05-station-models-and-predictions.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Notebook V - Using ARIMA to Generate Predictions for all Stations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# imports\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# modeling\n",
    "import pmdarima as pmd\n",
    "from pmdarima.utils import tsdisplay, plot_acf, plot_pacf\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from statsmodels.tsa.stattools import adfuller\n",
    "from statsmodels.tsa.seasonal import seasonal_decompose\n",
    "\n",
    "# graphing\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import MultipleLocator\n",
    "from pandas.plotting import register_matplotlib_converters\n",
    "register_matplotlib_converters()\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5a. Read and Process Predictions File"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1</th>\n",
       "      <th>1.1</th>\n",
       "      <th>17th &amp; Guadalupe</th>\n",
       "      <th>-1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2nd &amp; Congress</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5th &amp; Bowie</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8th &amp; Congress</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>City Hall / Lavaca &amp; 2nd</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Convention Center / 3rd &amp; Trinity</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   1  1.1                   17th & Guadalupe  -1\n",
       "0  1    1                     2nd & Congress  -1\n",
       "1  1    1                        5th & Bowie  -1\n",
       "2  1    1                     8th & Congress  -1\n",
       "3  1    1           City Hall / Lavaca & 2nd  -1\n",
       "4  1    1  Convention Center / 3rd & Trinity  -1"
      ]
     },
     "execution_count": 249,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read predictions\n",
    "predictions = pd.read_csv('./submission/predictions.csv')\n",
    "predictions.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>start_station_name</th>\n",
       "      <th>predicted_station_revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>17th &amp; Guadalupe</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2nd &amp; Congress</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5th &amp; Bowie</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8th &amp; Congress</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>City Hall / Lavaca &amp; 2nd</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  month day        start_station_name predicted_station_revenue\n",
       "0     1   1          17th & Guadalupe                        -1\n",
       "1     1   1            2nd & Congress                        -1\n",
       "2     1   1               5th & Bowie                        -1\n",
       "3     1   1            8th & Congress                        -1\n",
       "4     1   1  City Hall / Lavaca & 2nd                        -1"
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# move the header (17th & Guadalupe to row)\n",
    "predictions = pd.concat([predictions.columns.to_frame().T, predictions], ignore_index = True)\n",
    "\n",
    "# rename the columns\n",
    "predictions.columns = ['month', 'day', 'start_station_name', 'predicted_station_revenue']\n",
    "\n",
    "# change the \"1.1\" to 1 for day (odd format carries over from reading csv)\n",
    "predictions.loc[0, 'day'] = 1\n",
    "predictions.loc[0, 'month'] = 1\n",
    "\n",
    "# now preview!\n",
    "predictions.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['17th & Guadalupe',\n",
       " '2nd & Congress',\n",
       " '5th & Bowie',\n",
       " '8th & Congress',\n",
       " 'City Hall / Lavaca & 2nd',\n",
       " 'Convention Center / 3rd & Trinity',\n",
       " 'Davis at Rainey Street',\n",
       " 'Guadalupe & 21st',\n",
       " 'South Congress & Academy',\n",
       " 'West & 6th St.']"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's store the unq stations we want to predict for\n",
    "prediction_stations = list(predictions['start_station_name'].unique())\n",
    "prediction_stations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5a. (i) One Final Rabbit Hole"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At this stage, I had another epiphany. I initially wanted to verify that each of the stations I want to predict for have 730 rows (daily data for 2 years). If they did not, it meant something is broken. I found below that one station stood out: `Convention Center / 3rd & Trinity`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Convention Center / 3rd & Trinity: Not nice, what's going on?\n"
     ]
    }
   ],
