frequency_table.ipynb

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       "3    4\n",
       "1    3\n",
       "4    2\n",
       "2    1\n",
       "5    1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
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   "source": [
    "import pandas as pd\n",
    "\n",
    "# define series \n",
    "data = pd.Series([1,1,1,2,3,3,3,3,4,4,5])\n",
    "\n",
    "# Getting the frequencies of each value\n",
    "\n",
    "data.value_counts() # It's reading like 1 is 3 times in the series\n",
    "#data.value_counts(sort=False) # sort = False means it wont sort the data value by frequency\n"
   ]
  },
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Grade</th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>18</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A</td>\n",
       "      <td>18</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A</td>\n",
       "      <td>18</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>B</td>\n",
       "      <td>19</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B</td>\n",
       "      <td>19</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>B</td>\n",
       "      <td>20</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>B</td>\n",
       "      <td>18</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>C</td>\n",
       "      <td>18</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>D</td>\n",
       "      <td>19</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>D</td>\n",
       "      <td>19</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Grade  Age Gender\n",
       "0     A   18      M\n",
       "1     A   18      M\n",
       "2     A   18      F\n",
       "3     B   19      F\n",
       "4     B   19      F\n",
       "5     B   20      M\n",
       "6     B   18      M\n",
       "7     C   18      F\n",
       "8     D   19      M\n",
       "9     D   19      F"
      ]
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   "source": [
    "# Creating data \n",
    "\n",
    "'''\n",
    "df = dataframe \n",
    "'''\n",
    "\n",
    "df = pd.DataFrame({'Grade':['A','A','A','B','B', 'B', 'B', 'C', 'D', 'D'],\n",
    "                  'Age': [18, 18, 18, 19, 19, 20, 18, 18, 19, 19],\n",
    "                  'Gender': ['M','M', 'F', 'F', 'F', 'M', 'M', 'F', 'M', 'F']})\n",
    "\n",
    "df # to view the data\n"
   ]
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   "execution_count": 10,
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       "      <th>col_0</th>\n",
       "      <th>counts</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Grade</th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>3</td>\n",
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       "      <th>B</th>\n",
       "      <td>4</td>\n",
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       "      <th>C</th>\n",
       "      <td>1</td>\n",
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       "      <th>D</th>\n",
       "      <td>2</td>\n",
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      "text/plain": [
       "col_0  counts\n",
       "Grade        \n",
       "A           3\n",
       "B           4\n",
       "C           1\n",
       "D           2"
      ]
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   "source": [
    "'''\n",
    "The way to interpret this is\n",
    "--> 3 students recieved an 'A' in the class\n",
    "--> 4 students recieved a 'B' in the class\n",
    "'''\n",
    "\n",
    "\n",
    "# Now let's look for the each latter grade\n",
    "pd.crosstab(index=df['Grade'],columns='counts')"
   ]
  },
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   "cell_type": "code",
   "execution_count": 12,
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       "      <th>counts</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>4</td>\n",
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       "      <th>20</th>\n",
       "      <td>1</td>\n",
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      "text/plain": [
       "col_0  counts\n",
       "Age          \n",
       "18          5\n",
       "19          4\n",
       "20          1"
      ]
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     "execution_count": 12,
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   "source": [
    "'''\n",
    "The way is to interpret this is -\n",
    "--> 5 students are 18 years old\n",
    "--> 4 students are 19 years old\n",
    "--> 1 student is 20 years old\n",
    "'''\n",
    "\n",
    "\n",
    "\n",
    "# If we want to check age and then count it\n",
    "\n",
    "pd.crosstab(index=df['Age'],columns='counts')\n"
   ]
  },
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   "cell_type": "code",
   "execution_count": 15,
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       "      <th>count</th>\n",
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       "      <th>Age</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>10.0</td>\n",
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      "text/plain": [
       "col_0  count\n",
       "Age         \n",
       "18      50.0\n",
       "19      40.0\n",
       "20      10.0"
      ]
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   "source": [
    "'''\n",
    "The way to interpret this is --\n",
    "--> 50% of the students are 18 years old\n",
    "--> 40% of the students are 19 years old\n",
    "'''\n",
    "\n",
    "# If we want to find out the relative frequncy table then\n",
    "\n",
    "# Defining the crosstab\n",
    "tab = pd.crosstab(index=df['Age'],columns='count')\n",
    "\n",
    "# Lets get the proportion\n",
    "tab/tab.sum() * 100\n"
   ]
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   "id": "b3e85f22-3a7c-47a4-97af-e2c2f80808e0",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Grade</th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
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       "      <th>18</th>\n",
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       "      <td>0</td>\n",
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       "      <th>19</th>\n",
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      "text/plain": [
       "Grade  A  B  C  D\n",
       "Age              \n",
       "18     3  1  1  0\n",
       "19     0  2  0  2\n",
       "20     0  1  0  0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
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   "source": [
    "'''\n",
    "The way to interpret this is -\n",
    "--> There are 3 students who are 18 years old and recieved an 'A' \n",
    "in the class\n",
    "'''\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# Now we can look how we can see two way frequency that means we are going to \n",
    "# see how we can see grade and age frequency at the same time\n",
    "\n",
    "pd.crosstab(index=df['Age'], columns=df['Grade'])"
   ]
  },
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