FinalProject_group039.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# COGS 108 - Final Project "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Overview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to see whether or not there is a significant correlation between the weather and the number of rides made per day in New York, appropriate data has been gathered and analyzed. The weather is divided into three different attributes; temperature, wind speed, and humidity, for each hour during the day in May of 2014. These three attributes were analyzed against the number of rides made in each month, to see if we can visually see the correlation between these three attributes and the rides made."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Names\n",
    "\n",
    "- Zijian Liu\n",
    "- Pin-Hsuan Chen\n",
    "- Kyle Reed \n",
    "- Yunji Ryu\n",
    "- Jing Wei"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Research Question"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Research question: How does the weather condition, specifically wind speed, temperature, humidity, and hourly weather description, affect the overall number of rides during the day in NYC?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Background and Prior Work"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There were no prior Work done for our topic. But based on our experiences with acquiring rides in certain weather conditions, there are numerous reasons why one can consider getting a taxi/Uber compared to getting there by foot or other means. One of the main components that influences this decision is weather. Engineers in the New York State created a system to provide information about the road conditions in New York State, specifically catering towards the more haphazard winter season, providing whether the highway will be blocked or not due to snow and ice operations. They also included a report for each of the observed years to include a more detailed description of the road conditions and list how accurate their report was. But not much of these affected the road conditions during the non-winter seasons. Whether it's the wind travel advisory warnings, slippery driveways on a rainy day, drivers and the riders tend to be more wary of the environmental situations around the road regardless of the season, which affects their decision to get a ride or not, conclusively affecting the number of rides that are made in New York. In order to investigate this information, we decided to use our readily available public data to analyze and infer to the possible correlations listed in our research question and hypothesis.\n",
    "\n",
    "References (include links):\n",
    "- 1) https://www.dot.ny.gov/wta (New York State Winter Travel Advisory System for 2014 - 2015 winter season)\n",
    "- 2) https://www.dot.ny.gov/divisions/engineering/technical-services/highway-data-services/traffic-data (Traffic Data Report based on various regions in New York State in 2014)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hypothesis\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Temperature will have an effect on ride count.\n",
    "2. Wind speed will have an effect on ride count.\n",
    "3. Humidity will have an effect on ride count.\n",
    "4. Weather description will have an effect on ride count. \n",
    "\n",
    "We predict that these factor will correlate to the number of rides in some way. It is harder to say whether it will be a negative or positive correlation because unfavorable weather conditions can include either extremely low temperatures or extremely high temperatures. However, we predict ride counts to be higher on days with unfavorable weather conditions, such as thunderstorm or rain. For instance, if for a particular hour, the wind speed, humidity, and temperature are all relatively higher compared to the whole dataset, we predict the ride count to be higher as well.\n",
    "\n",
    "We believe that unfavorable weather conditions will lead to higher ride counts because people are less willing to walk or take public transportation in those conidtions. For example, if the weather was nice and sunny, people are more willing to save money and walk a couple of blocks. However, if it was rainy and cold, there is a higher chance of people getting a ride so that they can stay dry and warm. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dataset(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Fill in your dataset information here*\n",
    "\n",
    "(Copy this information for each dataset)\n",
    "- Dataset Name: Historical Hourly Weather Data 2012-2017\n",
    "- Link to the dataset: https://www.kaggle.com/selfishgene/historical-hourly-weather-data\n",
    "- Number of observations: [to be filled]\n",
    "- Description: This dataset includes hourly weather data for several cities\n",
    "\n",
    "\n",
    "- Dataset Name: Historical Uber Rides Data May 2014\n",
    "- Link to the dataset: https://github.com/fivethirtyeight/uber-tlc-foil-response\n",
    "- Number of observations: 652435\n",
    "- description: This data includes the Uber rides in New York City in May 2014, but the raw data is base on the time, latitude and longitude of each time people use Uber. We analyzed the raw data and counted the use of Uber by hour.\n",
    "\n",
    "\n",
    "1-2 sentences describing each dataset. \n",
    "\n",
    "If you plan to use multiple datasets, add 1-2 sentences about how you plan to combine these datasets."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "## YOUR CODE HERE\n",
    "import numpy as np\n",
    "import scipy as sp\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn as skl\n",
    "import datetime\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Describe your data cleaning steps here."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first dataset we are going to clean is the ride data. Our cleaning steps include:\n",
    "1. Load and read all the CSV file. We will also take a look at what the dataset looks like and how many enteries there are. Here we noticed that we need to condense our dataset in someway to match the hourly weather descriptions. \n",
    "2. We decided to condense the ride data by hour to show a ride count for each hour of everyday of a month. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step 1 \n",
    "pickup_df = pd.read_csv('uber-raw-data-may14.csv')\n",
    "pickup_df.head()\n",
    "print(pickup_df.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we are only interested in New York, we are going to drop data for other cities. \n",
    "We are only interested in data for May 2014, so we will modify the dataframe so that it only contains data for that particular month.\n",
    "Unfortunately, the format of date and time for pickup_df and weather_description_df do not match up. To solve this,\n",
    "    1. We figured out the type for the datetime object \n",
    "    2. Using to_datetime, we converted them into the same format.\n",
    "Now we have a minimal dataframe. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'ubermay' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-55-46d7cd838d08>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;31m#run through raw data and add each ride to its respective hour within the dict\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrow\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mubermay\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miterrows\u001b[0m\u001b[0;34m(\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     12\u001b[0m         \u001b[0mcounts\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\":\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'ubermay' is not defined"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "# Step 2 \n",
    "#initialize counts dict for each hour in may\n",
    "counts = {}\n",
    "for date in np.arange(1,32):\n",
    "    for time in np.arange(0,24):\n",
    "        counts[\"5/{}/2014 {}\".format(date,time)] = 0\n",
    "        \n",
    "#run through raw data and add each ride to its respective hour within the dict\n",
    "for index, row in ubermay.iterrows():\n",
    "        counts[row[0][:row[0].index(\":\")]] += 1\n",
    "\n",
    "\n",
    "weather_description_df = pd.read_csv('weather_description.csv')\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "###\n",
    "#data frame: pickup_df contains trip data, weather_description_df contains weather data\n",
    "\n",
    "# cleaning data, dropping everything not related to New York City \n",
    "weather_description_df = weather_description_df[['datetime','New York']].dropna()\n",
    "\n",
    "# reading raw data for uber rides\n",
    "pickup_df = pickup_df[['Date/Time']]\n",
    "pickup_df.head()\n",
    "\n",
    "\n",
    "\n",
    "# printing the first element in both data frame to see the format\n",
    "print(\"WEATHER: \"+ weather_description_df['datetime'].iloc[:3])\n",
    "print(\"PICKUP:  \"+ pickup_df['Date/Time'].iloc[:3])\n",
    "\n",
    "print(\"--------\")\n",
    "''' \n",
    "How to drop data whose year and month doesn't match? i.e. dropping rows in which years aren't 2014\n",
    "How to format the date so that everything is in sync with each other?\n",
    "After cleaning, how to relate trip numbers at a certain time period with that time?\n",
    "'''\n",
    "#pickup_df['Date/Time'] = datetime.strptime(str, '%m-%d-%y')\n",
    "weather_description_df['datetime'] = pd.to_datetime(weather_description_df['datetime'])\n",
    "\n",
    "# this is how you convert timestamps object to str and use contains with it\n",
    "weather_description_df = weather_description_df[weather_description_df['datetime'].astype(str).str.contains('2014-05')]\n",
    "pickup_df['Date/Time'] = pd.to_datetime(pickup_df['Date/Time'])\n",
    "print(\"AFTER CONVERSION: \")\n",
    "pickup_df['Date/Time']\n",
    "#weatherdesc_clean = \n",