   "source": [
    "for name in prediction_stations:\n",
    "    if daily_revenue[daily_revenue['start_station_name'] == name].shape[0] != 730:\n",
    "        print(f\"{name}: Not nice, what's going on?\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "        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>start_station_name</th>\n",
       "      <th>trip_revenue</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [start_station_name, trip_revenue]\n",
       "Index: []"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# verifying that the above is true\n",
    "daily_revenue[daily_revenue['start_station_name'] == 'Convention Center / 3rd & Trinity']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I went through the unique start station names in the data again by examining the unique names. My initial thought process was that it's possible there could be a mismatch in the names. So, I checked for any resemblance to 'Convention Center.'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Convention Center / 4th St. @ MetroRail\n"
     ]
    }
   ],
   "source": [
    "for name in list(daily_revenue['start_station_name'].unique()):\n",
    "    if 'convention' in name.lower():\n",
    "        print(name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I found \"Convention Center / 4th St. @ MetroRail\" to resemble the missing station name. There were a couple hypotheses here:\n",
    "1. There was a mistake - \"Convention Center / 3rd & Trinity\" was no longer active, and \"4th St. @ MetroRail\" replaced it\n",
    "2. \"Convention Center / 3rd & Trinity\" is just not in the dataset\n",
    "\n",
    "I checked Google Maps and found that there in fact is a [3rd & Trinity station](https://www.google.com/maps/place/Trinity+St+%26+E+3rd+St,+Austin,+TX+78701/@30.2645946,-97.7415888,19.01z/data=!4m6!3m5!1s0x8644b5a80b4639c7:0x4834f48bd0189e62!8m2!3d30.2642785!4d-97.7403589!16s%2Fg%2F11hb87s4xz?entry=ttu) as well as a [4th St. @ MetroRail](https://www.google.com/maps/place/ATX+MetroBike+Station/@30.2649572,-97.7390054,20.26z/data=!4m6!3m5!1s0x8644b5a7d4ad5db5:0x1716668ba7da4821!8m2!3d30.2648847!4d-97.7393963!16s%2Fg%2F11c4fc7ptt?entry=ttu), both right at the Convention Center. <br><br> After going back to the original dataset in a separate notebook and confirming that there were no trip records for \"Convention Center / 3rd & Trinity\", I decided to populate the predictions for this station as NaN."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## 5b. Read and Process Aggregated Trips Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>start_station_name</th>\n",
       "      <th>trip_revenue</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-01-01</th>\n",
       "      <td>6th &amp; Navasota St.</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-02</th>\n",
       "      <td>6th &amp; Navasota St.</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-03</th>\n",
       "      <td>6th &amp; Navasota St.</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-04</th>\n",
       "      <td>6th &amp; Navasota St.</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-05</th>\n",
       "      <td>6th &amp; Navasota St.</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-27</th>\n",
       "      <td>Red River @ LBJ Library</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-28</th>\n",
       "      <td>Red River @ LBJ Library</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-29</th>\n",
       "      <td>Red River @ LBJ Library</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-30</th>\n",
       "      <td>Red River @ LBJ Library</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-31</th>\n",
       "      <td>Red River @ LBJ Library</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>43800 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 start_station_name  trip_revenue\n",
       "date                                             \n",
       "2014-01-01       6th & Navasota St.           0.0\n",
       "2014-01-02       6th & Navasota St.           0.0\n",
       "2014-01-03       6th & Navasota St.           0.0\n",
       "2014-01-04       6th & Navasota St.           0.0\n",
       "2014-01-05       6th & Navasota St.           0.0\n",
       "...                             ...           ...\n",
       "2015-12-27  Red River @ LBJ Library           0.0\n",
       "2015-12-28  Red River @ LBJ Library           0.0\n",
       "2015-12-29  Red River @ LBJ Library           0.0\n",
       "2015-12-30  Red River @ LBJ Library           0.0\n",
       "2015-12-31  Red River @ LBJ Library           0.0\n",
       "\n",
       "[43800 rows x 2 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read cleaned aggregated data\n",
