diff --git a/your-code/main.ipynb b/your-code/main.ipynb
index b2b6f8d..0a439b4 100644
--- a/your-code/main.ipynb
+++ b/your-code/main.ipynb
@@ -1,279 +1,1181 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Before your start:\n",
-    "- Read the README.md file\n",
-    "- Comment as much as you can and use the resources (README.md file)\n",
-    "- Happy learning!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# import numpy and pandas\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Challenge 1 - Exploring the Data\n",
-    "\n",
-    "In this challenge, we will examine all salaries of employees of the City of Chicago. We will start by loading the dataset and examining its contents."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Your code here:\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Examine the `salaries` dataset using the `head` function below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Your code here:\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We see from looking at the `head` function that there is quite a bit of missing data. Let's examine how much missing data is in each column. Produce this output in the cell below"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Your code here:\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's also look at the count of hourly vs. salaried employees. Write the code in the cell below"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Your code here:\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "What this information indicates is that the table contains information about two types of employees - salaried and hourly. Some columns apply only to one type of employee while other columns only apply to another kind. This is why there are so many missing values. Therefore, we will not do anything to handle the missing values."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "There are different departments in the city. List all departments and the count of employees in each department."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Your code here:\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Challenge 2 - Hypothesis Tests\n",
-    "\n",
-    "In this section of the lab, we will test whether the hourly wage of all hourly workers is significantly different from $30/hr. Import the correct one sample test function from scipy and perform the hypothesis test for a 95% two sided confidence interval."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Your code here:\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We are also curious about salaries in the police force. The chief of police in Chicago claimed in a press briefing that salaries this year are higher than last year's mean of $86000/year a year for all salaried employees. Test this one sided hypothesis using a 95% confidence interval.\n",
-    "\n",
-    "Hint: A one tailed test has a p-value that is half of the two tailed p-value. If our hypothesis is greater than, then to reject, the test statistic must also be positive."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Your code here:\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Using the `crosstab` function, find the department that has the most hourly workers. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Your code here:\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The workers from the department with the most hourly workers have complained that their hourly wage is less than $35/hour. Using a one sample t-test, test this one-sided hypothesis at the 95% confidence level."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Your code here:\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Challenge 3: To practice - Constructing Confidence Intervals\n",
-    "\n",
-    "While testing our hypothesis is a great way to gather empirical evidence for accepting or rejecting the hypothesis, another way to gather evidence is by creating a confidence interval. A confidence interval gives us information about the true mean of the population. So for a 95% confidence interval, we are 95% sure that the mean of the population is within the confidence interval. \n",
-    ").\n",
-    "\n",
-    "To read more about confidence intervals, click [here](https://en.wikipedia.org/wiki/Confidence_interval).\n",
-    "\n",
-    "\n",
-    "In the cell below, we will construct a 95% confidence interval for the mean hourly wage of all hourly workers. \n",
-    "\n",
-    "The confidence interval is computed in SciPy using the `t.interval` function. You can read more about this function [here](https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.stats.t.html).\n",
-    "\n",
-    "To compute the confidence interval of the hourly wage, use the 0.95 for the confidence level, number of rows - 1 for degrees of freedom, the mean of the sample for the location parameter and the standard error for the scale. The standard error can be computed using [this](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.sem.html) function in SciPy."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Your code here:\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now construct the 95% confidence interval for all salaried employeed in the police in the cell below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Your code here:\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Bonus Challenge - Hypothesis Tests of Proportions\n",
