intro-data-analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import pandasql\n",
    "from pandasql import sqldf, load_meat, load_births\n",
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "nyc_jobs = pd.read_csv(\"NYC_Jobs.tsv\", sep='\\t')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The original columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Job ID', 'Agency', 'Posting Type', 'Business Title', 'Civil Service Title', 'Title Code No', 'Level', 'Job Category', 'Full-Time/Part-Time indicator', 'Salary Range From', 'Salary Range To', 'Salary Frequency', 'Work Location', 'Division/Work Unit', 'Job Description', 'Minimum Qual Requirements', 'Preferred Skills', 'Additional Information', 'To Apply', 'Hours/Shift', 'Work Location 1', 'Recruitment Contact', 'Residency Requirement', 'Posting Date', 'Post Until', 'Posting Updated', 'Process Date']\n"
     ]
    }
   ],
   "source": [
    "original_columns = list(nyc_jobs.columns)\n",
    "print(original_columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## To clean column names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_title(col_name):\n",
    "    col_name = col_name.strip().lower()\n",
    "    return re.sub(r'[^a-z0-9]', '_', col_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cleaned column names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['job_id', 'agency', 'posting_type', 'business_title', 'civil_service_title', 'title_code_no', 'level', 'job_category', 'full_time_part_time_indicator', 'salary_range_from', 'salary_range_to', 'salary_frequency', 'work_location', 'division_work_unit', 'job_description', 'minimum_qual_requirements', 'preferred_skills', 'additional_information', 'to_apply', 'hours_shift', 'work_location_1', 'recruitment_contact', 'residency_requirement', 'posting_date', 'post_until', 'posting_updated', 'process_date']\n"
     ]
    }
   ],
   "source": [
    "clean_columns = [clean_title(each_col) for each_col in original_columns]\n",
    "print(clean_columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "nyc_jobs.columns = clean_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\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>job_id</th>\n",
       "      <th>agency</th>\n",
       "      <th>posting_type</th>\n",
       "      <th>business_title</th>\n",
       "      <th>civil_service_title</th>\n",
       "      <th>title_code_no</th>\n",
       "      <th>level</th>\n",
       "      <th>job_category</th>\n",
       "      <th>full_time_part_time_indicator</th>\n",
       "      <th>salary_range_from</th>\n",
       "      <th>...</th>\n",
       "      <th>additional_information</th>\n",
       "      <th>to_apply</th>\n",
       "      <th>hours_shift</th>\n",
       "      <th>work_location_1</th>\n",
       "      <th>recruitment_contact</th>\n",
       "      <th>residency_requirement</th>\n",
       "      <th>posting_date</th>\n",
       "      <th>post_until</th>\n",
       "      <th>posting_updated</th>\n",
       "      <th>process_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>87990</td>\n",
       "      <td>DEPARTMENT OF BUSINESS SERV.</td>\n",
       "      <td>Internal</td>\n",
       "      <td>Account Manager</td>\n",
       "      <td>CONTRACT REVIEWER (OFFICE OF L</td>\n",
       "      <td>40563</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42405.00</td>\n",
       "      <td>...</td>\n",
       "      <td>Salary range for this position is: $42,405 - $...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>New York City residency is generally required ...</td>\n",
       "      <td>06/24/2011</td>\n",
       "      <td>NaN</td>\n",
       "      <td>06/24/2011</td>\n",
       "      <td>10/22/2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>97899</td>\n",
       "      <td>DEPARTMENT OF BUSINESS SERV.</td>\n",
       "      <td>Internal</td>\n",
       "      <td>EXECUTIVE DIRECTOR, BUSINESS DEVELOPMENT</td>\n",
       "      <td>ADMINISTRATIVE BUSINESS PROMOT</td>\n",
       "      <td>10009</td>\n",
       "      <td>M3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>60740.00</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>In addition to applying through this website, ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>New York City residency is generally required ...</td>\n",
       "      <td>01/26/2012</td>\n",
       "      <td>NaN</td>\n",
       "      <td>01/26/2012</td>\n",
       "      <td>10/22/2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>132292</td>\n",
       "      <td>NYC HOUSING AUTHORITY</td>\n",
       "      <td>Internal</td>\n",
       "      <td>Maintenance Worker - Technical Services-Heatin...</td>\n",
       "      <td>MAINTENANCE WORKER</td>\n",
       "      <td>90698</td>\n",
       "      <td>0</td>\n",
       "      <td>Maintenance &amp; Operations</td>\n",
       "      <td>F</td>\n",
       "      <td>51907.68</td>\n",
       "      <td>...</td>\n",
       "      <td>1.  A Motor Vehicle Driverâ€™s License valid i...</td>\n",
       "      <td>Click the \"Apply Now\" button.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NYCHA has no residency requirements.</td>\n",
       "      <td>10/24/2013</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12/12/2013</td>\n",
       "      <td>10/22/2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>132292</td>\n",
       "      <td>NYC HOUSING AUTHORITY</td>\n",
       "      <td>External</td>\n",
       "      <td>Maintenance Worker - Technical Services-Heatin...</td>\n",
       "      <td>MAINTENANCE WORKER</td>\n",
       "      <td>90698</td>\n",
       "      <td>0</td>\n",
       "      <td>Maintenance &amp; Operations</td>\n",
       "      <td>F</td>\n",
       "      <td>51907.68</td>\n",
       "      <td>...</td>\n",
       "      <td>1.  A Motor Vehicle Driverâ€™s License valid i...</td>\n",
       "      <td>Click the \"Apply Now\" button.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NYCHA has no residency requirements.</td>\n",
       "      <td>10/24/2013</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12/12/2013</td>\n",
       "      <td>10/22/2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>133921</td>\n",
       "      <td>NYC HOUSING AUTHORITY</td>\n",
       "      <td>Internal</td>\n",
       "      <td>Temporary Painter</td>\n",
       "      <td>PAINTER</td>\n",
       "      <td>91830</td>\n",