    "daily_revenue = pd.read_csv('./cleaned_data/trips_cleaned_aggregated.csv')\n",
    "daily_revenue = daily_revenue[['date', 'start_station_name', 'trip_revenue']] # select relevant columns\n",
    "\n",
    "# convert to datetime\n",
    "daily_revenue['date'] = pd.to_datetime(daily_revenue['date'])\n",
    "\n",
    "# set date as index\n",
    "daily_revenue.set_index('date', inplace = True)\n",
    "\n",
    "# preview\n",
    "daily_revenue"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## 5c. Writing a Function to Generate Predictions\n",
    "\n",
    "Note: I cannot fit the same model on all stations. Each station is technically its own time-series. They have different data with different starting/end points. Although I will assume the parameters to be the same for simplicity (as mentioned before), I want to train each model with the **station's own revenue** data from 2014-2015. <br>\n",
    "In this section, I will write a function that accepts a station name and computes the following: \n",
    "1. The station's revenue data in a separate data frame\n",
    "2. Train data set on the log of revenue data\n",
    "3. Empty predictions dataframe for 2016\n",
    "4. Instantiates an ARIMA class with chosen parameters (refer to Notebook IV)\n",
    "5. Fits and predicts data and populates the predictions dataframe with the exponentiated predictions\n",
    "6. Returns a dataframe for the station with predictions of daily revenue for 2016"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "def station_forecast(name):\n",
    "    \n",
    "    ### INITIAL CHECK FOR NAME ### \n",
    "    \n",
    "    # if the passed name is not found, return a dataframe with indexed dates, station name, and nan values for predicted revenue\n",
    "    if name not in list(daily_revenue['start_station_name'].unique()):\n",
    "        return pd.DataFrame({'predicted_station_revenue': float('nan'), 'start_station_name': name},\n",
    "                            index = pd.date_range(start = '2016-01-01', end = '2016-12-31', freq = 'D'))\n",
    "    \n",
    "    \n",
    "    ### Setting up DataFrames ###\n",
    "    \n",
    "    # set up df (keeping the name arbitrary)\n",
    "    df = daily_revenue[daily_revenue['start_station_name'] == name]\n",
    "    # train (on log1p of revenue)\n",
    "    train = np.log1p(df['trip_revenue'])\n",
    "#     # empty df w/ 2016 to be populated w/ preds later\n",
    "#     df_preds = pd.DataFrame(index = pd.date_range(start = '2016-01-01', end = '2016-12-31', freq = 'D'))\n",
    "    \n",
    "\n",
    "    ### Setting up the model ###\n",
    "    \n",
    "    # instantiate ARIMA (use normal ARIMA, we will hardcode parameters)\n",
    "    # please refer to Notebook 4 end, parameters selected from best model\n",
    "    arima = pmd.ARIMA(order = (1, 0, 1), seasonal_order = (2, 1, 1, 52))\n",
    "    \n",
    "    # fit arima on the train data for the model --> this step will likely take some time\n",
    "    arima.fit(y = train)\n",
    "    \n",
    "    # use model to make predictions!\n",
    "        # ensure predicting 366 (2016 leap yr)\n",
    "    df_preds = pd.DataFrame(np.exp(arima.predict(366)), columns = ['predicted_station_revenue'])\n",
    "    \n",
    "    # add name\n",
    "    df_preds['start_station_name'] = name\n",
    "    \n",
    "    \n",
    "    ### Return predictions data_frame ###\n",
    "    return df_preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'17th & Guadalupe': None,\n",
       " '2nd & Congress': None,\n",
       " '5th & Bowie': None,\n",
       " '8th & Congress': None,\n",
       " 'City Hall / Lavaca & 2nd': None,\n",
       " 'Convention Center / 3rd & Trinity': None,\n",
       " 'Davis at Rainey Street': None,\n",
       " 'Guadalupe & 21st': None,\n",
       " 'South Congress & Academy': None,\n",
       " 'West & 6th St.': None}"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create dictionary to store dataframes\n",
    "stations_revenue = {key: None for key in prediction_stations}\n",
    "stations_revenue"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, I will loop through the keys in the dictionary and use the key names to pass through my function. The resulting value (dataframe) gets stored with the associated key (name).\n",
    "\n",
