hw12.ipynb

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      "name": "Copy of hw12.ipynb",
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        {
          "file_id": "https://github.com/cis024c/summer2018hw/blob/master/hw12.ipynb",
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    "kernelspec": {
      "display_name": "Python 2",
      "language": "python",
      "name": "python2"
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      "source": [
        "\n",
        "# CIS024C - Spring 2018 - Friday 5:30-9:25pm \n",
        "\n",
        "## Homework 12\n",
        "\n",
        "Homework 12 covers plotting with matplotlib/pyplot\n",
        "\n",
        "The below sites have some interesting and useful information on working with plots\n",
        "\n",
        "* D3 Gallery - https://github.com/d3/d3/wiki/Gallery\n",
        "* Minard's Visualizaion of Napolean's 1812 March - https://robots.thoughtbot.com/analyzing-minards-visualization-of-napoleons-1812-march\n",
        "* Matplotlib Overview - https://matplotlib.org/users/intro.html\n",
        "\n",
        "\n",
        "You will need to download this notebook and use this as a starting point for your homework. You will just need to fill in the content of each code-block (cell) and execute. Once you have completed all the exercises, you will need to save and upload this to your github repository under a folder called hw10.\n",
        "\n",
        "Note also the exercises build on top of one another so you might be able to do the next exercise if you have not completed the previous exercise.\n",
        "\n",
        "Post any questions you have on our Slack at **cis-024c1.slack.com**\n",
        "\n",
        "** Slides ** for Week 11 can be found at \n",
        "\n",
        "https://docs.google.com/presentation/d/1Lz6li1lw7D5_abcG6W68E2rHrtCMgI-cJSA9VokKSi4/edit?usp=sharing\n",
        "\n",
        "**Please refer back to hw1 and slack for instructions on how to setup your computer for developing using Python.**"
      ]
    },
    {
      "metadata": {
        "id": "B-Kh34YiN7iu",
        "colab_type": "text"
      },
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      "source": [
        "### Helpful Jupyter Commands\n",
        "\n",
        "Below are some useful commands to know when using Jupyter\n",
        "\n",
        "1. You can add a new cell by clicking on the \"+\" icon on top.\n",
        "2. You can delete a cell by selecting that cell and clicking on the \"scissors\" icon on top.\n",
        "3. You can execute a cell by either pressing shift+enter or selecting the \"play\" button on top.\n",
        "4. You can create a new file in Jupyter via the File menu->New Notebook option. Make sure to select Python 2 when creating your notebook.\n",
        "5. Also, for your code blocks make sure that Code is selected instead of another option like Markdown.\n",
        "6. Use the Enter key to go to the next line in a cell to enter the next statement.\n",
        "7. You can clear results by clicking on the Cell menu item and selecting Current Output->Clear or All Output->Clear depending on whether you are trying to just clear the output for one cell or for all cells.\n",
        "8. In case your program has crashed for some reason (infinite loop, for example), you can restart your Python session by select Kernel in the menu and selecting Restart.\n"
      ]
    },
    {
      "metadata": {
        "id": "em5xHCJ4N7iw",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Check Python Version"
      ]
    },
    {
      "metadata": {
        "id": "hYClesinN7iw",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        },
        "outputId": "db7f00ed-a3db-46e4-d0bf-a1e625c8c6b7"
      },
      "cell_type": "code",
      "source": [
        "!python --version"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Python 2.7.12 :: Continuum Analytics, Inc.\r\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "6xZ9sS5WN7i2",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Class work\n",
        "\n",
        "The main classwork file is https://github.com/cis024c/spring2018classwork/blob/master/week11/classwork_04242018.ipynb"
      ]
    },
    {
      "metadata": {
        "id": "3SzSPVeXN7i4",
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      },
      "cell_type": "markdown",