-    "\n",
-    "Another type of one sample test is a hypothesis test of proportions. In this test, we examine whether the proportion of a group in our sample is significantly different than a fraction. \n",
-    "\n",
-    "You can read more about one sample proportion tests [here](http://sphweb.bumc.bu.edu/otlt/MPH-Modules/BS/SAS/SAS6-CategoricalData/SAS6-CategoricalData2.html).\n",
-    "\n",
-    "In the cell below, use the `proportions_ztest` function from `statsmodels` to perform a hypothesis test that will determine whether the number of hourly workers in the City of Chicago is significantly different from 25% at the 95% confidence level."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Your code here:\n",
-    "\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "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": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Before your start:\n",
+    "- Read the README.md file\n",
+    "- Comment as much as you can and use the resources (README.md file)\n",
+    "- Happy learning!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# import numpy and pandas\n",
+    "import pandas as pd\n",
+    "import numpy as np\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Challenge 1 - Exploring the Data\n",
+    "\n",
+    "In this challenge, we will examine all salaries of employees of the City of Chicago. We will start by loading the dataset and examining its contents."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Your code here:\n",
+    "data = pd.read_csv('Current_Employee_Names__Salaries__and_Position_Titles.csv')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Examine the `salaries` dataset using the `head` function below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "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>Name</th>\n",
+       "      <th>Job Titles</th>\n",
+       "      <th>Department</th>\n",
+       "      <th>Full or Part-Time</th>\n",
+       "      <th>Salary or Hourly</th>\n",
+       "      <th>Typical Hours</th>\n",
+       "      <th>Annual Salary</th>\n",
+       "      <th>Hourly Rate</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>AARON,  JEFFERY M</td>\n",
+       "      <td>SERGEANT</td>\n",
+       "      <td>POLICE</td>\n",
+       "      <td>F</td>\n",
+       "      <td>Salary</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>101442.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>AARON,  KARINA</td>\n",
+       "      <td>POLICE OFFICER (ASSIGNED AS DETECTIVE)</td>\n",
+       "      <td>POLICE</td>\n",
+       "      <td>F</td>\n",
+       "      <td>Salary</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>94122.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>AARON,  KIMBERLEI R</td>\n",
+       "      <td>CHIEF CONTRACT EXPEDITER</td>\n",
+       "      <td>GENERAL SERVICES</td>\n",
+       "      <td>F</td>\n",
+       "      <td>Salary</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>101592.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>ABAD JR,  VICENTE M</td>\n",
+       "      <td>CIVIL ENGINEER IV</td>\n",
+       "      <td>WATER MGMNT</td>\n",
+       "      <td>F</td>\n",
+       "      <td>Salary</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>110064.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>ABASCAL,  REECE E</td>\n",
+       "      <td>TRAFFIC CONTROL AIDE-HOURLY</td>\n",
+       "      <td>OEMC</td>\n",
+       "      <td>P</td>\n",
+       "      <td>Hourly</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>19.86</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  Name                              Job Titles  \\\n",
+       "0    AARON,  JEFFERY M                                SERGEANT   \n",
+       "1      AARON,  KARINA   POLICE OFFICER (ASSIGNED AS DETECTIVE)   \n",
+       "2  AARON,  KIMBERLEI R                CHIEF CONTRACT EXPEDITER   \n",
+       "3  ABAD JR,  VICENTE M                       CIVIL ENGINEER IV   \n",
+       "4    ABASCAL,  REECE E             TRAFFIC CONTROL AIDE-HOURLY   \n",
+       "\n",
+       "         Department Full or Part-Time Salary or Hourly  Typical Hours  \\\n",
+       "0            POLICE                 F           Salary            NaN   \n",
+       "1            POLICE                 F           Salary            NaN   \n",
+       "2  GENERAL SERVICES                 F           Salary            NaN   \n",
+       "3       WATER MGMNT                 F           Salary            NaN   \n",
+       "4              OEMC                 P           Hourly           20.0   \n",
+       "\n",
+       "   Annual Salary  Hourly Rate  \n",
+       "0       101442.0          NaN  \n",
+       "1        94122.0          NaN  \n",
+       "2       101592.0          NaN  \n",
+       "3       110064.0          NaN  \n",
+       "4            NaN        19.86  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Your code here:\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We see from looking at the `head` function that there is quite a bit of missing data. Let's examine how much missing data is in each column. Produce this output in the cell below"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 33183 entries, 0 to 33182\n",
+      "Data columns (total 8 columns):\n",
+      " #   Column             Non-Null Count  Dtype  \n",
+      "---  ------             --------------  -----  \n",
