AIS_03_Descriptive_Analytics-Colab.ipynb

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        "<div class='bar_title'></div>\n",
        "\n",
        "*Analytical Information Systems*\n",
        "\n",
        "# AIS Tutorial 03 - Descriptive Analytics\n",
        "\n",
        "Nico Elbert<br>\n",
        "Chair of Information Systems and Business Analytics\n",
        "\n",
        "Summer Semester 2023"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bDnZkh4OLXGa"
      },
      "source": [
        "### Recap: Data Visualization\n",
        "\n",
        "**Why use Data Visualization?**\n",
        "\n",
        "Data Visualization involves the depiction of information using spatial or graphical representations. This process facilitates comparison, pattern recognition, change detection, and other cognitive skills by making use of the visual system.\n",
        "\n",
        "**Problem**\n",
        "\n",
        "With big datasets, understanding them can be a challenge.\n",
        "\n",
        "**Solution**\n",
        "\n",
        "We take better advantage of the human perceptual system and convert information into a graphical representation.\n",
        "\n",
        "**Issues**\n",
        "\n",
        "There are several considerations when creating visualizations:\n",
        "\n",
        "1. How to convert abstract information into graphical form?\n",
        "2. Do visualizations do a better job than other methods?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FjajbvVILXGa"
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      "source": [
        "### Graphical Excellence\n",
        "\n",
        "Excellence in statistical graphics consists of complex ideas communicated with clarity, precision, and efficiency.\n",
        "\n",
        "Good graphical displays should:\n",
        "\n",
        "* Induce the viewer to think about the substance rather than about methodology, graphic design, the technology of graphic production or something else.\n",
        "* Avoid distorting what the data has to say.\n",
        "* Present many numbers in a small space.\n",
        "* Make large data sets coherent.\n",
        "* Encourage the eye to compare different pieces of data.\n",
        "* Reveal the data at several levels of detail, from a broad overview to the fine structure.\n",
        "* Serve a reasonably clear purpose: description, exploration, tabulation, or decoration."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QCaE0g96LXGb"
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      "source": [
        "### Graphical Integrity\n",
        "\n",
        "The concept of Graphical Integrity states:\n",
        "\n",
        "* The representation of numbers, as physically measured on the surface of the graphic itself, should be directly proportional to the numerical quantities represented.\n",
        "* Clear, detailed, and thorough labeling should be used to defeat graphical distortion and ambiguity.\n",
        "* Show data variation, not design variation.\n",
        "* In time-series displays of money, deflated and standardized units of monetary measurement are nearly always better than nominal units.\n",
        "* The number of information-carrying dimensions depicted should not exceed the number of dimensions in the data.\n",
        "* Graphics must not quote data out of context."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
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      "source": [
        "#install all packages\n",
        "!pip install matplotlib --q\n",
        "!pip install seaborn --q\n",
        "!pip install pandas --q"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Dmm1KY-MLXGn"
      },
      "source": [
        "# Data Visualization in Python"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "cell_style": "split",
        "id": "ei5YjUJFBVc3"
      },
      "source": [
        "Most of the material of this lecture is adopted from \n",
        "- https://github.com/LearnDataSci/article-resources \n",
        "- [Python Data Science Handbook](https://jakevdp.github.io/PythonDataScienceHandbook/) - Essential Tools for Working with Data\" By Jake VanderPlas,  O'Reilly Media"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "cell_style": "split",
        "id": "S6zH8-TgBVc3"
      },
      "source": [
        "\n",
        "\n",
        "<img src=\"https://jakevdp.github.io/PythonDataScienceHandbook/figures/PDSH-cover.png\" style=\"width:50%\" />"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AQPfMbFuBVc3"
      },
      "source": [
        "#### Two ways of exploring data\n",
        "\n",
        "<img src=\"https://raw.githubusercontent.com/wi3jmu/PDS1920/master/Lecture/images/02/explore_data.png\" style=\"width:60%\" />"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "cell_style": "center",
        "id": "CcOHe7nZBVc4"
      },
      "source": [
        "### Exploratory data analysis (EDA)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "cell_style": "split",
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      "source": [
        "EDA allows to\n",
        "* better understand the data\n",
        "* build an intuition about the data\n",
        "* generate hypotheses\n",
        "* assess assumptions\n",
        "* find insights"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
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      "source": [
        "With EDA we can\n",
        "* get comfortable with the data\n",
        "* find magic features\n",
        "* find mistakes or odd values"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ct322NKVBVc4"
      },
      "source": [
        "#### Building intuition about the data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "cell_style": "split",
        "id": "Jo2XbAcsBVc4"
      },
      "source": [
        "<img src=\"https://raw.githubusercontent.com/wi3jmu/PDS1920/master/Lecture/images/02/intuitive.png\" style=\"width:80%\" />"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "cell_style": "split",
        "id": "2CJAprW6BVc4"
      },
      "source": [
        "+ Is *336* a Typo?\n",
        "+ Do we misinterpret the feature (*age in years*)?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Zm7WOh0rBVc4"
      },
      "source": [
        "## Introducing Pandas\n",
        "\n",
        "The *pandas* package is the most important tool at the disposal of Data Scientists and Analysts working in Python today. The powerful machine learning and glamorous visualization tools may get all the attention, but pandas is the backbone of most data projects. \n",
        "\n",
        ">\\[*pandas*\\] is derived from the term \"**pan**el **da**ta\", an econometrics term for data sets that include observations over multiple time periods for the same individuals. — [Wikipedia](https://en.wikipedia.org/wiki/Pandas_%28software%29)\n",
        "\n",
        "We will cover the essential bits of information about pandas, including how to install it, its uses, and how it works with other common Python data analysis packages such as **matplotlib**."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
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      "source": [
        "### Typical Use Cases\n",
        "\n",
        "Pandas has so many uses that it might make sense to list the things it can't do instead of what it can do. \n",
        "\n",
        "For example, say you want to explore a dataset stored in a CSV on your computer. Pandas will extract the data from that CSV into a DataFrame — a table, basically — then let you do things like:\n",
        "\n",
        "- Calculate statistics and answer questions about the data, like\n",
        "    - What's the average, median, max, or min of each column? \n",
        "    - Does column A correlate with column B?\n",
        "    - What does the distribution of data in column C look like?\n",
        "- Clean the data by doing things like removing missing values and filtering rows or columns by some criteria\n",
        "- Visualize the data with help from Matplotlib. Plot bars, lines, histograms, bubbles, and more. \n",
        "- Store the cleaned, transformed data back into a CSV, other file or database\n",
        "\n",
        "Before you jump into predictive or prescriptive modeling you need to have a good understanding of the nature of your dataset and pandas is a great avenue through which to do that.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
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      "source": [
        "### Pandas within the data science toolkit\n",
        "\n",
        "Not only is the pandas library a central component of the data science toolkit but it is used in conjunction with other libraries in that collection. \n",
        "\n",
        "Pandas is built on top of the **NumPy** package, meaning a lot of the structure of NumPy is used or replicated in Pandas. Data in pandas is often used to feed statistical analysis in **SciPy**, plotting functions from **Matplotlib**, and machine learning algorithms in **Scikit-learn**.\n",
        "\n",
        "Jupyter Notebooks offer a good environment for using pandas to do data exploration and modeling, but pandas can also be used in text editors just as easily.\n",
        "\n",
        "Jupyter Notebooks give us the ability to execute code in a particular cell as opposed to running the entire file. This saves a lot of time when working with large datasets and complex transformations. Notebooks also provide an easy way to visualize pandas’ DataFrames and plots.\n",
        "\n"
      ]
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    {
      "cell_type": "markdown",
      "metadata": {
        "cell_style": "center",
        "id": "sX6SGYNmBVc5"
      },
      "source": [
        "### Installation and Import\n",
        "Pandas is an easy package to install. Open up your terminal program (for Mac users) or command line (for PC users) and install it using either of the following commands:\n",
        "\n",
        "`conda install pandas`\n",
        "\n",
        "OR \n",
        "\n",
        "`pip install pandas`\n",
        "\n",
        "Google Colab has pandas pre-installed.\n",
        "\n",
        "To import pandas we usually import it with a shorter name since it's used so much:"
      ]
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    {
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      "outputs": [],
      "source": [
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
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      "source": [
        "## Series and DataFrames\n",
        "\n",
        "The primary two components of pandas are the `Series` and `DataFrame`. \n",
        "\n",
        "A `Series` is essentially a column vector, and a `DataFrame` is a multi-dimensional table made up of a collection of Series. \n",
        "\n",
        "<img src=\"https://raw.githubusercontent.com/wi3jmu/PDS1920/master/Lecture/images/02/series-and-dataframe.png\" style=\"width:60%\" />\n",
        "\n",
        "DataFrames and Series are quite similar in that many operations that you can do with one you can do with the other, such as filling in null values and calculating the mean."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
