Kaggle Data Science Survey Project Lab Solution

data_science_survey_solution.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['experience_coding', 'python_user', 'r_user', 'sql_user', 'most_used', 'compensation']\n",
      "['6.1', 'TRUE', 'FALSE', 'TRUE', 'Scikit-learn', '124267']\n",
      "['12.3', 'TRUE', 'TRUE', 'TRUE', 'Scikit-learn', '236889']\n",
      "['2.2', 'TRUE', 'FALSE', 'FALSE', 'None', '74321']\n",
      "['2.7', 'FALSE', 'FALSE', 'TRUE', 'None', '62593']\n",
      "['1.2', 'TRUE', 'FALSE', 'FALSE', 'Scikit-learn', '36288']\n"
     ]
    }
   ],
   "source": [
    "import csv\n",
    "\n",
    "with open('kaggle2021-short.csv') as f:\n",
    "    reader = csv.reader(f, delimiter=\",\")\n",
    "    kaggle_data = list(reader)\n",
    "\n",
    "column_names = kaggle_data[0]\n",
    "survey_responses = kaggle_data[1:]\n",
    "\n",
    "print(column_names)\n",
    "for row in range(0,5):\n",
    "    print(survey_responses[row])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Iterate over the indices so that we can update all of the data\n",
    "num_rows = len(survey_responses)\n",
    "for i in range(num_rows):\n",
    "\n",
    "    # experience_coding\n",
    "    survey_responses[i][0] = float(survey_responses[i][0]) \n",
    "    \n",
    "    # python_user\n",
    "    if survey_responses[i][1] == \"TRUE\":\n",
    "        survey_responses[i][1] = True\n",
    "    else:\n",
    "        survey_responses[i][1] = False\n",
    "    \n",
    "    # r_user\n",
    "    if survey_responses[i][2] == \"TRUE\":\n",
    "        survey_responses[i][2] = True\n",
    "    else:\n",
    "        survey_responses[i][2] = False\n",
    "\n",
    "    # sql_user\n",
    "    if survey_responses[i][3] == \"TRUE\":\n",
    "        survey_responses[i][3] = True\n",
    "    else:\n",
    "        survey_responses[i][3] = False\n",
    "\n",
    "    # most_used\n",
    "    if survey_responses[i][4] == \"None\":\n",
    "        survey_responses[i][4] = None\n",
    "    else:\n",
    "        survey_responses[i][4] = survey_responses[i][4]\n",
    "\n",
    "\n",
    "    # compensation\n",
    "    survey_responses[i][5] = int(survey_responses[i][5]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['experience_coding', 'python_user', 'r_user', 'sql_user', 'most_used', 'compensation']\n",
      "[6.1, True, False, True, 'Scikit-learn', 124267]\n",
      "[12.3, True, True, True, 'Scikit-learn', 236889]\n",
      "[2.2, True, False, False, None, 74321]\n",
      "[2.7, False, False, True, None, 62593]\n"
     ]
    }
   ],
   "source": [
    "print(column_names)\n",
    "for row in range(0,4):\n",
    "    print(survey_responses[row])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Python users: 21860\n",
