00-exploratory-data-analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first step in any data science project is to getting know the data we work with.\n",
    "\n",
    "In this notebook you can see a basic explanatory data analysis on New York Taxi dataset.\n",
    "\n",
    "We will perform a basic statistical analysis that will help us to understand the behaviors of each feature and also examine and find outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import datetime\n",
    "import math\n",
    "import IPython\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we will load the data and immediately after that, as you can see, we will take a look of small portion of the data just to try and understand what it is about. After that we will start and examine it is main properties:\n",
    "1. The size of the data.\n",
    "2. The different types of features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>pickup_datetime</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>passenger_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009-06-15 17:26:21.0000001</td>\n",
       "      <td>4.5</td>\n",
       "      <td>2009-06-15 17:26:21 UTC</td>\n",
       "      <td>-73.844311</td>\n",
       "      <td>40.721319</td>\n",
       "      <td>-73.841610</td>\n",
       "      <td>40.712278</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-01-05 16:52:16.0000002</td>\n",
       "      <td>16.9</td>\n",
       "      <td>2010-01-05 16:52:16 UTC</td>\n",
       "      <td>-74.016048</td>\n",
       "      <td>40.711303</td>\n",
       "      <td>-73.979268</td>\n",
       "      <td>40.782004</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011-08-18 00:35:00.00000049</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2011-08-18 00:35:00 UTC</td>\n",
       "      <td>-73.982738</td>\n",
       "      <td>40.761270</td>\n",
       "      <td>-73.991242</td>\n",
       "      <td>40.750562</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012-04-21 04:30:42.0000001</td>\n",
       "      <td>7.7</td>\n",
       "      <td>2012-04-21 04:30:42 UTC</td>\n",
       "      <td>-73.987130</td>\n",
       "      <td>40.733143</td>\n",
       "      <td>-73.991567</td>\n",
       "      <td>40.758092</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-03-09 07:51:00.000000135</td>\n",
       "      <td>5.3</td>\n",
       "      <td>2010-03-09 07:51:00 UTC</td>\n",
       "      <td>-73.968095</td>\n",
       "      <td>40.768008</td>\n",
       "      <td>-73.956655</td>\n",
       "      <td>40.783762</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             key  fare_amount          pickup_datetime  \\\n",
       "0    2009-06-15 17:26:21.0000001          4.5  2009-06-15 17:26:21 UTC   \n",
       "1    2010-01-05 16:52:16.0000002         16.9  2010-01-05 16:52:16 UTC   \n",
       "2   2011-08-18 00:35:00.00000049          5.7  2011-08-18 00:35:00 UTC   \n",
       "3    2012-04-21 04:30:42.0000001          7.7  2012-04-21 04:30:42 UTC   \n",
       "4  2010-03-09 07:51:00.000000135          5.3  2010-03-09 07:51:00 UTC   \n",
       "\n",
       "   pickup_longitude  pickup_latitude  dropoff_longitude  dropoff_latitude  \\\n",
       "0        -73.844311        40.721319         -73.841610         40.712278   \n",
       "1        -74.016048        40.711303         -73.979268         40.782004   \n",
       "2        -73.982738        40.761270         -73.991242         40.750562   \n",
       "3        -73.987130        40.733143         -73.991567         40.758092   \n",
       "4        -73.968095        40.768008         -73.956655         40.783762   \n",
       "\n",
       "   passenger_count  \n",
       "0                1  \n",
       "1                1  \n",
       "2                2  \n",
       "3                1  \n",
       "4                1  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"./data/dataset.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(38000, 8)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to get a complete picture of our data, you need to understand exactly what each feature means, so in most cases where you will use data that is external to your organization, make sure that there is a full explanation as in the following [**link**](https://www.kaggle.com/competitions/new-york-city-taxi-fare-prediction/data) from which I got the data for this project.\n",