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      "source": [
        "### Creating DataFrames from scratch\n",
        "\n",
        "Creating DataFrames right in Python is good to know and quite useful when testing new methods and functions you find in the pandas docs. There are *many* ways to create a DataFrame from scratch, but a great option is to just use a simple `dict`. \n",
        "\n",
        "Let's say we are working on a fulfillment project. We want to have a row for each customer and one colum for order and delivery amount each. To organize this as a dictionary for pandas we could do something like:"
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              "   ordered  delivered\n",
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        "data = {\n",
        "    'ordered': [3, 2, 7, 3], \n",
        "    'delivered': [0, 2, 4, 2],\n",
        "}\n",
        "purchases = pd.DataFrame(data)\n",
        "purchases"
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        "### Indexing DataFrames\n",
        "\n",
        "Each *(key, value)* item in `data` corresponds to a *column* in the resulting DataFrame. The **Index** of this DataFrame was given to us on creation as the numbers 0-3, but we could also create our own when we initialize the DataFrame. \n",
        "\n",
        "Let's have customer names as our index: "
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d3d61c68-68d7-4008-8a48-2b762a77d9c9 button.colab-df-convert');\n",
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              "\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "purchases = pd.DataFrame(data, index=['June', 'Robert', 'Lily', 'David'])\n",
        "purchases"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k4t1RRI3BVc6"
      },
      "source": [
        "So now we could **loc**ate a customer's order status by using their name:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "OLKvXizFBVc6",
        "outputId": "e633a0ea-fbfa-4dc5-e2a6-8a024031f4bb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ordered      3\n",
              "delivered    0\n",
              "Name: June, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "purchases.loc['June']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G9AA8bHRLXGp",
        "outputId": "5f0db015-3fe3-4400-e3a9-d32cecd7ecd8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ordered      2\n",
              "delivered    2\n",
              "Name: Robert, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "purchases.iloc[1]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tKTASoKDBVc6"
      },
      "source": [
        "### Reading and writing CSVs\n",
        "It’s quite simple to load data from various file formats into a DataFrame. With CSV files all you need is a single line to load in the data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  customer  ordered  delivered\n",
              "0     June        3          0\n",
              "1   Robert        2          2\n",
              "2     Lily        7          4\n",
              "3    David        3          2"
            ],
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              "      <td>Lily</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "df = pd.read_csv('https://raw.githubusercontent.com/wi3jmu/PDS1920/master/Lecture/data/purchases.csv')\n",
        "df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MvaVD9vpBVc7"
      },
      "source": [
        "After extensive data preparation you likely want to save it as a file of your choice. Similar to the ways we read in data, pandas provides intuitive commands to save it:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "npCUVnFkBVc7"
      },
      "outputs": [],
      "source": [
        "df.to_csv('new_purchases.csv')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xeJZ4a22BVc7"
      },
      "source": [
        "## Basic DataFrame operations: Viewing\n",
        "\n",
        "DataFrames possess hundreds of methods and other operations that are crucial to any analysis. As a beginner, you should know the operations that perform simple transformations of your data and those that provide fundamental statistical analysis.\n",
        "\n",
        "Let's load in the IMDB movies dataset to begin. We're loading this dataset from a CSV and designating the movie titles to be our index."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "V2pJ2dzqBVc7"
      },
      "outputs": [],
      "source": [
        "movies_df = pd.read_csv(\"https://raw.githubusercontent.com/wi3jmu/PDS1920/master/Lecture/data/IMDB-Movie-Data.csv\", index_col=\"Title\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AQWErcCqBVc7"
      },
      "source": [
        "### Viewing your data\n",
        "\n",
        "The first thing to do when opening a new dataset is print out a few rows to keep as a visual reference.\n",
        "\n",
        "Typically when we load in a dataset, we like to view the first five or so rows to see what's under the hood. Here we can see the names of each column, the index, and examples of values in each row.\n",
        "\n",
        "`.head()` outputs the **first** five rows of your DataFrame by default, but we could also pass a number as well: `movies_df.head(10)` would output the top ten rows, for example. To see the **last** five rows use `.tail()`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "3OKo5Pn1BVc7",
        "outputId": "610599f8-aedb-4211-ba0e-c86dbc443ae2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                         Rank                     Genre  \\\n",
              "Title                                                     \n",
              "Guardians of the Galaxy     1   Action,Adventure,Sci-Fi   \n",
              "Prometheus                  2  Adventure,Mystery,Sci-Fi   \n",
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              "                                                               Description  \\\n",
              "Title                                                                        \n",
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              "\n",
              "                             Director  \\\n",
              "Title                                   \n",
              "Guardians of the Galaxy    James Gunn   \n",
              "Prometheus               Ridley Scott   \n",
              "\n",
              "                                                                    Actors  \\\n",
              "Title                                                                        \n",
              "Guardians of the Galaxy  Chris Pratt, Vin Diesel, Bradley Cooper, Zoe S...   \n",
              "Prometheus               Noomi Rapace, Logan Marshall-Green, Michael Fa...   \n",
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              "                         Year  Runtime (Minutes)  Rating   Votes  \\\n",
              "Title                                                              \n",
              "Guardians of the Galaxy  2014                121     8.1  757074   \n",
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              "\n",
              "                         Revenue (Millions)  Metascore  \n",
              "Title                                                   \n",
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              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "movies_df.head(2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5zd7IlMhBVc7"
      },
      "source": [
        "### Getting info about your data `.info()`\n",
        "\n",
        "`.info()` should be one of the very first commands you run after loading your data. `.info()` provides the essential details about your dataset, such as the number of rows and columns, the number of non-null values, what type of data is in each column, and how much memory your DataFrame is using. \n",
        "\n",
        "Notice in our movies dataset we have some obvious missing values in the `Revenue` and `Metascore` columns. We'll look at how to handle those in a bit."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "CxylqVQGBVc7",
        "outputId": "54ee55d8-2b6a-4c90-9abb-b96e0cac802a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 1000 entries, Guardians of the Galaxy to Nine Lives\n",
            "Data columns (total 11 columns):\n",
            " #   Column              Non-Null Count  Dtype  \n",
            "---  ------              --------------  -----  \n",
            " 0   Rank                1000 non-null   int64  \n",
            " 1   Genre               1000 non-null   object \n",
            " 2   Description         1000 non-null   object \n",
            " 3   Director            1000 non-null   object \n",
            " 4   Actors              1000 non-null   object \n",
            " 5   Year                1000 non-null   int64  \n",
            " 6   Runtime (Minutes)   1000 non-null   int64  \n",
            " 7   Rating              1000 non-null   float64\n",
            " 8   Votes               1000 non-null   int64  \n",
            " 9   Revenue (Millions)  872 non-null    float64\n",
            " 10  Metascore           936 non-null    float64\n",
            "dtypes: float64(3), int64(4), object(4)\n",
            "memory usage: 93.8+ KB\n"
          ]
        }
      ],
      "source": [
        "movies_df.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iZ5W3InRBVc7"
      },
      "source": [
        "### Getting info about your data: `.shape`\n",
        "\n",
        "Another fast and useful attribute is `.shape`, which outputs just a tuple of (rows, columns)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "SmgLAgU0BVc7",
        "outputId": "b41dc3ca-1732-4e28-a654-0e79778ddac1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1000, 11)"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "movies_df.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oDbdiMyaBVc7"
      },
      "source": [
        "Note that `.shape` has no parentheses and is a simple tuple of format (rows, columns). So we have **1000 rows** and **11 columns** in our movies DataFrame.\n",
        "\n",
        "The `.shape` command is used a lot when cleaning and transforming data. For example, you might filter some rows based on some criteria and then want to know quickly how many rows were removed."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iBn8En5OBVc7"
      },
      "source": [
        "### Describing your variables\n",
        "Using `describe()` on an entire DataFrame we can get a summary of the distribution of continuous variables. Understanding which numbers are continuous also comes in handy when thinking about the type of plot to use to represent your data visually. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "Hw0e54IeBVdG",
        "outputId": "cf6e0692-90f9-4105-b48c-35fc1cf86e49",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              Rank         Year  Runtime (Minutes)       Rating         Votes  \\\n",
              "count  1000.000000  1000.000000        1000.000000  1000.000000  1.000000e+03   \n",
              "mean    500.500000  2012.783000         113.172000     6.723200  1.698083e+05   \n",
              "std     288.819436     3.205962          18.810908     0.945429  1.887626e+05   \n",
