n",
       "      <td>\"$120,000\"</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>E0005</td>\n",
       "      <td>Adrian Star</td>\n",
       "      <td>DS</td>\n",
       "      <td>data scientist</td>\n",
       "      <td>\"$135,000\"</td>\n",
       "      <td>40.0</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>E0006</td>\n",
       "      <td>Albert Simon</td>\n",
       "      <td>DS</td>\n",
       "      <td>data scientist</td>\n",
       "      <td>125000dolar</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id          staff department                 job       salary   age\n",
       "0  M0001       Tom Blue         HR             manager   \"$150,000\"  52.0\n",
       "1  M0002     John Black         IT             manager   \"$180,000\"  48.0\n",
       "2  E0001  Micheal Brown         DS      data scientist   \"$150,000\"  35.0\n",
       "3  E0002   Jason Walker         HR           recruiter  130000dolar  38.0\n",
       "4  E0003     Alex Green         IT   backend developer   \"$110,000\"   NaN\n",
       "5  E0004   Oscar Smi̇th         IT  frontend developer   \"$120,000\"  32.0\n",
       "6  E0005    Adrian Star         DS      data scientist   \"$135,000\"  40.0\n",
       "7  E0006   Albert Simon         DS      data scientist  125000dolar  35.0"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    150,000\n",
       "1    180,000\n",
       "2    150,000\n",
       "3     130000\n",
       "4    110,000\n",
       "5    120,000\n",
       "6    135,000\n",
       "7     125000\n",
       "Name: salary, dtype: object"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.salary = df.salary.str.strip('$\"dolar')\n",
    "df.salary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    150000\n",
       "1    180000\n",
       "2    150000\n",
       "3    130000\n",
       "4    110000\n",
       "5    120000\n",
       "6    135000\n",
       "7    125000\n",
       "Name: salary, dtype: object"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"salary\"] = df.salary.str.replace(\",\", \"\")\n",
    "df.salary\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "df[\"salary\"] = df.salary.astype(\"int\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "137500.0"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.salary.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>M0001</td>\n",
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       "      <td>52.0</td>\n",
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       "      <td>John Black</td>\n",
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       "      <td>180000</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>E0001</td>\n",
       "      <td>Micheal Brown</td>\n",
       "      <td>DS</td>\n",
       "      <td>data scientist</td>\n",
       "      <td>150000</td>\n",
       "      <td>35.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>E0002</td>\n",
       "      <td>Jason Walker</td>\n",
       "      <td>HR</td>\n",
       "      <td>recruiter</td>\n",
       "      <td>130000</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>E0003</td>\n",
       "      <td>Alex Green</td>\n",
       "      <td>IT</td>\n",
       "      <td>backend developer</td>\n",
       "      <td>110000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>E0004</td>\n",
       "      <td>Oscar Smi̇th</td>\n",
       "      <td>IT</td>\n",
       "      <td>frontend developer</td>\n",
       "      <td>120000</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>E0005</td>\n",
       "      <td>Adrian Star</td>\n",
       "      <td>DS</td>\n",
       "      <td>data scientist</td>\n",
       "      <td>135000</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>E0006</td>\n",
       "      <td>Albert Simon</td>\n",
       "      <td>DS</td>\n",
       "      <td>data scientist</td>\n",
       "      <td>125000</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id          staff department                 job  salary   age\n",
       "0  M0001       Tom Blue         HR             manager  150000  52.0\n",
       "1  M0002     John Black         IT             manager  180000  48.0\n",
       "2  E0001  Micheal Brown         DS      data scientist  150000  35.0\n",
       "3  E0002   Jason Walker         HR           recruiter  130000  38.0\n",
       "4  E0003     Alex Green         IT   backend developer  110000   NaN\n",
       "5  E0004   Oscar Smi̇th         IT  frontend developer  120000  32.0\n",
       "6  E0005    Adrian Star         DS      data scientist  135000  40.0\n",
       "7  E0006   Albert Simon         DS      data scientist  125000  35.0"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         [Tom, Blue]\n",
       "1       [John, Black]\n",
       "2    [Micheal, Brown]\n",
       "3     [Jason, Walker]\n",
       "4       [Alex, Green]\n",
       "5     [Oscar, Smi̇th]\n",
       "6      [Adrian, Star]\n",
       "7     [Albert, Simon]\n",
       "Name: staff, dtype: object"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.staff.str.split()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Tom', 'Blue']"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.staff.str.split()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        Tom\n",
       "1       John\n",
       "2    Micheal\n",
       "3      Jason\n",
       "4       Alex\n",
       "5      Oscar\n",
       "6     Adrian\n",
       "7     Albert\n",
       "Name: staff, dtype: object"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.staff.str.split().str[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      Blue\n",
       "1     Black\n",
       "2     Brown\n",
       "3    Walker\n",
       "4     Green\n",
       "5    Smi̇th\n",
       "6      Star\n",
       "7     Simon\n",
       "Name: staff, dtype: object"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.staff.str.split().str[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        Tom\n",
       "1       John\n",
       "2    Micheal\n",
       "3      Jason\n",
       "4       Alex\n",
       "5      Oscar\n",
       "6     Adrian\n",
       "7     Albert\n",
       "Name: first_name, dtype: object"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"first_name\"] = df.staff.str.split().str[0]\n",
    "df.first_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      Blue\n",
       "1     Black\n",
       "2     Brown\n",
       "3    Walker\n",
       "4     Green\n",
       "5    Smi̇th\n",
       "6      Star\n",
       "7     Simon\n",
       "Name: last_name, dtype: object"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"last_name\"] = df.staff.str.split().str[1]\n",
    "df.last_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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",
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       "    }\n",
       "\n",
       "    .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>id</th>\n",
       "      <th>staff</th>\n",
       "      <th>department</th>\n",
       "      <th>job</th>\n",
       "      <th>salary</th>\n",
       "      <th>age</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M0001</td>\n",
       "      <td>Tom Blue</td>\n",
       "      <td>HR</td>\n",
       "      <td>manager</td>\n",
       "      <td>150000</td>\n",
       "      <td>52.0</td>\n",
       "      <td>Tom</td>\n",
       "      <td>Blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M0002</td>\n",
       "      <td>John Black</td>\n",
       "      <td>IT</td>\n",
       "      <td>manager</td>\n",
       "      <td>180000</td>\n",
       "      <td>48.0</td>\n",
       "      <td>John</td>\n",
       "      <td>Black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>E0001</td>\n",
       "      <td>Micheal Brown</td>\n",
       "      <td>DS</td>\n",
       "      <td>data scientist</td>\n",
       "      <td>150000</td>\n",
       "      <td>35.0</td>\n",
       "      <td>Micheal</td>\n",
       "      <td>Brown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>E0002</td>\n",
       "      <td>Jason Walker</td>\n",
       "      <td>HR</td>\n",
       "      <td>recruiter</td>\n",
       "      <td>130000</td>\n",
       "      <td>38.0</td>\n",
       "      <td>Jason</td>\n",
       "      <td>Walker</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>E0003</td>\n",
       "      <td>Alex Green</td>\n",
       "      <td>IT</td>\n",
       "      <td>backend developer</td>\n",
       "      <td>110000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Alex</td>\n",
