data_visualization/07-final-project.ipynb

{
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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": [
    "Now it's time for you to demonstrate your new skills with a project of your own!\n",
    "\n",
    "In this exercise, you will work with a dataset of your choosing.  Once you've selected a dataset, you'll design and create your own plot to tell interesting stories behind the data!\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",
    "from learntools.core import binder\n",
    "binder.bind(globals())\n",
    "from learntools.data_viz_to_coder.ex7 import *\n",
    "print(\"Setup Complete\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1: Attach a dataset to the notebook\n",
    "\n",
    "Begin by selecting a CSV dataset from [Kaggle Datasets](https://www.kaggle.com/datasets).  If you're unsure how to do this or would like to work with your own data, please revisit the instructions in the previous tutorial.\n",
    "\n",
    "Once you have selected a dataset, click on the **[+ ADD DATASET]** option in the top right corner.  This will generate a pop-up window that you can use to search for your chosen dataset.  \n",
    "\n",
    "![ex6_search_dataset](https://i.imgur.com/QDEKwYp.png)\n",
    "\n",
    "Once you have found the dataset, click on the **[Add]** button to attach it to the notebook.  You can check that it was successful by looking at the **Workspace** dropdown menu to the right of the notebook -- look for an **input** folder containing a subfolder that matches the name of the dataset.\n",
    "\n",
    "![ex6_dataset_added](https://i.imgur.com/oVlEBPx.png)\n",
    "\n",
    "You can click on the carat to the right of the name of the dataset to double-check that it contains a CSV file.  For instance, the image below shows that the example dataset contains two CSV files: (1) **dc-wikia-data.csv**, and (2) **marvel-wikia-data.csv**.\n",
    "\n",
    "![ex6_dataset_dropdown](https://i.imgur.com/4gpFw71.png)\n",
    "\n",
    "Once you've uploaded a dataset with a CSV file, run the code cell below **without changes** to receive credit for your work!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "<IPython.core.display.Javascript object>"
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      "text/markdown": [
       "<span style=\"color:#33cc33\">Correct:</span> \n",
       "\n"
      ],
      "text/plain": [
       "Correct: \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Check for a dataset with a CSV file\n",
    "step_1.check()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2: Specify the filepath\n",
    "\n",
    "Now that the dataset is attached to the notebook, you can find its filepath.  To do this, use the **Workspace** menu to list the set of files, and click on the CSV file you'd like to use.  This will open the CSV file in a tab below the notebook.  You can find the filepath towards the top of this new tab.  \n",
    "\n",
    "![ex6_filepath](https://i.imgur.com/pWe0sVb.png)\n",
    "\n",
    "After you find the filepath corresponding to your dataset, fill it in as the value for `my_filepath` in the code cell below, and run the code cell to check that you've provided a valid filepath.  For instance, in the case of this example dataset, we would set\n",
    "```\n",
    "my_filepath = \"../input/dc-wikia-data.csv\"\n",
    "```  \n",
    "Note that **you must enclose the filepath in quotation marks**; otherwise, the code will return an error.\n",
    "\n",
    "Once you've entered the filepath, you can close the tab below the notebook by clicking on the **[X]** at the top of the tab."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "parent.postMessage({\"jupyterEvent\": \"custom.exercise_interaction\", \"data\": {\"outcomeType\": 1, \"valueTowardsCompletion\": 0.25, \"interactionType\": 1, \"questionType\": 2, \"questionId\": \"2_Filepath\", \"learnToolsVersion\": \"0.3.4\", \"failureMessage\": \"\", \"exceptionClass\": \"\", \"trace\": \"\"}}, \"*\")"
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       "<IPython.core.display.Javascript object>"
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      "text/markdown": [
       "<span style=\"color:#33cc33\">Correct:</span> \n",
       "\n"
      ],
      "text/plain": [
       "Correct: \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Fill in the line below: Specify the path of the CSV file to read\n",
    "my_filepath = \"../input/fivethirtyeight-comic-characters-dataset/dc-wikia-data.csv\"\n",
    "\n",
    "# Check for a valid filepath to a CSV file in a dataset\n",
    "step_2.check()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3: Load the data\n",
    "\n",
    "Use the next code cell to load your data file into `my_data`.  Use the filepath that you specified in the previous step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "parent.postMessage({\"jupyterEvent\": \"custom.exercise_interaction\", \"data\": {\"outcomeType\": 1, \"valueTowardsCompletion\": 0.25, \"interactionType\": 1, \"questionType\": 2, \"questionId\": \"3_LoadData\", \"learnToolsVersion\": \"0.3.4\", \"failureMessage\": \"\", \"exceptionClass\": \"\", \"trace\": \"\"}}, \"*\")"
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       "<IPython.core.display.Javascript object>"
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      "text/markdown": [
       "<span style=\"color:#33cc33\">Correct:</span> \n",
       "\n"
      ],
      "text/plain": [
       "Correct: \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Fill in the line below: Read the file into a variable my_data\n",
    "my_data = pd.read_csv(my_filepath)\n",
    "\n",
    "# Check that a dataset has been uploaded into my_data\n",
    "step_3.check()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**_After the code cell above is marked correct_**, run the code cell below without changes to view the first five rows of the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th>page_id</th>\n",
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       "      <th>urlslug</th>\n",
       "      <th>ID</th>\n",
       "      <th>ALIGN</th>\n",
       "      <th>EYE</th>\n",