    "weather_description_df[weather_description_df['datetime'].astype(str).str.contains('2014')]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'New York'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-14-1afab3adb2ae>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# calling count() on the column containing New York data to see the specific content for weather description\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdescription_item\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mweather_description_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"New York\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcount\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      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdescription_item\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mgroupby\u001b[0;34m(self, by, axis, level, as_index, sort, group_keys, squeeze, observed, **kwargs)\u001b[0m\n\u001b[1;32m   7630\u001b[0m         return groupby(self, by=by, axis=axis, level=level, as_index=as_index,\n\u001b[1;32m   7631\u001b[0m                        \u001b[0msort\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msort\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgroup_keys\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mgroup_keys\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msqueeze\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msqueeze\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 7632\u001b[0;31m                        observed=observed, **kwargs)\n\u001b[0m\u001b[1;32m   7633\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   7634\u001b[0m     def asfreq(self, freq, method=None, how=None, normalize=False,\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/groupby/groupby.py\u001b[0m in \u001b[0;36mgroupby\u001b[0;34m(obj, by, **kwds)\u001b[0m\n\u001b[1;32m   2108\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'invalid type: {}'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\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   2109\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2110\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mklass\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mby\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\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/lib/python3.7/site-packages/pandas/core/groupby/groupby.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, obj, keys, axis, level, grouper, exclusions, selection, as_index, sort, group_keys, squeeze, observed, **kwargs)\u001b[0m\n\u001b[1;32m    358\u001b[0m                                                     \u001b[0msort\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msort\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    359\u001b[0m                                                     \u001b[0mobserved\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mobserved\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 360\u001b[0;31m                                                     mutated=self.mutated)\n\u001b[0m\u001b[1;32m    361\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    362\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/groupby/grouper.py\u001b[0m in \u001b[0;36m_get_grouper\u001b[0;34m(obj, key, axis, level, sort, observed, mutated, validate)\u001b[0m\n\u001b[1;32m    576\u001b[0m                 \u001b[0min_axis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgpr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgpr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    577\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 578\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgpr\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    579\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgpr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mGrouper\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mgpr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkey\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    580\u001b[0m             \u001b[0;31m# Add key to exclusions\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'New York'"
     ]
    }
   ],
   "source": [
    "# calling count() on the column containing New York data to see the specific content for weather description\n",
    "description_item = weather_description_df.groupby([\"New York\"]).count()\n",
    "print(description_item)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# trying to understand data so that we could clean it\n",
    "print(type(pickup_df['Date/Time'].iloc[0]))\n",
    "print(pd.to_datetime(pickup_df['Date/Time'].iloc[0]))\n",
    "#type(weather_description_df['datetime'].astype(str).iloc[0])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The steps included to clean the temperature dataset are as follows:\n",
    "1. Load the CSV file. \n",
    "2. Keep only the datasets that are for May. \n",
    "3. Convert enteries in temperature from Kelvin to fahrenheit so its easier to interpret result. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 datetime    New York\n",
      "2880  2014-05-01 00:00:00  283.736000\n",
      "2881  2014-05-01 01:00:00  284.739000\n",
      "2882  2014-05-01 02:00:00  284.554000\n",
      "2883  2014-05-01 03:00:00  285.655500\n",
      "2884  2014-05-01 04:00:00  285.041667\n",
      "(744, 2)\n"
     ]
    }
   ],
   "source": [
    "#Step 1 \n",
    "temperature_df = pd.read_csv('NewYork_Temperature_2014.csv')\n",
    "\n",
    "#Step 2 \n",
    "temperature_df = temperature_df[temperature_df['datetime'].astype(str).str.contains('2014-05')]\n",
    "temperature_df = temperature_df[['datetime','New York']]\n",
    "print(temperature_df.head())\n",
    "print(temperature_df.shape)\n",
    "\n",
    "#Step 3\n",
    "for i in temperature_df.index.values:\n",
    "    k_temp = temperature_df.loc[i,'New York']\n",
    "    f_temp = 9/5*(k_temp-273.15)+32\n",
    "    f_temp_2 = int(str(f_temp)[:2])\n",
    "    temperature_df.loc[i,'New York'] = f_temp_2\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The steps included to clean the wind speed dataset are as follows:\n",
    "1. Load the CSV file. \n",
    "2. Keep only the datasets that are for May. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 datetime  New York\n",
      "2880  2014-05-01 00:00:00         4\n",
      "2881  2014-05-01 01:00:00         5\n",
      "2882  2014-05-01 02:00:00         5\n",
      "2883  2014-05-01 03:00:00         5\n",
      "2884  2014-05-01 04:00:00         4\n",
      "(744, 2)\n"
     ]
    }
   ],
   "source": [
    "# Step 1 \n",
    "wind_speed_df = pd.read_csv('Wind_speed_for_2014-2015.csv')\n",
    "\n",
    "# Step 2 \n",
    "wind_speed_df = wind_speed_df[['datetime','New York']]\n",
    "wind_speed_df = wind_speed_df[wind_speed_df['datetime'].astype(str).str.contains('2014-05')]\n",
    "print(wind_speed_df.head())\n",
    "print(wind_speed_df.shape)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The steps included to clean the humidity dataset are as follows:\n",
    "1. Load the CSV file. \n",
    "2. Keep only the datasets that are for May. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(744, 2)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Step 1 \n",
    "humidity_df = pd.read_csv('humidity.csv')\n",
    "\n",
    "#Step 2 \n",
    "humidity_df = humidity_df[['datetime','New York']]\n",
    "humidity_df = humidity_df[humidity_df['datetime'].astype(str).str.contains('2014-05')]\n",
    "\n",
    "#checking if there is null values \n",
    "print(sum(humidity_df['New York'].isnull()))\n",
    "\n",
    "#replacing null with -1 \n",
    "humidity_df['New York'] = humidity_df['New York'].replace(np.NaN, -1)\n",
    "\n",
    "humidity_df.head()\n",
    "humidity_df.shape\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can put all the datasets into one dataframe. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# renaming all the columns so that it correctly matches with types of data stored.\n",
    "weather_description_df.rename(columns={'New York':'description'}, inplace=True)\n",
    "temperature_df.rename(columns={'New York':'temperature'}, inplace=True)\n",
    "wind_speed_df.rename(columns={'New York':'wind speed'}, inplace=True)\n",
    "humidity_df.rename(columns={'New York':'humidity'}, inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>datetime</th>\n",
       "      <th>description</th>\n",
       "      <th>temperature</th>\n",
       "      <th>wind speed</th>\n",
       "      <th>humidity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-05-01 00:00:00</td>\n",
       "      <td>moderate rain</td>\n",
       "      <td>51.0</td>\n",
       "      <td>4</td>\n",
       "      <td>95.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-05-01 01:00:00</td>\n",
       "      <td>moderate rain</td>\n",
       "      <td>52.0</td>\n",
       "      <td>5</td>\n",
       "      <td>94.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-05-01 02:00:00</td>\n",
       "      <td>moderate rain</td>\n",
       "      <td>52.0</td>\n",
       "      <td>5</td>\n",
       "      <td>95.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-05-01 03:00:00</td>\n",
       "      <td>heavy intensity rain</td>\n",
       "      <td>54.0</td>\n",
       "      <td>5</td>\n",
       "      <td>94.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-05-01 04:00:00</td>\n",
       "      <td>heavy intensity rain</td>\n",
       "      <td>53.0</td>\n",
       "      <td>4</td>\n",
       "      <td>94.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2014-05-01 05:00:00</td>\n",
       "      <td>heavy intensity rain</td>\n",
       "      <td>54.0</td>\n",
       "      <td>4</td>\n",
       "      <td>94.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2014-05-01 06:00:00</td>\n",
       "      <td>heavy intensity rain</td>\n",
       "      <td>55.0</td>\n",
       "      <td>4</td>\n",
       "      <td>93.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2014-05-01 07:00:00</td>\n",
       "      <td>heavy intensity rain</td>\n",
       "      <td>56.0</td>\n",
       "      <td>4</td>\n",
       "      <td>93.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2014-05-01 08:00:00</td>\n",
       "      <td>heavy intensity rain</td>\n",
       "      <td>57.0</td>\n",
       "      <td>5</td>\n",
       "      <td>92.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2014-05-01 09:00:00</td>\n",
       "      <td>heavy intensity rain</td>\n",
       "      <td>57.0</td>\n",
       "      <td>4</td>\n",
       "      <td>93.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2014-05-01 10:00:00</td>\n",
       "      <td>heavy intensity rain</td>\n",
       "      <td>58.0</td>\n",
       "      <td>4</td>\n",
       "      <td>92.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2014-05-01 11:00:00</td>\n",
       "      <td>heavy intensity rain</td>\n",
       "      <td>59.0</td>\n",
       "      <td>4</td>\n",
       "      <td>92.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2014-05-01 12:00:00</td>\n",
       "      <td>heavy intensity rain</td>\n",
       "      <td>60.0</td>\n",
       "      <td>4</td>\n",
       "      <td>93.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2014-05-01 13:00:00</td>\n",
       "      <td>heavy intensity rain</td>\n",
       "      <td>60.0</td>\n",
       "      <td>3</td>\n",
       "      <td>93.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2014-05-01 14:00:00</td>\n",
       "      <td>heavy intensity rain</td>\n",
       "      <td>61.0</td>\n",
       "      <td>3</td>\n",
       "      <td>93.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2014-05-01 15:00:00</td>\n",
       "      <td>heavy intensity rain</td>\n",
       "      <td>62.0</td>\n",
       "      <td>3</td>\n",
       "      <td>94.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2014-05-01 16:00:00</td>\n",
       "      <td>heavy intensity rain</td>\n",
       "      <td>63.0</td>\n",
       "      <td>2</td>\n",
       "      <td>94.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2014-05-01 17:00:00</td>\n",
       "      <td>heavy intensity rain</td>\n",
       "      <td>63.0</td>\n",