              "min       1.000000  2006.000000          66.000000     1.900000  6.100000e+01   \n",
              "25%     250.750000  2010.000000         100.000000     6.200000  3.630900e+04   \n",
              "50%     500.500000  2014.000000         111.000000     6.800000  1.107990e+05   \n",
              "75%     750.250000  2016.000000         123.000000     7.400000  2.399098e+05   \n",
              "max    1000.000000  2016.000000         191.000000     9.000000  1.791916e+06   \n",
              "\n",
              "       Revenue (Millions)   Metascore  \n",
              "count          872.000000  936.000000  \n",
              "mean            82.956376   58.985043  \n",
              "std            103.253540   17.194757  \n",
              "min              0.000000   11.000000  \n",
              "25%             13.270000   47.000000  \n",
              "50%             47.985000   59.500000  \n",
              "75%            113.715000   72.000000  \n",
              "max            936.630000  100.000000  "
            ],
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              "      <th>Rank</th>\n",
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              "      <th>25%</th>\n",
              "      <td>250.750000</td>\n",
              "      <td>2010.000000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>6.200000</td>\n",
              "      <td>3.630900e+04</td>\n",
              "      <td>13.270000</td>\n",
              "      <td>47.000000</td>\n",
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              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>500.500000</td>\n",
              "      <td>2014.000000</td>\n",
              "      <td>111.000000</td>\n",
              "      <td>6.800000</td>\n",
              "      <td>1.107990e+05</td>\n",
              "      <td>47.985000</td>\n",
              "      <td>59.500000</td>\n",
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              "      <th>75%</th>\n",
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              "      <td>2016.000000</td>\n",
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              "      <td>7.400000</td>\n",
              "      <td>2.399098e+05</td>\n",
              "      <td>113.715000</td>\n",
              "      <td>72.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1000.000000</td>\n",
              "      <td>2016.000000</td>\n",
              "      <td>191.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>1.791916e+06</td>\n",
              "      <td>936.630000</td>\n",
              "      <td>100.000000</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "movies_df.describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DeMJEsPDBVdG"
      },
      "source": [
        "### Describing Categorical Variables\n",
        "`.describe()` can also be used on a categorical variable to get the count of rows, unique count of categories, top category, and freq of top category:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "VXvwf-zXBVdG",
        "outputId": "df47c075-00d9-4502-9e3d-8090d3bf096a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count                        1000\n",
              "unique                        207\n",
              "top       Action,Adventure,Sci-Fi\n",
              "freq                           50\n",
              "Name: Genre, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "movies_df['Genre'].describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LTElz3AdBVdG"
      },
      "source": [
        "`.value_counts()` can tell us the frequency of all values in a column:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "U0-SnE-VBVdG",
        "outputId": "58c4c1e3-0eda-470f-ce20-488e3a232987",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Action,Adventure,Sci-Fi       50\n",
              "Drama                         48\n",
              "Comedy,Drama,Romance          35\n",
              "Comedy                        32\n",
              "Drama,Romance                 31\n",
              "Animation,Adventure,Comedy    27\n",
              "Action,Adventure,Fantasy      27\n",
              "Comedy,Drama                  27\n",
              "Comedy,Romance                26\n",
              "Crime,Drama,Thriller          24\n",
              "Name: Genre, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "movies_df['Genre'].value_counts().head(10)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yM04PGl-BVdG"
      },
      "source": [
        "## Basic DataFrame operations: Data Cleaning\n",
        "\n",
        "### Handling duplicates (1)\n",
        "Duplicate management is key in most settings involving real data. It is a central data intgegration challenge and we want to be able to perform some basic activities using pandas. This dataset does not have duplicate rows, but it is always important to verify you aren't aggregating duplicate rows. \n",
        "\n",
        "To demonstrate, let's simply just double up our movies DataFrame by appending it to itself. Using `append()` will return a copy without affecting the original DataFrame. We are capturing this copy in `temp` so we aren't working with the real data:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "jDXPa_BXBVdH",
        "outputId": "cb3d93cd-99a5-4fca-cbc5-ebae0c320d9b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2000, 11)"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "temp_df = pd.concat([movies_df,movies_df])\n",
        "temp_df.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OTtmoBh2BVdH"
      },
      "source": [
        "Now we can try dropping duplicates. Just like `append()`, the `drop_duplicates()` method will also return a copy of your DataFrame, but this time with duplicates removed. Calling `.shape` confirms we're back to the 1000 rows of our original dataset."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "O5kqfL2iBVdH",
        "outputId": "54bfd5c7-4877-4a80-9317-610894db9d80",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1000, 11)"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "temp_df = temp_df.drop_duplicates()\n",
        "temp_df.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q4hw0GBsBVdH"
      },
      "source": [
        "### Handling duplicates (2)\n",
        "\n",
        "Another important argument for `drop_duplicates()` is `keep`, which has three possible options:\n",
        "* `first`: (default) Drop duplicates except for the first occurrence.\n",
        "* `last`: Drop duplicates except for the last occurrence.\n",
        "* `False`: Drop all duplicates.\n",
        "\n",
        "Since we didn't define the `keep` arugment in the previous example it was defaulted to `first`. This means that if two rows are the same pandas will drop the second row and keep the first row. Watch what happens to `temp_df`:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "hF-uG1INBVdH",
        "outputId": "4f6828be-c5ba-4215-f36e-f4cad6b501cf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0, 11)"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "temp_df = pd.concat([movies_df,movies_df])  # make a new copy\n",
        "temp_df = temp_df.drop_duplicates(keep=False)\n",
        "temp_df.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EE8Xy-r7BVdI"
      },
      "source": [
        "Since all rows were duplicates, `keep=False` dropped them all resulting in zero rows being left over. If you're wondering why you would want to do this, one reason is that it allows you to locate all duplicates in your dataset. When conditional selections are shown below you'll see how to do that."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PyYbYH1fBVdI"
      },
      "source": [
        "### Inplace Operations\n",
        "\n",
        "It's a little verbose to keep assigning DataFrames to the same variable like in this example. For this reason, pandas has the `inplace` keyword argument on many of its methods. Using `inplace=True` will modify the DataFrame object in place:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "r5_SkXA6BVdI",
        "outputId": "962c5729-85c6-450d-f745-a01431a9009e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0, 11)"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "temp_df = pd.concat([movies_df,movies_df])  # make a new copy\n",
        "\n",
        "temp_df.drop_duplicates(inplace=True, keep=False)\n",
        "\n",
        "temp_df.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qkEQy5j6BVdI"
      },
      "source": [
        "### Column cleanup\n",
        "\n",
        "Many times datasets will have verbose column names with symbols, upper and lowercase words, spaces, and typos. To make selecting data by column name easier we can spend a little time cleaning up their names. Here's how to print the column names of our dataset:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "0grwPiJlBVdI",
        "outputId": "90eb8ec9-0a7f-4ba8-dec0-45f2560dc158",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Rank', 'Genre', 'Description', 'Director', 'Actors', 'Year',\n",
              "       'Runtime (Minutes)', 'Rating', 'Votes', 'Revenue (Millions)',\n",
              "       'Metascore'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "movies_df.columns"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DInvpA6IBVdI"
      },
      "source": [
        "Not only does `.columns` come in handy if you want to rename columns by allowing for simple copy and paste, it's also useful if you need to understand why you are receiving a `Key Error` when selecting data by column."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qKGVADTsBVdI"
      },
      "source": [
        "### Renaming Columns\n",
        "\n",
        "We can use the `.rename()` method to rename certain or all columns via a `dict`. We don't want parentheses, so let's rename those:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "ro77dBhiBVdI",
        "outputId": "496ebee4-afcc-41e6-f244-e3c07d5a5ceb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Rank', 'Genre', 'Description', 'Director', 'Actors', 'Year', 'Runtime',\n",
              "       'Rating', 'Votes', 'Revenue_millions', 'Metascore'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "movies_df.rename(columns={\n",
        "        'Runtime (Minutes)': 'Runtime', \n",
        "        'Revenue (Millions)': 'Revenue_millions'\n",
        "    }, inplace=True)\n",
        "movies_df.columns"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ywaG73bCBVdI"
      },
      "source": [
        "Excellent. But what if we want to lowercase all names? Instead of using `.rename()` we could also set a list of names to the columns like so:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "v9WgY9rgBVdI",
        "outputId": "89d56183-da5a-43dc-d889-c984a93445d9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['rank', 'genre', 'description', 'director', 'actors', 'year', 'runtime',\n",
              "       'rating', 'votes', 'revenue_millions', 'metascore'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "movies_df.columns = ['rank', 'genre', 'description', 'director', 'actors', 'year', 'runtime', \n",
        "                     'rating', 'votes', 'revenue_millions', 'metascore']\n",