    "#### Disclaimer: Running the code below issues several 'ValueWarnings'. One goal for the future is to figure out specifically how to prevent this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/sktimeenv/lib/python3.9/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "/opt/anaconda3/envs/sktimeenv/lib/python3.9/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "/opt/anaconda3/envs/sktimeenv/lib/python3.9/site-packages/statsmodels/base/model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "/opt/anaconda3/envs/sktimeenv/lib/python3.9/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "/opt/anaconda3/envs/sktimeenv/lib/python3.9/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "/opt/anaconda3/envs/sktimeenv/lib/python3.9/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "/opt/anaconda3/envs/sktimeenv/lib/python3.9/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "/opt/anaconda3/envs/sktimeenv/lib/python3.9/site-packages/statsmodels/base/model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "/opt/anaconda3/envs/sktimeenv/lib/python3.9/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "/opt/anaconda3/envs/sktimeenv/lib/python3.9/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "/opt/anaconda3/envs/sktimeenv/lib/python3.9/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "/opt/anaconda3/envs/sktimeenv/lib/python3.9/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "/opt/anaconda3/envs/sktimeenv/lib/python3.9/site-packages/statsmodels/base/model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "/opt/anaconda3/envs/sktimeenv/lib/python3.9/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "/opt/anaconda3/envs/sktimeenv/lib/python3.9/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "/opt/anaconda3/envs/sktimeenv/lib/python3.9/site-packages/statsmodels/base/model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "/opt/anaconda3/envs/sktimeenv/lib/python3.9/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "/opt/anaconda3/envs/sktimeenv/lib/python3.9/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "/opt/anaconda3/envs/sktimeenv/lib/python3.9/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "/opt/anaconda3/envs/sktimeenv/lib/python3.9/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "/opt/anaconda3/envs/sktimeenv/lib/python3.9/site-packages/statsmodels/base/model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "/opt/anaconda3/envs/sktimeenv/lib/python3.9/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "/opt/anaconda3/envs/sktimeenv/lib/python3.9/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  self._init_dates(dates, freq)\n"
     ]
    }
   ],
   "source": [
    "for k in stations_revenue.keys():\n",
    "    stations_revenue[k] = station_forecast(k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'17th & Guadalupe':             predicted_station_revenue start_station_name\n",
       " 2016-01-01                  13.347392   17th & Guadalupe\n",
       " 2016-01-02                  14.000361   17th & Guadalupe\n",
       " 2016-01-03                  11.144340   17th & Guadalupe\n",
       " 2016-01-04                  15.473278   17th & Guadalupe\n",
       " 2016-01-05                  11.074507   17th & Guadalupe\n",
       " ...                               ...                ...\n",
       " 2016-12-27                  19.662020   17th & Guadalupe\n",
       " 2016-12-28                  12.762220   17th & Guadalupe\n",
       " 2016-12-29                  12.909232   17th & Guadalupe\n",
       " 2016-12-30                  14.454571   17th & Guadalupe\n",
       " 2016-12-31                  13.561875   17th & Guadalupe\n",
       " \n",
       " [366 rows x 2 columns],\n",
       " '2nd & Congress':             predicted_station_revenue start_station_name\n",
       " 2016-01-01                  22.018151     2nd & Congress\n",
       " 2016-01-02                  36.363665     2nd & Congress\n",
       " 2016-01-03                  27.182522     2nd & Congress\n",
       " 2016-01-04                  34.210598     2nd & Congress\n",
       " 2016-01-05                  47.488203     2nd & Congress\n",
       " ...                               ...                ...\n",
       " 2016-12-27                  82.387224     2nd & Congress\n",
       " 2016-12-28                 105.051899     2nd & Congress\n",
       " 2016-12-29                 100.398530     2nd & Congress\n",
       " 2016-12-30                  41.111794     2nd & Congress\n",
       " 2016-12-31                  64.406929     2nd & Congress\n",
       " \n",
       " [366 rows x 2 columns],\n",
       " '5th & Bowie':             predicted_station_revenue start_station_name\n",
       " 2016-01-01                  50.568520        5th & Bowie\n",
       " 2016-01-02                  58.571281        5th & Bowie\n",
       " 2016-01-03                  47.510183        5th & Bowie\n",
       " 2016-01-04                  50.497129        5th & Bowie\n",
       " 2016-01-05                  46.096621        5th & Bowie\n",
       " ...                               ...                ...\n",