      "source": [
        "**Exercise 1 - Testing your knowledge of plots**\n",
        "\n",
        "Answer the below questions\n",
        "\n",
        "1. What is the goal of data visualization?\n",
        "2. Why is it critical to ensure that every bit of ink in a visualization contributes to the intended goal?\n",
        "3. What is the difference between a histogram and a barplot?\n",
        "4. Name four types of charts"
      ]
    },
    {
      "metadata": {
        "id": "LReaRDtHN7i6",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "<< Double click this cell and enter your result >>\n",
        "1. The goal of data visualization is to see complex data in a visual way that may be easier to understand depending on the purpose\n",
        "2. This is critical because your chart can become misleading and much harder to understand\n",
        "3. The difference between a histogram and a bar plot is that a histogram is for finding the **frequency in bins** and getting the **distribution** while a bar chart is for **comparing the values** of **categorical data**\n",
        "4. \n",
        "\n",
        "  A. Line Chart\n",
        "\n",
        "  B. Bar Chart\n",
        "  \n",
        "  C. Histogram\n",
        "  \n",
        "  D. Pie Chart"
      ]
    },
    {
      "metadata": {
        "id": "d9ykCvSKN7i6",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "** Exercise 2 - Line chart **\n",
        "\n",
        "Consider the following dataset of the weights of 7 cars and their corresponding fuel consumption\n",
        "\n",
        "```\n",
        "weight = [3170,3455,3222,3983,2441,2500,2390]\n",
        "fuel_consumption (miles per gallon) = [26,30,28,23,36,33,38]\n",
        "```\n",
        "\n",
        "1. Create a line chart that plots the weight of cars against their fuel consumption.\n",
        "2. Comment on your findings. How does fuel consumption change as the weight of the car increases?\n",
        "\n",
        "Make sure to add labels and a title to your chart"
      ]
    },
    {
      "metadata": {
        "id": "j1ZyNlvqN7i7",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "### YOUR CODE GOES BELOW\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "RlGJ1dh65xra",
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          "autoexec": {
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          }
        }
      },
      "cell_type": "code",
      "source": [
        "weight = [3170, 3455, 3222, 3983, 2441, 2500, 2390]\n",
        "mileage = [26, 30, 28, 23, 36, 33, 38] # miles per gallon\n",
        "\n",
        "new_x, new_y = zip(*sorted(zip(weight, mileage)))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "AkttuYw86KyZ",
        "colab_type": "code",
        "colab": {
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            "startup": false,
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          },
          "base_uri": "https://localhost:8080/",
          "height": 376
        },
        "outputId": "dc642d27-3dbe-4e26-a536-cc8b53c9ca17",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1531978011107,
          "user_tz": 420,
          "elapsed": 358,
          "user": {
            "displayName": "Sampann Nigam",
            "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s128",
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      },
      "cell_type": "code",
      "source": [
        "plt.title(\"Car mileage vs. weight\")\n",
        "plt.xlabel(\"Weight\")\n",
        "plt.ylabel(\"Mileage\")\n",
        "\n",
        "plt.plot(new_x, new_y)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
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292qfbRERcQx2K+0NGzYQHR3N/PnzeeONN5gxYwaLFy8mICCApUuXMnjwYOLi4ux1+kvW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\nsWMHMTEx9s4nDsJ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Y4mDq778Qkf8xmUxc36UprZr48c7yBFZvPsGBk9k8eFM0QX7uRser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\nhBBCaEJKWwghhNDEPwHsTKKqH0UwDgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f001c6b22d0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "ce0eMF4A6LgA",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Though some points are off a bit, the pattern that I see is that if the weight of the car is more, the mileage is less."