        "movies_df.columns"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UHoNMz16BVdI"
      },
      "source": [
        "### Operating on many columns\n",
        "With thousands of columns the previous approach is too much work. Instead of just renaming each column manually we can do a list comprehension:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "AkL-vDkgBVdI",
        "outputId": "d7b0f4f8-fa6c-4b65-d5f6-9a48372f915f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['rank', 'genre', 'description', 'director', 'actors', 'year', 'runtime',\n",
              "       'rating', 'votes', 'revenue_millions', 'metascore'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "movies_df.columns = [col.lower() for col in movies_df]\n",
        "\n",
        "movies_df.columns"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZDzeDXLUBVdI"
      },
      "source": [
        "`list` (and `dict`) comprehensions come in handy a lot when working with pandas and data in general.\n",
        "\n",
        "It's a good idea to lowercase, remove special characters, and replace spaces with underscores if you'll be working with a dataset for some time."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4OOEfRBOBVdI"
      },
      "source": [
        "### Working with missing values\n",
        "\n",
        "When exploring data, you’ll most likely encounter missing or null values, which are essentially placeholders for non-existent values. Most commonly you'll see Python's `None` or NumPy's `np.nan`, each of which are handled differently in some situations.\n",
        "\n",
        "There are two options in dealing with nulls: \n",
        "\n",
        "1. Get rid of rows or columns with nulls\n",
        "2. Replace nulls with non-null values, a technique known as **imputation**\n",
        "\n",
        "Let's calculate to total number of nulls in each column of our dataset. The first step is to check which cells in our DataFrame are null:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9CtlqI2HBVdJ"
      },
      "source": [
        "### Identify missing values\n",
        "`isnull()` returns a DataFrame where each cell is either True or False depending on that cell's null status.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "aJl213_oBVdJ",
        "outputId": "5f37d4c2-b2e6-4192-fe07-a480dd45cab4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                          rank  genre  description  director  actors   year  \\\n",
              "Title                                                                         \n",
              "Guardians of the Galaxy  False  False        False     False   False  False   \n",
              "Prometheus               False  False        False     False   False  False   \n",
              "Split                    False  False        False     False   False  False   \n",
              "Sing                     False  False        False     False   False  False   \n",
              "Suicide Squad            False  False        False     False   False  False   \n",
              "\n",
              "                         runtime  rating  votes  revenue_millions  metascore  \n",
              "Title                                                                         \n",
              "Guardians of the Galaxy    False   False  False             False      False  \n",
              "Prometheus                 False   False  False             False      False  \n",
              "Split                      False   False  False             False      False  \n",
              "Sing                       False   False  False             False      False  \n",
              "Suicide Squad              False   False  False             False      False  "
            ],
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          },
          "metadata": {},
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        }
      ],
      "source": [
        "movies_df.isnull().head(5)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "putZsOMEBVdJ"
      },
      "source": [
        "`.isnull()` just by iteself isn't very useful, and is usually used in conjunction with other methods, like `sum()`. To count the number of nulls in each column we use an aggregate function for summing: "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "HUm8IysUBVdJ",
        "outputId": "8215a90c-7524-491e-8a50-bd09d7bdac95",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "rank                  0\n",
              "genre                 0\n",
              "description           0\n",
              "director              0\n",
              "actors                0\n",
              "year                  0\n",
              "runtime               0\n",
              "rating                0\n",
              "votes                 0\n",
              "revenue_millions    128\n",
              "metascore            64\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "movies_df.isnull().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1CHIZM66BVdJ"
      },
      "source": [
        "### Removing null values\n",
        "\n",
        "Data Scientists and Analysts regularly face the dilemma of dropping or imputing null values, and is a decision that requires intimate knowledge of your data and its context. Overall, removing null data is only suggested if you have a small amount of missing data.\n",
        "\n",
        "Remove nulls is pretty simple:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "3_K1iPRNBVdJ",
        "outputId": "fb863940-345d-4b3a-96cd-de8fa13ec1ba",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(838, 11)"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "df_clean = movies_df.dropna()\n",
        "df_clean.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5hYg1rFeBVdJ"
      },
      "source": [
        "This operation deletes any **row** with at least a single null value. In our case, it removes 128 rows where `revenue_millions` is null and 64 rows where `metascore` is null. This obviously seems like a waste since there's perfectly good data in the other columns of those dropped rows. That's why we'll look at imputation next."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PQP44_teBVdJ"
      },
      "source": [
        "### Imputation\n",
        "\n",
        "Imputation is a conventional feature engineering technique used to keep valuable data that have null values. There may be instances where dropping every row with a null value removes too big a chunk from your dataset, so instead we can impute that null with another value, usually the **mean** or the **median** of that column. \n",
        "\n",
        "Let's look at imputing the missing values in the `revenue_millions` column:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "qB_qRv0uBVdJ"
      },
      "outputs": [],
      "source": [
        "revenue = movies_df['revenue_millions']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wggtAq-JBVdJ"
      },
      "source": [
        "Using square brackets is the general way we select columns in a DataFrame. `revenue` is a Series and we can calculate its mean and fill the nulls using `fillna()`:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "I4c-HTLaBVdJ",
        "outputId": "fb19e533-3647-4d28-e7eb-3b77a93094cf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "82.95637614678898\n"
          ]
        }
      ],
      "source": [
        "revenue_mean = revenue.mean()\n",
        "print(revenue_mean)\n",
        "revenue.fillna(revenue_mean, inplace=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wj1Q2Zc7BVdJ"
      },
      "source": [
        "We have now replaced all nulls in `revenue` with the mean of the column:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "ue6blwouBVdJ",
        "outputId": "f37baccc-7e67-48df-bb32-726200fcc9be",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "rank                 0\n",
              "genre                0\n",
              "description          0\n",
              "director             0\n",
              "actors               0\n",
              "year                 0\n",
              "runtime              0\n",
              "rating               0\n",
              "votes                0\n",
              "revenue_millions     0\n",
              "metascore           64\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ],
      "source": [
        "movies_df.isnull().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_0WYG_8WBVdJ"
      },
      "source": [
        "Imputing an entire column with the same value like this is a basic example. It would be a better idea to try a more granular imputation by Genre or Director. For example, you would find the mean of the revenue generated in each genre individually and impute the nulls in each genre with that genre's mean.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i905CuN3BVdJ"
      },
      "source": [
        "## Basic DataFrame operations: Slicing, selecting, extracting\n",
        "\n",
        "Up until now we've focused on some basic summaries of our data. We've learned about simple column extraction using single brackets, and we imputed null values in a column using `fillna()`. Below are the other methods of slicing, selecting, and extracting you'll need to use constantly.\n",
        "\n",
        "It's important to note that, although many methods are the same, DataFrames and Series have different attributes, so you'll need be sure to know which type you are working with or else you will receive attribute errors. \n",
        "\n",
        "Let's look at working with columns first."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "asC39SNFBVdJ"
      },
      "source": [
        "### By column\n",
        "\n",
        "You already saw how to extract a column using square brackets like this:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "4j1W3PrKBVdK",
        "outputId": "0c14dbb7-1c7c-47d5-cd2a-039d63236b2b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.series.Series"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ],
      "source": [
        "genre_col = movies_df['genre']\n",
        "type(genre_col)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vbc8lQi6BVdK"
      },
      "source": [
        "This will return a *Series*. To extract a column as a *DataFrame*, you need to pass a list of column names:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "CGeFpyRPBVdK",
        "outputId": "0df8a1d3-6013-44cf-a529-f0f1785eb68e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.frame.DataFrame"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ],
      "source": [
        "genre_col = movies_df[['genre']]\n",
        "type(genre_col)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zqtPU02vBVdK"
      },
      "source": [
        "Since it's just a list, adding another column name is easy:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "I1a-dk6QBVdK",
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        "colab": {
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        {
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          "data": {
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              "                                            genre  rating\n",
              "Title                                                    \n",