       "      <td>2</td>\n",
       "      <td>95.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2014-05-01 18:00:00</td>\n",
       "      <td>heavy intensity rain</td>\n",
       "      <td>63.0</td>\n",
       "      <td>2</td>\n",
       "      <td>92.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2014-05-01 19:00:00</td>\n",
       "      <td>heavy intensity rain</td>\n",
       "      <td>64.0</td>\n",
       "      <td>2</td>\n",
       "      <td>94.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2014-05-01 20:00:00</td>\n",
       "      <td>heavy intensity rain</td>\n",
       "      <td>64.0</td>\n",
       "      <td>2</td>\n",
       "      <td>92.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2014-05-01 21:00:00</td>\n",
       "      <td>heavy intensity rain</td>\n",
       "      <td>62.0</td>\n",
       "      <td>2</td>\n",
       "      <td>94.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2014-05-01 22:00:00</td>\n",
       "      <td>heavy intensity rain</td>\n",
       "      <td>62.0</td>\n",
       "      <td>2</td>\n",
       "      <td>91.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2014-05-01 23:00:00</td>\n",
       "      <td>heavy intensity rain</td>\n",
       "      <td>61.0</td>\n",
       "      <td>2</td>\n",
       "      <td>92.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2014-05-02 00:00:00</td>\n",
       "      <td>heavy intensity rain</td>\n",
       "      <td>60.0</td>\n",
       "      <td>2</td>\n",
       "      <td>91.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2014-05-02 01:00:00</td>\n",
       "      <td>heavy intensity rain</td>\n",
       "      <td>58.0</td>\n",
       "      <td>2</td>\n",
       "      <td>92.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2014-05-02 02:00:00</td>\n",
       "      <td>heavy intensity rain</td>\n",
       "      <td>57.0</td>\n",
       "      <td>2</td>\n",
       "      <td>92.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2014-05-02 03:00:00</td>\n",
       "      <td>light rain</td>\n",
       "      <td>57.0</td>\n",
       "      <td>2</td>\n",
       "      <td>93.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2014-05-02 04:00:00</td>\n",
       "      <td>light rain</td>\n",
       "      <td>56.0</td>\n",
       "      <td>2</td>\n",
       "      <td>92.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2014-05-02 05:00:00</td>\n",
       "      <td>light rain</td>\n",
       "      <td>56.0</td>\n",
       "      <td>2</td>\n",
       "      <td>93.0</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>714</th>\n",
       "      <td>2014-05-30 18:00:00</td>\n",
       "      <td>broken clouds</td>\n",
       "      <td>72.0</td>\n",
       "      <td>2</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>715</th>\n",
       "      <td>2014-05-30 19:00:00</td>\n",
       "      <td>broken clouds</td>\n",
       "      <td>73.0</td>\n",
       "      <td>3</td>\n",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>716</th>\n",
       "      <td>2014-05-30 20:00:00</td>\n",
       "      <td>broken clouds</td>\n",
       "      <td>74.0</td>\n",
       "      <td>2</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>717</th>\n",
       "      <td>2014-05-30 21:00:00</td>\n",
       "      <td>broken clouds</td>\n",
       "      <td>74.0</td>\n",
       "      <td>4</td>\n",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>718</th>\n",
       "      <td>2014-05-30 22:00:00</td>\n",
       "      <td>scattered clouds</td>\n",
       "      <td>73.0</td>\n",
       "      <td>4</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>719</th>\n",
       "      <td>2014-05-30 23:00:00</td>\n",
       "      <td>light rain</td>\n",
       "      <td>71.0</td>\n",
       "      <td>4</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>720</th>\n",
       "      <td>2014-05-31 00:00:00</td>\n",
       "      <td>moderate rain</td>\n",
       "      <td>64.0</td>\n",
       "      <td>3</td>\n",
       "      <td>63.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>721</th>\n",
       "      <td>2014-05-31 01:00:00</td>\n",
       "      <td>light rain</td>\n",
       "      <td>62.0</td>\n",
       "      <td>4</td>\n",
       "      <td>68.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>722</th>\n",
       "      <td>2014-05-31 02:00:00</td>\n",
       "      <td>scattered clouds</td>\n",
       "      <td>61.0</td>\n",
       "      <td>5</td>\n",
       "      <td>68.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>723</th>\n",
       "      <td>2014-05-31 03:00:00</td>\n",
       "      <td>few clouds</td>\n",
       "      <td>60.0</td>\n",
       "      <td>5</td>\n",
       "      <td>67.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>724</th>\n",
       "      <td>2014-05-31 04:00:00</td>\n",
       "      <td>sky is clear</td>\n",
       "      <td>58.0</td>\n",
       "      <td>5</td>\n",
       "      <td>67.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>725</th>\n",
       "      <td>2014-05-31 05:00:00</td>\n",
       "      <td>sky is clear</td>\n",
       "      <td>57.0</td>\n",
       "      <td>5</td>\n",
       "      <td>63.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>726</th>\n",
       "      <td>2014-05-31 06:00:00</td>\n",
       "      <td>sky is clear</td>\n",
       "      <td>56.0</td>\n",
       "      <td>4</td>\n",
       "      <td>63.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>727</th>\n",
       "      <td>2014-05-31 07:00:00</td>\n",
       "      <td>light rain</td>\n",
       "      <td>55.0</td>\n",
       "      <td>5</td>\n",
       "      <td>67.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>728</th>\n",
       "      <td>2014-05-31 08:00:00</td>\n",
       "      <td>sky is clear</td>\n",
       "      <td>55.0</td>\n",
       "      <td>6</td>\n",
       "      <td>67.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>729</th>\n",
       "      <td>2014-05-31 09:00:00</td>\n",
       "      <td>light rain</td>\n",
       "      <td>55.0</td>\n",
       "      <td>5</td>\n",
       "      <td>67.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>730</th>\n",
       "      <td>2014-05-31 10:00:00</td>\n",
       "      <td>light rain</td>\n",
       "      <td>56.0</td>\n",
       "      <td>7</td>\n",
       "      <td>59.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>731</th>\n",
       "      <td>2014-05-31 11:00:00</td>\n",
       "      <td>few clouds</td>\n",
       "      <td>59.0</td>\n",
       "      <td>5</td>\n",
       "      <td>59.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>732</th>\n",
       "      <td>2014-05-31 12:00:00</td>\n",
       "      <td>few clouds</td>\n",
       "      <td>62.0</td>\n",
       "      <td>6</td>\n",
       "      <td>55.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>733</th>\n",
       "      <td>2014-05-31 13:00:00</td>\n",
       "      <td>few clouds</td>\n",
       "      <td>64.0</td>\n",
       "      <td>9</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>734</th>\n",
       "      <td>2014-05-31 14:00:00</td>\n",
       "      <td>few clouds</td>\n",
       "      <td>67.0</td>\n",
       "      <td>9</td>\n",
       "      <td>37.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>735</th>\n",
       "      <td>2014-05-31 15:00:00</td>\n",
       "      <td>few clouds</td>\n",
       "      <td>68.0</td>\n",
       "      <td>10</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>736</th>\n",
       "      <td>2014-05-31 16:00:00</td>\n",
       "      <td>few clouds</td>\n",
       "      <td>69.0</td>\n",
       "      <td>9</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>737</th>\n",
       "      <td>2014-05-31 17:00:00</td>\n",
       "      <td>scattered clouds</td>\n",
       "      <td>70.0</td>\n",
       "      <td>6</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>738</th>\n",
       "      <td>2014-05-31 18:00:00</td>\n",
       "      <td>scattered clouds</td>\n",
       "      <td>70.0</td>\n",
       "      <td>6</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>739</th>\n",
       "      <td>2014-05-31 19:00:00</td>\n",
       "      <td>light rain</td>\n",
       "      <td>69.0</td>\n",
       "      <td>2</td>\n",
       "      <td>63.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>740</th>\n",
       "      <td>2014-05-31 20:00:00</td>\n",
       "      <td>light rain</td>\n",
       "      <td>67.0</td>\n",
       "      <td>3</td>\n",
       "      <td>52.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>741</th>\n",
       "      <td>2014-05-31 21:00:00</td>\n",
       "      <td>broken clouds</td>\n",
       "      <td>68.0</td>\n",
       "      <td>2</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>742</th>\n",
       "      <td>2014-05-31 22:00:00</td>\n",
       "      <td>scattered clouds</td>\n",
       "      <td>68.0</td>\n",
       "      <td>3</td>\n",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>743</th>\n",
       "      <td>2014-05-31 23:00:00</td>\n",
       "      <td>scattered clouds</td>\n",
       "      <td>67.0</td>\n",
       "      <td>5</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>744 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               datetime           description  temperature  wind speed  \\\n",
       "0   2014-05-01 00:00:00         moderate rain         51.0           4   \n",
       "1   2014-05-01 01:00:00         moderate rain         52.0           5   \n",
       "2   2014-05-01 02:00:00         moderate rain         52.0           5   \n",
       "3   2014-05-01 03:00:00  heavy intensity rain         54.0           5   \n",
       "4   2014-05-01 04:00:00  heavy intensity rain         53.0           4   \n",
       "5   2014-05-01 05:00:00  heavy intensity rain         54.0           4   \n",
       "6   2014-05-01 06:00:00  heavy intensity rain         55.0           4   \n",
       "7   2014-05-01 07:00:00  heavy intensity rain         56.0           4   \n",
       "8   2014-05-01 08:00:00  heavy intensity rain         57.0           5   \n",
       "9   2014-05-01 09:00:00  heavy intensity rain         57.0           4   \n",
       "10  2014-05-01 10:00:00  heavy intensity rain         58.0           4   \n",
       "11  2014-05-01 11:00:00  heavy intensity rain         59.0           4   \n",
       "12  2014-05-01 12:00:00  heavy intensity rain         60.0           4   \n",
       "13  2014-05-01 13:00:00  heavy intensity rain         60.0           3   \n",
       "14  2014-05-01 14:00:00  heavy intensity rain         61.0           3   \n",
       "15  2014-05-01 15:00:00  heavy intensity rain         62.0           3   \n",
       "16  2014-05-01 16:00:00  heavy intensity rain         63.0           2   \n",
       "17  2014-05-01 17:00:00  heavy intensity rain         63.0           2   \n",
       "18  2014-05-01 18:00:00  heavy intensity rain         63.0           2   \n",
       "19  2014-05-01 19:00:00  heavy intensity rain         64.0           2   \n",