       "      <td>Green</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>E0004</td>\n",
       "      <td>Oscar Smi̇th</td>\n",
       "      <td>IT</td>\n",
       "      <td>frontend developer</td>\n",
       "      <td>120000</td>\n",
       "      <td>32.0</td>\n",
       "      <td>Oscar</td>\n",
       "      <td>Smi̇th</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>E0005</td>\n",
       "      <td>Adrian Star</td>\n",
       "      <td>DS</td>\n",
       "      <td>data scientist</td>\n",
       "      <td>135000</td>\n",
       "      <td>40.0</td>\n",
       "      <td>Adrian</td>\n",
       "      <td>Star</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>E0006</td>\n",
       "      <td>Albert Simon</td>\n",
       "      <td>DS</td>\n",
       "      <td>data scientist</td>\n",
       "      <td>125000</td>\n",
       "      <td>35.0</td>\n",
       "      <td>Albert</td>\n",
       "      <td>Simon</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id          staff department                 job  salary   age  \\\n",
       "0  M0001       Tom Blue         HR             manager  150000  52.0   \n",
       "1  M0002     John Black         IT             manager  180000  48.0   \n",
       "2  E0001  Micheal Brown         DS      data scientist  150000  35.0   \n",
       "3  E0002   Jason Walker         HR           recruiter  130000  38.0   \n",
       "4  E0003     Alex Green         IT   backend developer  110000   NaN   \n",
       "5  E0004   Oscar Smi̇th         IT  frontend developer  120000  32.0   \n",
       "6  E0005    Adrian Star         DS      data scientist  135000  40.0   \n",
       "7  E0006   Albert Simon         DS      data scientist  125000  35.0   \n",
       "\n",
       "  first_name last_name  \n",
       "0        Tom      Blue  \n",
       "1       John     Black  \n",
       "2    Micheal     Brown  \n",
       "3      Jason    Walker  \n",
       "4       Alex     Green  \n",
       "5      Oscar    Smi̇th  \n",
       "6     Adrian      Star  \n",
       "7     Albert     Simon  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "del df[\"staff\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>id</th>\n",
       "      <th>department</th>\n",
       "      <th>job</th>\n",
       "      <th>salary</th>\n",
       "      <th>age</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M0001</td>\n",
       "      <td>HR</td>\n",
       "      <td>manager</td>\n",
       "      <td>150000</td>\n",
       "      <td>52.0</td>\n",
       "      <td>Tom</td>\n",
       "      <td>Blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M0002</td>\n",
       "      <td>IT</td>\n",
       "      <td>manager</td>\n",
       "      <td>180000</td>\n",
       "      <td>48.0</td>\n",
       "      <td>John</td>\n",
       "      <td>Black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>E0001</td>\n",
       "      <td>DS</td>\n",
       "      <td>data scientist</td>\n",
       "      <td>150000</td>\n",
       "      <td>35.0</td>\n",
       "      <td>Micheal</td>\n",
       "      <td>Brown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>E0002</td>\n",
       "      <td>HR</td>\n",
       "      <td>recruiter</td>\n",
       "      <td>130000</td>\n",
       "      <td>38.0</td>\n",
       "      <td>Jason</td>\n",
       "      <td>Walker</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>E0003</td>\n",
       "      <td>IT</td>\n",
       "      <td>backend developer</td>\n",
       "      <td>110000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Alex</td>\n",
       "      <td>Green</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>E0004</td>\n",
       "      <td>IT</td>\n",
       "      <td>frontend developer</td>\n",
       "      <td>120000</td>\n",
       "      <td>32.0</td>\n",
       "      <td>Oscar</td>\n",
       "      <td>Smi̇th</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>E0005</td>\n",
       "      <td>DS</td>\n",
       "      <td>data scientist</td>\n",
       "      <td>135000</td>\n",
       "      <td>40.0</td>\n",
       "      <td>Adrian</td>\n",
       "      <td>Star</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>E0006</td>\n",
       "      <td>DS</td>\n",
       "      <td>data scientist</td>\n",
       "      <td>125000</td>\n",
       "      <td>35.0</td>\n",
       "      <td>Albert</td>\n",
       "      <td>Simon</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id department                 job  salary   age first_name last_name\n",
       "0  M0001         HR             manager  150000  52.0        Tom      Blue\n",
       "1  M0002         IT             manager  180000  48.0       John     Black\n",
       "2  E0001         DS      data scientist  150000  35.0    Micheal     Brown\n",
       "3  E0002         HR           recruiter  130000  38.0      Jason    Walker\n",
       "4  E0003         IT   backend developer  110000   NaN       Alex     Green\n",
       "5  E0004         IT  frontend developer  120000  32.0      Oscar    Smi̇th\n",
       "6  E0005         DS      data scientist  135000  40.0     Adrian      Star\n",
       "7  E0006         DS      data scientist  125000  35.0     Albert     Simon"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'department', 'job', 'salary', 'age', 'first_name', 'last_name'], dtype='object')"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0               manager\n",
       "1               manager\n",
       "2        data scientist\n",
       "3             recruiter\n",
       "4     backend developer\n",
       "5    frontend developer\n",
       "6        data scientist\n",
       "7        data scientist\n",
       "Name: job, dtype: object"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.job"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   -1\n",
       "1   -1\n",
       "2   -1\n",
       "3   -1\n",
       "4    8\n",
       "5    9\n",
       "6   -1\n",
       "7   -1\n",
       "Name: job, dtype: int64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.job.str.find(\"developer\")\n",
    "#-1 dönen satırların içinde \"developer\" kelimesi yok\n",
    "# 4 ve 5 indexli satırlarda var . 4.satırda 8. indexten itibaren ifade başlıuyr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   -1\n",
       "1   -1\n",
       "2    2\n",
       "3    6\n",
       "4   -1\n",
       "5    4\n",
       "6    2\n",
       "7    2\n",
       "Name: job, dtype: int64"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.job.str.find(\"t\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    -1\n",
       "1    -1\n",
       "2    13\n",
       "3     6\n",
       "4    -1\n",
       "5     4\n",
       "6    13\n",
       "7    13\n",
       "Name: job, dtype: int64"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.job.str.rfind(\"t\")\n",
    "# ifadeyi son bulduğu index numarasını döndürür."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on method_descriptor:\n",
      "\n",
      "rfind(...)\n",
      "    S.rfind(sub[, start[, end]]) -> int\n",
      "    \n",
      "    Return the highest index in S where substring sub is found,\n",
      "    such that sub is contained within S[start:end].  Optional\n",
      "    arguments start and end are interpreted as in slice notation.\n",
      "    \n",
      "    Return -1 on failure.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(str.rfind)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0               manager\n",
       "1               manager\n",
       "2        data scientist\n",
       "3             recruiter\n",
       "4     backend developer\n",
       "5    frontend developer\n",
       "6        data scientist\n",
       "7        data scientist\n",
       "Name: job, dtype: object"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.job"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        []\n",
       "1        []\n",
       "2       [d]\n",
       "3        []\n",
       "4    [d, d]\n",
       "5    [d, d]\n",
       "6       [d]\n",
       "7       [d]\n",
       "Name: job, dtype: object"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.job.str.findall(\"d\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        []\n",
       "1        []\n",
       "2    [data]\n",
       "3        []\n",
       "4        []\n",
       "5        []\n",
       "6    [data]\n",
       "7    [data]\n",
       "Name: job, dtype: object"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.job.str.findall(\"data\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2\n",
