pandas_course2.ipynb

{
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Address</th>\n",
       "      <th>Qualification</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jai</td>\n",
       "      <td>27</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Msc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Princi</td>\n",
       "      <td>24</td>\n",
       "      <td>Kanpur</td>\n",
       "      <td>MA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Gaurav</td>\n",
       "      <td>22</td>\n",
       "      <td>Allahabad</td>\n",
       "      <td>MCA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Anuj</td>\n",
       "      <td>32</td>\n",
       "      <td>Kannauj</td>\n",
       "      <td>Phd</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "     Name  Age    Address Qualification\n",
       "0     Jai   27      Delhi           Msc\n",
       "1  Princi   24     Kanpur            MA\n",
       "2  Gaurav   22  Allahabad           MCA\n",
       "3    Anuj   32    Kannauj           Phd"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import pandas package \n",
    "import pandas as pd \n",
    "\n",
    "# Define a dictionary containing employee data \n",
    "data = {'Name':['Jai', 'Princi', 'Gaurav', 'Anuj'], \n",
    "\t\t'Age':[27, 24, 22, 32], \n",
    "\t\t'Address':['Delhi', 'Kanpur', 'Allahabad', 'Kannauj'], \n",
    "\t\t'Qualification':['Msc', 'MA', 'MCA', 'Phd']} \n",
    "\n",
    "# Convert the dictionary into DataFrame \n",
    "df = pd.DataFrame(data) \n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Address</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>27</td>\n",
       "      <td>Delhi</td>\n",
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       "      <th>1</th>\n",
       "      <td>24</td>\n",
       "      <td>Kanpur</td>\n",
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      "text/plain": [
       "   Age Address\n",
       "0   27   Delhi\n",
       "1   24  Kanpur"
      ]
     },
     "execution_count": 100,
     "metadata": {},
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    }
   ],
   "source": [
    "# iloc[row slicing, column slicing] \n",
    "df.iloc [0:2, 1:3] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>temp_c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Portland</th>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Berkeley</th>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "          temp_c\n",
       "Portland    17.0\n",
       "Berkeley    25.0"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'temp_c': [17.0, 25.0]}, index=['Portland', 'Berkeley'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
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       "      <th>Portland</th>\n",
       "      <td>17.0</td>\n",
       "      <td>62.6</td>\n",
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       "    <tr>\n",
       "      <th>Berkeley</th>\n",
       "      <td>25.0</td>\n",
       "      <td>77.0</td>\n",
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      "text/plain": [
       "          temp_c  temp_f\n",
       "Portland    17.0    62.6\n",
       "Berkeley    25.0    77.0"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "df=df.assign(temp_f=lambda x: x.temp_c * 9 / 5 + 32)\n",
    "df"
   ]
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  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
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       "          temp_c  temp_f\n",
       "Portland    17.0    62.6\n",
       "Berkeley    25.0    77.0"
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     "execution_count": 103,
     "metadata": {},
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   "source": [
    "df=df.assign(temp_f=df['temp_c'] * 9 / 5 + 32)\n",
    "df"
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  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
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       "          temp_c\n",
       "Portland    17.0\n",
       "Berkeley    25.0"
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     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df=df.drop(columns=['temp_f'])\n",
    "df"
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   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
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       "      <td>62.6</td>\n",
       "      <td>290.15</td>\n",
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       "    <tr>\n",
       "      <th>Berkeley</th>\n",
       "      <td>25.0</td>\n",
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       "          temp_c  temp_f  temp_k\n",
       "Portland    17.0    62.6  290.15\n",
       "Berkeley    25.0    77.0  298.15"
      ]
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     "execution_count": 105,
     "metadata": {},
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   "source": [
    "df=df.assign(temp_f=lambda x: x['temp_c'] * 9 / 5 + 32, temp_k=lambda x: (x['temp_f'] +  459.67) * 5 / 9)\n",
    "df"
   ]
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  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "      a     b     c     d\n",
       "0     1     2     3     4\n",
       "1   100   200   300   400\n",
       "2  1000  2000  3000  4000"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydict = [{'a': 1, 'b': 2, 'c': 3, 'd': 4},\n",
    "           {'a': 100, 'b': 200, 'c': 300, 'd': 400},\n",
    "           {'a': 1000, 'b': 2000, 'c': 3000, 'd': 4000 }]\n",
    "df = pd.DataFrame(mydict)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1\n",
       "b    2\n",
       "c    3\n",
       "d    4\n",
       "Name: 0, dtype: int64"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0]"