       "20  2014-05-01 20:00:00  heavy intensity rain         64.0           2   \n",
       "21  2014-05-01 21:00:00  heavy intensity rain         62.0           2   \n",
       "22  2014-05-01 22:00:00  heavy intensity rain         62.0           2   \n",
       "23  2014-05-01 23:00:00  heavy intensity rain         61.0           2   \n",
       "24  2014-05-02 00:00:00  heavy intensity rain         60.0           2   \n",
       "25  2014-05-02 01:00:00  heavy intensity rain         58.0           2   \n",
       "26  2014-05-02 02:00:00  heavy intensity rain         57.0           2   \n",
       "27  2014-05-02 03:00:00            light rain         57.0           2   \n",
       "28  2014-05-02 04:00:00            light rain         56.0           2   \n",
       "29  2014-05-02 05:00:00            light rain         56.0           2   \n",
       "..                  ...                   ...          ...         ...   \n",
       "714 2014-05-30 18:00:00         broken clouds         72.0           2   \n",
       "715 2014-05-30 19:00:00         broken clouds         73.0           3   \n",
       "716 2014-05-30 20:00:00         broken clouds         74.0           2   \n",
       "717 2014-05-30 21:00:00         broken clouds         74.0           4   \n",
       "718 2014-05-30 22:00:00      scattered clouds         73.0           4   \n",
       "719 2014-05-30 23:00:00            light rain         71.0           4   \n",
       "720 2014-05-31 00:00:00         moderate rain         64.0           3   \n",
       "721 2014-05-31 01:00:00            light rain         62.0           4   \n",
       "722 2014-05-31 02:00:00      scattered clouds         61.0           5   \n",
       "723 2014-05-31 03:00:00            few clouds         60.0           5   \n",
       "724 2014-05-31 04:00:00          sky is clear         58.0           5   \n",
       "725 2014-05-31 05:00:00          sky is clear         57.0           5   \n",
       "726 2014-05-31 06:00:00          sky is clear         56.0           4   \n",
       "727 2014-05-31 07:00:00            light rain         55.0           5   \n",
       "728 2014-05-31 08:00:00          sky is clear         55.0           6   \n",
       "729 2014-05-31 09:00:00            light rain         55.0           5   \n",
       "730 2014-05-31 10:00:00            light rain         56.0           7   \n",
       "731 2014-05-31 11:00:00            few clouds         59.0           5   \n",
       "732 2014-05-31 12:00:00            few clouds         62.0           6   \n",
       "733 2014-05-31 13:00:00            few clouds         64.0           9   \n",
       "734 2014-05-31 14:00:00            few clouds         67.0           9   \n",
       "735 2014-05-31 15:00:00            few clouds         68.0          10   \n",
       "736 2014-05-31 16:00:00            few clouds         69.0           9   \n",
       "737 2014-05-31 17:00:00      scattered clouds         70.0           6   \n",
       "738 2014-05-31 18:00:00      scattered clouds         70.0           6   \n",
       "739 2014-05-31 19:00:00            light rain         69.0           2   \n",
       "740 2014-05-31 20:00:00            light rain         67.0           3   \n",
       "741 2014-05-31 21:00:00         broken clouds         68.0           2   \n",
       "742 2014-05-31 22:00:00      scattered clouds         68.0           3   \n",
       "743 2014-05-31 23:00:00      scattered clouds         67.0           5   \n",
       "\n",
       "     humidity  \n",
       "0        95.0  \n",
       "1        94.0  \n",
       "2        95.0  \n",
       "3        94.0  \n",
       "4        94.0  \n",
       "5        94.0  \n",
       "6        93.0  \n",
       "7        93.0  \n",
       "8        92.0  \n",
       "9        93.0  \n",
       "10       92.0  \n",
       "11       92.0  \n",
       "12       93.0  \n",
       "13       93.0  \n",
       "14       93.0  \n",
       "15       94.0  \n",
       "16       94.0  \n",
       "17       95.0  \n",
       "18       92.0  \n",
       "19       94.0  \n",
       "20       92.0  \n",
       "21       94.0  \n",
       "22       91.0  \n",
       "23       92.0  \n",
       "24       91.0  \n",
       "25       92.0  \n",
       "26       92.0  \n",
       "27       93.0  \n",
       "28       92.0  \n",
       "29       93.0  \n",
       "..        ...  \n",
       "714      43.0  \n",
       "715      46.0  \n",
       "716      38.0  \n",
       "717      46.0  \n",
       "718      49.0  \n",
       "719      49.0  \n",
       "720      63.0  \n",
       "721      68.0  \n",
       "722      68.0  \n",
       "723      67.0  \n",
       "724      67.0  \n",
       "725      63.0  \n",
       "726      63.0  \n",
       "727      67.0  \n",
       "728      67.0  \n",
       "729      67.0  \n",
       "730      59.0  \n",
       "731      59.0  \n",
       "732      55.0  \n",
       "733      45.0  \n",
       "734      37.0  \n",
       "735      30.0  \n",
       "736      28.0  \n",
       "737      35.0  \n",
       "738      35.0  \n",
       "739      63.0  \n",
       "740      52.0  \n",
       "741      45.0  \n",
       "742      46.0  \n",
       "743      43.0  \n",
       "\n",
       "[744 rows x 5 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#After running above, we can see the index from all dataframes do not match up. \n",
    "#This is due to the different set up for each of them.\n",
    "weather_description_df = weather_description_df.reset_index(drop=True)\n",
    "temperature_df = temperature_df.reset_index(drop=True)\n",
    "wind_speed_df = wind_speed_df.reset_index(drop=True)\n",
    "humidity_df = humidity_df.reset_index(drop=True)\n",
    "\n",
    "# Then we can put them all together \n",
    "#weather_description_df = weather_description_df.join(temperature_df['temperature'])\n",
    "#weather_description_df = weather_description_df.join(wind_speed_df['wind speed'])\n",
    "#weather_description_df = weather_description_df.join(humidity_df['humidity'])\n",
    "\n",
    "# let's see what our dataframe looks like\n",
    "weather_description_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After cleaning all the different features for weather and joining them into a single dataframes, we can start to visualize them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>datetime</th>\n",
       "      <th>description</th>\n",
       "      <th>temperature</th>\n",
       "      <th>wind speed</th>\n",
       "      <th>humidity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-05-01 00:00:00</td>\n",
       "      <td>moderate rain</td>\n",
       "      <td>51.0</td>\n",
       "      <td>4</td>\n",
       "      <td>95.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-05-01 01:00:00</td>\n",
       "      <td>moderate rain</td>\n",
       "      <td>52.0</td>\n",
       "      <td>5</td>\n",
       "      <td>94.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-05-01 02:00:00</td>\n",
       "      <td>moderate rain</td>\n",
       "      <td>52.0</td>\n",
       "      <td>5</td>\n",
       "      <td>95.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-05-01 03:00:00</td>\n",
       "      <td>heavy intensity rain</td>\n",
       "      <td>54.0</td>\n",
       "      <td>5</td>\n",
       "      <td>94.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-05-01 04:00:00</td>\n",
       "      <td>heavy intensity rain</td>\n",
       "      <td>53.0</td>\n",
       "      <td>4</td>\n",
       "      <td>94.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2014-05-01 05:00:00</td>\n",
       "      <td>heavy intensity rain</td>\n",
       "      <td>54.0</td>\n",
       "      <td>4</td>\n",
       "      <td>94.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2014-05-01 06:00:00</td>\n",
       "      <td>heavy intensity rain</td>\n",
       "      <td>55.0</td>\n",
       "      <td>4</td>\n",
       "      <td>93.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2014-05-01 07:00:00</td>\n",
       "      <td>heavy intensity rain</td>\n",
       "      <td>56.0</td>\n",
       "      <td>4</td>\n",
       "      <td>93.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2014-05-01 08:00:00</td>\n",
       "      <td>heavy intensity rain</td>\n",
       "      <td>57.0</td>\n",
       "      <td>5</td>\n",
       "      <td>92.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2014-05-01 09:00:00</td>\n",
       "      <td>heavy intensity rain</td>\n",
       "      <td>57.0</td>\n",
       "      <td>4</td>\n",
       "      <td>93.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2014-05-01 10:00:00</td>\n",
       "      <td>heavy intensity rain</td>\n",
       "      <td>58.0</td>\n",
       "      <td>4</td>\n",
       "      <td>92.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2014-05-01 11:00:00</td>\n",
       "      <td>heavy intensity rain</td>\n",
       "      <td>59.0</td>\n",
       "      <td>4</td>\n",
       "      <td>92.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2014-05-01 12:00:00</td>\n",
       "      <td>heavy intensity rain</td>\n",
       "      <td>60.0</td>\n",
       "      <td>4</td>\n",
       "      <td>93.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2014-05-01 13:00:00</td>\n",
       "      <td>heavy intensity rain</td>\n",
       "      <td>60.0</td>\n",
       "      <td>3</td>\n",
       "      <td>93.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2014-05-01 14:00:00</td>\n",
       "      <td>heavy intensity rain</td>\n",
       "      <td>61.0</td>\n",
       "      <td>3</td>\n",
       "      <td>93.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2014-05-01 15:00:00</td>\n",
       "      <td>heavy intensity rain</td>\n",
       "      <td>62.0</td>\n",
       "      <td>3</td>\n",
       "      <td>94.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2014-05-01 16:00:00</td>\n",
       "      <td>heavy intensity rain</td>\n",
       "      <td>63.0</td>\n",
       "      <td>2</td>\n",
       "      <td>94.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2014-05-01 17:00:00</td>\n",
       "      <td>heavy intensity rain</td>\n",
       "      <td>63.0</td>\n",
       "      <td>2</td>\n",
       "      <td>95.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2014-05-01 18:00:00</td>\n",
       "      <td>heavy intensity rain</td>\n",
       "      <td>63.0</td>\n",
       "      <td>2</td>\n",
       "      <td>92.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2014-05-01 19:00:00</td>\n",
       "      <td>heavy intensity rain</td>\n",
       "      <td>64.0</td>\n",
       "      <td>2</td>\n",
       "      <td>94.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2014-05-01 20:00:00</td>\n",
       "      <td>heavy intensity rain</td>\n",
       "      <td>64.0</td>\n",
       "      <td>2</td>\n",
       "      <td>92.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2014-05-01 21:00:00</td>\n",
       "      <td>heavy intensity rain</td>\n",
       "      <td>62.0</td>\n",