              "Guardians of the Galaxy   Action,Adventure,Sci-Fi     8.1\n",
              "Prometheus               Adventure,Mystery,Sci-Fi     7.0\n",
              "Split                             Horror,Thriller     7.3\n",
              "Sing                      Animation,Comedy,Family     7.2\n",
              "Suicide Squad            Action,Adventure,Fantasy     6.2"
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          },
          "metadata": {},
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      ],
      "source": [
        "subset = movies_df[['genre', 'rating']]\n",
        "subset.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "roNU2WhgBVdK"
      },
      "source": [
        "### By row\n",
        "For rows, we have two options: \n",
        "- `.loc` - **loc**ates by name\n",
        "- `.iloc`- **loc**ates by numerical **i**ndex"
      ]
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      "execution_count": 33,
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "rank                                                                2\n",
              "genre                                        Adventure,Mystery,Sci-Fi\n",
              "description         Following clues to the origin of mankind, a te...\n",
              "director                                                 Ridley Scott\n",
              "actors              Noomi Rapace, Logan Marshall-Green, Michael Fa...\n",
              "year                                                             2012\n",
              "runtime                                                           124\n",
              "rating                                                            7.0\n",
              "votes                                                          485820\n",
              "revenue_millions                                               126.46\n",
              "metascore                                                        65.0\n",
              "Name: Prometheus, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ],
      "source": [
        "prom = movies_df.loc[\"Prometheus\"]\n",
        "prom"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OvzlCq0DBVdK"
      },
      "source": [
        "With `iloc` we give it the numerical index of Prometheus:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "fEgS_yisBVdK",
        "outputId": "d5f5cc54-f259-4962-91c7-c263372849e5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "rank                                                                2\n",
              "genre                                        Adventure,Mystery,Sci-Fi\n",
              "description         Following clues to the origin of mankind, a te...\n",
              "director                                                 Ridley Scott\n",
              "actors              Noomi Rapace, Logan Marshall-Green, Michael Fa...\n",
              "year                                                             2012\n",
              "runtime                                                           124\n",
              "rating                                                            7.0\n",
              "votes                                                          485820\n",
              "revenue_millions                                               126.46\n",
              "metascore                                                        65.0\n",
              "Name: Prometheus, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ],
      "source": [
        "prom = movies_df.iloc[1]\n",
        "prom"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YAVkTJiNBVdK"
      },
      "source": [
        "### Conditional selections\n",
        "We’ve gone over how to select columns and rows, but what if we want to make a conditional selection? \n",
        "\n",
        "For example, what if we want to filter our movies DataFrame to show only films directed by Ridley Scott or films with a rating greater than or equal to 8.0?\n",
        "\n",
        "To do that, we take a column from the DataFrame and apply a Boolean condition to it. Here's an example of a Boolean condition:"
      ]
    },
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      "execution_count": 35,
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            "text/plain": [
              "                        rank                     genre  \\\n",
              "Title                                                    \n",
              "Prometheus                 2  Adventure,Mystery,Sci-Fi   \n",
              "The Martian              103    Adventure,Drama,Sci-Fi   \n",
              "Robin Hood               388    Action,Adventure,Drama   \n",
              "American Gangster        471     Biography,Crime,Drama   \n",
              "Exodus: Gods and Kings   517    Action,Adventure,Drama   \n",
              "\n",
              "                                                              description  \\\n",
              "Title                                                                       \n",
              "Prometheus              Following clues to the origin of mankind, a te...   \n",
              "The Martian             An astronaut becomes stranded on Mars after hi...   \n",
              "Robin Hood              In 12th century England, Robin and his band of...   \n",
              "American Gangster       In 1970s America, a detective works to bring d...   \n",
              "Exodus: Gods and Kings  The defiant leader Moses rises up against the ...   \n",
              "\n",
              "                            director  \\\n",
              "Title                                  \n",
              "Prometheus              Ridley Scott   \n",
              "The Martian             Ridley Scott   \n",
              "Robin Hood              Ridley Scott   \n",
              "American Gangster       Ridley Scott   \n",
              "Exodus: Gods and Kings  Ridley Scott   \n",
              "\n",
              "                                                                   actors  \\\n",
              "Title                                                                       \n",
              "Prometheus              Noomi Rapace, Logan Marshall-Green, Michael Fa...   \n",
              "The Martian             Matt Damon, Jessica Chastain, Kristen Wiig, Ka...   \n",
              "Robin Hood              Russell Crowe, Cate Blanchett, Matthew Macfady...   \n",
              "American Gangster       Denzel Washington, Russell Crowe, Chiwetel Eji...   \n",
              "Exodus: Gods and Kings  Christian Bale, Joel Edgerton, Ben Kingsley, S...   \n",
              "\n",
              "                        year  runtime  rating   votes  revenue_millions  \\\n",
              "Title                                                                     \n",
              "Prometheus              2012      124     7.0  485820            126.46   \n",
              "The Martian             2015      144     8.0  556097            228.43   \n",
              "Robin Hood              2010      140     6.7  221117            105.22   \n",
              "American Gangster       2007      157     7.8  337835            130.13   \n",
              "Exodus: Gods and Kings  2014      150     6.0  137299             65.01   \n",
              "\n",
              "                        metascore  \n",
              "Title                              \n",
              "Prometheus                   65.0  \n",
              "The Martian                  80.0  \n",
              "Robin Hood                   53.0  \n",
              "American Gangster            76.0  \n",
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              "                                                     [key], {});\n",
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              "\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ],
      "source": [
        "movies_df[movies_df['director'] == \"Ridley Scott\"].head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HxgOdHWDBVdK"
      },
      "source": [
        "### Combining conditions\n",
        "We can make some richer conditionals by using logical operators `|` for \"or\" and `&` for \"and\".\n",
        "\n",
        "Let's filter the the DataFrame to show only movies by Christopher Nolan OR Ridley Scott:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
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        "id": "Ibg8BIreBVdK",
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        "colab": {
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      "outputs": [
        {
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          "data": {
            "text/plain": [
              "                 rank                     genre  \\\n",
              "Title                                             \n",
              "Prometheus          2  Adventure,Mystery,Sci-Fi   \n",
              "Interstellar       37    Adventure,Drama,Sci-Fi   \n",
              "The Dark Knight    55        Action,Crime,Drama   \n",
              "The Prestige       65      Drama,Mystery,Sci-Fi   \n",
              "Inception          81   Action,Adventure,Sci-Fi   \n",
              "\n",
              "                                                       description  \\\n",
              "Title                                                                \n",
              "Prometheus       Following clues to the origin of mankind, a te...   \n",
              "Interstellar     A team of explorers travel through a wormhole ...   \n",
              "The Dark Knight  When the menace known as the Joker wreaks havo...   \n",
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              "\n",
              "                          director  \\\n",
              "Title                                \n",
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              "The Prestige     Christopher Nolan   \n",
              "Inception        Christopher Nolan   \n",
              "\n",
              "                                                            actors  year  \\\n",
              "Title                                                                      \n",
              "Prometheus       Noomi Rapace, Logan Marshall-Green, Michael Fa...  2012   \n",
              "Interstellar     Matthew McConaughey, Anne Hathaway, Jessica Ch...  2014   \n",
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              "The Prestige     Christian Bale, Hugh Jackman, Scarlett Johanss...  2006   \n",
              "Inception        Leonardo DiCaprio, Joseph Gordon-Levitt, Ellen...  2010   \n",
              "\n",
              "                 runtime  rating    votes  revenue_millions  metascore  \n",
              "Title                                                                   \n",
              "Prometheus           124     7.0   485820            126.46       65.0  \n",
              "Interstellar         169     8.6  1047747            187.99       74.0  \n",
              "The Dark Knight      152     9.0  1791916            533.32       82.0  \n",
              "The Prestige         130     8.5   913152             53.08       66.0  \n",