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  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "   a  b  c  d\n",
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     "execution_count": 108,
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   "source": [
    "df.iloc[[0]]  #double []  [0] = row 0, columns = [a,b,c,d] = [0, :]"
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   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
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     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df.iloc[0,:]"
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      "text/plain": [
       "      a     b     c     d\n",
       "0     1     2     3     4\n",
       "1   100   200   300   400\n",
       "2  1000  2000  3000  4000"
      ]
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     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[:3]"
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  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
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       "      <td>3000</td>\n",
       "      <td>4000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      a     b     c     d\n",
       "0     1     2     3     4\n",
       "2  1000  2000  3000  4000"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[[True, False, True]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "prenom={'claude':79,'helene':73,'jeanfrans':54,'steph':50,'karine':41,'cindy':28,'yohan':22,\n",
    "    'thibo':21,'camille':24,'timothe':14,'aaron':4}\n",
    "prenom2={'aldo':46,'maria':46,'nat':42,'lea':16,'clea':17,'candice':15,'tom':10,'fouad':37}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'claude': 79,\n",
       "  'helene': 73,\n",
       "  'jeanfrans': 54,\n",
       "  'steph': 50,\n",
       "  'karine': 41,\n",
       "  'cindy': 28,\n",
       "  'yohan': 22,\n",
       "  'thibo': 21,\n",
       "  'camille': 24,\n",
       "  'timothe': 14,\n",
       "  'aaron': 4},\n",
       " {'aldo': 46,\n",
       "  'maria': 46,\n",
       "  'nat': 42,\n",
       "  'lea': 16,\n",
       "  'clea': 17,\n",
       "  'candice': 15,\n",
       "  'tom': 10,\n",
       "  'fouad': 37}]"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "delrey_dict=[prenom,prenom2]\n",
    "delrey_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>claude</th>\n",
       "      <th>helene</th>\n",
       "      <th>jeanfrans</th>\n",
       "      <th>steph</th>\n",
       "      <th>karine</th>\n",
       "      <th>cindy</th>\n",
       "      <th>yohan</th>\n",
       "      <th>thibo</th>\n",
       "      <th>camille</th>\n",
       "      <th>timothe</th>\n",
       "      <th>aaron</th>\n",
       "      <th>aldo</th>\n",
       "      <th>maria</th>\n",
       "      <th>nat</th>\n",
       "      <th>lea</th>\n",
       "      <th>clea</th>\n",
       "      <th>candice</th>\n",
       "      <th>tom</th>\n",
       "      <th>fouad</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>79.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>46.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>37.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   claude  helene  jeanfrans  steph  karine  cindy  yohan  thibo  camille  \\\n",
       "0    79.0    73.0       54.0   50.0    41.0   28.0   22.0   21.0     24.0   \n",
       "1     NaN     NaN        NaN    NaN     NaN    NaN    NaN    NaN      NaN   \n",
       "\n",
       "   timothe  aaron  aldo  maria   nat   lea  clea  candice   tom  fouad  \n",
       "0     14.0    4.0   NaN    NaN   NaN   NaN   NaN      NaN   NaN    NaN  \n",
       "1      NaN    NaN  46.0   46.0  42.0  16.0  17.0     15.0  10.0   37.0  "
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(delrey_dict)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>2</th>\n",
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       "      <td>D2</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A3</td>\n",
       "      <td>B3</td>\n",
       "      <td>C3</td>\n",
       "      <td>D3</td>\n",
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      "text/plain": [
       "    A   B   C   D\n",
       "0  A0  B0  C0  D0\n",
       "1  A1  B1  C1  D1\n",
       "2  A2  B2  C2  D2\n",
       "3  A3  B3  C3  D3"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Python program to concatenate (union with columns as key)\n",
    "# dataframes with same columns, different rows \n",
    "\n",
    "# Creating first dataframe \n",
    "df1 = pd.DataFrame({'A': ['A0', 'A1', 'A2', 'A3'], \n",
    "                    'B': ['B0', 'B1', 'B2', 'B3'], \n",
    "                    'C': ['C0', 'C1', 'C2', 'C3'], \n",
    "                    'D': ['D0', 'D1', 'D2', 'D3']}, \n",
    "                    index = [0, 1, 2, 3]) \n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th>5</th>\n",
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       "      <td>D5</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>A6</td>\n",
       "      <td>B6</td>\n",
       "      <td>C6</td>\n",
       "      <td>D6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "</div>"
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      "text/plain": [
       "    A   B   C   D\n",
       "4  A4  B4  C4  D4\n",
       "5  A5  B5  C5  D5\n",
       "6  A6  B6  C6  D6"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating second dataframe  3 rows\n",
    "df2 = pd.DataFrame({'A': ['A4', 'A5', 'A6'], \n",
    "                    'B': ['B4', 'B5', 'B6'], \n",
    "                    'C': ['C4', 'C5', 'C6'], \n",
    "                    'D': ['D4', 'D5', 'D6']}, \n",
    "                    index = [4, 5, 6]) \n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>C</th>\n",