       "      <td>2</td>\n",
       "      <td>94.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2014-05-01 22:00:00</td>\n",
       "      <td>heavy intensity rain</td>\n",
       "      <td>62.0</td>\n",
       "      <td>2</td>\n",
       "      <td>91.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2014-05-01 23:00:00</td>\n",
       "      <td>heavy intensity rain</td>\n",
       "      <td>61.0</td>\n",
       "      <td>2</td>\n",
       "      <td>92.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2014-05-02 00:00:00</td>\n",
       "      <td>heavy intensity rain</td>\n",
       "      <td>60.0</td>\n",
       "      <td>2</td>\n",
       "      <td>91.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2014-05-02 01:00:00</td>\n",
       "      <td>heavy intensity rain</td>\n",
       "      <td>58.0</td>\n",
       "      <td>2</td>\n",
       "      <td>92.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2014-05-02 02:00:00</td>\n",
       "      <td>heavy intensity rain</td>\n",
       "      <td>57.0</td>\n",
       "      <td>2</td>\n",
       "      <td>92.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2014-05-02 03:00:00</td>\n",
       "      <td>light rain</td>\n",
       "      <td>57.0</td>\n",
       "      <td>2</td>\n",
       "      <td>93.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2014-05-02 04:00:00</td>\n",
       "      <td>light rain</td>\n",
       "      <td>56.0</td>\n",
       "      <td>2</td>\n",
       "      <td>92.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2014-05-02 05:00:00</td>\n",
       "      <td>light rain</td>\n",
       "      <td>56.0</td>\n",
       "      <td>2</td>\n",
       "      <td>93.0</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>714</th>\n",
       "      <td>2014-05-30 18:00:00</td>\n",
       "      <td>broken clouds</td>\n",
       "      <td>72.0</td>\n",
       "      <td>2</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>715</th>\n",
       "      <td>2014-05-30 19:00:00</td>\n",
       "      <td>broken clouds</td>\n",
       "      <td>73.0</td>\n",
       "      <td>3</td>\n",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>716</th>\n",
       "      <td>2014-05-30 20:00:00</td>\n",
       "      <td>broken clouds</td>\n",
       "      <td>74.0</td>\n",
       "      <td>2</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>717</th>\n",
       "      <td>2014-05-30 21:00:00</td>\n",
       "      <td>broken clouds</td>\n",
       "      <td>74.0</td>\n",
       "      <td>4</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>718</th>\n",
       "      <td>2014-05-30 22:00:00</td>\n",
       "      <td>scattered clouds</td>\n",
       "      <td>73.0</td>\n",
       "      <td>4</td>\n",
       "      <td>63.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>719</th>\n",
       "      <td>2014-05-30 23:00:00</td>\n",
       "      <td>light rain</td>\n",
       "      <td>71.0</td>\n",
       "      <td>4</td>\n",
       "      <td>68.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>720</th>\n",
       "      <td>2014-05-31 00:00:00</td>\n",
       "      <td>moderate rain</td>\n",
       "      <td>64.0</td>\n",
       "      <td>3</td>\n",
       "      <td>68.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>721</th>\n",
       "      <td>2014-05-31 01:00:00</td>\n",
       "      <td>light rain</td>\n",
       "      <td>62.0</td>\n",
       "      <td>4</td>\n",
       "      <td>67.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>722</th>\n",
       "      <td>2014-05-31 02:00:00</td>\n",
       "      <td>scattered clouds</td>\n",
       "      <td>61.0</td>\n",
       "      <td>5</td>\n",
       "      <td>67.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>723</th>\n",
       "      <td>2014-05-31 03:00:00</td>\n",
       "      <td>few clouds</td>\n",
       "      <td>60.0</td>\n",
       "      <td>5</td>\n",
       "      <td>63.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>724</th>\n",
       "      <td>2014-05-31 04:00:00</td>\n",
       "      <td>sky is clear</td>\n",
       "      <td>58.0</td>\n",
       "      <td>5</td>\n",
       "      <td>63.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>725</th>\n",
       "      <td>2014-05-31 05:00:00</td>\n",
       "      <td>sky is clear</td>\n",
       "      <td>57.0</td>\n",
       "      <td>5</td>\n",
       "      <td>67.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>726</th>\n",
       "      <td>2014-05-31 06:00:00</td>\n",
       "      <td>sky is clear</td>\n",
       "      <td>56.0</td>\n",
       "      <td>4</td>\n",
       "      <td>67.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>727</th>\n",
       "      <td>2014-05-31 07:00:00</td>\n",
       "      <td>light rain</td>\n",
       "      <td>55.0</td>\n",
       "      <td>5</td>\n",
       "      <td>67.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>728</th>\n",
       "      <td>2014-05-31 08:00:00</td>\n",
       "      <td>sky is clear</td>\n",
       "      <td>55.0</td>\n",
       "      <td>6</td>\n",
       "      <td>59.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>729</th>\n",
       "      <td>2014-05-31 09:00:00</td>\n",
       "      <td>light rain</td>\n",
       "      <td>55.0</td>\n",
       "      <td>5</td>\n",
       "      <td>59.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>730</th>\n",
       "      <td>2014-05-31 10:00:00</td>\n",
       "      <td>light rain</td>\n",
       "      <td>56.0</td>\n",
       "      <td>7</td>\n",
       "      <td>55.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>731</th>\n",
       "      <td>2014-05-31 11:00:00</td>\n",
       "      <td>few clouds</td>\n",
       "      <td>59.0</td>\n",
       "      <td>5</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>732</th>\n",
       "      <td>2014-05-31 12:00:00</td>\n",
       "      <td>few clouds</td>\n",
       "      <td>62.0</td>\n",
       "      <td>6</td>\n",
       "      <td>37.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>733</th>\n",
       "      <td>2014-05-31 13:00:00</td>\n",
       "      <td>few clouds</td>\n",
       "      <td>64.0</td>\n",
       "      <td>9</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>734</th>\n",
       "      <td>2014-05-31 14:00:00</td>\n",
       "      <td>few clouds</td>\n",
       "      <td>67.0</td>\n",
       "      <td>9</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>735</th>\n",
       "      <td>2014-05-31 15:00:00</td>\n",
       "      <td>few clouds</td>\n",
       "      <td>68.0</td>\n",
       "      <td>10</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>736</th>\n",
       "      <td>2014-05-31 16:00:00</td>\n",
       "      <td>few clouds</td>\n",
       "      <td>69.0</td>\n",
       "      <td>9</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>737</th>\n",
       "      <td>2014-05-31 17:00:00</td>\n",
       "      <td>scattered clouds</td>\n",
       "      <td>70.0</td>\n",
       "      <td>6</td>\n",
       "      <td>63.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>738</th>\n",
       "      <td>2014-05-31 18:00:00</td>\n",
       "      <td>scattered clouds</td>\n",
       "      <td>70.0</td>\n",
       "      <td>6</td>\n",
       "      <td>52.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>739</th>\n",
       "      <td>2014-05-31 19:00:00</td>\n",
       "      <td>light rain</td>\n",
       "      <td>69.0</td>\n",
       "      <td>2</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>740</th>\n",
       "      <td>2014-05-31 20:00:00</td>\n",
       "      <td>light rain</td>\n",
       "      <td>67.0</td>\n",
       "      <td>3</td>\n",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>741</th>\n",
       "      <td>2014-05-31 21:00:00</td>\n",
       "      <td>broken clouds</td>\n",
       "      <td>68.0</td>\n",
       "      <td>2</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>742</th>\n",
       "      <td>2014-05-31 22:00:00</td>\n",
       "      <td>scattered clouds</td>\n",
       "      <td>68.0</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>743</th>\n",
       "      <td>2014-05-31 23:00:00</td>\n",
       "      <td>scattered clouds</td>\n",
       "      <td>67.0</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>744 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               datetime           description  temperature  wind speed  \\\n",
       "0   2014-05-01 00:00:00         moderate rain         51.0           4   \n",
       "1   2014-05-01 01:00:00         moderate rain         52.0           5   \n",
       "2   2014-05-01 02:00:00         moderate rain         52.0           5   \n",
       "3   2014-05-01 03:00:00  heavy intensity rain         54.0           5   \n",
       "4   2014-05-01 04:00:00  heavy intensity rain         53.0           4   \n",
       "5   2014-05-01 05:00:00  heavy intensity rain         54.0           4   \n",
       "6   2014-05-01 06:00:00  heavy intensity rain         55.0           4   \n",
       "7   2014-05-01 07:00:00  heavy intensity rain         56.0           4   \n",
       "8   2014-05-01 08:00:00  heavy intensity rain         57.0           5   \n",
       "9   2014-05-01 09:00:00  heavy intensity rain         57.0           4   \n",
       "10  2014-05-01 10:00:00  heavy intensity rain         58.0           4   \n",
       "11  2014-05-01 11:00:00  heavy intensity rain         59.0           4   \n",
       "12  2014-05-01 12:00:00  heavy intensity rain         60.0           4   \n",
       "13  2014-05-01 13:00:00  heavy intensity rain         60.0           3   \n",
       "14  2014-05-01 14:00:00  heavy intensity rain         61.0           3   \n",
       "15  2014-05-01 15:00:00  heavy intensity rain         62.0           3   \n",
       "16  2014-05-01 16:00:00  heavy intensity rain         63.0           2   \n",
       "17  2014-05-01 17:00:00  heavy intensity rain         63.0           2   \n",
       "18  2014-05-01 18:00:00  heavy intensity rain         63.0           2   \n",
       "19  2014-05-01 19:00:00  heavy intensity rain         64.0           2   \n",
       "20  2014-05-01 20:00:00  heavy intensity rain         64.0           2   \n",
       "21  2014-05-01 21:00:00  heavy intensity rain         62.0           2   \n",
       "22  2014-05-01 22:00:00  heavy intensity rain         62.0           2   \n",
       "23  2014-05-01 23:00:00  heavy intensity rain         61.0           2   \n",
       "24  2014-05-02 00:00:00  heavy intensity rain         60.0           2   \n",
       "25  2014-05-02 01:00:00  heavy intensity rain         58.0           2   \n",
       "26  2014-05-02 02:00:00  heavy intensity rain         57.0           2   \n",
       "27  2014-05-02 03:00:00            light rain         57.0           2   \n",
       "28  2014-05-02 04:00:00            light rain         56.0           2   \n",
       "29  2014-05-02 05:00:00            light rain         56.0           2   \n",
       "..                  ...                   ...          ...         ...   \n",