    "\n",
    "\n",
    "### Data fields\n",
    "#### ID\n",
    "    1. key - Unique string identifying each row in both the training and test sets. Comprised of pickup_datetime plus a unique integer, but this doesn't matter, it should just be used as a unique ID field.\n",
    "\n",
    "#### Features\n",
    "    1. pickup_datetime - timestamp value indicating when the taxi ride started.\n",
    "    2. pickup_longitude - float for longitude coordinate of where the taxi ride started.\n",
    "    3. pickup_latitude - float for latitude coordinate of where the taxi ride started.\n",
    "    4. dropoff_longitude - float for longitude coordinate of where the taxi ride ended.\n",
    "    5. dropoff_latitude - float for latitude coordinate of where the taxi ride ended.\n",
    "    6. passenger_count - integer indicating the number of passengers in the taxi ride.\n",
    "\n",
    "#### Target\n",
    "    1. fare_amount - float dollar amount of the cost of the taxi ride. This value is only in the training set; this is what you are predicting in the test set and it is required in your submission CSV.\n",
    "\n",
    "And of course beside the meanig of each feature you need exmine the types of the feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "key                   object\n",
       "fare_amount          float64\n",
       "pickup_datetime       object\n",
       "pickup_longitude     float64\n",
       "pickup_latitude      float64\n",
       "dropoff_longitude    float64\n",
       "dropoff_latitude     float64\n",
       "passenger_count        int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will start and examine the statistical characteristics of each feature, initial information that can give us different directions in finding outliers, dummies, etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>passenger_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>38000.000000</td>\n",
       "      <td>38000.000000</td>\n",
       "      <td>38000.000000</td>\n",
       "      <td>38000.000000</td>\n",
       "      <td>38000.000000</td>\n",
       "      <td>38000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>11.348630</td>\n",
       "      <td>-72.507625</td>\n",
       "      <td>39.932528</td>\n",
       "      <td>-72.495517</td>\n",
       "      <td>39.919080</td>\n",
       "      <td>1.663237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.639597</td>\n",
       "      <td>10.410071</td>\n",
       "      <td>6.329258</td>\n",
       "      <td>10.448033</td>\n",
       "      <td>6.066357</td>\n",
       "      <td>1.283981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-3.000000</td>\n",
       "      <td>-75.414728</td>\n",
       "      <td>-74.006893</td>\n",
       "      <td>-84.654241</td>\n",
       "      <td>-74.006377</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>-73.992065</td>\n",
       "      <td>40.734809</td>\n",
       "      <td>-73.991182</td>\n",
       "      <td>40.734324</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>8.500000</td>\n",
       "      <td>-73.981737</td>\n",
       "      <td>40.752701</td>\n",
       "      <td>-73.980210</td>\n",
       "      <td>40.753339</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>12.500000</td>\n",
       "      <td>-73.967051</td>\n",
       "      <td>40.767342</td>\n",
       "      <td>-73.963799</td>\n",
       "      <td>40.768162</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>200.000000</td>\n",
       "      <td>40.783472</td>\n",
       "      <td>401.083332</td>\n",
       "      <td>40.851027</td>\n",
       "      <td>43.415190</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        fare_amount  pickup_longitude  pickup_latitude  dropoff_longitude  \\\n",
       "count  38000.000000      38000.000000     38000.000000       38000.000000   \n",
       "mean      11.348630        -72.507625        39.932528         -72.495517   \n",
       "std        9.639597         10.410071         6.329258          10.448033   \n",
       "min       -3.000000        -75.414728       -74.006893         -84.654241   \n",
       "25%        6.000000        -73.992065        40.734809         -73.991182   \n",