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       "      <td>B8</td>\n",
       "      <td>C8</td>\n",
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       "      <th>9</th>\n",
       "      <td>A9</td>\n",
       "      <td>B9</td>\n",
       "      <td>C9</td>\n",
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       "      <th>10</th>\n",
       "      <td>A10</td>\n",
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       "      <td>C10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>A11</td>\n",
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       "      <td>C11</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "      A    B    C\n",
       "8    A8   B8   C8\n",
       "9    A9   B9   C9\n",
       "10  A10  B10  C10\n",
       "11  A11  B11  C11"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating third dataframe  3 columns\n",
    "df3 = pd.DataFrame({'A': ['A8', 'A9', 'A10', 'A11'], \n",
    "                    'B': ['B8', 'B9', 'B10', 'B11'], \n",
    "                    'C': ['C8', 'C9', 'C10', 'C11'] \n",
    "                    }, \n",
    "                    index = [8, 9, 10, 11]) \n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>B4</td>\n",
       "      <td>C4</td>\n",
       "      <td>D4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>A5</td>\n",
       "      <td>B5</td>\n",
       "      <td>C5</td>\n",
       "      <td>D5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>A6</td>\n",
       "      <td>B6</td>\n",
       "      <td>C6</td>\n",
       "      <td>D6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>A8</td>\n",
       "      <td>B8</td>\n",
       "      <td>C8</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>A9</td>\n",
       "      <td>B9</td>\n",
       "      <td>C9</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>A10</td>\n",
       "      <td>B10</td>\n",
       "      <td>C10</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>A11</td>\n",
       "      <td>B11</td>\n",
       "      <td>C11</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      A    B    C    D\n",
       "0    A0   B0   C0   D0\n",
       "1    A1   B1   C1   D1\n",
       "2    A2   B2   C2   D2\n",
       "3    A3   B3   C3   D3\n",
       "4    A4   B4   C4   D4\n",
       "5    A5   B5   C5   D5\n",
       "6    A6   B6   C6   D6\n",
       "8    A8   B8   C8  NaN\n",
       "9    A9   B9   C9  NaN\n",
       "10  A10  B10  C10  NaN\n",
       "11  A11  B11  C11  NaN"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Concatenating the dataframes with different number of columns or different number of rows\n",
    "df=pd.concat([df1, df2, df3], sort=False) \n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "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",
       "    .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>Key</th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>K0</td>\n",
       "      <td>A0</td>\n",
       "      <td>B0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>K1</td>\n",
       "      <td>A1</td>\n",
       "      <td>B1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>K2</td>\n",
       "      <td>A2</td>\n",
       "      <td>B2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>K3</td>\n",
       "      <td>A3</td>\n",
       "      <td>B3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Key   A   B\n",
       "0  K0  A0  B0\n",
       "1  K1  A1  B1\n",
       "2  K2  A2  B2\n",
       "3  K3  A3  B3"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Python program to merge  (union with intersection key)  \n",
    "# dataframes with same rows, different columns and same key column\n",
    "# Dataframe created \n",
    "left = pd.DataFrame({'Key': ['K0', 'K1', 'K2', 'K3'], \n",
    "                    'A': ['A0', 'A1', 'A2', 'A3'], \n",
    "                    'B': ['B0', 'B1', 'B2', 'B3']}) \n",
    "left"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "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>Key</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>K0</td>\n",
       "      <td>C0</td>\n",
       "      <td>D0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>K1</td>\n",
       "      <td>C1</td>\n",
       "      <td>D1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>K2</td>\n",
       "      <td>C2</td>\n",
       "      <td>D2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>K3</td>\n",
       "      <td>C3</td>\n",
       "      <td>D3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Key   C   D\n",
       "0  K0  C0  D0\n",
       "1  K1  C1  D1\n",
       "2  K2  C2  D2\n",
       "3  K3  C3  D3"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "right = pd.DataFrame({'Key': ['K0', 'K1', 'K2', 'K3'],   #same column\n",
    "                    'C': ['C0', 'C1', 'C2', 'C3'], \n",
    "                    'D': ['D0', 'D1', 'D2', 'D3']}) \n",
    "right"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "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>Key</th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>K0</td>\n",
       "      <td>A0</td>\n",
       "      <td>B0</td>\n",
       "      <td>C0</td>\n",
       "      <td>D0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>K1</td>\n",
       "      <td>A1</td>\n",
       "      <td>B1</td>\n",
       "      <td>C1</td>\n",
       "      <td>D1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>K2</td>\n",
       "      <td>A2</td>\n",
       "      <td>B2</td>\n",
       "      <td>C2</td>\n",
       "      <td>D2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>K3</td>\n",
       "      <td>A3</td>\n",
       "      <td>B3</td>\n",
       "      <td>C3</td>\n",
       "      <td>D3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Key   A   B   C   D\n",