       "714 2014-05-30 18:00:00         broken clouds         72.0           2   \n",
       "715 2014-05-30 19:00:00         broken clouds         73.0           3   \n",
       "716 2014-05-30 20:00:00         broken clouds         74.0           2   \n",
       "717 2014-05-30 21:00:00         broken clouds         74.0           4   \n",
       "718 2014-05-30 22:00:00      scattered clouds         73.0           4   \n",
       "719 2014-05-30 23:00:00            light rain         71.0           4   \n",
       "720 2014-05-31 00:00:00         moderate rain         64.0           3   \n",
       "721 2014-05-31 01:00:00            light rain         62.0           4   \n",
       "722 2014-05-31 02:00:00      scattered clouds         61.0           5   \n",
       "723 2014-05-31 03:00:00            few clouds         60.0           5   \n",
       "724 2014-05-31 04:00:00          sky is clear         58.0           5   \n",
       "725 2014-05-31 05:00:00          sky is clear         57.0           5   \n",
       "726 2014-05-31 06:00:00          sky is clear         56.0           4   \n",
       "727 2014-05-31 07:00:00            light rain         55.0           5   \n",
       "728 2014-05-31 08:00:00          sky is clear         55.0           6   \n",
       "729 2014-05-31 09:00:00            light rain         55.0           5   \n",
       "730 2014-05-31 10:00:00            light rain         56.0           7   \n",
       "731 2014-05-31 11:00:00            few clouds         59.0           5   \n",
       "732 2014-05-31 12:00:00            few clouds         62.0           6   \n",
       "733 2014-05-31 13:00:00            few clouds         64.0           9   \n",
       "734 2014-05-31 14:00:00            few clouds         67.0           9   \n",
       "735 2014-05-31 15:00:00            few clouds         68.0          10   \n",
       "736 2014-05-31 16:00:00            few clouds         69.0           9   \n",
       "737 2014-05-31 17:00:00      scattered clouds         70.0           6   \n",
       "738 2014-05-31 18:00:00      scattered clouds         70.0           6   \n",
       "739 2014-05-31 19:00:00            light rain         69.0           2   \n",
       "740 2014-05-31 20:00:00            light rain         67.0           3   \n",
       "741 2014-05-31 21:00:00         broken clouds         68.0           2   \n",
       "742 2014-05-31 22:00:00      scattered clouds         68.0           3   \n",
       "743 2014-05-31 23:00:00      scattered clouds         67.0           5   \n",
       "\n",
       "     humidity  \n",
       "0        95.0  \n",
       "1        94.0  \n",
       "2        95.0  \n",
       "3        94.0  \n",
       "4        94.0  \n",
       "5        94.0  \n",
       "6        93.0  \n",
       "7        93.0  \n",
       "8        92.0  \n",
       "9        93.0  \n",
       "10       92.0  \n",
       "11       92.0  \n",
       "12       93.0  \n",
       "13       93.0  \n",
       "14       93.0  \n",
       "15       94.0  \n",
       "16       94.0  \n",
       "17       95.0  \n",
       "18       92.0  \n",
       "19       94.0  \n",
       "20       92.0  \n",
       "21       94.0  \n",
       "22       91.0  \n",
       "23       92.0  \n",
       "24       91.0  \n",
       "25       92.0  \n",
       "26       92.0  \n",
       "27       93.0  \n",
       "28       92.0  \n",
       "29       93.0  \n",
       "..        ...  \n",
       "714      38.0  \n",
       "715      46.0  \n",
       "716      49.0  \n",
       "717      49.0  \n",
       "718      63.0  \n",
       "719      68.0  \n",
       "720      68.0  \n",
       "721      67.0  \n",
       "722      67.0  \n",
       "723      63.0  \n",
       "724      63.0  \n",
       "725      67.0  \n",
       "726      67.0  \n",
       "727      67.0  \n",
       "728      59.0  \n",
       "729      59.0  \n",
       "730      55.0  \n",
       "731      45.0  \n",
       "732      37.0  \n",
       "733      30.0  \n",
       "734      28.0  \n",
       "735      35.0  \n",
       "736      35.0  \n",
       "737      63.0  \n",
       "738      52.0  \n",
       "739      45.0  \n",
       "740      46.0  \n",
       "741      43.0  \n",
       "742       NaN  \n",
       "743       NaN  \n",
       "\n",
       "[744 rows x 5 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "humidity_df = pd.read_csv('humidity.csv')\n",
    "humidity_df = humidity_df[['datetime','New York']].dropna()\n",
    "humidity_df = humidity_df[humidity_df['datetime'].astype(str).str.contains('2014-05')]\n",
    "humidity_df.rename(columns={'New York':'humidity'}, inplace=True)\n",
    "humidity_df = humidity_df.reset_index(drop=True)\n",
    "humidity_df\n",
    "#comment below line after running for the first time\n",
    "#rerunning will fail due to two NaN values\n",
    "weather_description_df = weather_description_df.join(humidity_df['humidity'])\n",
    "weather_description_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>description</th>\n",
       "      <th>temperature</th>\n",
       "      <th>wind speed</th>\n",
       "      <th>humidity</th>\n",
       "      <th>ride count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-05-01 00:00:00</td>\n",
       "      <td>moderate rain</td>\n",
       "      <td>51.0</td>\n",
       "      <td>4</td>\n",
       "      <td>95.0</td>\n",
       "      <td>348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-05-01 01:00:00</td>\n",
       "      <td>moderate rain</td>\n",
       "      <td>52.0</td>\n",
       "      <td>5</td>\n",
       "      <td>94.0</td>\n",
       "      <td>179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-05-01 02:00:00</td>\n",
       "      <td>moderate rain</td>\n",
       "      <td>52.0</td>\n",
       "      <td>5</td>\n",
       "      <td>95.0</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-05-01 03:00:00</td>\n",
       "      <td>heavy intensity rain</td>\n",
       "      <td>54.0</td>\n",
       "      <td>5</td>\n",
       "      <td>94.0</td>\n",
       "      <td>172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-05-01 04:00:00</td>\n",
       "      <td>heavy intensity rain</td>\n",
       "      <td>53.0</td>\n",
       "      <td>4</td>\n",
       "      <td>94.0</td>\n",
       "      <td>241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2014-05-01 05:00:00</td>\n",
       "      <td>heavy intensity rain</td>\n",
       "      <td>54.0</td>\n",
       "      <td>4</td>\n",
       "      <td>94.0</td>\n",
       "      <td>438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2014-05-01 06:00:00</td>\n",
       "      <td>heavy intensity rain</td>\n",
       "      <td>55.0</td>\n",
       "      <td>4</td>\n",
       "      <td>93.0</td>\n",
       "      <td>822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2014-05-01 07:00:00</td>\n",
       "      <td>heavy intensity rain</td>\n",
       "      <td>56.0</td>\n",
       "      <td>4</td>\n",
       "      <td>93.0</td>\n",
       "      <td>1274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2014-05-01 08:00:00</td>\n",
       "      <td>heavy intensity rain</td>\n",
       "      <td>57.0</td>\n",
       "      <td>5</td>\n",
       "      <td>92.0</td>\n",
       "      <td>1237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2014-05-01 09:00:00</td>\n",
       "      <td>heavy intensity rain</td>\n",
       "      <td>57.0</td>\n",
       "      <td>4</td>\n",
       "      <td>93.0</td>\n",
       "      <td>853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2014-05-01 10:00:00</td>\n",
       "      <td>heavy intensity rain</td>\n",
       "      <td>58.0</td>\n",
       "      <td>4</td>\n",
       "      <td>92.0</td>\n",
       "      <td>771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2014-05-01 11:00:00</td>\n",
       "      <td>heavy intensity rain</td>\n",
       "      <td>59.0</td>\n",
       "      <td>4</td>\n",
       "      <td>92.0</td>\n",
       "      <td>662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2014-05-01 12:00:00</td>\n",
       "      <td>heavy intensity rain</td>\n",
       "      <td>60.0</td>\n",
       "      <td>4</td>\n",
       "      <td>93.0</td>\n",
       "      <td>716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2014-05-01 13:00:00</td>\n",
       "      <td>heavy intensity rain</td>\n",
       "      <td>60.0</td>\n",
       "      <td>3</td>\n",
       "      <td>93.0</td>\n",
       "      <td>820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2014-05-01 14:00:00</td>\n",
       "      <td>heavy intensity rain</td>\n",
       "      <td>61.0</td>\n",
       "      <td>3</td>\n",
       "      <td>93.0</td>\n",
       "      <td>1060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2014-05-01 15:00:00</td>\n",
       "      <td>heavy intensity rain</td>\n",
       "      <td>62.0</td>\n",
       "      <td>3</td>\n",
       "      <td>94.0</td>\n",
       "      <td>1348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2014-05-01 16:00:00</td>\n",
       "      <td>heavy intensity rain</td>\n",
       "      <td>63.0</td>\n",
       "      <td>2</td>\n",
       "      <td>94.0</td>\n",
       "      <td>1584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2014-05-01 17:00:00</td>\n",
       "      <td>heavy intensity rain</td>\n",
       "      <td>63.0</td>\n",
       "      <td>2</td>\n",
       "      <td>95.0</td>\n",
       "      <td>1845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2014-05-01 18:00:00</td>\n",
       "      <td>heavy intensity rain</td>\n",
       "      <td>63.0</td>\n",
       "      <td>2</td>\n",
       "      <td>92.0</td>\n",
       "      <td>1710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2014-05-01 19:00:00</td>\n",
       "      <td>heavy intensity rain</td>\n",
       "      <td>64.0</td>\n",
       "      <td>2</td>\n",
       "      <td>94.0</td>\n",
       "      <td>1584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2014-05-01 20:00:00</td>\n",
       "      <td>heavy intensity rain</td>\n",
       "      <td>64.0</td>\n",
       "      <td>2</td>\n",
       "      <td>92.0</td>\n",
       "      <td>1546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2014-05-01 21:00:00</td>\n",
       "      <td>heavy intensity rain</td>\n",
       "      <td>62.0</td>\n",
       "      <td>2</td>\n",
       "      <td>94.0</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2014-05-01 22:00:00</td>\n",
       "      <td>heavy intensity rain</td>\n",
       "      <td>62.0</td>\n",
       "      <td>2</td>\n",
       "      <td>91.0</td>\n",
       "      <td>1403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2014-05-01 23:00:00</td>\n",
       "      <td>heavy intensity rain</td>\n",
       "      <td>61.0</td>\n",
       "      <td>2</td>\n",
       "      <td>92.0</td>\n",
       "      <td>781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2014-05-02 00:00:00</td>\n",
       "      <td>heavy intensity rain</td>\n",