       "50%        8.500000        -73.981737        40.752701         -73.980210   \n",
       "75%       12.500000        -73.967051        40.767342         -73.963799   \n",
       "max      200.000000         40.783472       401.083332          40.851027   \n",
       "\n",
       "       dropoff_latitude  passenger_count  \n",
       "count      38000.000000     38000.000000  \n",
       "mean          39.919080         1.663237  \n",
       "std            6.066357         1.283981  \n",
       "min          -74.006377         0.000000  \n",
       "25%           40.734324         1.000000  \n",
       "50%           40.753339         1.000000  \n",
       "75%           40.768162         2.000000  \n",
       "max           43.415190         6.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It can be seen that there is a fairly large difference between the average and the maximum and minimum of all the geographic indices, \n",
    "which raises the question of whether these are outliers.\n",
    "\n",
    "In order to understand if this is indeed the case, we will take a look at the joint probability of each pair of geographic variables : \n",
    "\n",
    "    1. pickup_longitude, pickup_latitude.\n",
    "    2. dropoff_longitude, dropoff_latitude"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x7fcdeddffb90>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(data=df, x=\"pickup_longitude\", y=\"pickup_latitude\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x7fcded401c50>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(data=df, x=\"dropoff_longitude\", y=\"dropoff_latitude\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another outlires we can see is the in the target where we got fare amount that is lower than 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Distribution of the fare amount')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['fare_amount'])\n",
    "plt.title('Distribution of the fare amount')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When we have a dimension of time in the data, a good practice is to look at the distribution of features against our time, of course after breaking down the time into days, hours, months, etc.\n",
    "\n",
    "Here you can see the distribution of the the fare amount vs time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"pickup_datetime\"] = pd.to_datetime(\n",
    "        df[\"pickup_datetime\"], format=\"%Y-%m-%d %H:%M:%S UTC\"\n",
    "    )\n",
    "\n",
    "df[\"hour\"] = df.pickup_datetime.dt.hour\n",
    "df[\"day\"] = df.pickup_datetime.dt.day\n",
    "df[\"month\"] = df.pickup_datetime.dt.month\n",
    "df[\"weekday\"] = df.pickup_datetime.dt.weekday\n",
    "df[\"year\"] = df.pickup_datetime.dt.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure,ax=plt.subplots(nrows=5,ncols=1,figsize=(10,10))\n",
    "sns.countplot(x='weekday',data=df,ax=ax[0])\n",
    "ax[0].set_title('Number of Pickups done on each day of the week')\n",
    "sns.countplot(x='day',data=df,ax=ax[1])\n",
    "ax[1].set_title('Number of dropoffs done on each day of the month')\n",
    "sns.countplot(x='hour',data=df,ax=ax[2])\n",
    "ax[2].set_title('Number of dropoffs done on each hour of the day')\n",
    "sns.countplot(x='month',data=df,ax=ax[3])\n",
    "ax[3].set_title('Number of dropoffs done on each month')\n",
    "sns.countplot(x='year',data=df,ax=ax[4])\n",
    "ax[4].set_title('Number of dropoffs done on each year')\n",
    "plt.tight_layout()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In addition, it is worth looking at the correlation matrix between the various features, with the help of which maybe to dilute some of them in order to or maybe even connect them to a single feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 864x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = df.drop('fare_amount', axis=1).corr()\n",
    "plt.figure(figsize=(12, 10))\n",
    "\n",
    "sns.heatmap(corr[(corr >= 0.5) | (corr <= -0.4)], \n",
    "            cmap='viridis', vmax=1.0, vmin=-1.0, linewidths=0.1,\n",
    "            annot=True, annot_kws={\"size\": 8}, square=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conclusions that arose from the following analysis:\n",
    "\n",
    "    1. It can be seen that all of our geographic parameters have outliers when longitude<-200 and latitude>200\n",