       "1    2\n",
       "2    2\n",
       "3    0\n",
       "4    1\n",
       "5    0\n",
       "6    2\n",
       "7    2\n",
       "Name: job, dtype: int64"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.job.str.findall(\"a\").apply(len)\n",
    "# a ifadesi hangi satırda kaç dfa geçmiş onu buldu."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe thead th {\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>id</th>\n",
       "      <th>department</th>\n",
       "      <th>job</th>\n",
       "      <th>salary</th>\n",
       "      <th>age</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>skills</th>\n",
       "      <th>Skills</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M0001</td>\n",
       "      <td>HR</td>\n",
       "      <td>manager</td>\n",
       "      <td>150000</td>\n",
       "      <td>52.0</td>\n",
       "      <td>Tom</td>\n",
       "      <td>Blue</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M0002</td>\n",
       "      <td>IT</td>\n",
       "      <td>manager</td>\n",
       "      <td>180000</td>\n",
       "      <td>48.0</td>\n",
       "      <td>John</td>\n",
       "      <td>Black</td>\n",
       "      <td>[Java, C++]</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>E0001</td>\n",
       "      <td>DS</td>\n",
       "      <td>data scientist</td>\n",
       "      <td>150000</td>\n",
       "      <td>35.0</td>\n",
       "      <td>Micheal</td>\n",
       "      <td>Brown</td>\n",
       "      <td>[Python, Tableau, SQL]</td>\n",
       "      <td>[Python, Tableau, SQL]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>E0002</td>\n",
       "      <td>HR</td>\n",
       "      <td>recruiter</td>\n",
       "      <td>130000</td>\n",
       "      <td>38.0</td>\n",
       "      <td>Jason</td>\n",
       "      <td>Walker</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>E0003</td>\n",
       "      <td>IT</td>\n",
       "      <td>backend developer</td>\n",
       "      <td>110000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Alex</td>\n",
       "      <td>Green</td>\n",
       "      <td>[React, Django]</td>\n",
       "      <td>[React, Django]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>E0004</td>\n",
       "      <td>IT</td>\n",
       "      <td>frontend developer</td>\n",
       "      <td>120000</td>\n",
       "      <td>32.0</td>\n",
       "      <td>Oscar</td>\n",
       "      <td>Smi̇th</td>\n",
       "      <td>[JavaScript, Python]</td>\n",
       "      <td>[JavaScript, Python]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>E0005</td>\n",
       "      <td>DS</td>\n",
       "      <td>data scientist</td>\n",
       "      <td>135000</td>\n",
       "      <td>40.0</td>\n",
       "      <td>Adrian</td>\n",
       "      <td>Star</td>\n",
       "      <td>[R, SQL]</td>\n",
       "      <td>[R, SQL]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>E0006</td>\n",
       "      <td>DS</td>\n",
       "      <td>data scientist</td>\n",
       "      <td>125000</td>\n",
       "      <td>35.0</td>\n",
       "      <td>Albert</td>\n",
       "      <td>Simon</td>\n",
       "      <td>[SQL, Python]</td>\n",
       "      <td>[SQL, Python]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id department                 job  salary   age first_name last_name  \\\n",
       "0  M0001         HR             manager  150000  52.0        Tom      Blue   \n",
       "1  M0002         IT             manager  180000  48.0       John     Black   \n",
       "2  E0001         DS      data scientist  150000  35.0    Micheal     Brown   \n",
       "3  E0002         HR           recruiter  130000  38.0      Jason    Walker   \n",
       "4  E0003         IT   backend developer  110000   NaN       Alex     Green   \n",
       "5  E0004         IT  frontend developer  120000  32.0      Oscar    Smi̇th   \n",
       "6  E0005         DS      data scientist  135000  40.0     Adrian      Star   \n",
       "7  E0006         DS      data scientist  125000  35.0     Albert     Simon   \n",
       "\n",
       "                   skills                  Skills  \n",
       "0                      []                      []  \n",
       "1             [Java, C++]                      []  \n",
       "2  [Python, Tableau, SQL]  [Python, Tableau, SQL]  \n",
       "3                      []                      []  \n",
       "4         [React, Django]         [React, Django]  \n",
       "5    [JavaScript, Python]    [JavaScript, Python]  \n",
       "6                [R, SQL]                [R, SQL]  \n",
       "7           [SQL, Python]           [SQL, Python]  "
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"skills\"] = [[],[\"Java\",\"C++\"],[\"Python\",\"Tableau\",\"SQL\"],[],[\"React\",\"Django\"],[\"JavaScript\",\"Python\"],[\"R\",\"SQL\"],[\"SQL\",\"Python\"]]\n",
    "df[\"Skills\"] = [[],[],[\"Python\",\"Tableau\",\"SQL\"],[],[\"React\",\"Django\"],[\"JavaScript\",\"Python\"],[\"R\",\"SQL\"],[\"SQL\",\"Python\"]]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "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>id</th>\n",
       "      <th>department</th>\n",
       "      <th>job</th>\n",
       "      <th>salary</th>\n",
       "      <th>age</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>skills</th>\n",
       "      <th>Skills</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M0001</td>\n",
       "      <td>HR</td>\n",
       "      <td>manager</td>\n",
       "      <td>150000</td>\n",
       "      <td>52.0</td>\n",
       "      <td>Tom</td>\n",
       "      <td>Blue</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M0002</td>\n",
       "      <td>IT</td>\n",
       "      <td>manager</td>\n",
       "      <td>180000</td>\n",
       "      <td>48.0</td>\n",
       "      <td>John</td>\n",
       "      <td>Black</td>\n",
       "      <td>[Java, C++]</td>\n",
       "      <td>Java, C++</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>E0001</td>\n",
       "      <td>DS</td>\n",
       "      <td>data scientist</td>\n",
       "      <td>150000</td>\n",
       "      <td>35.0</td>\n",
       "      <td>Micheal</td>\n",
       "      <td>Brown</td>\n",
       "      <td>[Python, Tableau, SQL]</td>\n",
       "      <td>[Python, Tableau, SQL]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>E0002</td>\n",
       "      <td>HR</td>\n",
       "      <td>recruiter</td>\n",
       "      <td>130000</td>\n",
       "      <td>38.0</td>\n",
       "      <td>Jason</td>\n",
       "      <td>Walker</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>E0003</td>\n",
       "      <td>IT</td>\n",
       "      <td>backend developer</td>\n",
       "      <td>110000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Alex</td>\n",
       "      <td>Green</td>\n",
       "      <td>[React, Django]</td>\n",
       "      <td>[React, Django]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>E0004</td>\n",
       "      <td>IT</td>\n",
       "      <td>frontend developer</td>\n",
       "      <td>120000</td>\n",
       "      <td>32.0</td>\n",
       "      <td>Oscar</td>\n",
       "      <td>Smi̇th</td>\n",
       "      <td>[JavaScript, Python]</td>\n",
       "      <td>[JavaScript, Python]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>E0005</td>\n",
       "      <td>DS</td>\n",
       "      <td>data scientist</td>\n",
       "      <td>135000</td>\n",
       "      <td>40.0</td>\n",
       "      <td>Adrian</td>\n",
       "      <td>Star</td>\n",
       "      <td>[R, SQL]</td>\n",
       "      <td>[R, SQL]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>E0006</td>\n",
       "      <td>DS</td>\n",
       "      <td>data scientist</td>\n",
       "      <td>125000</td>\n",
       "      <td>35.0</td>\n",
       "      <td>Albert</td>\n",
       "      <td>Simon</td>\n",
       "      <td>[SQL, Python]</td>\n",
       "      <td>[SQL, Python]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id department                 job  salary   age first_name last_name  \\\n",
       "0  M0001         HR             manager  150000  52.0        Tom      Blue   \n",
       "1  M0002         IT             manager  180000  48.0       John     Black   \n",
       "2  E0001         DS      data scientist  150000  35.0    Micheal     Brown   \n",
       "3  E0002         HR           recruiter  130000  38.0      Jason    Walker   \n",
       "4  E0003         IT   backend developer  110000   NaN       Alex     Green   \n",
