DATA602 Integrating Databases.ipynb

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   "source": [
    "# DATA INTEGRATION\n",
    "import pandas as pd\n",
    "# Load the files\n",
    "df1 = pd.read_csv(\"d:/machine learning/student.csv\")\n",
    "df2 = pd.read_csv(\"d:/machine learning/mark.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Student_id</th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Grade</th>\n",
       "      <th>Employed</th>\n",
       "      <th>Unnamed: 5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>Male</td>\n",
       "      <td>1st Class</td>\n",
       "      <td>yes</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>Female</td>\n",
       "      <td>2nd Class</td>\n",
       "      <td>no</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>18</td>\n",
       "      <td>Male</td>\n",
       "      <td>1st Class</td>\n",
       "      <td>no</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>21</td>\n",
       "      <td>Female</td>\n",
       "      <td>2nd Class</td>\n",
       "      <td>no</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
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       "      <td>Male</td>\n",
       "      <td>1st Class</td>\n",
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       "      <td>NaN</td>\n",
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      "text/plain": [
       "   Student_id  Age  Gender      Grade Employed  Unnamed: 5\n",
       "0           1   19    Male  1st Class      yes         NaN\n",
       "1           2   20  Female  2nd Class       no         NaN\n",
       "2           3   18    Male  1st Class       no         NaN\n",
       "3           4   21  Female  2nd Class       no         NaN\n",
       "4           5   19    Male  1st Class       no         NaN"
      ]
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   ],
   "source": [
    "df1.head()"
   ]
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Student_id</th>\n",
       "      <th>Mark</th>\n",
       "      <th>City</th>\n",
       "      <th>Unnamed: 3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>95</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>70</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>98</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>75</td>\n",
       "      <td>Pune</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>89</td>\n",
       "      <td>Kochi</td>\n",
       "      <td>NaN</td>\n",
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      "text/plain": [
       "   Student_id  Mark     City  Unnamed: 3\n",
       "0           1    95  Chennai         NaN\n",
       "1           2    70    Delhi         NaN\n",
       "2           3    98   Mumbai         NaN\n",
       "3           4    75     Pune         NaN\n",
       "4           5    89    Kochi         NaN"
      ]
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   "source": [
    "df2.head()"
   ]
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Student_id</th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Grade</th>\n",
       "      <th>Employed</th>\n",
       "      <th>Unnamed: 5</th>\n",
       "      <th>Mark</th>\n",
       "      <th>City</th>\n",
       "      <th>Unnamed: 3</th>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>Male</td>\n",
       "      <td>1st Class</td>\n",
       "      <td>yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>95</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
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       "      <td>Female</td>\n",
       "      <td>2nd Class</td>\n",
       "      <td>no</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70</td>\n",
       "      <td>Delhi</td>\n",
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       "      <th>2</th>\n",
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       "      <td>1st Class</td>\n",
       "      <td>no</td>\n",
       "      <td>NaN</td>\n",
       "      <td>98</td>\n",
       "      <td>Mumbai</td>\n",
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       "      <td>2nd Class</td>\n",
       "      <td>no</td>\n",
       "      <td>NaN</td>\n",
       "      <td>75</td>\n",
       "      <td>Pune</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>5</td>\n",
       "      <td>19</td>\n",
       "      <td>Male</td>\n",
       "      <td>1st Class</td>\n",
       "      <td>no</td>\n",
       "      <td>NaN</td>\n",
       "      <td>89</td>\n",
       "      <td>Kochi</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>20</td>\n",
       "      <td>Male</td>\n",
       "      <td>2nd Class</td>\n",
       "      <td>yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>69</td>\n",
       "      <td>Gwalior</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>19</td>\n",
       "      <td>Female</td>\n",
       "      <td>3rd Class</td>\n",
       "      <td>yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>52</td>\n",
       "      <td>Bhopal</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>21</td>\n",
       "      <td>Male</td>\n",
       "      <td>3rd Class</td>\n",
       "      <td>yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>54</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>22</td>\n",
       "      <td>Female</td>\n",
       "      <td>3rd Class</td>\n",
       "      <td>yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>55</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>21</td>\n",
       "      <td>Male</td>\n",
       "      <td>1st Class</td>\n",
       "      <td>no</td>\n",
       "      <td>NaN</td>\n",
       "      <td>94</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>NaN</td>\n",
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      ],
      "text/plain": [
       "   Student_id  Age  Gender      Grade Employed  Unnamed: 5  Mark     City  \\\n",
       "0           1   19    Male  1st Class      yes         NaN    95  Chennai   \n",
       "1           2   20  Female  2nd Class       no         NaN    70    Delhi   \n",
       "2           3   18    Male  1st Class       no         NaN    98   Mumbai   \n",
       "3           4   21  Female  2nd Class       no         NaN    75     Pune   \n",
       "4           5   19    Male  1st Class       no         NaN    89    Kochi   \n",
       "5           6   20    Male  2nd Class      yes         NaN    69  Gwalior   \n",
       "6           7   19  Female  3rd Class      yes         NaN    52   Bhopal   \n",
       "7           8   21    Male  3rd Class      yes         NaN    54  Chennai   \n",
       "8           9   22  Female  3rd Class      yes         NaN    55    Delhi   \n",
       "9          10   21    Male  1st Class       no         NaN    94   Mumbai   \n",
       "\n",
       "   Unnamed: 3  \n",
       "0         NaN  \n",
       "1         NaN  \n",
       "2         NaN  \n",
       "3         NaN  \n",
       "4         NaN  \n",
       "5         NaN  \n",
       "6         NaN  \n",
       "7         NaN  \n",
       "8         NaN  \n",
       "9         NaN  "
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   "source": [
    " # Student_id is common to both datasets. Perform data integration on both the\n",
    "# DataFrames with respect to the Student_id column using the pd.merge() function,\n",
    "# and then print the first 10 values of the new DataFrame\n",
    "df = pd.merge(df1, df2, on = 'Student_id')\n",
    "df.head(10)"
   ]
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