       " 2016-12-27                  47.490672        5th & Bowie\n",
       " 2016-12-28                  71.888964        5th & Bowie\n",
       " 2016-12-29                  43.432946        5th & Bowie\n",
       " 2016-12-30                  37.319619        5th & Bowie\n",
       " 2016-12-31                  46.538069        5th & Bowie\n",
       " \n",
       " [366 rows x 2 columns],\n",
       " '8th & Congress':             predicted_station_revenue start_station_name\n",
       " 2016-01-01                  14.542900     8th & Congress\n",
       " 2016-01-02                  15.464435     8th & Congress\n",
       " 2016-01-03                  16.775498     8th & Congress\n",
       " 2016-01-04                  17.794475     8th & Congress\n",
       " 2016-01-05                  19.024153     8th & Congress\n",
       " ...                               ...                ...\n",
       " 2016-12-27                  22.922634     8th & Congress\n",
       " 2016-12-28                  26.477259     8th & Congress\n",
       " 2016-12-29                  19.144423     8th & Congress\n",
       " 2016-12-30                  13.881293     8th & Congress\n",
       " 2016-12-31                  14.618205     8th & Congress\n",
       " \n",
       " [366 rows x 2 columns],\n",
       " 'City Hall / Lavaca & 2nd':             predicted_station_revenue        start_station_name\n",
       " 2016-01-01                  37.422684  City Hall / Lavaca & 2nd\n",
       " 2016-01-02                  42.343538  City Hall / Lavaca & 2nd\n",
       " 2016-01-03                  37.976601  City Hall / Lavaca & 2nd\n",
       " 2016-01-04                  45.192928  City Hall / Lavaca & 2nd\n",
       " 2016-01-05                  30.320043  City Hall / Lavaca & 2nd\n",
       " ...                               ...                       ...\n",
       " 2016-12-27                  35.652815  City Hall / Lavaca & 2nd\n",
       " 2016-12-28                  60.644328  City Hall / Lavaca & 2nd\n",
       " 2016-12-29                  33.392070  City Hall / Lavaca & 2nd\n",
       " 2016-12-30                  28.649956  City Hall / Lavaca & 2nd\n",
       " 2016-12-31                  34.338197  City Hall / Lavaca & 2nd\n",
       " \n",
       " [366 rows x 2 columns],\n",
       " 'Convention Center / 3rd & Trinity':             predicted_station_revenue                 start_station_name\n",
       " 2016-01-01                        NaN  Convention Center / 3rd & Trinity\n",
       " 2016-01-02                        NaN  Convention Center / 3rd & Trinity\n",
       " 2016-01-03                        NaN  Convention Center / 3rd & Trinity\n",
       " 2016-01-04                        NaN  Convention Center / 3rd & Trinity\n",
       " 2016-01-05                        NaN  Convention Center / 3rd & Trinity\n",
       " ...                               ...                                ...\n",
       " 2016-12-27                        NaN  Convention Center / 3rd & Trinity\n",
       " 2016-12-28                        NaN  Convention Center / 3rd & Trinity\n",
       " 2016-12-29                        NaN  Convention Center / 3rd & Trinity\n",
       " 2016-12-30                        NaN  Convention Center / 3rd & Trinity\n",
       " 2016-12-31                        NaN  Convention Center / 3rd & Trinity\n",
       " \n",
       " [366 rows x 2 columns],\n",
       " 'Davis at Rainey Street':             predicted_station_revenue      start_station_name\n",
       " 2016-01-01                  15.302471  Davis at Rainey Street\n",
       " 2016-01-02                  19.089031  Davis at Rainey Street\n",
       " 2016-01-03                  12.577189  Davis at Rainey Street\n",
       " 2016-01-04                  11.141223  Davis at Rainey Street\n",
       " 2016-01-05                  11.007652  Davis at Rainey Street\n",
       " ...                               ...                     ...\n",
       " 2016-12-27                  49.209996  Davis at Rainey Street\n",
       " 2016-12-28                  36.964908  Davis at Rainey Street\n",
       " 2016-12-29                  35.373826  Davis at Rainey Street\n",
       " 2016-12-30                  33.400955  Davis at Rainey Street\n",
       " 2016-12-31                  43.760609  Davis at Rainey Street\n",
       " \n",
       " [366 rows x 2 columns],\n",
       " 'Guadalupe & 21st':             predicted_station_revenue start_station_name\n",
       " 2016-01-01                   5.824142   Guadalupe & 21st\n",
       " 2016-01-02                   6.216320   Guadalupe & 21st\n",
       " 2016-01-03                   6.680746   Guadalupe & 21st\n",