       "      <td>60.0</td>\n",
       "      <td>2</td>\n",
       "      <td>91.0</td>\n",
       "      <td>357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2014-05-02 01:00:00</td>\n",
       "      <td>heavy intensity rain</td>\n",
       "      <td>58.0</td>\n",
       "      <td>2</td>\n",
       "      <td>92.0</td>\n",
       "      <td>199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2014-05-02 02:00:00</td>\n",
       "      <td>heavy intensity rain</td>\n",
       "      <td>57.0</td>\n",
       "      <td>2</td>\n",
       "      <td>92.0</td>\n",
       "      <td>139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2014-05-02 03:00:00</td>\n",
       "      <td>light rain</td>\n",
       "      <td>57.0</td>\n",
       "      <td>2</td>\n",
       "      <td>93.0</td>\n",
       "      <td>183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2014-05-02 04:00:00</td>\n",
       "      <td>light rain</td>\n",
       "      <td>56.0</td>\n",
       "      <td>2</td>\n",
       "      <td>92.0</td>\n",
       "      <td>215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2014-05-02 05:00:00</td>\n",
       "      <td>light rain</td>\n",
       "      <td>56.0</td>\n",
       "      <td>2</td>\n",
       "      <td>93.0</td>\n",
       "      <td>359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>714</th>\n",
       "      <td>2014-05-30 18:00:00</td>\n",
       "      <td>broken clouds</td>\n",
       "      <td>72.0</td>\n",
       "      <td>2</td>\n",
       "      <td>38.0</td>\n",
       "      <td>2245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>715</th>\n",
       "      <td>2014-05-30 19:00:00</td>\n",
       "      <td>broken clouds</td>\n",
       "      <td>73.0</td>\n",
       "      <td>3</td>\n",
       "      <td>46.0</td>\n",
       "      <td>1443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>716</th>\n",
       "      <td>2014-05-30 20:00:00</td>\n",
       "      <td>broken clouds</td>\n",
       "      <td>74.0</td>\n",
       "      <td>2</td>\n",
       "      <td>49.0</td>\n",
       "      <td>1316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>717</th>\n",
       "      <td>2014-05-30 21:00:00</td>\n",
       "      <td>broken clouds</td>\n",
       "      <td>74.0</td>\n",
       "      <td>4</td>\n",
       "      <td>49.0</td>\n",
       "      <td>1495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>718</th>\n",
       "      <td>2014-05-30 22:00:00</td>\n",
       "      <td>scattered clouds</td>\n",
       "      <td>73.0</td>\n",
       "      <td>4</td>\n",
       "      <td>63.0</td>\n",
       "      <td>1470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>719</th>\n",
       "      <td>2014-05-30 23:00:00</td>\n",
       "      <td>light rain</td>\n",
       "      <td>71.0</td>\n",
       "      <td>4</td>\n",
       "      <td>68.0</td>\n",
       "      <td>1243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>720</th>\n",
       "      <td>2014-05-31 00:00:00</td>\n",
       "      <td>moderate rain</td>\n",
       "      <td>64.0</td>\n",
       "      <td>3</td>\n",
       "      <td>68.0</td>\n",
       "      <td>768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>721</th>\n",
       "      <td>2014-05-31 01:00:00</td>\n",
       "      <td>light rain</td>\n",
       "      <td>62.0</td>\n",
       "      <td>4</td>\n",
       "      <td>67.0</td>\n",
       "      <td>462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>722</th>\n",
       "      <td>2014-05-31 02:00:00</td>\n",
       "      <td>scattered clouds</td>\n",
       "      <td>61.0</td>\n",
       "      <td>5</td>\n",
       "      <td>67.0</td>\n",
       "      <td>331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>723</th>\n",
       "      <td>2014-05-31 03:00:00</td>\n",
       "      <td>few clouds</td>\n",
       "      <td>60.0</td>\n",
       "      <td>5</td>\n",
       "      <td>63.0</td>\n",
       "      <td>261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>724</th>\n",
       "      <td>2014-05-31 04:00:00</td>\n",
       "      <td>sky is clear</td>\n",
       "      <td>58.0</td>\n",
       "      <td>5</td>\n",
       "      <td>63.0</td>\n",
       "      <td>145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>725</th>\n",
       "      <td>2014-05-31 05:00:00</td>\n",
       "      <td>sky is clear</td>\n",
       "      <td>57.0</td>\n",
       "      <td>5</td>\n",
       "      <td>67.0</td>\n",
       "      <td>173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>726</th>\n",
       "      <td>2014-05-31 06:00:00</td>\n",
       "      <td>sky is clear</td>\n",
       "      <td>56.0</td>\n",
       "      <td>4</td>\n",
       "      <td>67.0</td>\n",
       "      <td>255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>727</th>\n",
       "      <td>2014-05-31 07:00:00</td>\n",
       "      <td>light rain</td>\n",
       "      <td>55.0</td>\n",
       "      <td>5</td>\n",
       "      <td>67.0</td>\n",
       "      <td>365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>728</th>\n",
       "      <td>2014-05-31 08:00:00</td>\n",
       "      <td>sky is clear</td>\n",
       "      <td>55.0</td>\n",
       "      <td>6</td>\n",
       "      <td>59.0</td>\n",
       "      <td>497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>729</th>\n",
       "      <td>2014-05-31 09:00:00</td>\n",
       "      <td>light rain</td>\n",
       "      <td>55.0</td>\n",
       "      <td>5</td>\n",
       "      <td>59.0</td>\n",
       "      <td>559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>730</th>\n",
       "      <td>2014-05-31 10:00:00</td>\n",
       "      <td>light rain</td>\n",
       "      <td>56.0</td>\n",
       "      <td>7</td>\n",
       "      <td>55.0</td>\n",
       "      <td>626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>731</th>\n",
       "      <td>2014-05-31 11:00:00</td>\n",
       "      <td>few clouds</td>\n",
       "      <td>59.0</td>\n",
       "      <td>5</td>\n",
       "      <td>45.0</td>\n",
       "      <td>713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>732</th>\n",
       "      <td>2014-05-31 12:00:00</td>\n",
       "      <td>few clouds</td>\n",
       "      <td>62.0</td>\n",
       "      <td>6</td>\n",
       "      <td>37.0</td>\n",
       "      <td>775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>733</th>\n",
       "      <td>2014-05-31 13:00:00</td>\n",
       "      <td>few clouds</td>\n",
       "      <td>64.0</td>\n",
       "      <td>9</td>\n",
       "      <td>30.0</td>\n",
       "      <td>816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>734</th>\n",
       "      <td>2014-05-31 14:00:00</td>\n",
       "      <td>few clouds</td>\n",
       "      <td>67.0</td>\n",
       "      <td>9</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>735</th>\n",
       "      <td>2014-05-31 15:00:00</td>\n",
       "      <td>few clouds</td>\n",
       "      <td>68.0</td>\n",
       "      <td>10</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>736</th>\n",
       "      <td>2014-05-31 16:00:00</td>\n",
       "      <td>few clouds</td>\n",
       "      <td>69.0</td>\n",
       "      <td>9</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>737</th>\n",
       "      <td>2014-05-31 17:00:00</td>\n",
       "      <td>scattered clouds</td>\n",
       "      <td>70.0</td>\n",
       "      <td>6</td>\n",
       "      <td>63.0</td>\n",
       "      <td>1348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>738</th>\n",
       "      <td>2014-05-31 18:00:00</td>\n",
       "      <td>scattered clouds</td>\n",
       "      <td>70.0</td>\n",
       "      <td>6</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>739</th>\n",
       "      <td>2014-05-31 19:00:00</td>\n",
       "      <td>light rain</td>\n",
       "      <td>69.0</td>\n",
       "      <td>2</td>\n",
       "      <td>45.0</td>\n",
       "      <td>1219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>740</th>\n",
       "      <td>2014-05-31 20:00:00</td>\n",
       "      <td>light rain</td>\n",
       "      <td>67.0</td>\n",
       "      <td>3</td>\n",
       "      <td>46.0</td>\n",
       "      <td>1203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>741</th>\n",
       "      <td>2014-05-31 21:00:00</td>\n",
       "      <td>broken clouds</td>\n",
       "      <td>68.0</td>\n",
       "      <td>2</td>\n",
       "      <td>43.0</td>\n",
       "      <td>1466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>742</th>\n",
       "      <td>2014-05-31 22:00:00</td>\n",
       "      <td>scattered clouds</td>\n",
       "      <td>68.0</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>743</th>\n",
       "      <td>2014-05-31 23:00:00</td>\n",
       "      <td>scattered clouds</td>\n",
       "      <td>67.0</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1616</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>744 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               datetime           description  temperature  wind speed  \\\n",
       "0   2014-05-01 00:00:00         moderate rain         51.0           4   \n",
       "1   2014-05-01 01:00:00         moderate rain         52.0           5   \n",
       "2   2014-05-01 02:00:00         moderate rain         52.0           5   \n",
       "3   2014-05-01 03:00:00  heavy intensity rain         54.0           5   \n",
       "4   2014-05-01 04:00:00  heavy intensity rain         53.0           4   \n",
       "5   2014-05-01 05:00:00  heavy intensity rain         54.0           4   \n",
       "6   2014-05-01 06:00:00  heavy intensity rain         55.0           4   \n",
       "7   2014-05-01 07:00:00  heavy intensity rain         56.0           4   \n",
       "8   2014-05-01 08:00:00  heavy intensity rain         57.0           5   \n",
       "9   2014-05-01 09:00:00  heavy intensity rain         57.0           4   \n",
       "10  2014-05-01 10:00:00  heavy intensity rain         58.0           4   \n",
       "11  2014-05-01 11:00:00  heavy intensity rain         59.0           4   \n",
       "12  2014-05-01 12:00:00  heavy intensity rain         60.0           4   \n",
       "13  2014-05-01 13:00:00  heavy intensity rain         60.0           3   \n",
       "14  2014-05-01 14:00:00  heavy intensity rain         61.0           3   \n",
       "15  2014-05-01 15:00:00  heavy intensity rain         62.0           3   \n",
       "16  2014-05-01 16:00:00  heavy intensity rain         63.0           2   \n",
       "17  2014-05-01 17:00:00  heavy intensity rain         63.0           2   \n",
       "18  2014-05-01 18:00:00  heavy intensity rain         63.0           2   \n",
       "19  2014-05-01 19:00:00  heavy intensity rain         64.0           2   \n",
       "20  2014-05-01 20:00:00  heavy intensity rain         64.0           2   \n",
       "21  2014-05-01 21:00:00  heavy intensity rain         62.0           2   \n",
       "22  2014-05-01 22:00:00  heavy intensity rain         62.0           2   \n",