      "Proportion of Python users: 0.8416432449081739\n",
      "\n",
      "Number of R users: 5335\n",
      "Proportion of R users: 0.20540561352173412\n",
      "\n",
      "Number of SQL users: 10757\n",
      "Proportion of SQL users: 0.4141608593539445\n",
      "\n"
     ]
    }
   ],
   "source": [
    "python_user_count = 0\n",
    "r_user_count = 0\n",
    "sql_user_count = 0\n",
    "\n",
    "for i in range(num_rows):\n",
    "\n",
    "    # Detect if python_user column is True\n",
    "    if survey_responses[i][1]:\n",
    "        python_user_count = python_user_count + 1\n",
    "    \n",
    "    # Detect if r_user column is True\n",
    "    if survey_responses[i][2]:\n",
    "        r_user_count = r_user_count + 1\n",
    "\n",
    "    # Detect if sql_user column is True\n",
    "    if survey_responses[i][3]:\n",
    "        sql_user_count = sql_user_count + 1\n",
    "\n",
    "user_counts = {\n",
    "    \"Python\": python_user_count,\n",
    "    \"R\": r_user_count,\n",
    "    \"SQL\": sql_user_count\n",
    "}\n",
    "\n",
    "for language, count in user_counts.items():\n",
    "    print(f\"Number of {language} users: {count}\")\n",
    "    print(f\"Proportion of {language} users: {count / num_rows}\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[6.1, 12.3, 2.2, 2.7, 1.2]\n",
      "[124267, 236889, 74321, 62593, 36288]\n"
     ]
    }
   ],
   "source": [
    "# Aggregating all years of experience and compensation together into a single list\n",
    "experience_coding_column = []\n",
    "compensation_column = []\n",
    "\n",
    "for i in range(num_rows):\n",
    "    experience_coding_column.append(survey_responses[i][0])\n",
    "    compensation_column.append(survey_responses[i][5])\n",
    "    \n",
    "# testing that the loop acted as-expected\n",
    "print(experience_coding_column[0:5])\n",
    "print(compensation_column[0:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum years of experience: 0.0\n",
      "Maximum years of experience: 30.0\n",
      "Average years of experience: 5.297231740653729\n"
     ]
    }
   ],
   "source": [
    "# Summarizing the experience_coding column\n",
    "min_experience_coding = min(experience_coding_column)\n",
    "max_experience_coding = max(experience_coding_column)\n",
    "avg_experience_coding = sum(experience_coding_column) / num_rows\n",
    "\n",
    "print(f\"Minimum years of experience: {min_experience_coding}\")\n",
    "print(f\"Maximum years of experience: {max_experience_coding}\")\n",