       " 2016-01-04                  13.160581   Guadalupe & 21st\n",
       " 2016-01-05                  10.536781   Guadalupe & 21st\n",
       " ...                               ...                ...\n",
       " 2016-12-27                  17.486193   Guadalupe & 21st\n",
       " 2016-12-28                  26.222999   Guadalupe & 21st\n",
       " 2016-12-29                  21.467741   Guadalupe & 21st\n",
       " 2016-12-30                  11.343286   Guadalupe & 21st\n",
       " 2016-12-31                  12.571315   Guadalupe & 21st\n",
       " \n",
       " [366 rows x 2 columns],\n",
       " 'South Congress & Academy':             predicted_station_revenue        start_station_name\n",
       " 2016-01-01                  13.666690  South Congress & Academy\n",
       " 2016-01-02                  18.071504  South Congress & Academy\n",
       " 2016-01-03                  24.145310  South Congress & Academy\n",
       " 2016-01-04                  24.655114  South Congress & Academy\n",
       " 2016-01-05                  23.286489  South Congress & Academy\n",
       " ...                               ...                       ...\n",
       " 2016-12-27                  97.001127  South Congress & Academy\n",
       " 2016-12-28                 114.356440  South Congress & Academy\n",
       " 2016-12-29                  95.751999  South Congress & Academy\n",
       " 2016-12-30                  84.272661  South Congress & Academy\n",
       " 2016-12-31                 101.354740  South Congress & Academy\n",
       " \n",
       " [366 rows x 2 columns],\n",
       " 'West & 6th St.':             predicted_station_revenue start_station_name\n",
       " 2016-01-01                   8.782015     West & 6th St.\n",
       " 2016-01-02                  10.566332     West & 6th St.\n",
       " 2016-01-03                   7.602694     West & 6th St.\n",
       " 2016-01-04                  10.853230     West & 6th St.\n",
       " 2016-01-05                  10.790607     West & 6th St.\n",
       " ...                               ...                ...\n",
       " 2016-12-27                  42.758178     West & 6th St.\n",
       " 2016-12-28                  35.347591     West & 6th St.\n",
       " 2016-12-29                  27.339568     West & 6th St.\n",
       " 2016-12-30                  30.202653     West & 6th St.\n",
       " 2016-12-31                  29.850664     West & 6th St.\n",
       " \n",
       " [366 rows x 2 columns]}"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# preview!\n",
    "stations_revenue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [],
   "source": [
    "# use pd.concat() to combine all prediction DFs into 1\n",
    "daily_revenue_predictions = pd.concat(stations_revenue.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "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>predicted_station_revenue</th>\n",
       "      <th>start_station_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-01</th>\n",
       "      <td>13.347392</td>\n",
       "      <td>17th &amp; Guadalupe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-02</th>\n",
       "      <td>14.000361</td>\n",
       "      <td>17th &amp; Guadalupe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-03</th>\n",
       "      <td>11.144340</td>\n",
       "      <td>17th &amp; Guadalupe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-04</th>\n",
       "      <td>15.473278</td>\n",
       "      <td>17th &amp; Guadalupe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
       "      <td>11.074507</td>\n",
       "      <td>17th &amp; Guadalupe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-27</th>\n",
       "      <td>42.758178</td>\n",
       "      <td>West &amp; 6th St.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-28</th>\n",
       "      <td>35.347591</td>\n",
       "      <td>West &amp; 6th St.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-29</th>\n",
       "      <td>27.339568</td>\n",
       "      <td>West &amp; 6th St.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30</th>\n",
       "      <td>30.202653</td>\n",
       "      <td>West &amp; 6th St.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31</th>\n",
       "      <td>29.850664</td>\n",
       "      <td>West &amp; 6th St.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3660 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            predicted_station_revenue start_station_name\n",
       "2016-01-01                  13.347392   17th & Guadalupe\n",
       "2016-01-02                  14.000361   17th & Guadalupe\n",
       "2016-01-03                  11.144340   17th & Guadalupe\n",