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          },
          "metadata": {},
          "execution_count": 36
        }
      ],
      "source": [
        "movies_df[(movies_df['director'] == 'Christopher Nolan') | (movies_df['director'] == 'Ridley Scott')].head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-gex3C0qBVdK"
      },
      "source": [
        "### Combining conditions (2)\n",
        "We need to make sure to group evaluations with parentheses so Python knows how to evaluate the conditional.\n",
        "\n",
        "`(movies_df['director'] == 'Christopher Nolan') | (movies_df['director'] == 'Ridley Scott')`\n",
        "\n",
        "Using the `isin()` method we could make this more concise:"
      ]
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      "execution_count": 37,
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        "id": "Dz52MCkgBVdK",
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        "colab": {
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          "height": 238
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 rank                     genre  \\\n",
              "Title                                             \n",
              "Prometheus          2  Adventure,Mystery,Sci-Fi   \n",
              "Interstellar       37    Adventure,Drama,Sci-Fi   \n",
              "The Dark Knight    55        Action,Crime,Drama   \n",
              "The Prestige       65      Drama,Mystery,Sci-Fi   \n",
              "Inception          81   Action,Adventure,Sci-Fi   \n",
              "\n",
              "                                                       description  \\\n",
              "Title                                                                \n",
              "Prometheus       Following clues to the origin of mankind, a te...   \n",
              "Interstellar     A team of explorers travel through a wormhole ...   \n",
              "The Dark Knight  When the menace known as the Joker wreaks havo...   \n",
              "The Prestige     Two stage magicians engage in competitive one-...   \n",
              "Inception        A thief, who steals corporate secrets through ...   \n",
              "\n",
              "                          director  \\\n",
              "Title                                \n",
              "Prometheus            Ridley Scott   \n",
              "Interstellar     Christopher Nolan   \n",
              "The Dark Knight  Christopher Nolan   \n",
              "The Prestige     Christopher Nolan   \n",
              "Inception        Christopher Nolan   \n",
              "\n",
              "                                                            actors  year  \\\n",
              "Title                                                                      \n",
              "Prometheus       Noomi Rapace, Logan Marshall-Green, Michael Fa...  2012   \n",
              "Interstellar     Matthew McConaughey, Anne Hathaway, Jessica Ch...  2014   \n",
              "The Dark Knight  Christian Bale, Heath Ledger, Aaron Eckhart,Mi...  2008   \n",
              "The Prestige     Christian Bale, Hugh Jackman, Scarlett Johanss...  2006   \n",
              "Inception        Leonardo DiCaprio, Joseph Gordon-Levitt, Ellen...  2010   \n",
              "\n",
              "                 runtime  rating    votes  revenue_millions  metascore  \n",
              "Title                                                                   \n",
              "Prometheus           124     7.0   485820            126.46       65.0  \n",
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              "    <tr>\n",
              "      <th>The Dark Knight</th>\n",
              "      <td>55</td>\n",
              "      <td>Action,Crime,Drama</td>\n",
              "      <td>When the menace known as the Joker wreaks havo...</td>\n",
              "      <td>Christopher Nolan</td>\n",
              "      <td>Christian Bale, Heath Ledger, Aaron Eckhart,Mi...</td>\n",
              "      <td>2008</td>\n",
              "      <td>152</td>\n",
              "      <td>9.0</td>\n",
              "      <td>1791916</td>\n",
              "      <td>533.32</td>\n",
              "      <td>82.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>The Prestige</th>\n",
              "      <td>65</td>\n",
              "      <td>Drama,Mystery,Sci-Fi</td>\n",
              "      <td>Two stage magicians engage in competitive one-...</td>\n",
              "      <td>Christopher Nolan</td>\n",
              "      <td>Christian Bale, Hugh Jackman, Scarlett Johanss...</td>\n",
              "      <td>2006</td>\n",
              "      <td>130</td>\n",
              "      <td>8.5</td>\n",
              "      <td>913152</td>\n",
              "      <td>53.08</td>\n",
              "      <td>66.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Inception</th>\n",
              "      <td>81</td>\n",
              "      <td>Action,Adventure,Sci-Fi</td>\n",
              "      <td>A thief, who steals corporate secrets through ...</td>\n",
              "      <td>Christopher Nolan</td>\n",
              "      <td>Leonardo DiCaprio, Joseph Gordon-Levitt, Ellen...</td>\n",
              "      <td>2010</td>\n",
              "      <td>148</td>\n",
              "      <td>8.8</td>\n",
              "      <td>1583625</td>\n",
              "      <td>292.57</td>\n",
              "      <td>74.0</td>\n",
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              "\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-07faaf18-67db-4d3c-b50f-7634a56e7bb9');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          element.appendChild(docLink);\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ],
      "source": [
        "selection = ['Christopher Nolan', 'Ridley Scott']\n",
        "movies_df[movies_df['director'].isin(selection)].head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-_GrsFaTBVdK"
      },
      "source": [
        "### Combining conditions (3)\n",
        "Let's say we want all movies that were released between 2005 and 2010, have a rating above 8.0, but made below the 25th percentile in revenue."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "id": "TdMSAlm-BVdL",
        "outputId": "82cba3e3-d168-4187-9478-df3001575929",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                     rank               genre  \\\n",
              "Title                                           \n",
              "3 Idiots              431        Comedy,Drama   \n",
              "The Lives of Others   477      Drama,Thriller   \n",
              "Incendies             714   Drama,Mystery,War   \n",
              "Taare Zameen Par      992  Drama,Family,Music   \n",
              "\n",
              "                                                           description  \\\n",
              "Title                                                                    \n",
              "3 Idiots             Two friends are searching for their long lost ...   \n",
              "The Lives of Others  In 1984 East Berlin, an agent of the secret po...   \n",
              "Incendies            Twins journey to the Middle East to discover t...   \n",
              "Taare Zameen Par     An eight-year-old boy is thought to be a lazy ...   \n",
              "\n",
              "                                             director  \\\n",
              "Title                                                   \n",
              "3 Idiots                              Rajkumar Hirani   \n",
              "The Lives of Others  Florian Henckel von Donnersmarck   \n",
              "Incendies                            Denis Villeneuve   \n",
              "Taare Zameen Par                           Aamir Khan   \n",
              "\n",
              "                                                                actors  year  \\\n",
              "Title                                                                          \n",
              "3 Idiots               Aamir Khan, Madhavan, Mona Singh, Sharman Joshi  2009   \n",
              "The Lives of Others  Ulrich Mühe, Martina Gedeck,Sebastian Koch, Ul...  2006   \n",
              "Incendies            Lubna Azabal, Mélissa Désormeaux-Poulin, Maxim...  2010   \n",
              "Taare Zameen Par     Darsheel Safary, Aamir Khan, Tanay Chheda, Sac...  2007   \n",
              "\n",
              "                     runtime  rating   votes  revenue_millions  metascore  \n",
              "Title                                                                      \n",
              "3 Idiots                 170     8.4  238789              6.52       67.0  \n",
              "The Lives of Others      137     8.5  278103             11.28       89.0  \n",
              "Incendies                131     8.2   92863              6.86       80.0  \n",
              "Taare Zameen Par         165     8.5  102697              1.20       42.0  "
            ],
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ],
      "source": [
        "movies_df[\n",
        "    ((movies_df['year'] >= 2005) & (movies_df['year'] <= 2010))\n",
        "    & (movies_df['rating'] > 8.0)\n",
        "    & (movies_df['revenue_millions'] < movies_df['revenue_millions'].quantile(0.25))\n",
        "]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_p5pU50pBVdL"
      },
      "source": [
        "## Aggregation and Grouping\n",
        "An essential piece of analysis of large data is efficient summarization: computing aggregations like ``sum()``, ``mean()``, ``median()``, ``min()``, and ``max()``, in which a single number gives insight into the nature of a potentially large dataset.\n",
        "In this section, we'll explore aggregations in Pandas, from simple operations akin to what we've seen on NumPy arrays, to more sophisticated operations based on the concept of a ``groupby``."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fnn8ztLNBVdL"
      },
      "source": [
        "The following table summarizes some other built-in Pandas aggregations:\n",
        "\n",
        "| Aggregation              | Description                     |\n",
        "|--------------------------|---------------------------------|\n",
        "| ``count()``              | Total number of items           |\n",
        "| ``first()``, ``last()``  | First and last item             |\n",
        "| ``mean()``, ``median()`` | Mean and median                 |\n",
        "| ``min()``, ``max()``     | Minimum and maximum             |\n",
        "| ``std()``, ``var()``     | Standard deviation and variance |\n",
        "| ``mad()``                | Mean absolute deviation         |\n",
        "| ``prod()``               | Product of all items            |\n",
        "| ``sum()``                | Sum of all items                |\n",
        "\n",
        "These are all methods of ``DataFrame`` and ``Series`` objects."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AmHYTYR7BVdL"
      },
      "source": [
        "Let's calculate some statistics "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jeucVIEWLXGx",
        "outputId": "f7cf0e6a-92e3-4c71-cab1-a8da161b40b4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "60.375"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ],