+      " 0   Name               33183 non-null  object \n",
+      " 1   Job Titles         33183 non-null  object \n",
+      " 2   Department         33183 non-null  object \n",
+      " 3   Full or Part-Time  33183 non-null  object \n",
+      " 4   Salary or Hourly   33183 non-null  object \n",
+      " 5   Typical Hours      8022 non-null   float64\n",
+      " 6   Annual Salary      25161 non-null  float64\n",
+      " 7   Hourly Rate        8022 non-null   float64\n",
+      "dtypes: float64(3), object(5)\n",
+      "memory usage: 2.0+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Your code here:\n",
+    "\n",
+    "data.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's also look at the count of hourly vs. salaried employees. Write the code in the cell below"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "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>Name</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Salary or Hourly</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Hourly</th>\n",
+       "      <td>8022</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Salary</th>\n",
+       "      <td>25161</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   Name\n",
+       "Salary or Hourly       \n",
+       "Hourly             8022\n",
+       "Salary            25161"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Your code here:\n",
+    "data.groupby('Salary or Hourly').agg({'Name': 'count'}).head()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "What this information indicates is that the table contains information about two types of employees - salaried and hourly. Some columns apply only to one type of employee while other columns only apply to another kind. This is why there are so many missing values. Therefore, we will not do anything to handle the missing values."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There are different departments in the city. List all departments and the count of employees in each department."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "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>Name</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Department</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>ADMIN HEARNG</th>\n",
+       "      <td>39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ANIMAL CONTRL</th>\n",
+       "      <td>81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AVIATION</th>\n",
+       "      <td>1629</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>BOARD OF ELECTION</th>\n",
+       "      <td>107</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>BOARD OF ETHICS</th>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>BUDGET &amp; MGMT</th>\n",
+       "      <td>46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>BUILDINGS</th>\n",
+       "      <td>269</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>BUSINESS AFFAIRS</th>\n",
+       "      <td>171</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CITY CLERK</th>\n",
+       "      <td>84</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CITY COUNCIL</th>\n",
+       "      <td>411</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>COMMUNITY DEVELOPMENT</th>\n",
+       "      <td>207</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>COPA</th>\n",
+       "      <td>116</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CULTURAL AFFAIRS</th>\n",
+       "      <td>65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DISABILITIES</th>\n",
+       "      <td>28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DoIT</th>\n",
+       "      <td>99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>FAMILY &amp; SUPPORT</th>\n",
+       "      <td>615</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>FINANCE</th>\n",
+       "      <td>560</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>FIRE</th>\n",
+       "      <td>4641</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>GENERAL SERVICES</th>\n",
+       "      <td>980</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>HEALTH</th>\n",
+       "      <td>488</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>HUMAN RELATIONS</th>\n",
+       "      <td>16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>HUMAN RESOURCES</th>\n",
+       "      <td>79</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>INSPECTOR GEN</th>\n",
+       "      <td>87</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LAW</th>\n",
+       "      <td>407</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LICENSE APPL COMM</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MAYOR'S OFFICE</th>\n",
+       "      <td>85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>OEMC</th>\n",
+       "      <td>2102</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>POLICE</th>\n",
+       "      <td>13414</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>POLICE BOARD</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>PROCUREMENT</th>\n",
+       "      <td>92</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>PUBLIC LIBRARY</th>\n",
+       "      <td>1015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>STREETS &amp; SAN</th>\n",
+       "      <td>2198</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TRANSPORTN</th>\n",
+       "      <td>1140</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TREASURER</th>\n",