       "      <td>0</td>\n",
       "      <td>Maintenance &amp; Operations</td>\n",
       "      <td>F</td>\n",
       "      <td>35.00</td>\n",
       "      <td>...</td>\n",
       "      <td>SPECIAL NOTE:    1.  This is a temporary assig...</td>\n",
       "      <td>Click the \"Apply Now\" button.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NYCHA has no residency requirement.</td>\n",
       "      <td>01/09/2014</td>\n",
       "      <td>NaN</td>\n",
       "      <td>01/08/2014</td>\n",
       "      <td>10/22/2019</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   job_id                        agency posting_type  \\\n",
       "0   87990  DEPARTMENT OF BUSINESS SERV.     Internal   \n",
       "1   97899  DEPARTMENT OF BUSINESS SERV.     Internal   \n",
       "2  132292         NYC HOUSING AUTHORITY     Internal   \n",
       "3  132292         NYC HOUSING AUTHORITY     External   \n",
       "4  133921         NYC HOUSING AUTHORITY     Internal   \n",
       "\n",
       "                                      business_title  \\\n",
       "0                                    Account Manager   \n",
       "1           EXECUTIVE DIRECTOR, BUSINESS DEVELOPMENT   \n",
       "2  Maintenance Worker - Technical Services-Heatin...   \n",
       "3  Maintenance Worker - Technical Services-Heatin...   \n",
       "4                                  Temporary Painter   \n",
       "\n",
       "              civil_service_title title_code_no level  \\\n",
       "0  CONTRACT REVIEWER (OFFICE OF L         40563     1   \n",
       "1  ADMINISTRATIVE BUSINESS PROMOT         10009    M3   \n",
       "2              MAINTENANCE WORKER         90698     0   \n",
       "3              MAINTENANCE WORKER         90698     0   \n",
       "4                         PAINTER         91830     0   \n",
       "\n",
       "               job_category full_time_part_time_indicator  salary_range_from  \\\n",
       "0                       NaN                           NaN           42405.00   \n",
       "1                       NaN                             F           60740.00   \n",
       "2  Maintenance & Operations                             F           51907.68   \n",
       "3  Maintenance & Operations                             F           51907.68   \n",
       "4  Maintenance & Operations                             F              35.00   \n",
       "\n",
       "   ...                             additional_information  \\\n",
       "0  ...  Salary range for this position is: $42,405 - $...   \n",
       "1  ...                                                NaN   \n",
       "2  ...  1.  A Motor Vehicle Driverâ€™s License valid i...   \n",
       "3  ...  1.  A Motor Vehicle Driverâ€™s License valid i...   \n",
       "4  ...  SPECIAL NOTE:    1.  This is a temporary assig...   \n",
       "\n",
       "                                            to_apply hours_shift  \\\n",
       "0                                                NaN         NaN   \n",
       "1  In addition to applying through this website, ...         NaN   \n",
       "2                      Click the \"Apply Now\" button.         NaN   \n",
       "3                      Click the \"Apply Now\" button.         NaN   \n",
       "4                      Click the \"Apply Now\" button.         NaN   \n",
       "\n",
       "  work_location_1 recruitment_contact  \\\n",
       "0             NaN                 NaN   \n",
       "1             NaN                 NaN   \n",
       "2             NaN                 NaN   \n",
       "3             NaN                 NaN   \n",
       "4             NaN                 NaN   \n",
       "\n",
       "                               residency_requirement posting_date post_until  \\\n",
       "0  New York City residency is generally required ...   06/24/2011        NaN   \n",
       "1  New York City residency is generally required ...   01/26/2012        NaN   \n",
       "2               NYCHA has no residency requirements.   10/24/2013        NaN   \n",
       "3               NYCHA has no residency requirements.   10/24/2013        NaN   \n",
       "4                NYCHA has no residency requirement.   01/09/2014        NaN   \n",
       "\n",
       "  posting_updated process_date  \n",
       "0      06/24/2011   10/22/2019  \n",
       "1      01/26/2012   10/22/2019  \n",
       "2      12/12/2013   10/22/2019  \n",
       "3      12/12/2013   10/22/2019  \n",
       "4      01/08/2014   10/22/2019  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nyc_jobs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "nyc_jobs['date_time'] = nyc_jobs['posting_date'].astype('datetime64[ns]')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SQL on Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>salary_range_from</th>\n",
       "      <th>salary_range_to</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>87203.0</td>\n",
       "      <td>131623.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>87203.0</td>\n",
       "      <td>131623.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>43292.0</td>\n",
       "      <td>53000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>81290.0</td>\n",
       "      <td>95896.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>45174.0</td>\n",
       "      <td>62370.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   salary_range_from  salary_range_to\n",
       "0            87203.0         131623.0\n",
       "1            87203.0         131623.0\n",
       "2            43292.0          53000.0\n",
       "3            81290.0          95896.0\n",
       "4            45174.0          62370.0"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pysqldf = lambda q: sqldf(q, globals())\n",
    "pysqldf(\"SELECT salary_range_from, salary_range_to FROM nyc_jobs where job_category like '%Technology%' LIMIT 10;\").head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercises\n",
    "\n",
    "Come up with questions to answers with this dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num_posting_per_day</th>\n",
       "      <th>posting_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>12/31/2018</td>\n",
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       "    <tr>\n",
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       "      <td>2</td>\n",