      ]
    },
    {
      "metadata": {
        "id": "ZN_vsrJ45jgI",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "### END CODE"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "fZCizAYYN7i9",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Exercise 3 - Scatter Plot\n",
        "\n",
        "Consider the following dataset of the weights of individuals and the number of times they used the remote contorl in a period of one hour\n",
        "\n",
        "```\n",
        "weight = [121,127,128,131,130,131,133,141,161,167,169,171,173,175,177,181]\n",
        "remote_use = [4,3,7,5,3,8,3,4,22,21,17,23,19,22,18,23]\n",
        "```\n",
        "\n",
        "1. Create a scatterplot with the weight on the y-axis and the remote_use variable on the x_axis.\n",
        "2. What can you conclude from the weight the points are distributed on the graph? Does a particular pattern standout?\n",
        "\n",
        "Make sure to add labels and a title to your chart\n"
      ]
    },
    {
      "metadata": {
        "id": "Gpy-o8qCN7i9",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "### YOUR CODE GOES BELOW\n",
        "weight = [121, 127, 128, 131, 130, 131, 133, 141, 161, 167, 169, 171, 173, 175, 177, 181]\n",
        "remote_use = [4, 3, 7, 5, 3, 8, 3, 4, 22, 21, 17, 23, 19, 22, 18, 23]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "YyVMRlUdBGk8",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 376
        },
        "outputId": "96943de6-c5c4-400b-a7db-bf4d5654cf42",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1531978905020,
          "user_tz": 420,
          "elapsed": 472,
          "user": {
            "displayName": "Sampann Nigam",
            "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s128",
            "userId": "102273316183648148768"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "plt.title(\"Remote use vs. weight\")\n",
        "plt.xlabel(\"# of times remote used\")\n",
        "plt.ylabel(\"Weights of individuals\")\n",
        "\n",
        "plt.scatter(remote_use, weight)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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yiIiImlXtoDp3tYS5sQfVMVqQV1RUYM2aNRgyZIhm3pYtWzT/X7ZsGaZOnYqbN2/i0KFD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\nIiIiEft/9DjyfulX4XQAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f001a4f2350>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "uNxxlXCACCDG",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Most people 120 - 140 pounds used the remote 3 - 8 times and most people 160 - 185 pounds used the remote 17 - 23 times. In summary, people who weight less used the remote less times and people who weigh more used the remote more times"
      ]
    },
    {
      "metadata": {
        "id": "pZCz-miYA1Jw",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "### END CODE"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "B8tM1-qGN7i_",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Exercise 4 - Bar Charts\n",
        "\n",
        "Consider the following dataset that show the excuses for being late in class and the number of times the excuse was used.\n",
        "\n",
        "```\n",
        "excuse = ['No clean pants to wear','thought it was Saturday','forgot to set alarm','too dark, thought it was night','stuck in traffic']\n",
        "frequency_of_use = [12,8,26,5,14]\n",
        "```\n",
        "\n",
        "Create a bar chart with the excuse on the y axis and the frequency of use on the x axis. Make sure the add labels and a title to the chart."
      ]
    },
    {
      "metadata": {
        "id": "Y_BI8jRiN7jB",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "### YOUR CODE GOES BELOW\n",
        "excuse = ['No clean pants to wear','thought it was Saturday','forgot to set alarm','too dark, thought it was night','stuck in traffic']\n",
        "frequency_of_use = [12,8,26,5,14]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "vcW2A6MUGfHu",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 376
        },
        "outputId": "439381c6-c097-4117-834e-0b483a302a09",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1531979924132,
          "user_tz": 420,
          "elapsed": 420,
          "user": {
            "displayName": "Sampann Nigam",
            "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s128",
            "userId": "102273316183648148768"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "plt.title(\"What excuses people use\")\n",
        "plt.xlabel(\"Frequency of use\")\n",
        "plt.ylabel(\"Excuse\")\n",
        "\n",
        "plt.barh(excuse, frequency_of_use)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Vo0iRIvTs2ZN+/frx9NNPm11WnnHt2jU6d+7MggULKFQo67X4tkO+vk9VyYNu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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f001a3338d0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "k4f5S3GlGaK2",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "### END CODE"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "g-Z8-aocN7jD",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Exercise 5 -  Creating subplots\n",
        "\n",
        "Consider the below data that shows the relationship between scores obtained and hours spent studying a week.\n",
        "\n",
        "```\n",
        "hours = [1,2,3,4,5,6,7,8,9,10]\n",
        "score = [40,45,50,55,60,65,70,80,90,100]\n",
        "```\n",
        "\n",
        "Create 4 subplots in a 2x2 grid, consisting of below four plots\n",
        "\n",
        "1. Histogram of scores.\n",
        "2. Line chart plotting hours on the x_axis and the score on the y_axis\n",
        "3. Bar chart plotting the hours on the x_axis and the score on the y_axis\n",
        "4. A scatter plot with the hours on the x_axis and the score on the y_axis\n",
        "\n",
        "Make sure to add labels and a title to each of the charts."