       "5  E0004         IT  frontend developer  120000  32.0      Oscar    Smi̇th   \n",
       "6  E0005         DS      data scientist  135000  40.0     Adrian      Star   \n",
       "7  E0006         DS      data scientist  125000  35.0     Albert     Simon   \n",
       "\n",
       "                   skills                  Skills  \n",
       "0                      []                      []  \n",
       "1             [Java, C++]               Java, C++  \n",
       "2  [Python, Tableau, SQL]  [Python, Tableau, SQL]  \n",
       "3                      []                      []  \n",
       "4         [React, Django]         [React, Django]  \n",
       "5    [JavaScript, Python]    [JavaScript, Python]  \n",
       "6                [R, SQL]                [R, SQL]  \n",
       "7           [SQL, Python]           [SQL, Python]  "
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[1, \"Skills\"] = \"Java, C++\"\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df.skills[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                      \n",
       "1              Java,C++\n",
       "2    Python,Tableau,SQL\n",
       "3                      \n",
       "4          React,Django\n",
       "5     JavaScript,Python\n",
       "6                 R,SQL\n",
       "7            SQL,Python\n",
       "Name: skills, dtype: object"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.skills.str.join(\",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                      \n",
       "1              Java-C++\n",
       "2    Python-Tableau-SQL\n",
       "3                      \n",
       "4          React-Django\n",
       "5     JavaScript-Python\n",
       "6                 R-SQL\n",
       "7            SQL-Python\n",
       "Name: skills, dtype: object"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.skills.str.join(\"-\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                        []\n",
       "1                 Java, C++\n",
       "2    [Python, Tableau, SQL]\n",
       "3                        []\n",
       "4           [React, Django]\n",
       "5      [JavaScript, Python]\n",
       "6                  [R, SQL]\n",
       "7             [SQL, Python]\n",
       "Name: Skills, dtype: object"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Skills"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                      \n",
       "1     J,a,v,a,,, ,C,+,+\n",
       "2    Python,Tableau,SQL\n",
       "3                      \n",
       "4          React,Django\n",
       "5     JavaScript,Python\n",
       "6                 R,SQL\n",
       "7            SQL,Python\n",
       "Name: Skills, dtype: object"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Skills.str.join(\",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.Skills.apply(lambda x : "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'str'>\n"
     ]
    }
   ],
   "source": [
    "names = [\"Fırat, Serhat\"]\n",
    "new_names = \",\".join(names)\n",
    "print(type(new_names))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                        []\n",
       "1                 Java, C++\n",
       "2    [Python, Tableau, SQL]\n",
       "3                        []\n",
       "4           [React, Django]\n",
       "5      [JavaScript, Python]\n",
       "6                  [R, SQL]\n",
       "7             [SQL, Python]\n",
       "Name: Skills, dtype: object"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Skills"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                      \n",
       "1             Java, C++\n",
       "2    Python,Tableau,SQL\n",
       "3                      \n",
       "4          React,Django\n",
       "5     JavaScript,Python\n",
       "6                 R,SQL\n",
       "7            SQL,Python\n",
       "Name: Skills, dtype: object"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Skills.apply(lambda x : \",\".join(x) if type(x) == list else x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                      \n",
       "1             Java, C++\n",
       "2    Python-Tableau-SQL\n",
       "3                      \n",
       "4          React-Django\n",
       "5     JavaScript-Python\n",
       "6                 R-SQL\n",
       "7            SQL-Python\n",
       "Name: Skills, dtype: object"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Skills.apply(lambda x : \"-\".join(x) if type(x) == list else x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['',\n",
       " 'Java, C++',\n",
       " 'Python;Tableau;SQL',\n",
       " '',\n",
       " 'React;Django',\n",
       " 'JavaScript;Python',\n",
       " 'R;SQL',\n",
       " 'SQL;Python']"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[\";\".join(x) if type(x) == list else x for x in df.Skills]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                        []\n",
       "1                 Java, C++\n",
       "2    [Python, Tableau, SQL]\n",
       "3                        []\n",
       "4           [React, Django]\n",
       "5      [JavaScript, Python]\n",
       "6                  [R, SQL]\n",
       "7             [SQL, Python]\n",
       "Name: Skills, dtype: object"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Skills"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                      \n",
       "1             Java, C++\n",
       "2    Python,Tableau,SQL\n",
       "3                      \n",
       "4          React,Django\n",
       "5     JavaScript,Python\n",
       "6                 R,SQL\n",
       "7            SQL,Python\n",
       "Name: Skills, dtype: object"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Skills\"] = df.Skills.apply(lambda x : \",\".join(x) if type(x) == list else x)\n",
    "df.Skills"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    HR\n",
       "1    IT\n",
       "2    DS\n",
       "3    HR\n",
       "4    IT\n",
       "5    IT\n",
       "6    DS\n",
       "7    DS\n",
       "Name: department, dtype: object"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.department"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "   DS  HR  IT\n",
       "0   0   1   0\n",
       "1   0   0   1\n",
       "2   1   0   0\n",
       "3   0   1   0\n",
       "4   0   0   1\n",
       "5   0   0   1\n",
       "6   1   0   0\n",
       "7   1   0   0"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.get_dummies(df.department)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   HR  IT\n",
       "0   1   0\n",
       "1   0   1\n",
       "2   0   0\n",
       "3   1   0\n",
       "4   0   1\n",
       "5   0   1\n",
       "6   0   0\n",
       "7   0   0"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.get_dummies(df.department, drop_first=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Working With Time Data ###"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>id_product</th>\n",
       "      <th>order_date</th>\n",
       "      <th>product_quantity</th>\n",
       "      <th>product_price</th>\n",
       "      <th>entry_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>401</td>\n",
       "      <td>2021-01-23</td>\n",
       "      <td>1.0</td>\n",
       "      <td>541.487603</td>\n",
       "      <td>2018-12-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>416</td>\n",
       "      <td>2020-04-02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>131.181818</td>\n",
       "      <td>2018-12-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>717</td>\n",
       "      <td>2019-03-10</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2035.488500</td>\n",
       "      <td>2018-12-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>778</td>\n",
       "      <td>2019-12-27</td>\n",
       "      <td>1.0</td>\n",
       "      <td>335.988000</td>\n",
       "      <td>2018-12-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>826</td>\n",
       "      <td>2020-02-19</td>\n",
       "      <td>1.0</td>\n",
       "      <td>342.292302</td>\n",
       "      <td>2018-12-04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id_product  order_date  product_quantity  product_price  entry_date\n",
       "0         401  2021-01-23               1.0     541.487603  2018-12-04\n",
       "1         416  2020-04-02               1.0     131.181818  2018-12-04\n",
       "2         717  2019-03-10               1.0    2035.488500  2018-12-04\n",
       "3         778  2019-12-27               1.0     335.988000  2018-12-04\n",