       "0  K0  A0  B0  C0  D0\n",
       "1  K1  A1  B1  C1  D1\n",
       "2  K2  A2  B2  C2  D2\n",
       "3  K3  A3  B3  C3  D3"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merging the dataframes                   \n",
    "df=pd.merge(left, right, how ='inner', on ='Key') \n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>K0</th>\n",
       "      <td>A0</td>\n",
       "      <td>B0</td>\n",
       "      <td>C0</td>\n",
       "      <td>D0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>K1</th>\n",
       "      <td>A1</td>\n",
       "      <td>B1</td>\n",
       "      <td>C1</td>\n",
       "      <td>D1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>K2</th>\n",
       "      <td>A2</td>\n",
       "      <td>B2</td>\n",
       "      <td>C2</td>\n",
       "      <td>D2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>K3</th>\n",
       "      <td>A3</td>\n",
       "      <td>B3</td>\n",
       "      <td>C3</td>\n",
       "      <td>D3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A   B   C   D\n",
       "K0  A0  B0  C0  D0\n",
       "K1  A1  B1  C1  D1\n",
       "K2  A2  B2  C2  D2\n",
       "K3  A3  B3  C3  D3"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Python program to join (union)\n",
    "# dataframes with different columns, different rows\n",
    "\n",
    "left = pd.DataFrame({'A': ['A0', 'A1', 'A2', 'A3'], \n",
    "\t\t\t\t\t'B': ['B0', 'B1', 'B2', 'B3']}, \n",
    "\t\t\t\t\tindex = ['K0', 'K1', 'K2', 'K3']) \n",
    "\n",
    "right = pd.DataFrame({'C': ['C0', 'C1', 'C2', 'C3'], \n",
    "\t\t\t\t\t'D': ['D0', 'D1', 'D2', 'D3']}, \n",
    "\t\t\t\t\tindex = ['K0', 'K1', 'K2', 'K3']) \n",
    "\t\t\t\t\t\t\n",
    "# Joining the dataframes \n",
    "left.join(right) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'claude': 79,\n",
       " 'helene': 73,\n",
       " 'jeanfrans': 54,\n",
       " 'steph': 50,\n",
       " 'karine': 41,\n",
       " 'cindy': 28,\n",
       " 'yohan': 22,\n",
       " 'thibo': 21,\n",
       " 'camille': 24,\n",
       " 'timothe': 14,\n",
       " 'aaron': 4}"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kernel=delrey_dict[0]\n",
    "kernel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'aldo': 46,\n",
       " 'maria': 46,\n",
       " 'nat': 42,\n",
       " 'lea': 16,\n",
       " 'clea': 17,\n",
       " 'candice': 15,\n",
       " 'tom': 10,\n",
       " 'fouad': 37}"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "next=delrey_dict[1]\n",
    "next"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "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>claude</th>\n",
       "      <th>helene</th>\n",
       "      <th>jeanfrans</th>\n",
       "      <th>steph</th>\n",
       "      <th>karine</th>\n",
       "      <th>cindy</th>\n",
       "      <th>yohan</th>\n",
       "      <th>thibo</th>\n",
       "      <th>camille</th>\n",
       "      <th>timothe</th>\n",
       "      <th>aaron</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>79</td>\n",
       "      <td>73</td>\n",
       "      <td>54</td>\n",
       "      <td>50</td>\n",
       "      <td>41</td>\n",
       "      <td>28</td>\n",
       "      <td>22</td>\n",
       "      <td>21</td>\n",
       "      <td>24</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   claude  helene  jeanfrans  steph  karine  cindy  yohan  thibo  camille  \\\n",
       "0      79      73         54     50      41     28     22     21       24   \n",
       "\n",
       "   timothe  aaron  \n",
       "0       14      4  "
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_kernel=pd.DataFrame([kernel])\n",
    "df_kernel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "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>aldo</th>\n",
       "      <th>maria</th>\n",
       "      <th>nat</th>\n",
       "      <th>lea</th>\n",
       "      <th>clea</th>\n",
       "      <th>candice</th>\n",
       "      <th>tom</th>\n",
       "      <th>fouad</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>46</td>\n",
       "      <td>46</td>\n",
       "      <td>42</td>\n",
       "      <td>16</td>\n",
       "      <td>17</td>\n",
       "      <td>15</td>\n",
       "      <td>10</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   aldo  maria  nat  lea  clea  candice  tom  fouad\n",
       "0    46     46   42   16    17       15   10     37"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_next=pd.DataFrame([next])\n",
    "df_next"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "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>claude</th>\n",
       "      <th>helene</th>\n",
       "      <th>jeanfrans</th>\n",
       "      <th>steph</th>\n",
       "      <th>karine</th>\n",
       "      <th>cindy</th>\n",
       "      <th>yohan</th>\n",
       "      <th>thibo</th>\n",
       "      <th>camille</th>\n",
       "      <th>timothe</th>\n",
       "      <th>aaron</th>\n",
       "      <th>aldo</th>\n",
       "      <th>maria</th>\n",
       "      <th>nat</th>\n",
       "      <th>lea</th>\n",
       "      <th>clea</th>\n",
       "      <th>candice</th>\n",
       "      <th>tom</th>\n",
       "      <th>fouad</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>79</td>\n",
       "      <td>73</td>\n",
       "      <td>54</td>\n",
       "      <td>50</td>\n",
       "      <td>41</td>\n",
       "      <td>28</td>\n",
       "      <td>22</td>\n",
       "      <td>21</td>\n",
       "      <td>24</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>46</td>\n",
       "      <td>46</td>\n",
       "      <td>42</td>\n",
       "      <td>16</td>\n",
       "      <td>17</td>\n",
       "      <td>15</td>\n",
