data_visualization/02-line-charts.ipynb

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  {
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   "source": [
    "**[Data Visualization: From Non-Coder to Coder Home Page](https://www.kaggle.com/learn/data-visualization-from-non-coder-to-coder)**\n",
    "\n",
    "---\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this exercise, you will use your new knowledge to propose a solution to a real-world scenario. To succeed, you will need to import data into Python, answer questions using the data, and generate **line charts** to understand patterns in the data.\n",
    "\n",
    "## Scenario\n",
    "\n",
    "You have recently been hired to manage the museums in the City of Los Angeles. Your first project focuses on the four museums pictured in the images below.\n",
    "\n",
    "![ex1_museums](https://i.imgur.com/pFYL8J1.png)\n",
    "\n",
    "You will leverage data from the Los Angeles [Data Portal](https://data.lacity.org/) that tracks monthly visitors to each museum.  \n",
    "\n",
    "![ex1_xlsx](https://i.imgur.com/mGWYlym.png)\n",
    "\n",
    "## Setup\n",
    "\n",
    "Run the next cell to import and configure the Python libraries that you need to complete the exercise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setup Complete\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "print(\"Setup Complete\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The questions below will give you feedback on your work. Run the following cell to set up the feedback system."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setup Complete\n"
     ]
    }
   ],
   "source": [
    "# Set up code checking\n",
    "import os\n",
    "if not os.path.exists(\"../input/museum_visitors.csv\"):\n",
    "    os.symlink(\"../input/data-for-datavis/museum_visitors.csv\", \"../input/museum_visitors.csv\") \n",
    "from learntools.core import binder\n",
    "binder.bind(globals())\n",
    "from learntools.data_viz_to_coder.ex2 import *\n",
    "print(\"Setup Complete\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1: Load the data\n",
    "\n",
    "Your first assignment is to read the LA Museum Visitors data file into `museum_data`.  Note that:\n",
    "- The filepath to the dataset is stored as `museum_filepath`.  Please **do not** change the provided value of the filepath.\n",
    "- The name of the column to use as row labels is `\"Date\"`.  (This can be seen in cell A1 when the file is opened in Excel.)\n",
    "\n",
    "To help with this, you may find it useful to revisit some relevant code from [the tutorial](https://www.kaggle.com/alexisbcook/hello-seaborn), which we have pasted below:\n",
    "\n",
    "```python\n",
    "# Path of the file to read\n",
    "spotify_filepath = \"../input/spotify.csv\"\n",
    "\n",
    "# Read the file into a variable spotify_data\n",
    "spotify_data = pd.read_csv(spotify_filepath, index_col=\"Date\", parse_dates=True)\n",
    "```\n",
    "\n",
    "The code you need to write now looks very similar!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
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       "<span style=\"color:#33cc33\">Correct</span>"
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       "Correct"
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   "source": [
    "# Path of the file to read\n",
    "museum_filepath = \"../input/museum_visitors.csv\"\n",
    "\n",
    "# Fill in the line below to read the file into a variable museum_data\n",
    "museum_data = pd.read_csv(museum_filepath, index_col=\"Date\", parse_dates=True)\n",
    "\n",
    "# Run the line below with no changes to check that you've loaded the data correctly\n",
    "step_1.check()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Uncomment the line below to receive a hint\n",
    "# step_1.hint()\n",
    "# Uncomment the line below to see the solution\n",
    "# step_1.solution()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2: Review the data\n",
    "\n",
    "Use a Python command to print the last 5 rows of the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>Avila Adobe</th>\n",
       "      <th>Firehouse Museum</th>\n",
       "      <th>Chinese American Museum</th>\n",
       "      <th>America Tropical Interpretive Center</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-07-01</th>\n",