      ]
    },
    {
      "metadata": {
        "id": "z9bnlDLeN7jE",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "### YOUR CODE GOES BELOW\n",
        "hours = [1,2,3,4,5,6,7,8,9,10]\n",
        "scores = [40,45,50,55,60,65,70,80,90,100]\n",
        "\n",
        "new_x, new_y = zip(*sorted(zip(hours, score)))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "pJuBRntiKWiD",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 376
        },
        "outputId": "3e586742-1768-4a9d-cf76-47a45292a2f8",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1531981804714,
          "user_tz": 420,
          "elapsed": 753,
          "user": {
            "displayName": "Sampann Nigam",
            "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s128",
            "userId": "102273316183648148768"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "f, axarr = plt.subplots(2, 2)\n",
        "\n",
        "ax = axarr[0][0]\n",
        "ax.set_title(\"Histogram of Scores\")\n",
        "ax.set_xlabel(\"Scores\")\n",
        "ax.set_ylabel(\"Frequency\")\n",
        "ax.hist(scores)\n",
        "\n",
        "ax = axarr[0][1]\n",
        "ax.set_title(\"Hours vs. Score Line Chart\")\n",
        "ax.set_xlabel(\"Hours\")\n",
        "ax.set_ylabel(\"Scores\")\n",
        "ax.plot(new_x, new_y)\n",
        "\n",
        "ax = axarr[1][0]\n",
        "ax.set_title(\"Hours vs. Score Bar Chart\")\n",
        "ax.set_xlabel(\"Hours\")\n",
        "ax.set_ylabel(\"Scores\")\n",
        "ax.bar(hours, scores)\n",
        "\n",
        "ax = axarr[1][1]\n",
        "ax.set_title(\"Hours vs. Score Scatter Plot\")\n",
        "ax.set_xlabel(\"Hours\")\n",
        "ax.set_ylabel(\"Scores\")\n",
        "ax.scatter(hours, scores)\n",
        "\n",
        "plt.show()\n",
        "### END CODE"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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XL8/y5cuxtLTMca8AwH//+1+WLl3K4MGDgcwgb9euHR999BGQeUxvypQpuLi4YGVlxeLF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\nYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f0019e50590>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "ObRO9HSjN7jI",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## OPTIONAL EXERCISES\n",
        "\n",
        "Below is a set of optional exercises. These will not be graded but the solutions will be posted. I would strongly encourage you to try these out if you are done with the mandatory homework exercises to improve your understanding of python."
      ]
    },
    {
      "metadata": {
        "id": "vwM8QEcgN7jI",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Exercise 6 -  Pie Plots\n",
        "\n",
        "Consider the following distribution of populations of 10 countries in the European Union in 2016 (See https://en.wikipedia.org/wiki/List_of_European_Union_member_states_by_population)\n",
        "\n",
        "```\n",
        "1\t Germany\t82,301,678\t\n",
        "2\t France\t66,991,000\t\n",
        "3\t Italy\t60,795,612\t\n",
        "4\t Spain\t46,468,102\t\n",
        "5\t Poland\t38,567,614\t\n",
        "6\t Romania\t19,861,408\t\n",
        "7\t Netherlands\t17,100,475\t\n",
        "8\t Belgium\t11,258,434\t\n",
        "9\t Greece\t10,812,46\n",
        "10\t Czech Republic\t10,538,275\n",
        "```\n",
        "\n",
        "1. Create a pie-chart that shows the distribution of populations across the 10 countries\n",
        "2. Comment on your findings\n",
        "3. Why could a pie-chart be problematic when trying to visualize the results"
      ]
    },
    {
      "metadata": {
        "id": "JJY6q0yZN7jK",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "WCAJjK1lN7jN",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Exercise 7 -  Large data sets and sub Plots\n",
        "\n",
        "For this exercise you will download historical stock data from the internet from the below link. The data is available at - https://github.com/cis024c/spring2018hw/blob/master/all_stocks_1yr.csv. The data has the following fields - Date, Open, High, Low, Close, Volume, Name. The Name is the stock ticker or stock symbol.\n",
        "\n",
        "Your subplot will involve two plots in a 1X2 grid (1 row and two columns)\n",
        "\n",
        "* The first column will show a plot of date versus the price at close\n",
        "* The second column show show a plot of date versus the volume\n",
        "\n",
        "You will create an interactive way for the user to enter the ticker symbol and then look up for the data corresponding to that symbol and show the corresponding plot for that chart."
      ]
    },
    {
      "metadata": {
        "id": "dpPgJGL7N7jN",
        "colab_type": "code",
        "colab": {
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      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
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}