       "      <td>10</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   claude  helene  jeanfrans  steph  karine  cindy  yohan  thibo  camille  \\\n",
       "0      79      73         54     50      41     28     22     21       24   \n",
       "\n",
       "   timothe  aaron  aldo  maria  nat  lea  clea  candice  tom  fouad  \n",
       "0       14      4    46     46   42   16    17       15   10     37  "
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# joining\n",
    "df=df_kernel.join(df_next)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>K0</th>\n",
       "      <td>A0</td>\n",
       "      <td>B0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>K1</th>\n",
       "      <td>A1</td>\n",
       "      <td>B1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>K2</th>\n",
       "      <td>A2</td>\n",
       "      <td>B2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>K3</th>\n",
       "      <td>A3</td>\n",
       "      <td>B3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A   B\n",
       "K0  A0  B0\n",
       "K1  A1  B1\n",
       "K2  A2  B2\n",
       "K3  A3  B3"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "left"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "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>variable</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>A0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A</td>\n",
       "      <td>A1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A</td>\n",
       "      <td>A2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A</td>\n",
       "      <td>A3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B</td>\n",
       "      <td>B0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>B</td>\n",
       "      <td>B1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>B</td>\n",
       "      <td>B2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>B</td>\n",
       "      <td>B3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  variable value\n",
       "0        A    A0\n",
       "1        A    A1\n",
       "2        A    A2\n",
       "3        A    A3\n",
       "4        B    B0\n",
       "5        B    B1\n",
       "6        B    B2\n",
       "7        B    B3"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m_left = pd.melt(left)\n",
    "m_left"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
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   "outputs": [
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       "variable    A    B\n",
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       "7         NaN   B3"
      ]
     },
     "execution_count": 130,
     "metadata": {},
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    "m_left.pivot(columns='variable', values='value')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
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    "df1"
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       "5  A5  B5  C5  D5\n",
       "6  A6  B6  C6  D6"
      ]
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     "execution_count": 133,
     "metadata": {},
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    "pd.concat([df1,df2])"
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       "     A    B    C    D    A    B    C    D\n",
       "0   A0   B0   C0   D0  NaN  NaN  NaN  NaN\n",
       "1   A1   B1   C1   D1  NaN  NaN  NaN  NaN\n",
       "2   A2   B2   C2   D2  NaN  NaN  NaN  NaN\n",
       "3   A3   B3   C3   D3  NaN  NaN  NaN  NaN\n",
       "4  NaN  NaN  NaN  NaN   A4   B4   C4   D4\n",
       "5  NaN  NaN  NaN  NaN   A5   B5   C5   D5\n",
       "6  NaN  NaN  NaN  NaN   A6   B6   C6   D6"
      ]
     },
     "execution_count": 134,
     "metadata": {},
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    }
   ],
   "source": [
    "pd.concat([df1,df2], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== Executing: ===\n",
      "# Creating second dataframe  3 rows\n",
      "df2 = pd.DataFrame({'A': ['A4', 'A5', 'A6'], \n",
      "                    'B': ['B4', 'B5', 'B6'], \n",
      "                    'C': ['C4', 'C5', 'C6'], \n",
      "                    'D': ['D4', 'D5', 'D6']}, \n",
      "                    index = [4, 5, 6]) \n",
      "df2\n",
      "=== Output: ===\n"
     ]
    },
    {
     "data": {
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       "    A   B   C   D\n",
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     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>A6</td>\n",
       "      <td>B6</td>\n",
       "      <td>C6</td>\n",
       "      <td>D6</td>\n",
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      "text/plain": [
       "   index   A   B   C   D\n",
       "0      4  A4  B4  C4  D4\n",
       "1      5  A5  B5  C5  D5\n",
       "2      6  A6  B6  C6  D6"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# https://ipython.readthedocs.io/en/stable/interactive/magics.html\n",
    "# %recall 19   # for edit\n",
    "\n",
    "%rerun 19\n",
    "df2=df2.reset_index()\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== Executing: ===\n",
      "# Creating second dataframe  3 rows\n",
      "df2 = pd.DataFrame({'A': ['A4', 'A5', 'A6'], \n",
      "                    'B': ['B4', 'B5', 'B6'], \n",
      "                    'C': ['C4', 'C5', 'C6'], \n",
      "                    'D': ['D4', 'D5', 'D6']}, \n",
      "                    index = [4, 5, 6]) \n",
      "df2\n",
      "=== Output: ===\n"
     ]
    },
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     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
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     "execution_count": 145,
     "metadata": {},
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   ],
   "source": [