       "4         826  2020-02-19               1.0     342.292302  2018-12-04"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"time_exercise.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 911 entries, 0 to 910\n",
      "Data columns (total 5 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   id_product        911 non-null    int64  \n",
      " 1   order_date        911 non-null    object \n",
      " 2   product_quantity  911 non-null    float64\n",
      " 3   product_price     911 non-null    float64\n",
      " 4   entry_date        911 non-null    object \n",
      "dtypes: float64(2), int64(1), object(2)\n",
      "memory usage: 35.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     2021-01-23\n",
       "1     2020-04-02\n",
       "2     2019-03-10\n",
       "3     2019-12-27\n",
       "4     2020-02-19\n",
       "         ...    \n",
       "906   2020-11-24\n",
       "907   2020-11-24\n",
       "908   2020-11-22\n",
       "909   2021-01-26\n",
       "910   2020-12-06\n",
       "Name: order_date, Length: 911, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.to_datetime(df.order_date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.entry_date = pd.to_datetime(df.entry_date)\n",
    "df.order_date = pd.to_datetime(df.order_date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id_product                   int64\n",
       "order_date          datetime64[ns]\n",
       "product_quantity           float64\n",
       "product_price              float64\n",
       "entry_date          datetime64[ns]\n",
       "dtype: object"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2018-12-04 00:00:00')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.entry_date.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2020-11-26 00:00:00')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.entry_date.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2018-11-14 00:00:00')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.order_date.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2021-02-01 00:00:00')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.order_date.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timedelta('723 days 00:00:00')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.entry_date.max() - df.entry_date.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    15-03-2020\n",
       "1    18-05-2019\n",
       "2    24-07-2018\n",
       "dtype: object"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = pd.Series([\"15-03-2020\", \"18-05-2019\", \"24-07-2018\"])\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'24-07-2018'"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.max() # sonuç döndürdü ancak string içindeki ifadelerin ascci değerlerine göre"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   2020-03-15\n",
       "1   2019-05-18\n",
       "2   2018-07-24\n",
       "dtype: datetime64[ns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.to_datetime(a, infer_datetime_format=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   2020-03-15\n",
       "1   2019-05-18\n",
       "2   2018-07-24\n",
       "dtype: datetime64[ns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.to_datetime(a, format=\"%d-%m-%Y\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Series.dt() ###"
   ]
  },
  {
   "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>id_product</th>\n",
       "      <th>order_date</th>\n",
       "      <th>product_quantity</th>\n",
       "      <th>product_price</th>\n",
       "      <th>entry_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>401</td>\n",
       "      <td>2021-01-23</td>\n",
       "      <td>1.0</td>\n",
       "      <td>541.487603</td>\n",
       "      <td>2018-12-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>416</td>\n",
       "      <td>2020-04-02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>131.181818</td>\n",
       "      <td>2018-12-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>717</td>\n",
       "      <td>2019-03-10</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2035.488500</td>\n",
       "      <td>2018-12-04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id_product order_date  product_quantity  product_price entry_date\n",
       "0         401 2021-01-23               1.0     541.487603 2018-12-04\n",
       "1         416 2020-04-02               1.0     131.181818 2018-12-04\n",
       "2         717 2019-03-10               1.0    2035.488500 2018-12-04"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      2018-12-04\n",
       "1      2018-12-04\n",
       "2      2018-12-04\n",
       "3      2018-12-04\n",
       "4      2018-12-04\n",
       "          ...    \n",
       "906    2020-10-07\n",
       "907    2020-10-07\n",
       "908    2020-11-13\n",
       "909    2020-11-24\n",
       "910    2020-11-26\n",
       "Name: entry_date, Length: 911, dtype: object"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.entry_date.dt.date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      2018\n",
       "1      2018\n",
       "2      2018\n",
       "3      2018\n",
       "4      2018\n",
       "       ... \n",
       "906    2020\n",
       "907    2020\n",
       "908    2020\n",
       "909    2020\n",
       "910    2020\n",
       "Name: entry_date, Length: 911, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.entry_date.dt.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       4\n",
       "1       4\n",
       "2       4\n",
       "3       4\n",
       "4       4\n",
       "       ..\n",
       "906     7\n",
       "907     7\n",
       "908    13\n",
       "909    24\n",
       "910    26\n",
       "Name: entry_date, Length: 911, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.entry_date.dt.day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        Tuesday\n",
       "1        Tuesday\n",
       "2        Tuesday\n",
       "3        Tuesday\n",
       "4        Tuesday\n",
       "         ...    \n",
       "906    Wednesday\n",
       "907    Wednesday\n",
       "908       Friday\n",
       "909      Tuesday\n",
       "910     Thursday\n",
       "Name: entry_date, Length: 911, dtype: object"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.entry_date.dt.day_name()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>id_product</th>\n",
       "      <th>order_date</th>\n",
       "      <th>product_quantity</th>\n",
       "      <th>product_price</th>\n",
       "      <th>entry_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>401</td>\n",
       "      <td>2021-01-23</td>\n",
       "      <td>1.0</td>\n",
       "      <td>541.487603</td>\n",
       "      <td>2018-12-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>416</td>\n",
       "      <td>2020-04-02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>131.181818</td>\n",
       "      <td>2018-12-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>717</td>\n",
       "      <td>2019-03-10</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2035.488500</td>\n",
       "      <td>2018-12-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>778</td>\n",
       "      <td>2019-12-27</td>\n",
       "      <td>1.0</td>\n",
       "      <td>335.988000</td>\n",
       "      <td>2018-12-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>826</td>\n",
       "      <td>2020-02-19</td>\n",
       "      <td>1.0</td>\n",
       "      <td>342.292302</td>\n",
       "      <td>2018-12-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>906</th>\n",
       "      <td>1536842</td>\n",
       "      <td>2020-11-24</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1186.776860</td>\n",
       "      <td>2020-10-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>907</th>\n",
       "      <td>1536842</td>\n",
       "      <td>2020-11-24</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1186.776860</td>\n",
       "      <td>2020-10-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>908</th>\n",
       "      <td>1536887</td>\n",
       "      <td>2020-11-22</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2020-11-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>909</th>\n",
       "      <td>1536952</td>\n",
       "      <td>2021-01-26</td>\n",
       "      <td>1.0</td>\n",
       "      <td>988.429752</td>\n",
       "      <td>2020-11-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>910</th>\n",
       "      <td>1536974</td>\n",