+       "      <td>22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>WATER MGMNT</th>\n",
+       "      <td>1879</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                        Name\n",
+       "Department                  \n",
+       "ADMIN HEARNG              39\n",
+       "ANIMAL CONTRL             81\n",
+       "AVIATION                1629\n",
+       "BOARD OF ELECTION        107\n",
+       "BOARD OF ETHICS            8\n",
+       "BUDGET & MGMT             46\n",
+       "BUILDINGS                269\n",
+       "BUSINESS AFFAIRS         171\n",
+       "CITY CLERK                84\n",
+       "CITY COUNCIL             411\n",
+       "COMMUNITY DEVELOPMENT    207\n",
+       "COPA                     116\n",
+       "CULTURAL AFFAIRS          65\n",
+       "DISABILITIES              28\n",
+       "DoIT                      99\n",
+       "FAMILY & SUPPORT         615\n",
+       "FINANCE                  560\n",
+       "FIRE                    4641\n",
+       "GENERAL SERVICES         980\n",
+       "HEALTH                   488\n",
+       "HUMAN RELATIONS           16\n",
+       "HUMAN RESOURCES           79\n",
+       "INSPECTOR GEN             87\n",
+       "LAW                      407\n",
+       "LICENSE APPL COMM          1\n",
+       "MAYOR'S OFFICE            85\n",
+       "OEMC                    2102\n",
+       "POLICE                 13414\n",
+       "POLICE BOARD               2\n",
+       "PROCUREMENT               92\n",
+       "PUBLIC LIBRARY          1015\n",
+       "STREETS & SAN           2198\n",
+       "TRANSPORTN              1140\n",
+       "TREASURER                 22\n",
+       "WATER MGMNT             1879"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Your code here:\n",
+    "data.groupby('Department').agg({'Name': 'count'}).head(100)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Challenge 2 - Hypothesis Tests\n",
+    "\n",
+    "In this section of the lab, we will test whether the hourly wage of all hourly workers is significantly different from $30/hr. Import the correct one sample test function from scipy and perform the hypothesis test for a 95% two sided confidence interval."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4     19.86\n",
+       "6     46.10\n",
+       "7     35.60\n",
+       "10     2.65\n",
+       "18    17.68\n",
+       "Name: Hourly Rate, dtype: float64"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Your code here:\n",
+    "from scipy.stats import ttest_1samp\n",
+    "dataset = data[data['Salary or Hourly'] == 'Hourly']['Hourly Rate']\n",
+    "dataset.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "TtestResult(statistic=20.6198057854942, pvalue=4.3230240486229894e-92, df=8021)"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test_result = ttest_1samp(dataset,30.00)\n",
+    "test_result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Since P value of 2.1615120243114947e-92 is less than 0.05 then we reject the null hypothesis.\n"
+     ]
+    }
+   ],
+   "source": [
+    "p_value = test_result[1]/2\n",
+    "print(f'Since P value of {p_value} is less than 0.05 then we reject the null hypothesis.')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We are also curious about salaries in the police force. The chief of police in Chicago claimed in a press briefing that salaries this year are higher than last year's mean of $86000/year a year for all salaried employees. Test this one sided hypothesis using a 95% confidence interval.\n",
+    "\n",
+    "Hint: A one tailed test has a p-value that is half of the two tailed p-value. If our hypothesis is greater than, then to reject, the test statistic must also be positive."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0     101442.0\n",
+       "1      94122.0\n",
+       "9      93354.0\n",
+       "11     84054.0\n",
+       "12     87006.0\n",
+       "Name: Annual Salary, dtype: float64"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Your code here:\n",
+    "police_data = data[(data['Department'] == 'POLICE') & (data['Salary or Hourly'] == 'Salary')]['Annual Salary']\n",
+    "police_data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "TtestResult(statistic=3.081997005712994, pvalue=0.0020603403550965155, df=13403)"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "police_test_result = ttest_1samp(police_data, 86000.00)\n",
+    "police_test_result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.0010301701775482577"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "p_value = police_test_result[1]/2\n",
+    "p_value"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Since P value of 0.0010301701775482577 is less than 0.05 and our statistics is positive then we reject the null hypothesis\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f'Since P value of {p_value} is less than 0.05 and our statistics is positive then we reject the null hypothesis')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Using the `crosstab` function, find the department that has the most hourly workers. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "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>Salary or Hourly</th>\n",
+       "      <th>Hourly</th>\n",
+       "      <th>Salary</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Department</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>ADMIN HEARNG</th>\n",