       "2016-01-04                  15.473278   17th & Guadalupe\n",
       "2016-01-05                  11.074507   17th & Guadalupe\n",
       "...                               ...                ...\n",
       "2016-12-27                  42.758178     West & 6th St.\n",
       "2016-12-28                  35.347591     West & 6th St.\n",
       "2016-12-29                  27.339568     West & 6th St.\n",
       "2016-12-30                  30.202653     West & 6th St.\n",
       "2016-12-31                  29.850664     West & 6th St.\n",
       "\n",
       "[3660 rows x 2 columns]"
      ]
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# preview\n",
    "daily_revenue_predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we need to add the same month & day columns. We will use month, day, and start_station_name for joining onto the `predictions` dataframe we read in earlier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "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>predicted_station_revenue</th>\n",
       "      <th>start_station_name</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-01</th>\n",
       "      <td>13.347392</td>\n",
       "      <td>17th &amp; Guadalupe</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-02</th>\n",
       "      <td>14.000361</td>\n",
       "      <td>17th &amp; Guadalupe</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-03</th>\n",
       "      <td>11.144340</td>\n",
       "      <td>17th &amp; Guadalupe</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-04</th>\n",
       "      <td>15.473278</td>\n",
       "      <td>17th &amp; Guadalupe</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
       "      <td>11.074507</td>\n",
       "      <td>17th &amp; Guadalupe</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-27</th>\n",
       "      <td>42.758178</td>\n",
       "      <td>West &amp; 6th St.</td>\n",
       "      <td>12</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-28</th>\n",
       "      <td>35.347591</td>\n",
       "      <td>West &amp; 6th St.</td>\n",
       "      <td>12</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-29</th>\n",
       "      <td>27.339568</td>\n",
       "      <td>West &amp; 6th St.</td>\n",
       "      <td>12</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30</th>\n",
       "      <td>30.202653</td>\n",
       "      <td>West &amp; 6th St.</td>\n",
       "      <td>12</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31</th>\n",
       "      <td>29.850664</td>\n",
       "      <td>West &amp; 6th St.</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3660 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            predicted_station_revenue start_station_name  month  day\n",
       "2016-01-01                  13.347392   17th & Guadalupe      1    1\n",
       "2016-01-02                  14.000361   17th & Guadalupe      1    2\n",
       "2016-01-03                  11.144340   17th & Guadalupe      1    3\n",
       "2016-01-04                  15.473278   17th & Guadalupe      1    4\n",
       "2016-01-05                  11.074507   17th & Guadalupe      1    5\n",
       "...                               ...                ...    ...  ...\n",
       "2016-12-27                  42.758178     West & 6th St.     12   27\n",
       "2016-12-28                  35.347591     West & 6th St.     12   28\n",
       "2016-12-29                  27.339568     West & 6th St.     12   29\n",
       "2016-12-30                  30.202653     West & 6th St.     12   30\n",
       "2016-12-31                  29.850664     West & 6th St.     12   31\n",
       "\n",
       "[3660 rows x 4 columns]"
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_revenue_predictions['month'] = daily_revenue_predictions.index.month\n",
    "daily_revenue_predictions['day'] = daily_revenue_predictions.index.day\n",
    "daily_revenue_predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save off daily_revenue_predictions dataset for future use\n",
    "daily_revenue_predictions.to_csv('./cleaned_data/all-2016-predictions.csv', index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, left join the `predicted_station_revenue` onto the dataframe from the `predictions.csv` file by joining on month, day, and start_station_name."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [],
   "source": [
    "# merge, make sure left join\n",
    "predictions = pd.merge(predictions, daily_revenue_predictions, on = ['month', 'day', 'start_station_name'], how = 'left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "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>month</th>\n",
       "      <th>day</th>\n",
