4.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "22d0462b-252a-4f6e-9762-8215eed389f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d70a90db-6b7f-47b7-a770-e57da5fa6d6f",
   "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>mid</th>\n",
       "      <th>Q1</th>\n",
       "      <th>Q2</th>\n",
       "      <th>HW1</th>\n",
       "      <th>HW2</th>\n",
       "      <th>end</th>\n",
       "      <th>Final Grade</th>\n",
       "      <th>Grade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>90.0</td>\n",
       "      <td>100</td>\n",
       "      <td>40.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>42.86</td>\n",
       "      <td>93.75</td>\n",
       "      <td>86.56</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100.0</td>\n",
       "      <td>100</td>\n",
       "      <td>65.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>57.14</td>\n",
       "      <td>73.75</td>\n",
       "      <td>86.31</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>92.5</td>\n",
       "      <td>100</td>\n",
       "      <td>60.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>42.86</td>\n",
       "      <td>83.75</td>\n",
       "      <td>85.56</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>97.5</td>\n",
       "      <td>100</td>\n",
       "      <td>40.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>76.19</td>\n",
       "      <td>70.00</td>\n",
       "      <td>83.75</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>70.0</td>\n",
       "      <td>95</td>\n",
       "      <td>75.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>38.09</td>\n",
       "      <td>95.00</td>\n",
       "      <td>80.75</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>10.0</td>\n",
       "      <td>85</td>\n",
       "      <td>67.5</td>\n",
       "      <td>20.0</td>\n",
       "      <td>4.05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>9.63</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>57.5</td>\n",
       "      <td>60.0</td>\n",
       "      <td>33.33</td>\n",
       "      <td>0.00</td>\n",
       "      <td>7.63</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>7.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>163 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       mid   Q1    Q2   HW1    HW2    end  Final Grade Grade\n",
       "0     90.0  100  40.0  70.0  42.86  93.75        86.56     A\n",
       "1    100.0  100  65.0  85.0  57.14  73.75        86.31     A\n",
       "2     92.5  100  60.0  80.0  42.86  83.75        85.56     A\n",
       "3     97.5  100  40.0  85.0  76.19  70.00        83.75     A\n",
       "4     70.0   95  75.0  85.0  38.09  95.00        80.75     A\n",
       "..     ...  ...   ...   ...    ...    ...          ...   ...\n",
       "158   10.0   85  67.5  20.0   4.05   0.00         9.63     F\n",
       "159    0.0    0  57.5  60.0  33.33   0.00         7.63     F\n",
       "160    7.5    0   0.0   0.0   0.00   0.00         3.00     F\n",
       "161    0.0    0   0.0   0.0   0.00   0.00         0.00     F\n",
       "162    0.0    0   0.0   0.0   0.00   0.00         0.00     F\n",
       "\n",
       "[163 rows x 8 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_excel('Grade_data.xlsx')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f870fc02",
   "metadata": {},
   "outputs": [],
   "source": [
    "grade = []\n",
    "for i in df['Final Grade'].values:\n",
    "    if i>= 0 and i< 40:\n",
    "        grade.append('F')\n",
    "    elif i>= 40 and i< 60:\n",
    "        grade.append('c')\n",
    "    else: \n",
    "        grade.append('A')\n",
    "\n",
    "\n",
    "df_Copy = df\n",
    "se = pd.Series(grade)\n",
    "df_Copy['Grade'] = se.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a898a992-d862-4490-9b8b-4a29d4590129",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Grade\n",
       "c    74\n",
       "F    56\n",
       "A    33\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Grade'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0b891818-54c3-4e67-b17f-4df1b4074e16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Grade\n",
       "c    0.453988\n",
       "F    0.343558\n",
       "A    0.202454\n",
       "Name: proportion, dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Grade'].value_counts(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fa0f0a8e-d28a-4ac2-bf94-170fceb7a4e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Grade'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "counts=pd.Series(df['Grade']).value_counts()\n",
    "counts.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5266ddde-8b69-4bff-9ab0-def45792f71f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Grade'].value_counts().plot(kind='pie', autopct='%1.1f%%', startangle=140)\n",
    "plt.ylabel('')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}