       "      <td>23136</td>\n",
       "      <td>4191</td>\n",
       "      <td>2620</td>\n",
       "      <td>4718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-01</th>\n",
       "      <td>20815</td>\n",
       "      <td>4866</td>\n",
       "      <td>2409</td>\n",
       "      <td>3891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-01</th>\n",
       "      <td>21020</td>\n",
       "      <td>4956</td>\n",
       "      <td>2146</td>\n",
       "      <td>3180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-01</th>\n",
       "      <td>19280</td>\n",
       "      <td>4622</td>\n",
       "      <td>2364</td>\n",
       "      <td>3775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01</th>\n",
       "      <td>17163</td>\n",
       "      <td>4082</td>\n",
       "      <td>2385</td>\n",
       "      <td>4562</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Avila Adobe  Firehouse Museum  Chinese American Museum  \\\n",
       "Date                                                                 \n",
       "2018-07-01        23136              4191                     2620   \n",
       "2018-08-01        20815              4866                     2409   \n",
       "2018-09-01        21020              4956                     2146   \n",
       "2018-10-01        19280              4622                     2364   \n",
       "2018-11-01        17163              4082                     2385   \n",
       "\n",
       "            America Tropical Interpretive Center  \n",
       "Date                                              \n",
       "2018-07-01                                  4718  \n",
       "2018-08-01                                  3891  \n",
       "2018-09-01                                  3180  \n",
       "2018-10-01                                  3775  \n",
       "2018-11-01                                  4562  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print the last five rows of the data \n",
    "museum_data.tail() # Your code here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The last row (for `2018-11-01`) tracks the number of visitors to each museum in November 2018, the next-to-last row (for `2018-10-01`) tracks the number of visitors to each museum in October 2018, _and so on_.\n",
    "\n",
    "Use the last 5 rows of the data to answer the questions below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
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       "<span style=\"color:#33cc33\">Correct</span>"
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       "Correct"
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   ],
   "source": [
    "# Fill in the line below: How many visitors did the Chinese American Museum \n",
    "# receive in July 2018?\n",
    "ca_museum_jul18 = 2620 \n",
    "\n",
    "# Fill in the line below: In October 2018, how many more visitors did Avila \n",
    "# Adobe receive than the Firehouse Museum? Yes\n",
    "avila_oct18 = 14658 # The correct value is: 19280\n",
    "\n",
    "# Check your answers\n",
    "step_2.check()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Lines below will give you a hint or solution code\n",
    "# step_2.hint()\n",
    "# step_2.solution()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3: Convince the museum board \n",
    "\n",
    "The Firehouse Museum claims they ran an event in 2014 that brought an incredible number of visitors, and that they should get extra budget to run a similar event again.  The other museums think these types of events aren't that important, and budgets should be split purely based on recent visitors on an average day.  \n",
    "\n",
    "To show the museum board how the event compared to regular traffic at each museum, create a line chart that shows how the number of visitors to each museum evolved over time.  Your figure should have four lines (one for each museum).\n",
    "\n",
    "> **(Optional) Note**: If you have some prior experience with plotting figures in Python, you might be familiar with the `plt.show()` command.  If you decide to use this command, please place it **after** the line of code that checks your answer (in this case, place it after `step_3.check()` below) -- otherwise, the checking code will return an error!"