    "%rerun 19\n",
    "df2=df2.reset_index(drop=True)\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   1: left\n",
      "   2:\n",
      "# Import pandas package \n",
      "import pandas as pd \n",
      "\n",
      "# Define a dictionary containing employee data \n",
      "data = {'Name':['Jai', 'Princi', 'Gaurav', 'Anuj'], \n",
      "        'Age':[27, 24, 22, 32], \n",
      "        'Address':['Delhi', 'Kanpur', 'Allahabad', 'Kannauj'], \n",
      "        'Qualification':['Msc', 'MA', 'MCA', 'Phd']} \n",
      "\n",
      "# Convert the dictionary into DataFrame \n",
      "df = pd.DataFrame(data) \n",
      "df\n",
      "   3:\n",
      "# iloc[row slicing, column slicing] \n",
      "df.iloc [0:2, 1:3]\n",
      "   4:\n",
      "df = pd.DataFrame({'temp_c': [17.0, 25.0]}, index=['Portland', 'Berkeley'])\n",
      "df\n",
      "   5:\n",
      "df=df.assign(temp_f=lambda x: x.temp_c * 9 / 5 + 32)\n",
      "df\n",
      "   6:\n",
      "df=df.assign(temp_f=df['temp_c'] * 9 / 5 + 32)\n",
      "df\n",
      "   7:\n",
      "df=df.drop(columns=['temp_f'])\n",
      "df\n",
      "   8:\n",
      "df=df.assign(temp_f=lambda x: x['temp_c'] * 9 / 5 + 32, temp_k=lambda x: (x['temp_f'] +  459.67) * 5 / 9)\n",
      "df\n",
      "   9:\n",
      "mydict = [{'a': 1, 'b': 2, 'c': 3, 'd': 4},\n",
      "           {'a': 100, 'b': 200, 'c': 300, 'd': 400},\n",
      "           {'a': 1000, 'b': 2000, 'c': 3000, 'd': 4000 }]\n",
      "df = pd.DataFrame(mydict)\n",
      "df\n",
      "  10: df.iloc[0]\n",
      "  11: df.iloc[[0]]  #double []  [0] = row 0, columns = [a,b,c,d] = [0, :]\n",
      "  12: df.iloc[0,:]\n",
      "  13: df.iloc[:3]\n",
      "  14: df.iloc[[True, False, True]]\n",
      "  15:\n",
      "prenom={'claude':79,'helene':73,'jeanfrans':54,'steph':50,'karine':41,'cindy':28,'yohan':22,\n",
      "    'thibo':21,'camille':24,'timothe':14,'aaron':4}\n",
      "prenom2={'aldo':46,'maria':46,'nat':42,'lea':16,'clea':17,'candice':15,'tom':10,'fouad':37}\n",
      "  16:\n",
      "delrey_dict=[prenom,prenom2]\n",
      "delrey_dict\n",
      "  17:\n",
      "df = pd.DataFrame(delrey_dict)\n",
      "df\n",
      "  18:\n",
      "# Python program to concatenate (union with columns as key)\n",
      "# dataframes with same columns, different rows \n",
      "\n",
      "# Creating first dataframe \n",
      "df1 = pd.DataFrame({'A': ['A0', 'A1', 'A2', 'A3'], \n",
      "                    'B': ['B0', 'B1', 'B2', 'B3'], \n",
      "                    'C': ['C0', 'C1', 'C2', 'C3'], \n",
      "                    'D': ['D0', 'D1', 'D2', 'D3']}, \n",
      "                    index = [0, 1, 2, 3]) \n",
      "df1\n",
      "  19:\n",
      "# Creating second dataframe  3 rows\n",
      "df2 = pd.DataFrame({'A': ['A4', 'A5', 'A6'], \n",
      "                    'B': ['B4', 'B5', 'B6'], \n",
      "                    'C': ['C4', 'C5', 'C6'], \n",
      "                    'D': ['D4', 'D5', 'D6']}, \n",
      "                    index = [4, 5, 6]) \n",
      "df2\n",
      "  20:\n",
      "# Creating third dataframe  3 columns\n",
      "df3 = pd.DataFrame({'A': ['A8', 'A9', 'A10', 'A11'], \n",
      "                    'B': ['B8', 'B9', 'B10', 'B11'], \n",
      "                    'C': ['C8', 'C9', 'C10', 'C11'] \n",
      "                    }, \n",
      "                    index = [8, 9, 10, 11]) \n",
      "df3\n",
      "  21:\n",
      "# Concatenating the dataframes with different number of columns or different number of rows\n",
      "df=pd.concat([df1, df2, df3], sort=False) \n",
      "df\n",
      "  22:\n",
      "# Python program to merge  (union with intersection key)  \n",
      "# dataframes with same rows, different columns and same key column\n",
      "# Dataframe created \n",
      "left = pd.DataFrame({'Key': ['K0', 'K1', 'K2', 'K3'], \n",
      "                    'A': ['A0', 'A1', 'A2', 'A3'], \n",
      "                    'B': ['B0', 'B1', 'B2', 'B3']}) \n",
      "left\n",
      "  23:\n",
      "right = pd.DataFrame({'Key': ['K0', 'K1', 'K2', 'K3'],   #same column\n",
      "                    'C': ['C0', 'C1', 'C2', 'C3'], \n",
      "                    'D': ['D0', 'D1', 'D2', 'D3']}) \n",
      "right\n",
      "  24:\n",
      "# Merging the dataframes                   \n",
      "df=pd.merge(left, right, how ='inner', on ='Key') \n",
      "df\n",
      "  25:\n",
      "# Python program to join (union)\n",
      "# dataframes with different columns, different rows\n",
      "\n",
      "left = pd.DataFrame({'A': ['A0', 'A1', 'A2', 'A3'], \n",
      "                    'B': ['B0', 'B1', 'B2', 'B3']}, \n",
      "                    index = ['K0', 'K1', 'K2', 'K3']) \n",
      "\n",
      "right = pd.DataFrame({'C': ['C0', 'C1', 'C2', 'C3'], \n",
      "                    'D': ['D0', 'D1', 'D2', 'D3']}, \n",
      "                    index = ['K0', 'K1', 'K2', 'K3']) \n",
      "                        \n",
      "# Joining the dataframes \n",
      "left.join(right)\n",
      "  26:\n",
      "kernel=delrey_dict[0]\n",
      "kernel\n",
      "  27:\n",
      "next=delrey_dict[1]\n",
      "next\n",
      "  28:\n",
      "df_kernel=pd.DataFrame([kernel])\n",
      "df_kernel\n",
      "  29:\n",
      "df_next=pd.DataFrame([next])\n",
      "df_next\n",
      "  30:\n",
      "# joining\n",
      "df=df_kernel.join(df_next)\n",
      "df\n",
      "  31: left\n",
      "  32:\n",
      "m_left = pd.melt(left)\n",
      "m_left\n",
      "  33: left.pivot(columns='var', values='val')\n",
      "  34: left.pivot(columns='A', values='K0')\n",
      "  35: left.pivot(columns='A', values='A0')\n",
      "  36: m_left.pivot(columns='variable', values='value')\n",
      "  37: df1\n",
      "  38: df2\n",
      "  39: pd.concat([df1,df2])\n",