       "      <th>HAIR</th>\n",
       "      <th>SEX</th>\n",
       "      <th>GSM</th>\n",
       "      <th>ALIVE</th>\n",
       "      <th>APPEARANCES</th>\n",
       "      <th>FIRST APPEARANCE</th>\n",
       "      <th>YEAR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>1422</td>\n",
       "      <td>Batman (Bruce Wayne)</td>\n",
       "      <td>\\/wiki\\/Batman_(Bruce_Wayne)</td>\n",
       "      <td>Secret Identity</td>\n",
       "      <td>Good Characters</td>\n",
       "      <td>Blue Eyes</td>\n",
       "      <td>Black Hair</td>\n",
       "      <td>Male Characters</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Living Characters</td>\n",
       "      <td>3093.0</td>\n",
       "      <td>1939, May</td>\n",
       "      <td>1939.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>23387</td>\n",
       "      <td>Superman (Clark Kent)</td>\n",
       "      <td>\\/wiki\\/Superman_(Clark_Kent)</td>\n",
       "      <td>Secret Identity</td>\n",
       "      <td>Good Characters</td>\n",
       "      <td>Blue Eyes</td>\n",
       "      <td>Black Hair</td>\n",
       "      <td>Male Characters</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Living Characters</td>\n",
       "      <td>2496.0</td>\n",
       "      <td>1986, October</td>\n",
       "      <td>1986.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1458</td>\n",
       "      <td>Green Lantern (Hal Jordan)</td>\n",
       "      <td>\\/wiki\\/Green_Lantern_(Hal_Jordan)</td>\n",
       "      <td>Secret Identity</td>\n",
       "      <td>Good Characters</td>\n",
       "      <td>Brown Eyes</td>\n",
       "      <td>Brown Hair</td>\n",
       "      <td>Male Characters</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Living Characters</td>\n",
       "      <td>1565.0</td>\n",
       "      <td>1959, October</td>\n",
       "      <td>1959.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1659</td>\n",
       "      <td>James Gordon (New Earth)</td>\n",
       "      <td>\\/wiki\\/James_Gordon_(New_Earth)</td>\n",
       "      <td>Public Identity</td>\n",
       "      <td>Good Characters</td>\n",
       "      <td>Brown Eyes</td>\n",
       "      <td>White Hair</td>\n",
       "      <td>Male Characters</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Living Characters</td>\n",
       "      <td>1316.0</td>\n",
       "      <td>1987, February</td>\n",
       "      <td>1987.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1576</td>\n",
       "      <td>Richard Grayson (New Earth)</td>\n",
       "      <td>\\/wiki\\/Richard_Grayson_(New_Earth)</td>\n",
       "      <td>Secret Identity</td>\n",
       "      <td>Good Characters</td>\n",
       "      <td>Blue Eyes</td>\n",
       "      <td>Black Hair</td>\n",
       "      <td>Male Characters</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Living Characters</td>\n",
       "      <td>1237.0</td>\n",
       "      <td>1940, April</td>\n",
       "      <td>1940.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
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       "   page_id                         name                              urlslug  \\\n",
       "0     1422         Batman (Bruce Wayne)         \\/wiki\\/Batman_(Bruce_Wayne)   \n",
       "1    23387        Superman (Clark Kent)        \\/wiki\\/Superman_(Clark_Kent)   \n",
       "2     1458   Green Lantern (Hal Jordan)   \\/wiki\\/Green_Lantern_(Hal_Jordan)   \n",
       "3     1659     James Gordon (New Earth)     \\/wiki\\/James_Gordon_(New_Earth)   \n",
       "4     1576  Richard Grayson (New Earth)  \\/wiki\\/Richard_Grayson_(New_Earth)   \n",
       "\n",
       "                ID            ALIGN         EYE        HAIR              SEX  \\\n",
       "0  Secret Identity  Good Characters   Blue Eyes  Black Hair  Male Characters   \n",
       "1  Secret Identity  Good Characters   Blue Eyes  Black Hair  Male Characters   \n",
       "2  Secret Identity  Good Characters  Brown Eyes  Brown Hair  Male Characters   \n",
       "3  Public Identity  Good Characters  Brown Eyes  White Hair  Male Characters   \n",
       "4  Secret Identity  Good Characters   Blue Eyes  Black Hair  Male Characters   \n",
       "\n",
       "   GSM              ALIVE  APPEARANCES FIRST APPEARANCE    YEAR  \n",
       "0  NaN  Living Characters       3093.0        1939, May  1939.0  \n",
       "1  NaN  Living Characters       2496.0    1986, October  1986.0  \n",
       "2  NaN  Living Characters       1565.0    1959, October  1959.0  \n",
       "3  NaN  Living Characters       1316.0   1987, February  1987.0  \n",
       "4  NaN  Living Characters       1237.0      1940, April  1940.0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print the first five rows of the data\n",
    "my_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 4: Visualize the data\n",
    "\n",
    "Use the next code cell to create a figure that tells a story behind your dataset.  You can use any chart type (_line chart, bar chart, heatmap, etc_) of your choosing!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
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      "text/markdown": [
       "<span style=\"color:#33cc33\">Correct:</span> \n",
       "\n"
      ],
      "text/plain": [
       "Correct: \n"
      ]
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a plot\n",
    "sns.kdeplot(my_data[\"YEAR\"])\n",
    "\n",
    "# Check that a figure appears below\n",
    "step_4.check()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5376a9ffd0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,6))\n",
    "sns.distplot(my_data[\"APPEARANCES\"], hist=False)\n",
    "#sns.distplot(my_data[\"YEAR\"], hist=False)\n",
    "\n",
    "#plt.figure(figsize=(12,12))\n",
    "#sns.jointplot(x=\"YEAR\", y=\"APPEARANCES\", data=my_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Keep going\n",
    "\n",
    "Learn how to use your skills after completing the micro-course to create data visualizations in a **[final tutorial](https://www.kaggle.com/alexisbcook/creating-your-own-notebooks)**."
   ]
  },
  {
   "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,
 "nbformat_minor": 4
}