       "      <td>2020-12-06</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2020-11-26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>911 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     id_product order_date  product_quantity  product_price entry_date\n",
       "0           401 2021-01-23               1.0     541.487603 2018-12-04\n",
       "1           416 2020-04-02               1.0     131.181818 2018-12-04\n",
       "2           717 2019-03-10               1.0    2035.488500 2018-12-04\n",
       "3           778 2019-12-27               1.0     335.988000 2018-12-04\n",
       "4           826 2020-02-19               1.0     342.292302 2018-12-04\n",
       "..          ...        ...               ...            ...        ...\n",
       "906     1536842 2020-11-24               1.0    1186.776860 2020-10-07\n",
       "907     1536842 2020-11-24               1.0    1186.776860 2020-10-07\n",
       "908     1536887 2020-11-22               1.0       0.000000 2020-11-13\n",
       "909     1536952 2021-01-26               1.0     988.429752 2020-11-24\n",
       "910     1536974 2020-12-06               1.0       0.000000 2020-11-26\n",
       "\n",
       "[911 rows x 5 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        Tuesday\n",
       "1        Tuesday\n",
       "2        Tuesday\n",
       "3        Tuesday\n",
       "4        Tuesday\n",
       "         ...    \n",
       "906    Wednesday\n",
       "907    Wednesday\n",
       "908       Friday\n",
       "909      Tuesday\n",
       "910     Thursday\n",
       "Name: day_name, Length: 911, dtype: object"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"day_name\"] = df.entry_date.dt.day_name()\n",
    "df.day_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>id_product</th>\n",
       "      <th>order_date</th>\n",
       "      <th>product_quantity</th>\n",
       "      <th>product_price</th>\n",
       "      <th>entry_date</th>\n",
       "      <th>day_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>401</td>\n",
       "      <td>2021-01-23</td>\n",
       "      <td>1.0</td>\n",
       "      <td>541.487603</td>\n",
       "      <td>2018-12-04</td>\n",
       "      <td>Tuesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>416</td>\n",
       "      <td>2020-04-02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>131.181818</td>\n",
       "      <td>2018-12-04</td>\n",
       "      <td>Tuesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>717</td>\n",
       "      <td>2019-03-10</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2035.488500</td>\n",
       "      <td>2018-12-04</td>\n",
       "      <td>Tuesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>778</td>\n",
       "      <td>2019-12-27</td>\n",
       "      <td>1.0</td>\n",
       "      <td>335.988000</td>\n",
       "      <td>2018-12-04</td>\n",
       "      <td>Tuesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>826</td>\n",
       "      <td>2020-02-19</td>\n",
       "      <td>1.0</td>\n",
       "      <td>342.292302</td>\n",
       "      <td>2018-12-04</td>\n",
       "      <td>Tuesday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id_product order_date  product_quantity  product_price entry_date day_name\n",
       "0         401 2021-01-23               1.0     541.487603 2018-12-04  Tuesday\n",
       "1         416 2020-04-02               1.0     131.181818 2018-12-04  Tuesday\n",
       "2         717 2019-03-10               1.0    2035.488500 2018-12-04  Tuesday\n",
       "3         778 2019-12-27               1.0     335.988000 2018-12-04  Tuesday\n",
       "4         826 2020-02-19               1.0     342.292302 2018-12-04  Tuesday"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "day_name",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Input \u001b[1;32mIn [42]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m df\u001b[38;5;241m.\u001b[39mday_name\n",
      "\u001b[1;31mAttributeError\u001b[0m: day_name"
     ]
    }
   ],
   "source": [
    "del df.day_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>id_product</th>\n",
       "      <th>order_date</th>\n",
       "      <th>product_quantity</th>\n",
       "      <th>product_price</th>\n",
       "      <th>entry_date</th>\n",
       "      <th>day_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>401</td>\n",
       "      <td>2021-01-23</td>\n",
       "      <td>1.0</td>\n",
       "      <td>541.487603</td>\n",
       "      <td>2018-12-04</td>\n",
       "      <td>Tuesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>416</td>\n",
       "      <td>2020-04-02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>131.181818</td>\n",
       "      <td>2018-12-04</td>\n",
       "      <td>Tuesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>717</td>\n",
       "      <td>2019-03-10</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2035.488500</td>\n",
       "      <td>2018-12-04</td>\n",
       "      <td>Tuesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>778</td>\n",
       "      <td>2019-12-27</td>\n",
       "      <td>1.0</td>\n",
       "      <td>335.988000</td>\n",
       "      <td>2018-12-04</td>\n",
       "      <td>Tuesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>826</td>\n",
       "      <td>2020-02-19</td>\n",
       "      <td>1.0</td>\n",
       "      <td>342.292302</td>\n",
       "      <td>2018-12-04</td>\n",
       "      <td>Tuesday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id_product order_date  product_quantity  product_price entry_date day_name\n",
       "0         401 2021-01-23               1.0     541.487603 2018-12-04  Tuesday\n",
       "1         416 2020-04-02               1.0     131.181818 2018-12-04  Tuesday\n",
       "2         717 2019-03-10               1.0    2035.488500 2018-12-04  Tuesday\n",
       "3         778 2019-12-27               1.0     335.988000 2018-12-04  Tuesday\n",
       "4         826 2020-02-19               1.0     342.292302 2018-12-04  Tuesday"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(labels=\"day_name\", axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>id_product</th>\n",
       "      <th>order_date</th>\n",
       "      <th>product_quantity</th>\n",
       "      <th>product_price</th>\n",
       "      <th>entry_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>401</td>\n",
       "      <td>2021-01-23</td>\n",
       "      <td>1.0</td>\n",
       "      <td>541.487603</td>\n",
       "      <td>2018-12-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>416</td>\n",
       "      <td>2020-04-02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>131.181818</td>\n",
       "      <td>2018-12-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>717</td>\n",
       "      <td>2019-03-10</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2035.488500</td>\n",
       "      <td>2018-12-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>778</td>\n",
       "      <td>2019-12-27</td>\n",
       "      <td>1.0</td>\n",
       "      <td>335.988000</td>\n",
       "      <td>2018-12-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>826</td>\n",
       "      <td>2020-02-19</td>\n",
       "      <td>1.0</td>\n",
       "      <td>342.292302</td>\n",
       "      <td>2018-12-04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id_product order_date  product_quantity  product_price entry_date\n",
       "0         401 2021-01-23               1.0     541.487603 2018-12-04\n",
       "1         416 2020-04-02               1.0     131.181818 2018-12-04\n",
       "2         717 2019-03-10               1.0    2035.488500 2018-12-04\n",
       "3         778 2019-12-27               1.0     335.988000 2018-12-04\n",
       "4         826 2020-02-19               1.0     342.292302 2018-12-04"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Datetime Module ###"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2022, 5, 14, 20, 10, 42, 382035)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datetime.now()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-05-14 20:10:59.959094\n"
     ]
    }
   ],
   "source": [
    "print(datetime.now())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-05-14 20:11:14.919942\n"
     ]
    }
   ],
   "source": [
    "print(datetime.today())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-05-14 20:11:46.766008\n"
     ]
    }
   ],
   "source": [
    "current_datetime = datetime.now()\n",