       "      <th>start_station_name</th>\n",
       "      <th>predicted_station_revenue_x</th>\n",
       "      <th>predicted_station_revenue_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>17th &amp; Guadalupe</td>\n",
       "      <td>-1</td>\n",
       "      <td>13.347392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>17th &amp; Guadalupe</td>\n",
       "      <td>-1</td>\n",
       "      <td>14.000361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>17th &amp; Guadalupe</td>\n",
       "      <td>-1</td>\n",
       "      <td>11.144340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>17th &amp; Guadalupe</td>\n",
       "      <td>-1</td>\n",
       "      <td>15.473278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>17th &amp; Guadalupe</td>\n",
       "      <td>-1</td>\n",
       "      <td>11.074507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1190</th>\n",
       "      <td>10</td>\n",
       "      <td>27</td>\n",
       "      <td>17th &amp; Guadalupe</td>\n",
       "      <td>-1</td>\n",
       "      <td>15.410924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1200</th>\n",
       "      <td>10</td>\n",
       "      <td>28</td>\n",
       "      <td>17th &amp; Guadalupe</td>\n",
       "      <td>-1</td>\n",
       "      <td>25.511229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1210</th>\n",
       "      <td>10</td>\n",
       "      <td>29</td>\n",
       "      <td>17th &amp; Guadalupe</td>\n",
       "      <td>-1</td>\n",
       "      <td>15.662095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1220</th>\n",
       "      <td>10</td>\n",
       "      <td>30</td>\n",
       "      <td>17th &amp; Guadalupe</td>\n",
       "      <td>-1</td>\n",
       "      <td>27.438606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1230</th>\n",
       "      <td>10</td>\n",
       "      <td>31</td>\n",
       "      <td>17th &amp; Guadalupe</td>\n",
       "      <td>-1</td>\n",
       "      <td>19.515802</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>124 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     month day start_station_name predicted_station_revenue_x  \\\n",
       "0        1   1   17th & Guadalupe                          -1   \n",
       "10       1   2   17th & Guadalupe                          -1   \n",
       "20       1   3   17th & Guadalupe                          -1   \n",
       "30       1   4   17th & Guadalupe                          -1   \n",
       "40       1   5   17th & Guadalupe                          -1   \n",
       "...    ...  ..                ...                         ...   \n",
       "1190    10  27   17th & Guadalupe                          -1   \n",
       "1200    10  28   17th & Guadalupe                          -1   \n",
       "1210    10  29   17th & Guadalupe                          -1   \n",
       "1220    10  30   17th & Guadalupe                          -1   \n",
       "1230    10  31   17th & Guadalupe                          -1   \n",
       "\n",
       "      predicted_station_revenue_y  \n",
       "0                       13.347392  \n",
       "10                      14.000361  \n",
       "20                      11.144340  \n",
       "30                      15.473278  \n",
       "40                      11.074507  \n",
       "...                           ...  \n",
       "1190                    15.410924  \n",
       "1200                    25.511229  \n",
       "1210                    15.662095  \n",
       "1220                    27.438606  \n",
       "1230                    19.515802  \n",
       "\n",
       "[124 rows x 5 columns]"
      ]
     },
     "execution_count": 255,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sample of 1 station\n",
    "predictions[predictions['start_station_name'] == '17th & Guadalupe']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop previous place holder column\n",
    "predictions.drop(columns = ['predicted_station_revenue_x'], axis = 1, inplace = True)\n",
    "\n",
    "# rename added column\n",
    "predictions.rename(columns = {'month': 'MONTH','day': 'DAY','start_station_name':'START STATION NAME',\n",
    "                              'predicted_station_revenue_y': 'PREDICTED STATION REVENUE'}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save off predictions file!\n",
    "predictions.to_csv('./submission/predictions.csv', index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This concludes my code and explanations for this bike-share daily revenue forecasting project. <br><br> Thank you for reading. Please contact me at patelprem922@gmail.com for questions, comments, concerns!"
   ]
  }
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