   ]
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       "<span style=\"color:#33cc33\">Correct</span>"
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       "Correct"
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     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Explicitly register matplotlib converters\n",
    "from pandas.plotting import register_matplotlib_converters\n",
    "register_matplotlib_converters()\n",
    "\n",
    "# Line chart showing the number of visitors to each museum over time\n",
    "sns.lineplot(data=museum_data) # Your code here\n",
    "\n",
    "# Check your answer\n",
    "step_3.check()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Lines below will give you a hint or solution code\n",
    "# step_3.hint()\n",
    "# step_3.solution_plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 4: Assess seasonality\n",
    "\n",
    "When meeting with the employees at Avila Adobe, you hear that one major pain point is that the number of museum visitors varies greatly with the seasons, with low seasons (when the employees are perfectly staffed and happy) and also high seasons (when the employees are understaffed and stressed).  You realize that if you can predict these high and low seasons, you can plan ahead to hire some additional seasonal employees to help out with the extra work.\n",
    "\n",
    "#### Part A\n",
    "Create a line chart that shows how the number of visitors to Avila Adobe has evolved over time.  (_If your code returns an error, the first thing that you should check is that you've spelled the name of the column correctly!  You must write the name of the column exactly as it appears in the dataset._)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Thank you for creating a line chart!  To see how your code compares to the official solution, please use the code cell below.\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "parent.postMessage({\"jupyterEvent\": \"custom.exercise_interaction\", \"data\": {\"outcomeType\": 1, \"valueTowardsCompletion\": 0.3333333333333333, \"interactionType\": 1, \"questionType\": 2, \"questionId\": \"4.1_PlotAvila\", \"learnToolsVersion\": \"0.3.4\", \"failureMessage\": \"\", \"exceptionClass\": \"\", \"trace\": \"\"}}, \"*\")"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "<span style=\"color:#33cc33\">Correct</span>"
      ],
      "text/plain": [
       "Correct"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Line plot showing the number of visitors to Avila Adobe over time\n",
    "# Your code here\n",
    "plt.figure(figsize=(12,6))\n",
    "sns.lineplot(data=museum_data[\"Avila Adobe\"], label=\"Avila Adobe\")\n",
    "plt.title(\"Monthly Visitors to Avila Adobe Museum\")\n",
    "plt.xlabel(\"Date\")\n",
    "plt.ylabel(\"Visitors\")\n",
    "\n",
    "# Check your answer\n",
    "step_4.a.check()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Lines below will give you a hint or solution code\n",
    "# step_4.a.hint()\n",
    "# step_4.a.solution_plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Part B\n",
    "\n",
    "Does Avila Adobe get more visitors:\n",
    "- in September-February (in LA, the fall and winter months), or \n",
    "- in March-August (in LA, the spring and summer)?  \n",
    "\n",
    "Using this information, when should the museum staff additional seasonal employees?  \n",
    "\n",
    "- **My answer:** Avila Adobe get more visitors in March-August (the spring and summer). Thus, the museum should staff additional employees for these seasons."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# step_4.b.hint()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "parent.postMessage({\"jupyterEvent\": \"custom.exercise_interaction\", \"data\": {\"interactionType\": 3, \"questionType\": 4, \"questionId\": \"4.2_ThinkAvila\", \"learnToolsVersion\": \"0.3.4\", \"valueTowardsCompletion\": 0.0, \"failureMessage\": \"\", \"exceptionClass\": \"\", \"trace\": \"\", \"outcomeType\": 4}}, \"*\")"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "<span style=\"color:#33cc99\">Solution:</span> The line chart generally dips to relatively low values around the early part of each year (in December and January), and reaches its highest values in the middle of the year (especially around May and June).  Thus, Avila Adobe usually gets more visitors in March-August (or the spring and summer months).  With this in mind, Avila Adobe could definitely benefit from hiring more seasonal employees to help with the extra work in March-August (the spring and summer)!"
      ],
      "text/plain": [
       "Solution: The line chart generally dips to relatively low values around the early part of each year (in December and January), and reaches its highest values in the middle of the year (especially around May and June).  Thus, Avila Adobe usually gets more visitors in March-August (or the spring and summer months).  With this in mind, Avila Adobe could definitely benefit from hiring more seasonal employees to help with the extra work in March-August (the spring and summer)!"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "step_4.b.solution()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Keep going\n",
    "\n",
    "Move on to learn about **[bar charts and heatmaps](https://www.kaggle.com/alexisbcook/bar-charts-and-heatmaps)** with a new dataset!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "**[Data Visualization: From Non-Coder to Coder Home Page](https://www.kaggle.com/learn/data-visualization-from-non-coder-to-coder)**\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "*Have questions or comments? Visit the [Learn Discussion forum](https://www.kaggle.com/learn-forum) to chat with other Learners.*"
   ]
  }
 ],
 "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": "3.6.6"
  }
 },
 "nbformat": 4,
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}