    "\n",
    "    2. Fare amount includes also outliers.\n",
    "\n",
    "    3. The number of passengers is a number between 1 to 6 can be considered as dummies.\n",
    "\n",
    "    4. There is a fairly equal distribution of the fare amount against time.\n",
    "\n",
    "Of course, this is a notebook that presents only a basic and initial analysis of the data, you can see more detailed examples of the analysis in the following links : \n",
    "1. https://medium.com/analytics-vidhya/exploratory-data-analysis-of-nyc-taxi-trip-duration-dataset-using-python-257fdef2749e\n",
    "2. https://www.analyticsvidhya.com/blog/2021/01/exploratory-data-analysis-on-nyc-taxi-trip-duration-dataset/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analyis with MLRUN\n",
    "\n",
    "You can see by simply running the describe function from our Market Place you can get a lot of information about the data and perform a qualitative and in-depth analysis on it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "> 2022-11-30 08:35:08,408 [info] loaded project ny-taxi-eda from MLRun DB\n",
      "> 2022-11-30 08:35:08,451 [info] starting run describe-analyze uid=d41d2a03181a4a1f8b58ac66fe15558a DB=http://mlrun-api:8080\n",
      "> 2022-11-30 08:35:15,084 [info] The data set is logged to the project under dataset name\n"
     ]
    },
    {
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       "  oReq.addEventListener(\"load\", reqListener);\n",
       "  oReq.open(\"GET\", el.title);\n",
       "  oReq.send();\n",
       "\n",
       "\n",
       "  //iframe.src = el.title;\n",
       "  const resultPane = document.querySelector(panelName + \"-pane\");\n",
       "  if (resultPane.classList.contains(\"hidden\")) {\n",
       "    resultPane.classList.remove(\"hidden\");\n",
       "  }\n",
       "}\n",
       "function closePanel(el) {\n",
       "  const panelName = \"#\" + el.getAttribute('paneName')\n",
       "  const resultPane = document.querySelector(panelName + \"-pane\");\n",
       "  if (!resultPane.classList.contains(\"hidden\")) {\n",
       "    resultPane.classList.add(\"hidden\");\n",
       "  }\n",
       "}\n",
       "\n",
       "</script>\n",
       "<div class=\"master-wrapper\">\n",
       "  <div class=\"block master-tbl\"><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>project</th>\n",
       "      <th>uid</th>\n",
       "      <th>iter</th>\n",
       "      <th>start</th>\n",
       "      <th>state</th>\n",
       "      <th>name</th>\n",
       "      <th>labels</th>\n",
       "      <th>inputs</th>\n",
       "      <th>parameters</th>\n",
       "      <th>results</th>\n",
       "      <th>artifacts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>ny-taxi-eda-davids</td>\n",
       "      <td><div title=\"d41d2a03181a4a1f8b58ac66fe15558a\"><a href=\"https://dashboard.default-tenant.app.dev6.lab.iguazeng.com/mlprojects/ny-taxi-eda-davids/jobs/monitor/d41d2a03181a4a1f8b58ac66fe15558a/overview\" target=\"_blank\" >...fe15558a</a></div></td>\n",
       "      <td>0</td>\n",
       "      <td>Nov 30 08:35:08</td>\n",
       "      <td>completed</td>\n",
       "      <td>describe-analyze</td>\n",
       "      <td><div class=\"dictlist\">v3io_user=davids</div><div class=\"dictlist\">kind=</div><div class=\"dictlist\">owner=davids</div><div class=\"dictlist\">host=jupyter-davids-6996d5569d-p4mfk</div></td>\n",
       "      <td><div class=\"artifact\" onclick=\"expandPanel(this)\" paneName=\"result557669dc\" title=\"/files/nyc-taxi-demo/data/data_set_describe.csv\">table</div></td>\n",
       "      <td><div class=\"dictlist\">label_column=fare_amount</div><div class=\"dictlist\">problem_type=regression</div></td>\n",
       "      <td></td>\n",