      "  40: pd.concat([df1,df2], axis=1)\n",
      "  41: m_left.pivot(columns='variable', values='value')\n",
      "  42: m_left.pivot(columns='variable', values='value', axis=1)\n",
      "  43:\n",
      "m_left.pivot(columns='variable', values='value')\n",
      "left.pivot(columns=['A','B'], values=['K0','K1','K2','K3'])\n",
      "  44:\n",
      "m_left.pivot(columns='variable', values='value')\n",
      "left.pivot(columns=['A'], values=['B'])\n",
      "  45:\n",
      "m_left.pivot(columns='variable', values='value')\n",
      "left.pivot(columns=[['A','B'], values=['B'])\n",
      "  46:\n",
      "m_left.pivot(columns='variable', values='value')\n",
      "left.pivot(columns=[['A','B'], values='B')\n",
      "  47:\n",
      "m_left.pivot(columns='variable', values='value')\n",
      "left.pivot(columns=[['A','B'])\n",
      "  48:\n",
      "m_left.pivot(columns='variable', values='value')\n",
      "left.pivot(columns=[['A','B'], values )\n",
      "  49:\n",
      "m_left.pivot(columns='variable', values='value')\n",
      "left.pivot(columns=[['A','B'], values=)\n",
      "  50:\n",
      "m_left.pivot(columns='variable', values='value')\n",
      "left.pivot(columns=[['A','B'], values[])\n",
      "  51:\n",
      "m_left.pivot(columns='variable', values='value')\n",
      "left.pivot(columns=[['A','B'], values['B']]\n",
      "  52:\n",
      "m_left.pivot(columns='variable', values='value')\n",
      "left.pivot(columns=[['A','B'], values['B']]]\n",
      "  53:\n",
      "m_left.pivot(columns='variable', values='value')\n",
      "left.pivot(columns=[['A','B'], values['B']])\n",
      "  54:\n",
      "m_left.pivot(columns='variable', values='value')\n",
      "left.pivot(columns=[['A','B'], B['B1']])\n",
      "  55: m_left.pivot(columns='variable', values='value')\n",
      "  56:\n",
      "df2.reindex()\n",
      "df2\n",
      "  57:\n",
      "df2.reindex([1,2,3])\n",
      "df2\n",
      "  58:\n",
      "df2.index([1,2,3])\n",
      "df2\n",
      "  59:\n",
      "df2.reset_index([1,2,3])\n",
      "df2\n",
      "  60:\n",
      "df2.reset_index()\n",
      "df2\n",
      "  61:\n",
      "df2=df2.reset_index()\n",
      "df2\n",
      "  62:\n",
      "df2=df2.reset_index(drop=True)\n",
      "df2\n",
      "  63:\n",
      "df2=df2.reset_index(drop=True, inplace =True)\n",
      "df2\n",
      "  64:\n",
      "# Creating second dataframe  3 rows\n",
      "df2 = pd.DataFrame({'A': ['A4', 'A5', 'A6'], \n",
      "                    'B': ['B4', 'B5', 'B6'], \n",
      "                    'C': ['C4', 'C5', 'C6'], \n",
      "                    'D': ['D4', 'D5', 'D6']}, \n",
      "                    index = [4, 5, 6]) \n",
      "df2\n",
      "  65:\n",
      "df2=df2.reset_index(drop=True, inplace =True)\n",
      "df2\n",
      "  66:\n",
      "df2=df2.reset_index(drop=True)\n",
      "df2\n",
      "  67:\n",
      "# Python program to concatenate (union with columns as key)\n",
      "# dataframes with same columns, different rows \n",
      "\n",
      "# Creating first dataframe \n",
      "df1 = pd.DataFrame({'A': ['A0', 'A1', 'A2', 'A3'], \n",
      "                    'B': ['B0', 'B1', 'B2', 'B3'], \n",
      "                    'C': ['C0', 'C1', 'C2', 'C3'], \n",
      "                    'D': ['D0', 'D1', 'D2', 'D3']}, \n",
      "                    index = [0, 1, 2, 3]) \n",
      "df1\n",
      "  68:\n",
      "df2=df2.reset_index(drop=True)\n",
      "df2\n",
      "  69:\n",
      "df2.reset_index(drop=True)\n",
      "df2\n",
      "  70: $64\n",
      "  71: [64]\n",
      "  72: $\n",
      "  73: %recall 64\n",
      "  74:\n",
      "%recall 64\n",
      "df2.reset_index(drop=True)\n",
      "df2\n",
      "  75: %run 64\n",
      "  76:\n",
      "# Creating second dataframe  3 rows\n",
      "df2 = pd.DataFrame({'A': ['A4', 'A5', 'A6'], \n",
      "                    'B': ['B4', 'B5', 'B6'], \n",
      "                    'C': ['C4', 'C5', 'C6'], \n",
      "                    'D': ['D4', 'D5', 'D6']}, \n",
      "                    index = [4, 5, 6]) \n",
      "df2\n",
      "  77:\n",
      "df2.reset_index(drop=True)\n",
      "df2\n",
      "  78: %rerun 64\n",
      "  79:\n",
      "# https://ipython.readthedocs.io/en/stable/interactive/magics.html\n",
      "%recall 64   # for edit\n",
      "  80: %rerun 64\n",
      "  81:\n",
      "df2.reset_index(drop=True)\n",
      "df2\n",
      "  82:\n",
      "df2.reset_index()\n",
      "df2\n",
      "  83:\n",
      "# https://ipython.readthedocs.io/en/stable/interactive/magics.html\n",
      "# %recall 64   # for edit\n",
      "\n",
      "%rerun 64\n",
      "  84:\n",
      "df2=df2.reset_index()\n",
      "df2\n",
      "  85: df2.drop('Index')\n",
      "  86:\n",
      "df2=df2.reset_index()\n",
      "df2\n",
      "  87: df2.index\n",
      "  88: df2.index()\n",
      "  89: df2.index[0]\n",
      "  90: df2.index['level_0']\n",
      "  91: df2['level_0']\n",
      "  92:\n",
      "# https://ipython.readthedocs.io/en/stable/interactive/magics.html\n",
      "# %recall 64   # for edit\n",
      "\n",
      "%rerun 64\n",
      "  93:\n",
      "df2=df2.reset_index()\n",
      "df2\n",
      "  94: df2['Index']\n",
      "  95: %rerun 40\n",
      "  96:\n",
      "# https://ipython.readthedocs.io/en/stable/interactive/magics.html\n",
      "# %recall 64   # for edit\n",
      "\n",
      "%rerun 64\n",
      "  97:\n",
      "df2=df2.reset_index(drop='index')\n",
      "df2\n",
      "  98: %rerun 40\n",
      "  99:\n",
      "# Import pandas package \n",
      "import pandas as pd \n",
      "\n",
      "# Define a dictionary containing employee data \n",
      "data = {'Name':['Jai', 'Princi', 'Gaurav', 'Anuj'], \n",
      "        'Age':[27, 24, 22, 32], \n",