       "23  2014-05-01 23:00:00  heavy intensity rain         61.0           2   \n",
       "24  2014-05-02 00:00:00  heavy intensity rain         60.0           2   \n",
       "25  2014-05-02 01:00:00  heavy intensity rain         58.0           2   \n",
       "26  2014-05-02 02:00:00  heavy intensity rain         57.0           2   \n",
       "27  2014-05-02 03:00:00            light rain         57.0           2   \n",
       "28  2014-05-02 04:00:00            light rain         56.0           2   \n",
       "29  2014-05-02 05:00:00            light rain         56.0           2   \n",
       "..                  ...                   ...          ...         ...   \n",
       "714 2014-05-30 18:00:00         broken clouds         72.0           2   \n",
       "715 2014-05-30 19:00:00         broken clouds         73.0           3   \n",
       "716 2014-05-30 20:00:00         broken clouds         74.0           2   \n",
       "717 2014-05-30 21:00:00         broken clouds         74.0           4   \n",
       "718 2014-05-30 22:00:00      scattered clouds         73.0           4   \n",
       "719 2014-05-30 23:00:00            light rain         71.0           4   \n",
       "720 2014-05-31 00:00:00         moderate rain         64.0           3   \n",
       "721 2014-05-31 01:00:00            light rain         62.0           4   \n",
       "722 2014-05-31 02:00:00      scattered clouds         61.0           5   \n",
       "723 2014-05-31 03:00:00            few clouds         60.0           5   \n",
       "724 2014-05-31 04:00:00          sky is clear         58.0           5   \n",
       "725 2014-05-31 05:00:00          sky is clear         57.0           5   \n",
       "726 2014-05-31 06:00:00          sky is clear         56.0           4   \n",
       "727 2014-05-31 07:00:00            light rain         55.0           5   \n",
       "728 2014-05-31 08:00:00          sky is clear         55.0           6   \n",
       "729 2014-05-31 09:00:00            light rain         55.0           5   \n",
       "730 2014-05-31 10:00:00            light rain         56.0           7   \n",
       "731 2014-05-31 11:00:00            few clouds         59.0           5   \n",
       "732 2014-05-31 12:00:00            few clouds         62.0           6   \n",
       "733 2014-05-31 13:00:00            few clouds         64.0           9   \n",
       "734 2014-05-31 14:00:00            few clouds         67.0           9   \n",
       "735 2014-05-31 15:00:00            few clouds         68.0          10   \n",
       "736 2014-05-31 16:00:00            few clouds         69.0           9   \n",
       "737 2014-05-31 17:00:00      scattered clouds         70.0           6   \n",
       "738 2014-05-31 18:00:00      scattered clouds         70.0           6   \n",
       "739 2014-05-31 19:00:00            light rain         69.0           2   \n",
       "740 2014-05-31 20:00:00            light rain         67.0           3   \n",
       "741 2014-05-31 21:00:00         broken clouds         68.0           2   \n",
       "742 2014-05-31 22:00:00      scattered clouds         68.0           3   \n",
       "743 2014-05-31 23:00:00      scattered clouds         67.0           5   \n",
       "\n",
       "     humidity  ride count  \n",
       "0        95.0         348  \n",
       "1        94.0         179  \n",
       "2        95.0         101  \n",
       "3        94.0         172  \n",
       "4        94.0         241  \n",
       "5        94.0         438  \n",
       "6        93.0         822  \n",
       "7        93.0        1274  \n",
       "8        92.0        1237  \n",
       "9        93.0         853  \n",
       "10       92.0         771  \n",
       "11       92.0         662  \n",
       "12       93.0         716  \n",
       "13       93.0         820  \n",
       "14       93.0        1060  \n",
       "15       94.0        1348  \n",
       "16       94.0        1584  \n",
       "17       95.0        1845  \n",
       "18       92.0        1710  \n",
       "19       94.0        1584  \n",
       "20       92.0        1546  \n",
       "21       94.0        1880  \n",
       "22       91.0        1403  \n",
       "23       92.0         781  \n",
       "24       91.0         357  \n",
       "25       92.0         199  \n",
       "26       92.0         139  \n",
       "27       93.0         183  \n",
       "28       92.0         215  \n",
       "29       93.0         359  \n",
       "..        ...         ...  \n",
       "714      38.0        2245  \n",
       "715      46.0        1443  \n",
       "716      49.0        1316  \n",
       "717      49.0        1495  \n",
       "718      63.0        1470  \n",
       "719      68.0        1243  \n",
       "720      68.0         768  \n",
       "721      67.0         462  \n",
       "722      67.0         331  \n",
       "723      63.0         261  \n",
       "724      63.0         145  \n",
       "725      67.0         173  \n",
       "726      67.0         255  \n",
       "727      67.0         365  \n",
       "728      59.0         497  \n",
       "729      59.0         559  \n",
       "730      55.0         626  \n",
       "731      45.0         713  \n",
       "732      37.0         775  \n",
       "733      30.0         816  \n",
       "734      28.0        1265  \n",
       "735      35.0        1584  \n",
       "736      35.0        1551  \n",
       "737      63.0        1348  \n",
       "738      52.0        1470  \n",
       "739      45.0        1219  \n",
       "740      46.0        1203  \n",
       "741      43.0        1466  \n",
       "742       NaN        1793  \n",
       "743       NaN        1616  \n",
       "\n",
       "[744 rows x 6 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# counts_df contains data for Uber ride count in May 2014 from pickup_df, cleaned in a hourly fashion\n",
    "counts_df = pd.read_csv('uber_hourly_counts_may.csv')\n",
    "counts_df\n",
    "\n",
    "humidity_df\n",
    "# why 2 NaN?\n",
    "# There were two missing entry for humidity in May\n",
    "\n",
    "\n",
    "counts_df = counts_df.reset_index(drop=True)\n",
    "counts_df.rename(columns={'Count':'ride count'}, inplace=True)\n",
    "\n",
    "#weather_description_df = weather_description_df.join(counts_df['ride count'])\n",
    "weather_description_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "## YOUR CODE HERE\n",
    "## FEEL FREE TO ADD MULTIPLE CELLS PER SECTION\n",
    "\n",
    "## NY's Temperature in 2014\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Analysis & Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\ncols_plot = ['description', 'temperature','wind speed', 'humidity']\\naxes = weather_description_df[cols_plot].plot(marker='.', alpha=0.1, linestyle='None', figsize=(11, 9), subplots=True)\\nfor y_axis in axes:\\n    y_axis.set_ylabel('counts')\\n\""
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "cols_plot = ['description', 'temperature','wind speed', 'humidity']\n",
    "axes = weather_description_df[cols_plot].plot(marker='.', alpha=0.1, linestyle='None', figsize=(11, 9), subplots=True)\n",
    "for y_axis in axes:\n",
    "    y_axis.set_ylabel('counts')\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "temperature_plot = ['temperature']\n",
    "axes_temperature = weather_description_df[temperature_plot].plot(marker='.', color=\"darkgreen\",\n",
    "                                                                 alpha=0.6, \n",
    "                                                                 linestyle='None', \n",
    "                                                                 figsize=(11, 9), \n",
    "                                                                 subplots=True)\n",
    "for y_axis in axes_temperature:\n",
    "    y_axis.set_ylabel('kelvin')\n",
    "for x_axis in axes_temperature:\n",
    "    x_axis.set_xlabel('ride count for that hour')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "wind_speed_plot = ['wind speed']\n",
    "axes_wind_speed = weather_description_df[wind_speed_plot].plot(marker='.', color=\"darkred\",\n",
    "                                                               alpha=0.6, \n",
    "                                                               linestyle='None', \n",
    "                                                               figsize=(11, 9), \n",
    "                                                               subplots=True)\n",
    "for y_axis in axes_wind_speed:\n",
    "    y_axis.set_ylabel('mph')\n",
    "for x_axis in axes_wind_speed:\n",
    "    x_axis.set_xlabel('ride count for that hour')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "humidity_plot = ['humidity']\n",
    "axes_humidity = weather_description_df[humidity_plot].plot(marker='.', color=\"blue\",\n",
    "                                                           alpha=0.6, \n",
    "                                                           linestyle='None', \n",
    "                                                           figsize=(11, 9), \n",
    "                                                           subplots=True)\n",
    "for y_axis in axes_humidity:\n",
    "    y_axis.set_ylabel('humidity')\n",
    "    \n",
    "for x_axis in axes_humidity:\n",
    "    x_axis.set_xlabel('ride count for that hour')\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Include cells that describe the steps in your data analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## YOUR CODE HERE\n",
    "## FEEL FREE TO ADD MULTIPLE CELLS PER SECTION"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ethics & Privacy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data set we are analyzing for this project is public data obtained from an open source. The data on its own does not reveal any private information about individual users as well as Uber drivers so we are not as concerned with personal privacy. However, analyzers could still easily extract a lot of information from these data. For instance, analyzing this data set could reveal users’ commute patterns and preferences. Though this does not point to a specific person, it could be information that could be sold to other companies for profit. From this, companies can target users based on their individual preferences. For example, they could distribute advertisements based on these preferences. Or, rideshare apps, like Uber, could take advantage of all these data and deliberately raise prices when the demand is high. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion & Discussion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Fill in your discussion information here*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since it is hard to parametrize how a description can effect ride count, we decided it is ambiguious to use description to predict ride count. To see the actual effect of description, we did a PCA on it."
   ]
  },
  {
   "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": []
  }
 ],
 "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}