       "      <td><div class=\"artifact\" onclick=\"expandPanel(this)\" paneName=\"result557669dc\" title=\"files/v3io/projects/ny-taxi-eda-davids/artifacts/describe-analyze/0/describe-csv.csv\">describe-csv</div><div class=\"artifact\" onclick=\"expandPanel(this)\" paneName=\"result557669dc\" title=\"files/v3io/projects/ny-taxi-eda-davids/artifacts/describe-analyze/0/hist.html\">hist</div><div class=\"artifact\" onclick=\"expandPanel(this)\" paneName=\"result557669dc\" title=\"files/v3io/projects/ny-taxi-eda-davids/artifacts/describe-analyze/0/histograms.html\">histograms</div><div class=\"artifact\" onclick=\"expandPanel(this)\" paneName=\"result557669dc\" title=\"files/v3io/projects/ny-taxi-eda-davids/artifacts/describe-analyze/0/scatter-2d.html\">scatter-2d</div><div class=\"artifact\" onclick=\"expandPanel(this)\" paneName=\"result557669dc\" title=\"files/v3io/projects/ny-taxi-eda-davids/artifacts/describe-analyze/0/violin.html\">violin</div><div class=\"artifact\" onclick=\"expandPanel(this)\" paneName=\"result557669dc\" title=\"files/v3io/projects/ny-taxi-eda-davids/artifacts/describe-analyze/0/imbalance.html\">imbalance</div><div class=\"artifact\" onclick=\"expandPanel(this)\" paneName=\"result557669dc\" title=\"files/v3io/projects/ny-taxi-eda-davids/artifacts/describe-analyze/0/imbalance-weights-vec.csv\">imbalance-weights-vec</div><div class=\"artifact\" onclick=\"expandPanel(this)\" paneName=\"result557669dc\" title=\"files/v3io/projects/ny-taxi-eda-davids/artifacts/describe-analyze/0/correlation-matrix-csv.csv\">correlation-matrix-csv</div><div class=\"artifact\" onclick=\"expandPanel(this)\" paneName=\"result557669dc\" title=\"files/v3io/projects/ny-taxi-eda-davids/artifacts/describe-analyze/0/correlation.html\">correlation</div><div title=\"v3io:///projects/ny-taxi-eda-davids/artifacts/describe-analyze/0/dataset.parquet\">dataset</div></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div></div>\n",
       "  <div id=\"result557669dc-pane\" class=\"right-pane block hidden\">\n",
       "    <div class=\"pane-header\">\n",
       "      <span id=\"result557669dc-title\" class=\"pane-header-title\">Title</span>\n",
       "      <span onclick=\"closePanel(this)\" paneName=\"result557669dc\" class=\"close clickable\">&times;</span>\n",
       "    </div>\n",
       "    <iframe class=\"fileview\" id=\"result557669dc-body\"></iframe>\n",
       "  </div>\n",
       "</div>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<b> > to track results use the .show() or .logs() methods  or <a href=\"https://dashboard.default-tenant.app.dev6.lab.iguazeng.com/mlprojects/ny-taxi-eda-davids/jobs/monitor/d41d2a03181a4a1f8b58ac66fe15558a/overview\" target=\"_blank\">click here</a> to open in UI</b>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "> 2022-11-30 08:35:15,185 [info] run executed, status=completed\n"
     ]
    }
   ],
   "source": [
    "df.to_csv('./data/data_set_describe.csv', index=False)\n",
    "\n",
    "\n",
    "import mlrun\n",
    "\n",
    "project = mlrun.get_or_create_project(\n",
    "    name=\"ny-taxi-eda\", user_project=True, context=\"./\"\n",
    ")\n",
    "\n",
    "# import the function\n",
    "describe = mlrun.import_function('hub://describe')\n",
    "\n",
    "describe_run = describe.run(params={'label_column': 'fare_amount', \"problem_type\": \"regression\"},\n",
    "                            inputs={\"table\": './data/data_set_describe.csv'}, local=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Few examples for plots we get from mlrun describe function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'describe-csv': 'v3io:///projects/ny-taxi-eda-davids/artifacts/describe-analyze/0/describe-csv.csv',\n",
       " 'hist': 'v3io:///projects/ny-taxi-eda-davids/artifacts/describe-analyze/0/hist.html',\n",
       " 'histograms': 'v3io:///projects/ny-taxi-eda-davids/artifacts/describe-analyze/0/histograms.html',\n",
       " 'scatter-2d': 'v3io:///projects/ny-taxi-eda-davids/artifacts/describe-analyze/0/scatter-2d.html',\n",
       " 'violin': 'v3io:///projects/ny-taxi-eda-davids/artifacts/describe-analyze/0/violin.html',\n",
       " 'correlation-matrix-csv': 'v3io:///projects/ny-taxi-eda-davids/artifacts/describe-analyze/0/correlation-matrix-csv.csv',\n",
       " 'correlation': 'v3io:///projects/ny-taxi-eda-davids/artifacts/describe-analyze/0/correlation.html',\n",
       " 'dataset': 'store://artifacts/ny-taxi-eda-davids/dataset:8de8a7c199cb41238ed6e0e21372debb'}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# view generated artifacts (charts)\n",
    "describe_run.outputs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"./images/describe.png\" alt=\"describe\" width=\"1000\"/>"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
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 "nbformat": 4,
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