    "print(f\"Average years of experience: {avg_experience_coding}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AgKEtrEHH7XZLUq8VldbWVmf1xe12q6urS36//6I1J0+e7HX+U6dOhdSc/z5+v1/d3d29VnrOiYuLU2JiYsgGAADMFdagk5GRIbfbrZqaGqetq6tLtbW1ysvLkyRlZ2crJiYmpKalpUWHDh1yanJzcxUIBLRv3z6nZu/evQoEAiE1hw4dUktLi1Oza9cuxcXFKTs7O5yXBQAAhqjo/h7Q0dGhDz/80Hnd1NSkxsZGJSUlafTo0SopKVFZWZnGjBmjMWPGqKysTMOHD1dRUZEkybIsLV68WKWlpUpOTlZSUpJWrVqlrKwszZgxQ5I0btw4zZ49W0uWLNGWLVskSUuXLlVBQYHGjh0rScrPz9f48ePl9Xr1wAMP6NNPP9WqVau0ZMkSVmoAAICkrxB03nzzTX3ve99zXq9cuVKStGjRIm3dulV33XWXOjs7tXz5cvn9fuXk5GjXrl1KSEhwjtm8ebOio6O1YMECdXZ2avr06dq6dauioqKcmu3bt6u4uNi5O6uwsDDk2T1RUVHasWOHli9frilTpmjYsGEqKirSgw8+2P9RAAAARvqTnqMz1PEcnd54jg4AYLCL2HN0AAAABhOCDgAAMBZBBwAAGIugAwAAjEXQAQAAxiLoAAAAYxF0AACAsQg6AADAWAQdAABgLIIOAAAwFkEHAAAYi6ADAACMRdABAADGIugAAABjEXQAAICxCDoAAMBYBB0AAGAsgg4AADAWQQcAABiLoAMAAIxF0AEAAMYi6AAAAGMRdAAAgLEIOgAAwFgEHQAAYCyCDgAAMBZBBwAAGIugAwAAjEXQAQAAxiLoAAAAYxF0AACAsQg6AADAWAQdAABgLIIOAAAwFkEHAAAYi6ADAACMRdABAADGCnvQ+fzzz/Vv//ZvysjI0LBhw/SNb3xD9913n86ePevU2LatdevWyePxaNiwYZo2bZoOHz4ccp5gMKgVK1YoJSVF8fHxKiws1PHjx0Nq/H6/vF6vLMuSZVnyer06ffp0uC8JAAAMUWEPOhs2bNAvfvELVVRU6MiRI9q4caMeeOAB/fznP3dqNm7cqE2bNqmiokL79++X2+3WzJkz1d7e7tSUlJSoqqpKlZWVqqurU0dHhwoKCtTT0+PUFBUVqbGxUdXV1aqurlZjY6O8Xm+4LwkAAAxRLtu27XCesKCgQGlpaXriiSectr/927/V8OHDtW3bNtm2LY/Ho5KSEq1evVrSF6s3aWlp2rBhg5YtW6ZAIKCRI0dq27ZtWrhwoSTpxIkTSk9P186dOzVr1iwdOXJE48ePV319vXJyciRJ9fX1ys3N1bvvvquxY8desq9tbW2yLEuBQECJiYnhHAZJ0rVrdoT9nAPt4/VzI90FAAAuqj+f32Ff0bnxxhv1P//zP3r//fclSf/3f/+nuro6/c3f/I0kqampST6fT/n5+c4xcXFxmjp1qnbv3i1JamhoUHd3d0iNx+NRZmamU7Nnzx5ZluWEHEmaPHmyLMtyas4XDAbV1tYWsgEAAHNFh/uEq1evViAQ0De/+U1FRUWpp6dH999/v374wx9Kknw+nyQpLS0t5Li0tDQdPXrUqYmNjdWIESN61Zw73ufzKTU1tdf7p6amOjXnKy8v17333vunXSAAABgywr6i88wzz+ipp57S008/rbfeektPPvmkHnzwQT355JMhdS6XK+S1bdu92s53fs2F6i92nrVr1yoQCDhbc3NzXy8LAAAMQWFf0bnzzju1Zs0a3XzzzZKkrKwsHT16VOXl5Vq0aJHcbrekL1ZkRo0a5RzX2trqrPK43W51dXXJ7/eHrOq0trYqLy/PqTl58mSv9z916lSv1aJz4uLiFBcXF54LBQAAg17YV3Q+++wzXXFF6GmjoqKc28szMjLkdrtVU1Pj7O/q6lJtba0TYrKzsxUTExNS09LSokOHDjk1ubm5CgQC2rdvn1Ozd+9eBQIBpwYAAFzewr6iM2/ePN1///0aPXq0rrvuOh04cECbNm3Sj3/8Y0lf/LmppKREZWVlGjNmjMaMGaOysjINHz5cRUVFkiTLsrR48WKVlpYqOTlZSUlJWrVqlbKysjRjxgxJ0rhx4zR79mwtWbJEW7ZskSQtXbpUBQUFfbrjCgAAmC/sQefnP/+5fvKTn2j58uVqbW2Vx+PRsmXL9NOf/tSpueuuu9TZ2anly5fL7/crJydHu3btUkJCglOzefNmRUdHa8GCBers7NT06dO1detWRUVFOTXbt29XcXGxc3dWYWGhKioqwn1JAABgiAr7c3SGEp6j0xvP0QEADHYRfY4OAADAYEHQAQAAxiLoAAAAYxF0AACAsQg6AADAWAQdAABgLIIOAAAwFkEHAAAYi6ADAACMRdABAADGIugAAABjEXQAAICxCDoAAMBYBB0AAGAsgg4AADAWQQcAABiLoAMAAIxF0AEAAMYi6AAAAGMRdAAAgLEIOgAAwFgEHQAAYCyCDgAAMBZBBwAAGIugAwAAjEXQAQAAxiLoAAAAYxF0AACAsQg6AADAWAQdAABgLIIOAAAwFkEHAAAYi6ADAACMRdABAADGIugAAABjEXQAAICxCDoAAMBYAxJ0fvvb3+of/uEflJycrOHDh+tb3/qWGhoanP22bWvdunXyeDwaNmyYpk2bpsOHD4ecIxgMasWKFUpJSVF8fLwKCwt1/PjxkBq/3y+v1yvLsmRZlrxer06fPj0QlwQAAIagsAcdv9+vKVOmKCYmRi+++KLeeecdPfTQQ7rqqqucmo0bN2rTpk2qqKjQ/v375Xa7NXPmTLW3tzs1JSUlqqqqUmVlperq6tTR0aGCggL19PQ4NUVFRWpsbFR1dbWqq6vV2Ngor9cb7ksCAABDlMu2bTucJ1yzZo3+93//V2+88cYF99u2LY/Ho5KSEq1evVrSF6s3aWlp2rBhg5YtW6ZAIKCRI0dq27ZtWrhwoSTpxIkTSk9P186dOzVr1iwdOXJE48ePV319vXJyciRJ9fX1ys3N1bvvvquxY8desq9tbW2yLEuBQECJiYlhGoEvXbtmR9jPOdA+Xj830l0AAOCi+vP5HfYVnRdeeEGTJk3S3//93ys1NVUTJ07U448/7uxvamqSz+dTfn6+0xYXF6epU6dq9+7dkqSGhgZ1d3eH1Hg8HmVmZjo1e/bskWVZTsiRpMmTJ8uyLKcGAABc3sIedD766CM9+uijGjNmjF566SXddtttKi4u1q9//WtJks/nkySlpaWFHJeWlubs8/l8io2N1YgRIy5ak5qa2uv9U1NTnZrzBYNBtbW1hWwAAMBc0eE+4dmzZzVp0iSVlZVJkiZOnKjDhw/r0Ucf1Y9+9COnzuVyhRxn23avtvOdX3Oh+oudp7y8XPfee2+frwUAAAxtYV/RGTVqlMaPHx/SNm7cOB07dkyS5Ha7JanXqktra6uzyuN2u9XV1SW/33/RmpMnT/Z6/1OnTvVaLTpn7dq1CgQCztbc3PwVrhAAAAwVYQ86U6ZM0XvvvRfS9v777+uaa66RJGVkZMjtdqumpsbZ39XVpdraWuXl5UmSsrOzFRMTE1LT0tKiQ4cOOTW5ubkKBALat2+fU7N3714FAgGn5nxxcXFKTEwM2QAAgLnC/qerf/mXf1FeXp7Kysq0YMEC7du3T4899pgee+wxSV/8uamkpERlZWUaM2aMxowZo7KyMg0fPlxFRUWSJMuytHjxYpWWlio5OVlJSUlatWqVsrKyNGPGDElfrBLNnj1bS5Ys0ZYtWyRJS5cuVUFBQZ/uuAIAAOYLe9C54YYbVFVVpbVr1+q+++5TRkaGHn74Yd1yyy1OzV133aXOzk4tX75cfr9fOTk52rVrlxISEpyazZs3Kzo6WgsWLFBnZ6emT5+urVu3KioqyqnZvn27iouLnbuzCgsLVVFREe5LAgAAQ1TYn6MzlPAcnd54jg4AYLCL6HN0AAAABguCDgAAMBZBBwAAGIugAwAAjEXQAQAAxiLoAAAAYxF0AACAsQg6AADAWAQdAABgLIIOAAAwFkEHAAAYi6ADAACMRdABAADGIugAAABjEXQAAICxCDoAAMBYBB0AAGAsgg4AADAWQQcAABiLoAMAAIxF0AEAAMaKjnQHMLhcu2ZHpLvQbx+vnxvpLgAABilWdAAAgLEIOgAAwFgEHQAAYCyCDgAAMBZBBwAAGIugAwAAjEXQAQAAxiLoAAAAYxF0AACAsQg6AADAWAQdAABgLIIOAAAwFkEHAAAYi6ADAACMRdABAADGIugAAABjDXjQKS8vl8vlUklJidNm27bWrVsnj8ejYcOGadq0aTp8+HDIccFgUCtWrFBKSori4+NVWFio48ePh9T4/X55vV5ZliXLsuT1enX69OmBviQAADBEDGjQ2b9/vx577DFdf/31Ie0bN27Upk2bVFFRof3798vtdmvmzJlqb293akpKSlRVVaXKykrV1dWpo6NDBQUF6unpcWqKiorU2Nio6upqVVdXq7GxUV6vdyAvCQAADCEDFnQ6Ojp0yy236PHHH9eIESOcdtu29fDDD+vuu+/WTTfdpMzMTD355JP67LPP9PTTT0uSAoGAnnjiCT300EOaMWOGJk6cqKeeekoHDx7Uyy+/LEk6cuSIqqur9ctf/lK5ubnKzc3V448/rt/85jd67733BuqyAADAEDJgQef222/X3LlzNWPGjJD2pqYm+Xw+5efnO21xcXGaOnWqdu/eLUlqaGhQd3d3SI3H41FmZqZTs2fPHlmWpZycHKdm8uTJsizLqTlfMBhUW1tbyAYAAMwVPRAnrays1FtvvaX9+/f32ufz+SRJaWlpIe1paWk6evSoUxMbGxuyEnSu5tzxPp9Pqampvc6fmprq1JyvvLxc9957b/8vCAAADElhX9Fpbm7WP//zP+upp57SlVde+UfrXC5XyGvbtnu1ne/8mgvVX+w8a9euVSAQcLbm5uaLvh8AABjawh50Ghoa1NraquzsbEVHRys6Olq1tbX693//d0VHRzsrOeevurS2tjr73G63urq65Pf7L1pz8uTJXu9/6tSpXqtF58TFxSkxMTFkAwAA5gp70Jk+fboOHjyoxsZGZ5s0aZJuueUWNTY26hvf+IbcbrdqamqcY7q6ulRbW6u8vDxJUnZ2tmJiYkJqWlpadOjQIacmNzdXgUBA+/btc2r27t2rQCDg1AAAgMtb2L+jk5CQoMzMzJC2+Ph4JScnO+0lJSUqKyvTmDFjNGbMGJWVlWn48OEqKiqSJFmWpcWLF6u0tFTJyclKSkrSqlWrlJWV5Xy5edy4cZo9e7aWLFmiLVu2SJKWLl2qgoICjR07NtyXBQAAhqAB+TLypdx1113q7OzU8uXL5ff7lZOTo127dikhIcGp2bx5s6Kjo7VgwQJ1dnZq+vTp2rp1q6Kiopya7du3q7i42Lk7q7CwUBUVFV/79QAAgMHJZdu2HelOREpbW5ssy1IgEBiQ7+tcu2ZH2M+J3j5ePzfSXQAAfI368/nNb10BAABjEXQAAICxCDoAAMBYBB0AAGAsgg4AADAWQQcAABiLoAMAAIxF0AEAAMYi6AAAAGMRdAAAgLEIOgAAwFgEHQAAYCyCDgAAMBZBBwAAGIugAwAAjEXQAQAAxiLoAAAAYxF0AACAsQg6AADAWAQdAABgLIIOAAAwFkEHAAAYi6ADAACMRdABAADGIugAAABjEXQAAICxCDoAAMBYBB0AAGAsgg4AADAWQQcAABiLoAMAAIxF0AEAAMYi6AAAAGMRdAAAgLEIOgAAwFgEHQAAYCyCDgAAMFbYg055ebluuOEGJSQkKDU1VfPnz9d7770XUmPbttatWyePx6Nhw4Zp2rRpOnz4cEhNMBjUihUrlJKSovj4eBUWFur48eMhNX6/X16vV5ZlybIseb1enT59OtyXBAAAhqiwB53a2lrdfvvtqq+vV01NjT7//HPl5+frzJkzTs3GjRu1adMmVVRUaP/+/XK73Zo5c6ba29udmpKSElVVVamyslJ1dXXq6OhQQUGBenp6nJqioiI1Njaqurpa1dXVamxslNfrDfclAQCAIcpl27Y9kG9w6tQppaamqra2Vt/97ndl27Y8Ho9KSkq0evVqSV+s3qSlpWnDhg1atmyZAoGARo4cqW3btmnhwoWSpBMnTig9PV07d+7UrFmzdOTIEY0fP1719fXKycmRJNXX1ys3N1fvvvuuxo4de8m+tbW1ybIsBQIBJSYmhv3ar12zI+znRG8fr58b6S4AAL5G/fn8HvDv6AQCAUlSUlKSJKmpqUk+n0/5+flOTVxcnKZOnardu3dLkhoaGtTd3R1S4/F4lJmZ6dTs2bNHlmU5IUeSJk+eLMuynJrzBYNBtbW1hWwAAMBcAxp0bNvWypUrdeONNyozM1OS5PP5JElpaWkhtWlpac4+n8+n2NhYjRgx4qI1qampvd4zNTXVqTlfeXm5830ey7KUnp7+p10gAAAY1AY06Nxxxx16++239Z//+Z+99rlcrpDXtm33ajvf+TUXqr/YedauXatAIOBszc3NfbkMAAAwRA1Y0FmxYoVeeOEFvfrqq7r66quddrfbLUm9Vl1aW1udVR63262uri75/f6L1pw8ebLX+546darXatE5cXFxSkxMDNkAAIC5wh50bNvWHXfcoeeee06vvPKKMjIyQvZnZGTI7XarpqbGaevq6lJtba3y8vIkSdnZ2YqJiQmpaWlp0aFDh5ya3NxcBQIB7du3z6nZu3evAoGAUwMAAC5v0eE+4e23366nn35a//Vf/6WEhARn5cayLA0bNkwul0slJSUqKyvTmDFjNGbMGJWVlWn48OEqKipyahcvXqzS0lIlJycrKSlJq1atUlZWlmbMmCFJGjdunGbPnq0lS5Zoy5YtkqSlS5eqoKCgT3dcwRxD8e427hQDgK9H2IPOo48+KkmaNm1aSPuvfvUr3XrrrZKku+66S52dnVq+fLn8fr9ycnK0a9cuJSQkOPWbN29WdHS0FixYoM7OTk2fPl1bt25VVFSUU7N9+3YVFxc7d2cVFhaqoqIi3JcEAACGqAF/js5gxnN0ECms6ADAVzeonqMDAAAQKQQdAABgLIIOAAAwFkEHAAAYi6ADAACMRdABAADGIugAAABjEXQAAICxCDoAAMBYBB0AAGAsgg4AADAWQQcAABiLoAMAAIxF0AEAAMYi6AAAAGNFR7oDwOXo2jU7It2Ffvt4/dxIdwEA+o0VHQAAYCyCDgAAMBZBBwAAGIugAwAAjEXQAQAAxiLoAAAAYxF0AACAsQg6AADAWAQdAABgLIIOAAAwFj8BAaBPhuLPVgxF/NQGEF4EHQAYRAiUX4+hGCiH6tyI9FjzpysAAGAsVnQAAJedobo6gv5jRQcAABiLoAMAAIxF0AEAAMYi6AAAAGMRdAAAgLEIOgAAwFgEHQAAYKwhH3QeeeQRZWRk6Morr1R2drbeeOONSHcJAAAMEkM66DzzzDMqKSnR3XffrQMHDug73/mO5syZo2PHjkW6awAAYBAY0kFn06ZNWrx4sf7xH/9R48aN08MPP6z09HQ9+uijke4aAAAYBIbsT0B0dXWpoaFBa9asCWnPz8/X7t27L3hMMBhUMBh0XgcCAUlSW1vbgPTxbPCzATkvAABDxUB8xp47p23bl6wdskHnd7/7nXp6epSWlhbSnpaWJp/Pd8FjysvLde+99/ZqT09PH5A+AgBwubMeHrhzt7e3y7Ksi9YM2aBzjsvlCnlt23avtnPWrl2rlStXOq/Pnj2rTz/9VMnJyX/0mK+qra1N6enpam5uVmJiYljPbRrGqu8Yq75jrPqOseo7xqp/Bmq8bNtWe3u7PB7PJWuHbNBJSUlRVFRUr9Wb1tbWXqs858TFxSkuLi6k7aqrrhqoLkqSEhMT+c/QR4xV3zFWfcdY9R1j1XeMVf8MxHhdaiXnnCH7ZeTY2FhlZ2erpqYmpL2mpkZ5eXkR6hUAABhMhuyKjiStXLlSXq9XkyZNUm5urh577DEdO3ZMt912W6S7BgAABoEhHXQWLlyoTz75RPfdd59aWlqUmZmpnTt36pprrol01xQXF6d77rmn15/K0Btj1XeMVd8xVn3HWPUdY9U/g2G8XHZf7s0CAAAYgobsd3QAAAAuhaADAACMRdABAADGIugAAABjEXQGwCOPPKKMjAxdeeWVys7O1htvvBHpLg0669atk8vlCtncbnekuzVovP7665o3b548Ho9cLpeef/75kP22bWvdunXyeDwaNmyYpk2bpsOHD0emsxF2qbG69dZbe821yZMnR6azEVReXq4bbrhBCQkJSk1N1fz58/Xee++F1DCvvtSX8WJufeHRRx/V9ddf7zwUMDc3Vy+++KKzP9LziqATZs8884xKSkp0991368CBA/rOd76jOXPm6NixY5Hu2qBz3XXXqaWlxdkOHjwY6S4NGmfOnNGECRNUUVFxwf0bN27Upk2bVFFRof3798vtdmvmzJlqb2//mnsaeZcaK0maPXt2yFzbuXPn19jDwaG2tla333676uvrVVNTo88//1z5+fk6c+aMU8O8+lJfxktibknS1VdfrfXr1+vNN