      "        'Address':['Delhi', 'Kanpur', 'Allahabad', 'Kannauj'], \n",
      "        'Qualification':['Msc', 'MA', 'MCA', 'Phd']} \n",
      "\n",
      "# Convert the dictionary into DataFrame \n",
      "df = pd.DataFrame(data) \n",
      "df\n",
      " 100:\n",
      "# iloc[row slicing, column slicing] \n",
      "df.iloc [0:2, 1:3]\n",
      " 101:\n",
      "df = pd.DataFrame({'temp_c': [17.0, 25.0]}, index=['Portland', 'Berkeley'])\n",
      "df\n",
      " 102:\n",
      "df=df.assign(temp_f=lambda x: x.temp_c * 9 / 5 + 32)\n",
      "df\n",
      " 103:\n",
      "df=df.assign(temp_f=df['temp_c'] * 9 / 5 + 32)\n",
      "df\n",
      " 104:\n",
      "df=df.drop(columns=['temp_f'])\n",
      "df\n",
      " 105:\n",
      "df=df.assign(temp_f=lambda x: x['temp_c'] * 9 / 5 + 32, temp_k=lambda x: (x['temp_f'] +  459.67) * 5 / 9)\n",
      "df\n",
      " 106:\n",
      "mydict = [{'a': 1, 'b': 2, 'c': 3, 'd': 4},\n",
      "           {'a': 100, 'b': 200, 'c': 300, 'd': 400},\n",
      "           {'a': 1000, 'b': 2000, 'c': 3000, 'd': 4000 }]\n",
      "df = pd.DataFrame(mydict)\n",
      "df\n",
      " 107: df.iloc[0]\n",
      " 108: df.iloc[[0]]  #double []  [0] = row 0, columns = [a,b,c,d] = [0, :]\n",
      " 109: df.iloc[0,:]\n",
      " 110: df.iloc[:3]\n",
      " 111: df.iloc[[True, False, True]]\n",
      " 112:\n",
      "prenom={'claude':79,'helene':73,'jeanfrans':54,'steph':50,'karine':41,'cindy':28,'yohan':22,\n",
      "    'thibo':21,'camille':24,'timothe':14,'aaron':4}\n",
      "prenom2={'aldo':46,'maria':46,'nat':42,'lea':16,'clea':17,'candice':15,'tom':10,'fouad':37}\n",
      " 113:\n",
      "delrey_dict=[prenom,prenom2]\n",
      "delrey_dict\n",
      " 114:\n",
      "df = pd.DataFrame(delrey_dict)\n",
      "df\n",
      " 115:\n",
      "# Python program to concatenate (union with columns as key)\n",
      "# dataframes with same columns, different rows \n",
      "\n",
      "# Creating first dataframe \n",
      "df1 = pd.DataFrame({'A': ['A0', 'A1', 'A2', 'A3'], \n",
      "                    'B': ['B0', 'B1', 'B2', 'B3'], \n",
      "                    'C': ['C0', 'C1', 'C2', 'C3'], \n",
      "                    'D': ['D0', 'D1', 'D2', 'D3']}, \n",
      "                    index = [0, 1, 2, 3]) \n",
      "df1\n",
      " 116:\n",
      "# Creating second dataframe  3 rows\n",
      "df2 = pd.DataFrame({'A': ['A4', 'A5', 'A6'], \n",
      "                    'B': ['B4', 'B5', 'B6'], \n",
      "                    'C': ['C4', 'C5', 'C6'], \n",
      "                    'D': ['D4', 'D5', 'D6']}, \n",
      "                    index = [4, 5, 6]) \n",
      "df2\n",
      " 117:\n",
      "# Creating third dataframe  3 columns\n",
      "df3 = pd.DataFrame({'A': ['A8', 'A9', 'A10', 'A11'], \n",
      "                    'B': ['B8', 'B9', 'B10', 'B11'], \n",
      "                    'C': ['C8', 'C9', 'C10', 'C11'] \n",
      "                    }, \n",
      "                    index = [8, 9, 10, 11]) \n",
      "df3\n",
      " 118:\n",
      "# Concatenating the dataframes with different number of columns or different number of rows\n",
      "df=pd.concat([df1, df2, df3], sort=False) \n",
      "df\n",
      " 119:\n",
      "# Python program to merge  (union with intersection key)  \n",
      "# dataframes with same rows, different columns and same key column\n",
      "# Dataframe created \n",
      "left = pd.DataFrame({'Key': ['K0', 'K1', 'K2', 'K3'], \n",
      "                    'A': ['A0', 'A1', 'A2', 'A3'], \n",
      "                    'B': ['B0', 'B1', 'B2', 'B3']}) \n",
      "left\n",
      " 120:\n",
      "right = pd.DataFrame({'Key': ['K0', 'K1', 'K2', 'K3'],   #same column\n",
      "                    'C': ['C0', 'C1', 'C2', 'C3'], \n",
      "                    'D': ['D0', 'D1', 'D2', 'D3']}) \n",
      "right\n",
      " 121:\n",
      "# Merging the dataframes                   \n",
      "df=pd.merge(left, right, how ='inner', on ='Key') \n",
      "df\n",
      " 122:\n",
      "# Python program to join (union)\n",
      "# dataframes with different columns, different rows\n",
      "\n",
      "left = pd.DataFrame({'A': ['A0', 'A1', 'A2', 'A3'], \n",
      "                    'B': ['B0', 'B1', 'B2', 'B3']}, \n",
      "                    index = ['K0', 'K1', 'K2', 'K3']) \n",
      "\n",
      "right = pd.DataFrame({'C': ['C0', 'C1', 'C2', 'C3'], \n",
      "                    'D': ['D0', 'D1', 'D2', 'D3']}, \n",
      "                    index = ['K0', 'K1', 'K2', 'K3']) \n",
      "                        \n",
      "# Joining the dataframes \n",
      "left.join(right)\n",
      " 123:\n",
      "kernel=delrey_dict[0]\n",
      "kernel\n",
      " 124:\n",
      "next=delrey_dict[1]\n",
      "next\n",
      " 125:\n",
      "df_kernel=pd.DataFrame([kernel])\n",
      "df_kernel\n",
      " 126:\n",
      "df_next=pd.DataFrame([next])\n",
      "df_next\n",
      " 127:\n",
      "# joining\n",
      "df=df_kernel.join(df_next)\n",
      "df\n",
      " 128: left\n",
      " 129:\n",
      "m_left = pd.melt(left)\n",
      "m_left\n",
      " 130: m_left.pivot(columns='variable', values='value')\n",
      " 131: df1\n",
      " 132: df2\n",
      " 133: pd.concat([df1,df2])\n",
      " 134: pd.concat([df1,df2], axis=1)\n",
      " 135:\n",
      "# https://ipython.readthedocs.io/en/stable/interactive/magics.html\n",
      "# %recall 64   # for edit\n",
      "\n",
      "%rerun 64\n",
      " 136:\n",
      "df2=df2.reset_index(drop='index')\n",
      "df2\n",
      " 137: %rerun 40\n",
      " 138: %history\n",
      " 139: %history -n\n"
     ]
    }
   ],
   "source": [
    "%history -n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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