       "      <td>12/27/2016</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>12/23/2016</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>12/22/2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>12/21/2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>478</th>\n",
       "      <td>2</td>\n",
       "      <td>01/06/2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>479</th>\n",
       "      <td>1</td>\n",
       "      <td>01/04/2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>480</th>\n",
       "      <td>4</td>\n",
       "      <td>01/03/2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>481</th>\n",
       "      <td>6</td>\n",
       "      <td>01/03/2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>482</th>\n",
       "      <td>3</td>\n",
       "      <td>01/02/2019</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>483 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     num_posting_per_day posting_date\n",
       "0                      2   12/31/2018\n",
       "1                      2   12/27/2016\n",
       "2                      1   12/23/2016\n",
       "3                      2   12/22/2016\n",
       "4                      1   12/21/2015\n",
       "..                   ...          ...\n",
       "478                    2   01/06/2017\n",
       "479                    1   01/04/2017\n",
       "480                    4   01/03/2019\n",
       "481                    6   01/03/2017\n",
       "482                    3   01/02/2019\n",
       "\n",
       "[483 rows x 2 columns]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "postings_per_day = pysqldf('''SELECT count(0) num_posting_per_day, posting_date FROM nyc_jobs '''\n",
    "                            ''' where posting_date is not null group by posting_date order by posting_date desc;''')\n",
    "postings_per_day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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>num_posting_per_day</th>\n",
       "      <th>posting_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>06/24/2011</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>01/26/2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>10/24/2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>12/09/2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>12/20/2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>478</th>\n",
       "      <td>70</td>\n",
       "      <td>10/16/2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>479</th>\n",
       "      <td>31</td>\n",
       "      <td>10/17/2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>480</th>\n",
       "      <td>57</td>\n",
       "      <td>10/18/2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>481</th>\n",
       "      <td>1</td>\n",
       "      <td>10/19/2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>482</th>\n",
       "      <td>54</td>\n",
       "      <td>10/21/2019</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>483 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     num_posting_per_day posting_date\n",
       "0                      1   06/24/2011\n",
       "1                      1   01/26/2012\n",
       "2                      2   10/24/2013\n",
       "3                      1   12/09/2013\n",
       "4                      1   12/20/2013\n",
       "..                   ...          ...\n",
       "478                   70   10/16/2019\n",
       "479                   31   10/17/2019\n",
       "480                   57   10/18/2019\n",
       "481                    1   10/19/2019\n",
       "482                   54   10/21/2019\n",
       "\n",
       "[483 rows x 2 columns]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "postings_per_day2 = pysqldf('''SELECT count(0) num_posting_per_day, posting_date FROM nyc_jobs '''\n",
    "                            ''' where posting_date is not null group by date_time order by date_time asc;''')\n",
    "postings_per_day2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "postings_per_day2.plot(kind='line',x='posting_date',y='num_posting_per_day',color='red')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'date_time'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m~/anaconda3/envs/intro-data-analysis/lib/python3.6/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2896\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2897\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2898\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'date_time'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-87-a405788a1ed4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpostings_per_day2\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'year_month'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpostings_per_day2\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'date_time'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;36m100\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0myear\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmonth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/envs/intro-data-analysis/lib/python3.6/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2993\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2994\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2995\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2996\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2997\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/intro-data-analysis/lib/python3.6/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2897\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2898\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2899\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2900\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2901\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'date_time'"
     ]
    }
   ],
   "source": [
    "postings_per_day2['year_month'] = postings_per_day2['date_time'].map(lambda x: 100*x.year + x.month)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Can we predict how many jobs will be posted in the future?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('<M8[ns]')"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nyc_jobs['date_time'].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "nyc_jobs['date_time']"
   ]
  }
 ],
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