    "print(current_datetime)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.date(2022, 5, 14)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "current_datetime.date()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "current_datetime.weekday()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "current_datetime.isoweekday()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.to_datetime(\"1993/05/03\").isoweekday()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### class datetime.timedelta ####"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import timedelta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.timedelta(days=2)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "timedelta(days=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2022, 5, 14, 20, 11, 46, 766008)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "current_datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2022, 5, 12, 20, 11, 46, 766008)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "two_days_before = current_datetime - timedelta(days=2)\n",
    "two_days_before"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2022, 6, 1, 0, 21, 46, 766008)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "current_datetime + timedelta(weeks=2, days=3, hours=4, minutes=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "current_date    2022-05-14 20:11:46.766008\n"
     ]
    }
   ],
   "source": [
    "print(f\"{'current_date': <15}\", current_datetime)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-05-14 20:11:46.766008\n"
     ]
    }
   ],
   "source": [
    "print(current_datetime)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "plus            14 days, 4:10:00\n"
     ]
    }
   ],
   "source": [
    "print(f\"{'plus': <15}\", timedelta(weeks=2, hours=4, minutes=10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.timedelta(days=14, seconds=15000)"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "timedelta(weeks=2, hours=4, minutes=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('1991-08-10 00:00:00')"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.to_datetime(\"08.10.1991\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('1942-10-13 00:00:00')"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.to_datetime(\"13/10/1942\", infer_datetime_format=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timedelta('11235 days 20:23:37.137152')"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datetime.now() - pd.to_datetime(\"08/10/1991\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### strftime() ####"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2022, 5, 14, 20, 11, 46, 766008)"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "current_datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-05-14 20:11:46.766008\n"
     ]
    }
   ],
   "source": [
    "print(current_datetime)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2022"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "current_datetime.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "current_datetime.month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(current_datetime)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "int"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(current_datetime.year)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2022'"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "current_datetime.strftime(\"%Y\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'14'"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "current_datetime.strftime(\"%d\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'05'"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "current_datetime.strftime(\"%m\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2022'"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "current_datetime.strftime(\"%Y\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(current_datetime.strftime(\"%Y\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2022"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "current_datetime.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "int"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(current_datetime.year)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'14'"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "current_datetime.strftime(\"%d\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "current_datetime.day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "int"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(current_datetime.day)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(current_datetime.strftime(\"%d\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'05/14/2022'"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "current_datetime.strftime(\"%m/%d/%Y\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'05.14.2022'"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "current_datetime.strftime(\"%m.%d.%Y\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-05-14 20:11:46.766008\n"
     ]
    }
   ],
   "source": [
    "print(current_datetime)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'05/14/2022, 20'"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "current_datetime.strftime(\"%m/%d/%Y, %H\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'05/14/2022, 20:11:46'"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "current_datetime.strftime(\"%m/%d/%Y, %H:%M:%S\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### strptime() ###"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'21 June, 2018'"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date_string = \"21 June, 2018\"\n",
    "date_string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2018, 6, 21, 0, 0)"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datetime.strptime(date_string, \"%d %B, %Y\")\n",
    "# aynı işlemleri pd.to_datetime ile de yapabiliriz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.date(2018, 6, 21)"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.to_datetime(date_string).date()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2018-06-21\n"
     ]
    }
   ],
   "source": [
    "print(pd.to_datetime(date_string).date())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Operation With Datetime Object ###"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "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>id_product</th>\n",
       "      <th>order_date</th>\n",
       "      <th>product_quantity</th>\n",
       "      <th>product_price</th>\n",
       "      <th>entry_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>401</td>\n",
       "      <td>2021-01-23</td>\n",
       "      <td>1.0</td>\n",
       "      <td>541.487603</td>\n",
       "      <td>2018-12-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>416</td>\n",
       "      <td>2020-04-02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>131.181818</td>\n",
       "      <td>2018-12-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>717</td>\n",
       "      <td>2019-03-10</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2035.488500</td>\n",
       "      <td>2018-12-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>778</td>\n",
       "      <td>2019-12-27</td>\n",
       "      <td>1.0</td>\n",
       "      <td>335.988000</td>\n",
       "      <td>2018-12-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>826</td>\n",
       "      <td>2020-02-19</td>\n",
       "      <td>1.0</td>\n",
       "      <td>342.292302</td>\n",
       "      <td>2018-12-04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id_product order_date  product_quantity  product_price entry_date\n",
       "0         401 2021-01-23               1.0     541.487603 2018-12-04\n",
       "1         416 2020-04-02               1.0     131.181818 2018-12-04\n",
       "2         717 2019-03-10               1.0    2035.488500 2018-12-04\n",
       "3         778 2019-12-27               1.0     335.988000 2018-12-04\n",
       "4         826 2020-02-19               1.0     342.292302 2018-12-04"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id_product                   int64\n",
       "order_date          datetime64[ns]\n",
       "product_quantity           float64\n",
       "product_price              float64\n",
       "entry_date          datetime64[ns]\n",
       "dtype: object"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     781 days\n",
       "1     485 days\n",
       "2      96 days\n",
       "3     388 days\n",
       "4     442 days\n",
       "        ...   \n",
       "906    48 days\n",