9/Um2++qe9///v6wQ9+4ISZiM8rG2H17W9/277ttttC2r75zW/aa9asiVCPBqd77rnHnjBhQqS7MSRIsquqqpzXZ8+etd1ut71+/Xqn7fe//71tWZb9i1/8IgI9HDzOHyvbtu1FixbZP/jBDyLSn8GstbXVlmTX1tbats28upTzx8u2mVsXM2LECPuXv/zloJhXrOiEUVdXlxoaGpSfnx/Snp+fr927d0eoV4PXBx98II/Ho4yMDN1888366KOPIt2lIaGpqUk+ny9knsXFxWnq1KnMsz/itddeU2pqqv7qr/5KS5YsUWtra6S7FHGBQECSlJSUJIl5dSnnj9c5zK1QPT09qqys1JkzZ5Sbmzso5hVBJ4x+97vfqaenp9ePiqalpfX68dHLXU5Ojn7961/rpZde0uOPPy6fz6e8vDx98sknke7aoHduLjHP+mbOnDnavn27XnnlFT300EPav3+/vv/97ysYDEa6axFj27ZWrlypG2+8UZmZmZKYVxdzofGSmFt/6ODBg/qzP/szxcXF6bbbblNVVZXGjx8/KObVkP4JiMHK5XKFvLZtu1fb5W7OnDnOv7OyspSbm6u/+Iu/0JNPPqmVK1dGsGdDB/OsbxYuXOj8OzMzU5MmTdI111yjHTt26KabbopgzyLnjjvu0Ntvv626urpe+5hXvf2x8WJufWns2LFqbGzU6dOn9eyzz2rRokWqra119kdyXrGiE0YpKSmKiorqlVJbW1t7pVmEio+PV1ZWlj744INId2XQO3d3GvPsqxk1apSuueaay3aurVixQi+88IJeffVVXX311U478+rC/th4XcjlPLdiY2P1l3/5l5o0aZLKy8s1YcIE/exnPxsU84qgE0axsbHKzs5WTU1NSHtNTY3y8vIi1KuhIRgM6siRIxo1alSkuzLoZWRkyO12h8yzrq4u1dbWMs/64JNPPlFzc/NlN9ds29Ydd9yh5557Tq+88ooyMjJC9jOvQl1qvC7kcp1bF2LbtoLB4OCYV1/LV54vI5WVlXZMTIz9xBNP2O+8845dUlJix8fH2x9//HGkuzaolJaW2q+99pr90Ucf2fX19XZBQYGdkJDAOP1/7e3t9oEDB+wDBw7YkuxNmzbZBw4csI8ePWrbtm2vX7/etizLfu655+yDBw/aP/zhD+1Ro0bZbW1tEe751+9iY9Xe3m6Xlpbau3fvtpuamuxXX33Vzs3Ntf/8z//8shurf/qnf7Ity7Jfe+01u6Wlxdk+++wzp4Z59aVLjRdz60tr1661X3/9dbupqcl+++237X/913+1r7jiCnvXrl22bUd+XhF0BsB//Md/2Ndcc40dGxtr//Vf/3XI7Yj4wsKFC+1Ro0bZMTExtsfjsW+66Sb78OHDke7WoPHqq6/aknptixYtsm37i1uB77nnHtvtdttxcXH2d7/7XfvgwYOR7XSEXGysPvvsMzs/P98eOXKkHRMTY48ePdpetGiRfezYsUh3+2t3oTGSZP/qV79yaphXX7rUeDG3vvTjH//Y+cwbOXKkPX36dCfk2Hbk55XLtm3761k7AgAA+HrxHR0AAGAsgg4AADAWQQcAABiLoAMAAIxF0AEAAMYi6AAAAGMRdAAAgLEIOgAAwFgEHQAAYCyCDgAAMBZBBwAAGIugAwAAjPX/AOOgO/ac3Nb/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.hist(experience_coding_column)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum compensation: 0\n",
      "Maximum compensation: 1492951\n",
      "Average compensation: 53252.82\n"
     ]
    }
   ],
   "source": [
    "# Summarizing the compensation column\n",
    "min_compensation = min(compensation_column)\n",
    "max_compensation = max(compensation_column)\n",
    "avg_compensation = sum(compensation_column) / num_rows\n",
    "\n",
    "print(f\"Minimum compensation: {min_compensation}\")\n",
    "print(f\"Maximum compensation: {max_compensation}\")\n",
    "print(f\"Average compensation: {round(avg_compensation, 2)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(compensation_column)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25973\n"
     ]
    }
   ],
   "source": [
    "print(num_rows)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(num_rows):\n",
    "\n",
    "    if survey_responses[i][0] < 5:\n",
    "        survey_responses[i].append(\"<5 Years\")\n",
    "    \n",
    "    elif survey_responses[i][0] >= 5 and survey_responses[i][0] < 10:\n",