+       "      <td>0</td>\n",
+       "      <td>39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>POLICE BOARD</th>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>BOARD OF ELECTION</th>\n",
+       "      <td>0</td>\n",
+       "      <td>107</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>BOARD OF ETHICS</th>\n",
+       "      <td>0</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LICENSE APPL COMM</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>BUILDINGS</th>\n",
+       "      <td>0</td>\n",
+       "      <td>269</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>INSPECTOR GEN</th>\n",
+       "      <td>0</td>\n",
+       "      <td>87</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CITY CLERK</th>\n",
+       "      <td>0</td>\n",
+       "      <td>84</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>HUMAN RELATIONS</th>\n",
+       "      <td>0</td>\n",
+       "      <td>16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TREASURER</th>\n",
+       "      <td>0</td>\n",
+       "      <td>22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>COPA</th>\n",
+       "      <td>0</td>\n",
+       "      <td>116</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DoIT</th>\n",
+       "      <td>0</td>\n",
+       "      <td>99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DISABILITIES</th>\n",
+       "      <td>0</td>\n",
+       "      <td>28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>PROCUREMENT</th>\n",
+       "      <td>2</td>\n",
+       "      <td>90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>FIRE</th>\n",
+       "      <td>2</td>\n",
+       "      <td>4639</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>BUDGET &amp; MGMT</th>\n",
+       "      <td>2</td>\n",
+       "      <td>44</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>HEALTH</th>\n",
+       "      <td>3</td>\n",
+       "      <td>485</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>COMMUNITY DEVELOPMENT</th>\n",
+       "      <td>4</td>\n",
+       "      <td>203</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>HUMAN RESOURCES</th>\n",
+       "      <td>4</td>\n",
+       "      <td>75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>BUSINESS AFFAIRS</th>\n",
+       "      <td>7</td>\n",
+       "      <td>164</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CULTURAL AFFAIRS</th>\n",
+       "      <td>7</td>\n",
+       "      <td>58</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MAYOR'S OFFICE</th>\n",
+       "      <td>8</td>\n",
+       "      <td>77</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>POLICE</th>\n",
+       "      <td>10</td>\n",
+       "      <td>13404</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ANIMAL CONTRL</th>\n",
+       "      <td>19</td>\n",
+       "      <td>62</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LAW</th>\n",
+       "      <td>40</td>\n",
+       "      <td>367</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>FINANCE</th>\n",
+       "      <td>44</td>\n",
+       "      <td>516</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CITY COUNCIL</th>\n",
+       "      <td>64</td>\n",
+       "      <td>347</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>FAMILY &amp; SUPPORT</th>\n",
+       "      <td>287</td>\n",
+       "      <td>328</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>PUBLIC LIBRARY</th>\n",
+       "      <td>299</td>\n",
+       "      <td>716</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TRANSPORTN</th>\n",
+       "      <td>725</td>\n",
+       "      <td>415</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>GENERAL SERVICES</th>\n",
+       "      <td>765</td>\n",
+       "      <td>215</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AVIATION</th>\n",
+       "      <td>1082</td>\n",
+       "      <td>547</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>OEMC</th>\n",
+       "      <td>1273</td>\n",
+       "      <td>829</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>WATER MGMNT</th>\n",
+       "      <td>1513</td>\n",
+       "      <td>366</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>STREETS &amp; SAN</th>\n",
+       "      <td>1862</td>\n",
+       "      <td>336</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Salary or Hourly       Hourly  Salary\n",
+       "Department                           \n",
+       "ADMIN HEARNG                0      39\n",
+       "POLICE BOARD                0       2\n",
+       "BOARD OF ELECTION           0     107\n",
+       "BOARD OF ETHICS             0       8\n",
+       "LICENSE APPL COMM           0       1\n",
+       "BUILDINGS                   0     269\n",
+       "INSPECTOR GEN               0      87\n",
+       "CITY CLERK                  0      84\n",
+       "HUMAN RELATIONS             0      16\n",
+       "TREASURER                   0      22\n",
+       "COPA                        0     116\n",
+       "DoIT                        0      99\n",
+       "DISABILITIES                0      28\n",
+       "PROCUREMENT                 2      90\n",
+       "FIRE                        2    4639\n",
+       "BUDGET & MGMT               2      44\n",
+       "HEALTH                      3     485\n",
+       "COMMUNITY DEVELOPMENT       4     203\n",
+       "HUMAN RESOURCES             4      75\n",