       "907    48 days\n",
       "908     9 days\n",
       "909    63 days\n",
       "910    10 days\n",
       "Length: 911, dtype: timedelta64[ns]"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.order_date - df.entry_date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     781 days\n",
       "1     485 days\n",
       "2      96 days\n",
       "3     388 days\n",
       "4     442 days\n",
       "        ...   \n",
       "906    48 days\n",
       "907    48 days\n",
       "908     9 days\n",
       "909    63 days\n",
       "910    10 days\n",
       "Name: time_delta, Length: 911, dtype: timedelta64[ns]"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"time_delta\"] =df.order_date - df.entry_date\n",
    "df.time_delta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "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>id_product</th>\n",
       "      <th>order_date</th>\n",
       "      <th>product_quantity</th>\n",
       "      <th>product_price</th>\n",
       "      <th>entry_date</th>\n",
       "      <th>time_delta</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>401</td>\n",
       "      <td>2021-01-23</td>\n",
       "      <td>1.0</td>\n",
       "      <td>541.487603</td>\n",
       "      <td>2018-12-04</td>\n",
       "      <td>781 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>416</td>\n",
       "      <td>2020-04-02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>131.181818</td>\n",
       "      <td>2018-12-04</td>\n",
       "      <td>485 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>717</td>\n",
       "      <td>2019-03-10</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2035.488500</td>\n",
       "      <td>2018-12-04</td>\n",
       "      <td>96 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>778</td>\n",
       "      <td>2019-12-27</td>\n",
       "      <td>1.0</td>\n",
       "      <td>335.988000</td>\n",
       "      <td>2018-12-04</td>\n",
       "      <td>388 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>826</td>\n",
       "      <td>2020-02-19</td>\n",
       "      <td>1.0</td>\n",
       "      <td>342.292302</td>\n",
       "      <td>2018-12-04</td>\n",
       "      <td>442 days</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id_product order_date  product_quantity  product_price entry_date  \\\n",
       "0         401 2021-01-23               1.0     541.487603 2018-12-04   \n",
       "1         416 2020-04-02               1.0     131.181818 2018-12-04   \n",
       "2         717 2019-03-10               1.0    2035.488500 2018-12-04   \n",
       "3         778 2019-12-27               1.0     335.988000 2018-12-04   \n",
       "4         826 2020-02-19               1.0     342.292302 2018-12-04   \n",
       "\n",
       "  time_delta  \n",
       "0   781 days  \n",
       "1   485 days  \n",
       "2    96 days  \n",
       "3   388 days  \n",
       "4   442 days  "
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id_product                    int64\n",
       "order_date           datetime64[ns]\n",
       "product_quantity            float64\n",
       "product_price               float64\n",
       "entry_date           datetime64[ns]\n",
       "time_delta          timedelta64[ns]\n",
       "dtype: object"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      [781, days]\n",
       "1      [485, days]\n",
       "2       [96, days]\n",
       "3      [388, days]\n",
       "4      [442, days]\n",
       "          ...     \n",
       "906     [48, days]\n",
       "907     [48, days]\n",
       "908      [9, days]\n",
       "909     [63, days]\n",
       "910     [10, days]\n",
       "Name: time_delta, Length: 911, dtype: object"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.time_delta.astype(\"str\").str.split()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      781\n",
       "1      485\n",
       "2       96\n",
       "3      388\n",
       "4      442\n",
       "      ... \n",
       "906     48\n",
       "907     48\n",
       "908      9\n",
       "909     63\n",
       "910     10\n",
       "Name: time_delta, Length: 911, dtype: int32"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.time_delta.astype(\"str\").str.split().str[0].astype(\"int\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.time_delta = df.time_delta.astype(\"str\").str.split().str[0].astype(\"int\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "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>id_product</th>\n",
       "      <th>order_date</th>\n",
       "      <th>product_quantity</th>\n",
       "      <th>product_price</th>\n",
       "      <th>entry_date</th>\n",
       "      <th>time_delta</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>401</td>\n",
       "      <td>2021-01-23</td>\n",
       "      <td>1.0</td>\n",
       "      <td>541.487603</td>\n",
       "      <td>2018-12-04</td>\n",
       "      <td>781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>416</td>\n",
       "      <td>2020-04-02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>131.181818</td>\n",
       "      <td>2018-12-04</td>\n",
       "      <td>485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>717</td>\n",
       "      <td>2019-03-10</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2035.488500</td>\n",
       "      <td>2018-12-04</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>778</td>\n",
       "      <td>2019-12-27</td>\n",
       "      <td>1.0</td>\n",
       "      <td>335.988000</td>\n",
       "      <td>2018-12-04</td>\n",
       "      <td>388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>826</td>\n",
       "      <td>2020-02-19</td>\n",
       "      <td>1.0</td>\n",
       "      <td>342.292302</td>\n",
       "      <td>2018-12-04</td>\n",
       "      <td>442</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id_product order_date  product_quantity  product_price entry_date  \\\n",
       "0         401 2021-01-23               1.0     541.487603 2018-12-04   \n",
       "1         416 2020-04-02               1.0     131.181818 2018-12-04   \n",
       "2         717 2019-03-10               1.0    2035.488500 2018-12-04   \n",
       "3         778 2019-12-27               1.0     335.988000 2018-12-04   \n",
       "4         826 2020-02-19               1.0     342.292302 2018-12-04   \n",
       "\n",
       "   time_delta  \n",
       "0         781  \n",
       "1         485  \n",
       "2          96  \n",
       "3         388  \n",
       "4         442  "
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 911 entries, 0 to 910\n",
      "Data columns (total 6 columns):\n",
      " #   Column            Non-Null Count  Dtype         \n",
      "---  ------            --------------  -----         \n",
      " 0   id_product        911 non-null    int64         \n",
      " 1   order_date        911 non-null    datetime64[ns]\n",
      " 2   product_quantity  911 non-null    float64       \n",
      " 3   product_price     911 non-null    float64       \n",
      " 4   entry_date        911 non-null    datetime64[ns]\n",
      " 5   time_delta        911 non-null    int32         \n",
      "dtypes: datetime64[ns](2), float64(2), int32(1), int64(1)\n",
      "memory usage: 39.3 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id_product', 'order_date', 'product_quantity', 'product_price',\n",
       "       'entry_date', 'time_delta'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id_product\n",
       "401        1\n",
       "416        1\n",
       "717        1\n",
       "778        1\n",
       "826        1\n",
       "          ..\n",
       "1536841    3\n",
       "1536842    2\n",
       "1536887    1\n",
       "1536952    1\n",
       "1536974    1\n",
       "Name: time_delta, Length: 498, dtype: int64"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"id_product\")[\"time_delta\"].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id_product\n",
       "1535922   -213\n",
       "1535919   -211\n",
       "1535914   -209\n",
       "1535912   -170\n",
       "1535957   -127\n",
       "          ... \n",
       "5655       781\n",
       "401        781\n",
       "12237      785\n",
       "5094       787\n",
       "4845       790\n",
       "Name: time_delta, Length: 498, dtype: int32"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"id_product\")[\"time_delta\"].min().sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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