    "        survey_responses[i].append(\"5-10 Years\")\n",
    "\n",
    "    elif survey_responses[i][0] >= 10 and survey_responses[i][0] < 15:\n",
    "        survey_responses[i].append(\"10-15 Years\")\n",
    "    \n",
    "    elif survey_responses[i][0] >= 15 and survey_responses[i][0] < 20:\n",
    "        survey_responses[i].append(\"15-20 Years\")\n",
    "\n",
    "    elif survey_responses[i][0] >= 20 and survey_responses[i][0] < 25:\n",
    "        survey_responses[i].append(\"20-25 Years\")\n",
    "    \n",
    "    else:\n",
    "        survey_responses[i].append(\"25+ Years\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "bin_0_to_5 = []\n",
    "bin_5_to_10 = []\n",
    "bin_10_to_15 = []\n",
    "bin_15_to_20 = []\n",
    "bin_20_to_25 = []\n",
    "bin_25_to_30 = []\n",
    "\n",
    "for i in range(num_rows):\n",
    "    \n",
    "    if survey_responses[i][6] == \"<5 Years\":\n",
    "        bin_0_to_5.append(survey_responses[i][5])\n",
    "    \n",
    "    elif survey_responses[i][6] == \"5-10 Years\":\n",
    "        bin_5_to_10.append(survey_responses[i][5])\n",
    "    \n",
    "    elif survey_responses[i][6] == \"10-15 Years\":\n",
    "        bin_10_to_15.append(survey_responses[i][5])\n",
    "    \n",
    "    elif survey_responses[i][6] == \"15-20 Years\":\n",
    "        bin_15_to_20.append(survey_responses[i][5])\n",
    "    \n",
    "    elif survey_responses[i][6] == \"20-25 Years\":\n",
    "        bin_20_to_25.append(survey_responses[i][5])\n",
    "\n",
    "    else:\n",
    "        bin_25_to_30.append(survey_responses[i][5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "People with < 5 years of experience: 18753\n",
      "People with 5 - 10 years of experience: 3167\n",
      "People with 10 - 15 years of experience: 1118\n",
      "People with 15 - 20 years of experience: 1069\n",
      "People with 20 - 25 years of experience: 925\n",
      "People with 25+ years of experience: 941\n"
     ]
    }
   ],
   "source": [
    "# Checking the distribution of experience in the dataset\n",
    "print(\"People with < 5 years of experience: \" + str(len(bin_0_to_5)))\n",
    "print(\"People with 5 - 10 years of experience: \" + str(len(bin_5_to_10)))\n",
    "print(\"People with 10 - 15 years of experience: \" + str(len(bin_10_to_15)))\n",
    "print(\"People with 15 - 20 years of experience: \" + str(len(bin_15_to_20)))\n",
    "print(\"People with 20 - 25 years of experience: \" + str(len(bin_20_to_25)))\n",
    "print(\"People with 25+ years of experience: \" + str(len(bin_25_to_30)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['<5', '5-10', '10-15', '15-20', '20-25', '25+']\n",
      "[18753, 3167, 1118, 1069, 925, 941]\n"
     ]
    }
   ],
   "source": [