+       "BUSINESS AFFAIRS            7     164\n",
+       "CULTURAL AFFAIRS            7      58\n",
+       "MAYOR'S OFFICE              8      77\n",
+       "POLICE                     10   13404\n",
+       "ANIMAL CONTRL              19      62\n",
+       "LAW                        40     367\n",
+       "FINANCE                    44     516\n",
+       "CITY COUNCIL               64     347\n",
+       "FAMILY & SUPPORT          287     328\n",
+       "PUBLIC LIBRARY            299     716\n",
+       "TRANSPORTN                725     415\n",
+       "GENERAL SERVICES          765     215\n",
+       "AVIATION                 1082     547\n",
+       "OEMC                     1273     829\n",
+       "WATER MGMNT              1513     366\n",
+       "STREETS & SAN            1862     336"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Your code here:\n",
+    "pd.crosstab(data['Department'], data['Salary or Hourly'] ).sort_values('Hourly')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The workers from the department with the most hourly workers have complained that their hourly wage is less than $35/hour. Using a one sample t-test, test this one-sided hypothesis at the 95% confidence level."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "TtestResult(statistic=-9.567447887848152, pvalue=3.3378530564707717e-21, df=1861)"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Your code here:\n",
+    "hourly_ds = data[(data['Department'] == 'STREETS & SAN') & (data['Salary or Hourly'] == 'Hourly')]['Hourly Rate']\n",
+    "h_tr = ttest_1samp(hourly_ds, 35.00)\n",
+    "h_tr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Considering the pvalue is smaller than 0.05 and the statistics is negative, then we reject the null hypothesis'"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"\"\"Considering the pvalue is smaller than 0.05 and the statistics is negative, then we reject the null hypothesis\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Challenge 3: To practice - Constructing Confidence Intervals\n",
+    "\n",
+    "While testing our hypothesis is a great way to gather empirical evidence for accepting or rejecting the hypothesis, another way to gather evidence is by creating a confidence interval. A confidence interval gives us information about the true mean of the population. So for a 95% confidence interval, we are 95% sure that the mean of the population is within the confidence interval. \n",
+    ").\n",
+    "\n",
+    "To read more about confidence intervals, click [here](https://en.wikipedia.org/wiki/Confidence_interval).\n",
+    "\n",
+    "\n",
+    "In the cell below, we will construct a 95% confidence interval for the mean hourly wage of all hourly workers. \n",
+    "\n",
+    "The confidence interval is computed in SciPy using the `t.interval` function. You can read more about this function [here](https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.stats.t.html).\n",
+    "\n",
+    "To compute the confidence interval of the hourly wage, use the 0.95 for the confidence level, number of rows - 1 for degrees of freedom, the mean of the sample for the location parameter and the standard error for the scale. The standard error can be computed using [this](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.sem.html) function in SciPy."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.1352368565101596"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Your code here:\n",
+    "hourly_rates = data[data['Salary or Hourly'] == 'Hourly']['Hourly Rate']\n",
+    "from scipy import stats\n",
+    "er = stats.sem(hourly_rates)\n",
+    "er"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(32.52345834488425, 33.05365708767623)"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stats.t.interval(0.95,hourly_rates.shape[0]-1, loc=hourly_rates.mean(), scale=er)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now construct the 95% confidence interval for all salaried employeed in the police in the cell below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "157.82445675052244"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Your code here:\n",
+    "p_er = stats.sem(police_data)\n",
+    "p_er"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(86177.05631531784, 86795.77269094894)"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stats.t.interval(0.95,police_data.shape[0]-1, loc=police_data.mean(), scale=p_er)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Bonus Challenge - Hypothesis Tests of Proportions\n",
+    "\n",
+    "Another type of one sample test is a hypothesis test of proportions. In this test, we examine whether the proportion of a group in our sample is significantly different than a fraction. \n",
+    "\n",
+    "You can read more about one sample proportion tests [here](http://sphweb.bumc.bu.edu/otlt/MPH-Modules/BS/SAS/SAS6-CategoricalData/SAS6-CategoricalData2.html).\n",
+    "\n",
+    "In the cell below, use the `proportions_ztest` function from `statsmodels` to perform a hypothesis test that will determine whether the number of hourly workers in the City of Chicago is significantly different from 25% at the 95% confidence level."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Your code here:\n",
+    "\n"
+   ]
+  }
+ ],
+ "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": "3.10.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}