    "bar_labels = [\"<5\", \"5-10\", \"10-15\", \"15-20\", \"20-25\", \"25+\"]\n",
    "experience_counts = [len(bin_0_to_5),\n",
    "               len(bin_5_to_10),\n",
    "               len(bin_10_to_15),\n",
    "               len(bin_15_to_20),\n",
    "               len(bin_20_to_25),\n",
    "               len(bin_25_to_30)]\n",
    "\n",
    "print(bar_labels)\n",
    "print(experience_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(bar_labels, experience_counts)\n",
    "plt.title(\"Years of Experience\")\n",
    "plt.xlabel(\"Years\")\n",
    "plt.ylabel(\"Count\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average salary of people with < 5 years of experience: 45047.87484669119\n",
      "Average salary of people with 5 - 10 years of experience: 59312.82033470161\n",
      "Average salary of people with 10 - 15 years of experience: 80226.75581395348\n",
      "Average salary of people with 15 - 20 years of experience: 75101.82694106642\n",
      "Average salary of people with 20 - 25 years of experience: 103159.80432432433\n",
      "Average salary of people with 25+ years of experience: 90444.98512221042\n"
     ]
    }
   ],
   "source": [
    "avg_0_5 = sum(bin_0_to_5) / len(bin_0_to_5)\n",
    "avg_5_10 = sum(bin_5_to_10) / len(bin_5_to_10)\n",
    "avg_10_15 = sum(bin_10_to_15) / len(bin_10_to_15)\n",
    "avg_15_20 = sum(bin_15_to_20) / len(bin_15_to_20)\n",
    "avg_20_25 = sum(bin_20_to_25) / len(bin_20_to_25)\n",
    "avg_25_30 = sum(bin_25_to_30) / len(bin_25_to_30)\n",
    "\n",
    "salary_averages = [avg_0_5,\n",
    "                   avg_5_10,\n",
    "                   avg_10_15,\n",
    "                   avg_15_20,\n",
    "                   avg_20_25,\n",
    "                   avg_25_30]\n",
    "\n",
    "# Checking the distribution of experience in the dataset\n",
    "print(f\"Average salary of people with < 5 years of experience: {avg_0_5}\")\n",
    "print(f\"Average salary of people with 5 - 10 years of experience: {avg_5_10}\")\n",
    "print(f\"Average salary of people with 10 - 15 years of experience: {avg_10_15}\")\n",
    "print(f\"Average salary of people with 15 - 20 years of experience: {avg_15_20}\")\n",
    "print(f\"Average salary of people with 20 - 25 years of experience: {avg_20_25}\")\n",
    "print(f\"Average salary of people with 25+ years of experience: {avg_25_30}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(bar_labels, salary_averages)\n",
    "plt.title(\"Average Salary by Years of Experience\")\n",
    "plt.xlabel(\"Years\")\n",
    "plt.ylabel(\"Salary Average\")\n",
    "plt.axhline(avg_compensation, linestyle=\"--\", color=\"black\", label=\"overall avg\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.10.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}