      "source": [
        "movies_df['revenue_millions'].median()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rkpuqurCBVdL"
      },
      "source": [
        "###### GroupBy: Split, Apply, Combine\n",
        "Simple aggregations can give you a flavor of your dataset, but often we would prefer to aggregate conditionally on some label or index: this is implemented in the so-called groupby operation. The name \"group by\" comes from a command in the SQL database language, but it is perhaps more illuminative to think of it in the terms first coined by Hadley Wickham of Rstats fame: split, apply, combine.\n",
        "\n",
        "<img src=\"https://github.com/jakevdp/PythonDataScienceHandbook/raw/master/notebooks/figures/03.08-split-apply-combine.png\" style=\"width:60%\" />\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oXWN4EYABVdL"
      },
      "source": [
        "##### Column indexing\n",
        "\n",
        "The ``GroupBy`` object supports column indexing in the same way as the ``DataFrame``, and returns a modified ``GroupBy`` object.\n",
        "For example:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "id": "QXnXgIG8BVdL",
        "outputId": "a77b338e-7c2c-4737-d36c-e11f97295d61",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x7fd528bf0c40>"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ],
      "source": [
        "movies_df.groupby('genre')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "id": "emTpFA4DBVdL",
        "outputId": "7ea9783c-7dfa-4be7-cb54-50fe32078fdb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<pandas.core.groupby.generic.SeriesGroupBy object at 0x7fd528bf05e0>"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ],
      "source": [
        "movies_df.groupby('genre')['revenue_millions']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "id": "fKz_CZsgBVdM",
        "outputId": "1e327de7-bde6-43a8-b715-bae394785837",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "genre\n",
              "Adventure,Drama,Fantasy       286.79\n",
              "Action,Adventure              262.03\n",
              "Adventure,Fantasy             258.36\n",
              "Animation,Action,Adventure    215.40\n",
              "Action,Fantasy,War            210.59\n",
              "                               ...  \n",
              "Animation,Comedy,Drama          0.29\n",
              "Horror,Mystery,Sci-Fi           0.15\n",
              "Adventure                       0.15\n",
              "Action,Biography,Crime          0.10\n",
              "Adventure,Drama                 0.06\n",
              "Name: revenue_millions, Length: 207, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ],
      "source": [
        "movies_df.groupby('genre')['revenue_millions'].median().sort_values(ascending=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xF4jYj__BVdM"
      },
      "source": [
        "## Applying functions\n",
        "\n",
        "It is possible to iterate over a DataFrame or Series as you would with a list, but doing so — especially on large datasets — is very slow.\n",
        "\n",
        "An efficient alternative is to `apply()` a function to the dataset. For example, we could use a function to convert movies with an 8.0 or greater to a string value of \"good\" and the rest to \"bad\" and use this transformed values to create a new column.\n",
        "\n",
        "First we would create a function that, when given a rating, determines if it's good or bad:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "id": "ULbdXtC5BVdM"
      },
      "outputs": [],
      "source": [
        "def rating_function(x):\n",
        "    if x >= 8.0:\n",
        "        return \"good\"\n",
        "    else:\n",
        "        return \"bad\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h2KRc7-bBVdM"
      },
      "source": [
        "Now we want to send the entire rating column through this function, which is what `apply()` does:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "id": "20j_A7zvBVdM",
        "outputId": "a9b9a110-33b4-48f0-f8d3-43aa45d24c28",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                         rank                     genre  \\\n",
              "Title                                                     \n",
              "Guardians of the Galaxy     1   Action,Adventure,Sci-Fi   \n",
              "Prometheus                  2  Adventure,Mystery,Sci-Fi   \n",
              "\n",
              "                                                               description  \\\n",
              "Title                                                                        \n",
              "Guardians of the Galaxy  A group of intergalactic criminals are forced ...   \n",
              "Prometheus               Following clues to the origin of mankind, a te...   \n",
              "\n",
              "                             director  \\\n",
              "Title                                   \n",
              "Guardians of the Galaxy    James Gunn   \n",
              "Prometheus               Ridley Scott   \n",
              "\n",
              "                                                                    actors  \\\n",
              "Title                                                                        \n",
              "Guardians of the Galaxy  Chris Pratt, Vin Diesel, Bradley Cooper, Zoe S...   \n",
              "Prometheus               Noomi Rapace, Logan Marshall-Green, Michael Fa...   \n",
              "\n",
              "                         year  runtime  rating   votes  revenue_millions  \\\n",
              "Title                                                                      \n",
              "Guardians of the Galaxy  2014      121     8.1  757074            333.13   \n",
              "Prometheus               2012      124     7.0  485820            126.46   \n",
              "\n",
              "                         metascore rating_category  \n",
              "Title                                               \n",
              "Guardians of the Galaxy       76.0            good  \n",
              "Prometheus                    65.0             bad  "
            ],
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          },
          "metadata": {},
          "execution_count": 44
        }
      ],
      "source": [
        "movies_df[\"rating_category\"] = movies_df[\"rating\"].apply(rating_function)\n",
        "movies_df.head(2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xbHYzw2hBVdM"
      },
      "source": [
        "Besides being much more concise than a loop structure, using `apply()` will also be much faster than iterating manually over rows because pandas is utilizing vectorization."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7S0O_Ng4LXGy"
      },
      "source": [
        "# Introduction into Matplolib\n",
        "Matplotlib is one of the most popular Python libraries for creating 2D plots and graphs. It's capable of producing a wide range of different plot types, and it's highly customizable.\n",
        "\n",
        "You can find the documentation here: https://matplotlib.org/stable/index.html\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "id": "yPWFyqesLXGy"
      },
      "outputs": [],
      "source": [
        "# First, we need to import the library.\n",
        "import matplotlib.pyplot as plt\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aJ0DUSZwLXGy"
      },
      "source": [
        "## Matplotlib Basics: Figures and Axes\n",
        "In matplotlib, a plot is contained within a \"Figure\" object. Within this Figure, there are one or more \"Axes\".\n",
        "\n",
        "A Figure in matplotlib can be thought of as a canvas upon which we draw our plot. Axes, on the other hand, are the actual plots that we draw within the Figure.\n",
        "\n",
        "The below line of code creates a Figure object, and then an Axes object within it. The Axes object is what we'll be interacting with most often when creating plots.\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 435
        },
        "id": "MfimlM-hLXGy",
        "outputId": "fd61ae5d-92e3-45c0-ab0b-a6a058706723"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Here's an example of creating a Figure and an Axes.\n",
        "fig, ax = plt.subplots()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w6vDgJOtLXGy"
      },
      "source": [
        "## Basic Plot\n",
        "Let's create a simple line plot. We'll use numpy to create some data first."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "QzmActAELXGy",
        "outputId": "ea7611e3-e12f-407b-8314-7d1e20b877a0"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import numpy as np\n",
        "\n",
        "# Creating data\n",
        "x = np.linspace(0, 10, 100)\n",
        "y = np.sin(x)\n",
        "\n",
        "# Creating plot\n",
        "fig, ax = plt.subplots()\n",
        "\n",
        "# Plotting data on the Axes\n",
        "ax.plot(x, y)\n",
        "\n",
        "# Always include the following line in your notebooks to make sure your plot is displayed.\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "nxfaaWR8LXGy",
        "outputId": "9b5ae7c5-0a62-44a0-941a-286c484916b5"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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PRerNFh79ahsWK1wyII7zU7TX9fyRKT0JMBnZdrSULzfJrip3IgmOlnUZDoOuV27//IhLFgFarVb+8eNurFa4YnBnxveMcvprno203tGM7xlJvdnKP3/erXY4QrTtyDrY9hmgg6nPgV6Dh11vf7joJeX2utfh6CaXvOybyzPZk1tOqJ+XfX2d1kQF+nD/xO4APP/LHspq6lWOSJwuDf6lCQdpT0HKRXDZmy6plfHLzjx+P1yMj5eehydrYy68LX+epnQU/mVnHusPFKodjhAnspib+kwNul6pd6VV3Sc2ji5ZYcGDTt/BmVtaw6tLMgB44uLeTmv70h5uGplIUoQ/BRV1vLp4v9rhiNMkCY7W+YfDjI8gKsXpL1VvtvDcwj0A3D4miZhgn1N8h7p6RAcy47x4AP7x026ZHxfao9PDqAeV9XQTn1Q7mlOb/AyYgpSFx/nObf77ypL91DZYGNI1lOkDNbQmqRXeRj2PX6SMML23+hCZx7VTHFG0TRIcd+PEHVWfbjjCwYJKIgK8+cO4bk57nfb0xwuS7fPj323NVjscIRzpdND3crhrNQREqh3NqfmHwxXvwv1blDo5TnKwoJLPNiptVx6dkuLSasVna0JKFBN6RtJgsfL0gl1qhyNOgyQ47sJcD9/fBy/2UppytrPymnpeamzH8ECakjS4g4gAE3dPUJKx5xfupbpOto0LjWg+xeMGH+B2yZMgKM6pL/Hion2YLVYm9IxkWKK6NW/OxOMX9cbLoGPZ3uMs2aPNRqWiiSQ47sLgpbRuaKhR2ji0szeXH6Cwso6kCH/7tI+7uGVUIp1CfMkpreHdVe2f/AlxxsqOwauDYf1bqlcjPydZG5VCgO1oR3YpP2xV2tA8PNn5U+/tKSkygJtHJQLwz5/3yLS4xkmC404m/J9yve0zKGi/hW65pTW805gYPDo1BS+De/1a+HgZ7MXBXl+WyfHyWpUjEh3e6peh+CDs+labu6ZOx4LZ8G5au1dd/tcvSlXgSwbE0TsuqF2f2xXumdCdQJORfXkV/LorV+1wxEm46V9eB9VpCCRPVToAL3+u3Z72xUV7qam3cF5CKJN6a68Oxem4uH8cAzoHU1ln5p2VMoojVFSeC5vmK7fHPaJqKOckbpByvfLfUFPWLk+57kAhy/cdx6jXMfuC5HZ5TlcL9vXixpEJALyyOAOryj28RNskwXE3thYO27+E/D3n/HRHi6v4arOyOPexqb3cYrFfa/R6HQ+k9QDgw3WHKamqUzki0WGtfkWZSo5PhcRxakdz9gZcCxHJSjX1tXPP+emsVivPN+7SnDEsnoQI/3N+TrXcOjoRP28Du3LKWLw7X+1wRBskwXE3sQOg18WAFZY9e85P987Kg5gtVkZ3j2BI19Bzj09FE3pGkRITSGWdmffXHFY7HNERVeTD7/OU2+Meda/FxS0ZjDDhz8rtta9BZcE5Pd2SPflsPlKCj5ee+8/v0Q4BqifU35sbRnQF4NUl+2UUR6MkwXFH4+cAOshcek7bxgsravl04xEA7hrvHtvCT0an03HPBKXi6HtrDlJZ26ByRKLDWfMqNFRDp6HQ7Xy1ozl3vS+F2IFKm4l1/z2np3p9WSYAN45MICpI2zW2TsftY5Lw8dKz9WgpK/afW/InnEMSHHcU3QeueAceSAe/s99i+f7aw9TUW+jfOZiR3cLbLz4VTesXS0K4HyVV9Xyy4Yja4YiOpKasaUGuu4/e2Oh0MPZPyu2N70Ld2fV923S4mN8PF+Nt0HNr4y4kdxcRYGJmqjKK88piGcXRIklw3FW/K88puamsbeD9NYcAuHNcN7dde9OSQa/jzsYihe+sPEhtg9TFES7iEwQ3/QDD74EeF6gdTfvpOQ3CksDL76x3b769Qln4P31QnEeM3tj8YWwS3kY9mw4XszZT2sVojSQ4nqAk64y/5ZMNRyitricxwp/JfWKcEJR6LhvcieggE7llNXyzWaobCxfqNASmPOMZozc2egPM/BIe2ApxA8/42w8WVPJL43bq28cktXNw6ooK8uHaxrphryyRHlVaIwmOO6sqgnfS4LXzzmgtTl2DhXdWHgSUMxCD3oMOxoDJaLAfSN9YnolZinEJZ7N4+EhheDcwep/Vt7676gBWK5yfEkWP6MB2Dkx9fxjXDS+DjnUHith02HmtdMSZkwTHnfmGQkOtsqhx03un/W3fpmeTW1ZDVKCJywZru8nd2bp2WBdC/bw4VFjFT9tz1A5HeDJzPcwdBgv+6NRecZpgboD9v8FprjcprKjli9+PAp43emMTF+LLZYOU4+h7qw+pG4xwIAmOO9PpYPjdyu0NbysH2lOwWKy8sVzZzXDr6ERMRoMzI1SNv8nITSOVxYyvL8uUBYDCeXb/AIUZyrW3+9Z2OSVzA/x3OHx0BWStP61v+d+6w9Q2WOjXKZjhSe7Tc+pM2Qr/LdyRS15ZjbrBCDtJcNxd38shIBrKc2Dnt6d8+G+78zhwvJJAHyPXpXZxfnwqunFkV3y89OzKKWPT4WK1wxGeav2byvXQW8BoUjcWZzIYoesI5faaV0/58Jp6Mx+sVepR3TE2yWM2MrSmT1wwwxLCaLBY+Wid1ODSCklw3J3RBOfdptxeN/eUQ8f/a/zjuy61C4E+Xs6OTlUhft5cMkDpivw/OegIZziWDlnrQG9UEhxPN+Je5XrPj1CYedKHfrnpKEWVdXQK8WVqX8/ayNAa2yjOxxuOyO5NjZAExxMMvQUMJji25aRDxwcLKlm5vwCdDq5vrN/g6WaNSADgp+05FFRIE07Rzja8pVz3ng6Bnv8hTmRP6DEZsJ60fYPFYuXdVcpGhltHJ2J0swa+Z2NSn2hignwoqKjjx22y7k8LXPJbN3fuXBISEvDx8SE1NZUNGza0+djx48ej0+lOuFx44YX2x9x0000nfH3KlCmueCva5B8B/a9Wbm/9tM2H2YZOxydHEh/m54rIVNe3UzAD40OoN1v5bOOZb6cXok2VBUpPOIDUO9WNxZVG3qdcp38M1SWtPmRVRgEHCyoJNBm5pnEbtafzMui5frgy7W+rMSbU5fQE57PPPmP27Nk8+eSTbN68mQEDBjB58mTy81tvUPb111+Tk5Njv+zYsQODwcBVV13l8LgpU6Y4PO6TTz5x9lvRtlEPwFXvw7QXWv1yTb2ZLzYpuxmuH94xRm9sZjX2jPlo3WHZMi7az6b5YK6FuMHQeaja0bhOwmiI7KXs3tz+RasPsVURv2xwJ/xNRldGp6oZw7rgbVDaN2w5Iuv+1Ob0BOfFF1/k9ttv5+abb6Z379688cYb+Pn5MW/evFYfHxYWRkxMjP2yaNEi/Pz8TkhwTCaTw+NCQ927UeQ5i+gBfaYrCwFbsWBbDqXV9XQK8WV8zyjXxqayaf1iCfXz4lhpDYt356kdjvAUA6+DsQ/D6D96VmG/U9HpYMhNyu2jG0/4cn55DYt2KX9nnr6RoaWIABMXDYgFZBRHC5ya4NTV1bFp0ybS0tKaXlCvJy0tjbVr157Wc7z77rvMmDEDf3/H7ZfLli0jKiqKnj17ctddd1FY2HaZ7NraWsrKyhwuHs1iPqHw2IfNFhd7WmG/U/HxMnB14zC5LDYW7SYoDs7/C/S+RO1IXG/ADLhjOVz+1glf+uL3ozRYrAzuEkJKTJAKwanrpsbFxj9uzyG/XLaMq8mpCU5BQQFms5no6GiH+6Ojo8nNzT3l92/YsIEdO3Zw2223Odw/ZcoUPvjgAxYvXsxzzz3H8uXLmTp1KmZz6yvXn332WYKDg+2X+HgPnhNe9zq81A/2LLDftSO7lPSsErwMOq4e6sHv/SSuT+2KTgcr9ytrA4QQ58A3pNW2DRaL1T49dV0H2cjQUv/OIQzqoqz7+2S9rPtTk6aXtr/77rv069ePYcOGOdw/Y8YMLrnkEvr168f06dNZsGABGzduZNmyZa0+z5w5cygtLbVfsrI8+JeuIh/KsmHzB/a7PlqvjFpM6RtLZKAH1+k4ifgwPyY0Ts19KKM44lwU7IePr4E9P6kdiTbUlNq7jK/MKOBocTWBPkYu7BercmDqsY3ifLLhiKz7U5FTE5yIiAgMBgN5eY7rHvLy8oiJOfmWysrKSj799FNuvfXWU75OUlISERERZGRktPp1k8lEUFCQw8VjDbpeuc5YDCVZlNXU8+2WYwBc38Hmw1u6oXFx9Re/Z1FdJ3UqxFna8iHsW+hwEtFhLX8e/p2i7KgCPm48mbpicGd8vT2zSvrpmNI3hhA/L3LLali5/7ja4XRYTk1wvL29GTJkCIsXL7bfZ7FYWLx4MSNGjDjp937xxRfU1tZy/fXXn/J1jh49SmFhIbGxHfeMwS68GySMAayQ/hFfbzpKdb2Z5OgAhiV6bqn00zEuOZL4MF/Kahr4YesxtcMR7sjcAFsbd2wOOvWxyeOZgqC+CjbNJ6+0mt92K7tjO9ri4pZMRgPTByr9qWy9uITrOX2Kavbs2bz99tu8//777N69m7vuuovKykpuvvlmAGbNmsWcOXNO+L53332X6dOnEx4e7nB/RUUFDz/8MOvWrePQoUMsXryYSy+9lO7duzN58mRnvx33MPhGAKxb/scn65RiW9cP7+rRpdJPh16v49phyoH3y01y0BFnIeM3qMgDvwhIluMN/a8Gow/k7WD5koWYLVaGdg0l2QO7hp+pq4Z2BmDRrjyKK+tUjqZjcnqCc8011/DCCy/wxBNPMHDgQNLT01m4cKF94fGRI0fIyXGs+rh3715WrVrV6vSUwWBg27ZtXHLJJSQnJ3PrrbcyZMgQVq5cicnUMdeXnKDXxeATgq70KNGF6/Dx0jN9kGd2DT9Tlw/qjF4HGw4VcbhQFhuLM7Tlf8r1gBlg8OxWJ6fFL0yp4gz47lB+NraTiI6uT1wwvWODqDNb+C49W+1wOiSdtQO2WS4rKyM4OJjS0lLPXY/z86Ow/g1+NA/j1z7P8/KMQWpHpBk3vLuelfsLuH9iD2ZfkKx2OMJdVByHF1PA0gB3rYXo3mpHpA1H1sG8yVRZTVyge5PFf74EH6+Ou/6mufmrD/LXH3bRJy6IH+8fo3Y4HuFMPr81vYtKnL26AdfzJWm82XAxVwzurHY4mnLlEOXn8fXmo1hkh4M4Xds+U5KbTkMkuWkuPpVj3l3x09Xyf/HbJblp5tKBnfA26Nl5rIydx0rVDqfDkQTHQy0piuBPNbeQF9ibUd0j1A5HUyb3iSHQZORocTXrDxapHY5wF8GdlbYMsrjYQWlNA/OrxwIwoXapytFoS6i/Nxf0VpZjyGJj15MEx0N9tVmZ850+qFOHq1x8Kj5eBns5dVlsLE5bn+lwx1IYfJPakWjKT9tz+KZ+BO+aZuF73ftqh6M5VzYuNv42PZvaBilP4UqS4Higwopalu5RtmvOjMuDHx6E7M3qBqUxtmmqn3fkUFnboHI0wq3o5bDZ3DebszlOCHUjHkQXIguMWxrbI5KYIB9KqupZvLv1JtPCOeQv1QN9v/UYDRYr/ToF0yXjQ9j0Hmz9VO2wNGVwl1ASI/ypqjPz845Ttw0RHVhdFfw+D6pL1I5Ec7KKqthwqAidDqYPilM7HE0y6HVcPljZxfr57x5cRV+DJMHxQF83Tk9dMbgT9LtauXPn10qRMgGATqdTfj7AVzJNJU5m9w+w4I8wT+retGTb/jwiKZzYYF/Y9T18eCVkndhlvCO7qrEH4Ip9x8ktlQacriIJjofZl1fO9uxSjHodlwzsBN0mgF84VB6HA8vUDk9TLhvcGZ0O1h4oJKuoSu1whFZt/0K5bqz3IhRWq5WvtygJzmW2Olt7foSMRbBNRoybS4zw57yEUCxWpCaOC0mC42FsoxETUqII8/dWipH1uVz54vbPVYxMezqF+DKym1Ip+5stctARragsgMwlyu1+V6kbi8ZsO1rKgeOVmIx6pvRt7C3Yv3HEeMdX0CDVe5uzFVv9XtrEuIwkOB7EbLHaP6gdat/YDjq7F9i7/gqFbbHxV5uP0gFrXopT2fUtWM0QOwAiuqsdjabYjjWT+sQQ6NNY1TlxHAREQ3Wx0tZC2E3rG4tRr2PnsTIyj1eoHU6HIAmOB1mVUUB+eS0hfl5MSIls+kLn8yCkK9RXwt6f1QtQgyb3icHP28Dhwiq2HpVCXKKF7V8p1zJ646DebLE3rL28eRsYg7HpZyXTVA5C/b0Z00OpSfZ9uoziuIIkOB7ku8Yzqov7x2EyNqsmqtMpB53geKUSq7Dz8zaS1kspxLVAho5Fc6VH4cgaQNc0zSsAWLn/OIWVdUQENH1o29lGjPculJ1nLVwyUNlp9sPWYzJi7AKS4HiImnozi3blAU1/RA7G/gke2KY0CRQOLuqvFP1bsC1HWjeIJlnrQaeHriMhWJrVNmfbqXnxgDiMhhYfIzH9IbIXmGth9/cqRKddF/SOwWTUc6Cgkp3HytQOx+NJguMhVuw7TnltAzFBPgzpEnriA7x8pUBZG8b1jCTQx0huWQ2bjhSrHY7Qir5XwOw9MO1fakeiKeU19faTqcsHtdLnTqeDAddAlxHgH+Xi6LQtwNQ0YiyLjZ1PPvE8xIJtOQBc2D8W/claM5jr4fAaF0XlHkxGA5N6K7tAfpCDjmguMBqi+6gdhaYs3p1PbYOFpAh/+nZqo5vzqAfhloXQc4pLY3MHFw9omqaSEWPnkgTHA1TXmfltt3JGZZtuaVVtBfw7Bd6bCsWHXRSde7i4sTfVT9tzaDBbVI5GqK5eirG1xXYydVH/WHS6Nk6m2rpfML5nJIEmIzmlNfx+WEaMnUkSHA+wbG8+VXVmOoX4MjA+pO0HmgIgqpdye8eXLonNXYzqHkGonxcFFXXSYbyjs1rhzTEw/yIozFQ7Gk0pr6lnxb7jAEw72cmUTWWBUgla2Pl4GZjcWDfo+61Sf8uZJMHxAKd1RmVjL8T1jZOjci9eBj1T+toWG8s0VYeWkw4F++DoRgiQNSTN/bY7jzqzhW6R/vSMDjz5g6uK4N894bProVQ+yJu7pHGa6qftuTJi7ESS4Li5ytoGFu+xTU+dRrO7lItAZ4C87XJ22sLFjWekP+/IpV4OOh3X9sbRzeQpYDrFh3gH8+M2pTHthf1O42TKLww6DVVuy24qByO7hRMR4E1RZR2rMwvVDsdjSYLj5hbvyaem3kLXcL+2F/w15xcGiWOV27u+c25wbiY1KZyIABMlVfWsyihQOxyhBosFdjaObva7Ut1YNKb59NSFp3MyBdBnunK981unxOSujAY90/opJ1RS9M95JMFxc7bidKd1RmXT+1LlWhIcBwa9jgv7yW6qDi37dyjLBu9A6H6B2tFoSvPpqeTogNP7JtuxJmsdlMnfVHO2aapfd+ZS22BWORrPJAmOGyuvqWdZ4xnVaU1P2aRcpBQwy0mHkiPOCc5N2bZwLtqZR029HHQ6HFvSnzwZvHzUjUVj7NNT/eNO/2QqKA7ihyu35YTKweAuoUQHmSivbWBNhkxTOYMkOG7st9151DVYSIr0p1fsGawVCIiEi1+GO1cr7RuE3eAuocQG+1Be28DyxuRRdBBWa9OOn96XqBuLxpQ1n57qdxq7p5qTaapW6fU6JvdRRowX7shVORrPJAmOG1uw1bZ76gzOqGwGz4KYvlKvogW9Xmc/gP+0PUflaIRLWcxKS5Oe06B7mtrRaMrixump7lEBpz89ZdOrMVnM3qTsrBJ2Uxq3i/+6S3ZTOYMkOG6qrKaeFftt01NneEYlTmpq4zqcJXvyqWuQg06HYTAqif+1n4C3v9rRaMqPjaUopp3JWj+b4E5wzYfw0F5lk4OwG5YQRqifF8VV9WyQ+lvtThIcN7V0Tz71ZmvjGdVZbmU9vAa+uh02zW/X2NzdoPhQIgNNlNc0sPaAzI2Ljk2ZnlJ2FZ71yVSvi8E/vB2j8gxGg97eJmbhTpmmam+S4LipXxr/GCb3iT77J8nZBts/h62ftlNUnkGv1zGpt/JzlbnxDqIwE9b+F0qy1I5Ec37b1Xx6qh3qAlml/1JztmmqhTtypTdVO5MExw3V1JtZtleZnrItUjsrvS5Wro+sgzJZb9Kc7ee6aFceZjnoeL4dX8Evc+DH2WpHojk/bVeS/Glnuri4pV3fw7ypsOHtdojKc4zsHk6gyUh+eS1bskrUDsejSILjhlbtL6CqzkxcsA/9OgWf/RMFd4LO5wFW2LOg3eLzBMOTwgnyMVJQUcvmI9IQz+PZKu32kt1TzVXVNbCyca3f1L7ncDIFUHoUjqyRqsYtmIwGzu+ltARZuENONNuTJDhuyDY9NalPzJkv+GtJiv61ytuoZ2IvZZrqF5mm8mxFByF3u9LCpOc0taPRlBX7CqhtsBAf5ktKzDlOT6VcqFwfXi27qVqwJY8Ld+ZilSm8diMJjptpMFv4bbfSe2rSuay/sbGdsR5eDRX55/58HsS2vumXXXLQ8Wi2EYWEUbIQtoVfdzWeTPVuh5Op0K4Q3Q+sFti3sB2i8xxjkyPx8dKTVVTNzmNlaofjMSTBcTMbDxVTXFVPiJ8XwxLaYctlaFeIHagcdPb+fO7P50GaH3R25chBx2Ptkump1jSYLSzZo5z02BbdnzPbKM6eH9vn+TyEn7eR8cnKNNUvspuq3bgkwZk7dy4JCQn4+PiQmprKhg0b2nzs/Pnz0el0DhcfH8eS6VarlSeeeILY2Fh8fX1JS0tj//79zn4bmmA7o5qYEo3R0E7/fSkXKWdWUvvDgZ+3kbE9IgH4ZWeeytEIpyjNVvpPoWtadC8A5WSqpKqeMH9vhnQNbZ8nTWmcAsxYDHVV7fOcHsK2m+pnmRJvN05PcD777DNmz57Nk08+yebNmxkwYACTJ08mP7/t6ZCgoCBycnLsl8OHDzt8/fnnn+eVV17hjTfeYP369fj7+zN58mRqamqc/XZUZbVa+bXxg/actoe3NOYhuGuVdE9uhe2gI+twPFTOVjB4Q/wwCDzHRbQepulkKqr9TqZi+ivtYRqq4cCy9nlOD3F+ryi8DDoy8ivIyC9XOxyP4PQE58UXX+T222/n5ptvpnfv3rzxxhv4+fkxb968Nr9Hp9MRExNjv0RHN32YW61WXnrpJf7yl79w6aWX0r9/fz744AOOHTvGt99+6+y3o6qdx8rILqnG18vA2OTI9ntivcxUtmViSjRGvY69eeUcLKhUOxzR3lKmwcOZcOlctSPRlOYnU5POpRRFSzod9L0ckqeCzznsAPVAQT5ejOoeAUj9rfbi1E+2uro6Nm3aRFpaU18XvV5PWloaa9eubfP7Kioq6Nq1K/Hx8Vx66aXs3LnT/rWDBw+Sm5vr8JzBwcGkpqa2+Zy1tbWUlZU5XNyRbW52XHIkPl6G9n+Buko4lt7+z+vGgv28GJ6kLDyVuXEP5RMEET3UjkJTduU0nUyN6RHRvk9+wVNw3afKom7hwFZ/67fdsuGjPTg1wSkoKMBsNjuMwABER0eTm9v6h0XPnj2ZN28e3333HR9++CEWi4WRI0dy9OhRAPv3nclzPvvsswQHB9sv8fHu2UHbXr24bztOT9nk7oDnk+DDK5Smg8Jusm2aShIcz2JuUDsCzbKN3ozpEeGckynRqokpykLj9KwS8ss9e8mFK2hubmLEiBHMmjWLgQMHMm7cOL7++msiIyN58803z/o558yZQ2lpqf2SleV+5dgPFlSyL68Co17H+T2dkOBE9gSjD1QVQFbbi8A7osmNO0i2HCkhr0wOOh7j27vgzXGyFqQVv+5ywvRUS8WH4dBq5z2/G4oK8mFAZ2XqbomM4pwzpyY4ERERGAwG8vIcd6Dk5eURE3N6fzheXl4MGjSIjIwMAPv3nclzmkwmgoKCHC7uxjZ6MKJbOMF+Xu3/AgYv6DFJub1XtnA2FxXkw4D4EAD7tlnh5sz1sP8XyEkHo6/a0WhKVlEVu3PK0OuaRhTaXcZieLk/fHe39KZqIa2xwKhMU507pyY43t7eDBkyhMWLF9vvs1gsLF68mBEjRpzWc5jNZrZv305srNIHJTExkZiYGIfnLCsrY/369af9nO7oN9sZVXvVo2iNbQvnnp/koNNCWuOBfvFu2S7uEY6shZpS8IuAzkPVjkZTFjUea4YlhhHq7+2cF4lPBYMJig9B/m7nvIabSms8xq/KOE51nSwXOBdOn6KaPXs2b7/9Nu+//z67d+/mrrvuorKykptvvhmAWbNmMWfOHPvjn3rqKX799VcOHDjA5s2buf766zl8+DC33XYboOywevDBB/n73//O999/z/bt25k1axZxcXFMnz7d2W9HFUWVdfZ+SLb2AU7RPU3ZMluUCQUdo67Q6bL93FdlFFBTLwcdt2crapk8BfSyxqS55tWLncYUAN0mKLel6J+DlJhAOoX4UlNvYXVGgdrhuDWnJzjXXHMNL7zwAk888QQDBw4kPT2dhQsX2hcJHzlyhJycpgZjxcXF3H777fTq1Ytp06ZRVlbGmjVr6N27t/0xjzzyCPfddx933HEH5513HhUVFSxcuPCEgoCeYtnefCxW6BUbRFyIE4fTTYGQMEa5LdNUDnrFBhIX7CMHHU9gtTZ9qPacqm4sGlNcWceGg0qfqAucOVoMzaoaS6Pf5nQ6HWmNzTcX75ER43PhkkXG9957L4cPH6a2tpb169eTmppq/9qyZcuYP3++/d//+c9/7I/Nzc3lxx9/ZNCgQQ7Pp9PpeOqpp8jNzaWmpobffvuN5ORkV7wVVSxuXPdh+6V3qubTVMJOp9PZR3FkbtzNHd8DJYeVKRLbKIIAYGmzk6n4MD/nvljyFECnrIMqPerc13Iztmmq33bnY7HIcoGzpbldVMJRXYOFFXuPA3C+sxb8NZdyEUz6B1x+9rvWPNXExgRzyZ48ab7pzvY2Ju9J46Q9SQu2kymnLS5uLiAKOp+n3N73i/Nfz42kJoYTYDJyvLyWbdmlaofjtiTB0bjfDxVRXttARIA3AzqHOP8FA2Ng5L0QluT813Izw5PC8fM2kFdWy45s9ywWKYC4wdD3Suh7hdqRaEq92cKKfY0nU64YLQZInqxcZ/zmmtdzE95GPeMaq9XLxoazJwmOxtmmQyb0jEKv16kcTcfm42VgdGMpdZkbd2PdJsCV78KAGWpHoim/HyqmvKaBcH8XnUyB0v/u6g/gMhkxbimtt5Jk2na1iTMnCY6GWa1W+wepU3dPtWRugM0fwBc3QX21617XDdhqVCyWdTjCwyxpPNaM7xmFwVUnU6EJ0PtSpV2GcDCh8f9hT245WUXSef1sSIKjYZnHKzlcWIW3Qd/+/WBORm+AZf+End/AoVWue103MCElCp0OtmeXSlVjd7Tja8jbJXWeWmFbf+OStX7ilEL8vBnSNRSQaaqzJQmOhtnOqIZ3C8ffZHTdC+t00OMC5bYs/nMQGWiyD9/LKI6bqa+Gb++G10dA/i61o9GUQwWVHDheiVGvY0yyC0+mAKpLYOmz8PE1kni2cIFtxFgqqJ8VSXA0zLb+xiU7Glrq0bj4b/8vctBpwV6jQs6q3MvBldBQDUGdIar3qR/fgdhakAxLDCPIxwmtYE7G4A2rX4J9CyXxbMG2XXzdgULKa+pVjsb9SIKjUSVVdWw6rFQvVmXIOGmcUiek5AgU7HP962tY86rGUkrdjez/VblOnqSMUgq7JWpOT3n7QeI45fa+ha5/fQ1LjPAnKcKferNVCoyeBUlwNGr5vuOYLVZ6Rgc6v+BWa7z9IWG0clumqRzYSqnXNkhVY7dhtSqjkdDUVFYAUF5Tz/qDhYCK629s28XlWHOC8T2V/5Ole46rHIn7kQRHo2zrOya6qh5Fa2wfBLYzXwHYqhpLKXW3UrBPGY00eEPiWLWj0ZRV+wuoN1uV0YLIAHWCsB1rjm6EykJ1YtCoCSlKPZyle/OlwOgZkgRHg+rNFpbt1UCCkzwJ0IHVAhaZimluQuOZ7rK9x+Wg4w5sSXrCaKle3IKq01M2IfEQ3Vc51kjRPwfDEsPw9TKQX17LrhwpMHomJMHRoM2HiymraSDM35uB8aHqBRKWBI8cgJt/ko7LLYxICsdk1JNTWsPevHK1wxGncmCZci3TUw4sFitL96q4maE5+4ixTFM1ZzIaGNVYYHTZXpmmOhOS4GjQssZy6eOSI11XcKstfmHqvr5G+XgZGNktHJCDjlu45iO4/mvoPV3tSDRlW3YpBRV1BJqMDE1Q+W89eQqYgsEUqG4cGmSbploi28XPiCQ4GrS08Zd4fM9IlSNpproYLBa1o9CUpsV/ctDRPC8f6D4RgmLVjkRTljSWOhiTHIG3UeWPg87nwSOZcPHL6sahQRMajzVbjhRTXFmncjTuQxIcjcktrWFPbjk6HYzpoZEE5+Nr4PlucGyL2pFoiu2g8/vhYsqkRoVwQ0v2NvW6U51eDwYX1+BxE3EhvqTEBGKxwor9MmJ8uiTB0Zjl+5QDzsD4EML8vVWOppHBG6xmmRtvoUu4H0mR/pgtVlbvl+3immS1wvyL4Jc/Q1WR2tFoSn55DTuylUWr47WQ4NhYrVCYqXYUmmP7P5Ip8dMnCY7G2GodjE/W0AFHalS0yfb/ZFuoKTQmdzscWgm/zwMvFepJadiKfUpS3q9TMJGBJpWjadRQB68MglcHQ+lRtaPRlAmNSxZsNdLEqUmCoyH15qbCcZpaf9O9sS9VTjqU56oaitbYFv/JdnGNsm0PTxqvrMMRdsubbWbQDKM3+DfGI9vFHQzuGkqgj5Giyjq2HS1ROxy3IAmOhmw6XEx5bQPh/t706xSsdjhNAqMhbpBye/8idWPRGKlRoXG2BMfWPFYAYLZYWdm4lkNTJ1PQ9H8lxxoHXgY9YxvXZcrGhtMjCY6G2OZWxyVHold7e3hLtlGczMXqxqExSo0K2S6uSVVFSmVckPo3LaRnlVBSVU+Qj5GB8SFqh+Ooe5pyfWC5MmUl7GzJ6FI51pwWSXA0xFa9eJzWzqig6awqcymYG9SNRWOaFv/JWZWmZCxWKuNG9YHgzmpHoynLG39XxyRHYjRo7GMgdiD4RUBdORzdoHY0mmI71mzPLiW/vEblaLRPY7/ZHVdOaTV7csvR67APQ2pK3GDodxWk/RUskuA0Zzur2nykhNIq2S6uGTI91SZNrr+x0euVmkUg01QtRAaa6N9ZWb6wXEZxTkkSHI2w/bIOiA8hVCvbw5szGOGKd2DozbJYs4XOoX70iApQ1jVkyEFHMwKjITBWEpwWCitq2ZZdCsB4LSY40DQlLguNTyDbxU+fJDgasVRLBbfEGbPPje+Rg45mTPo7zN4NXUaqHYmmrNh/HKsVescGERWk0ZOVbufDsD/AxCfVjkRzbKNuqzIKaDBLdfmTkQRHA+oaLKzOKAQ0uKOhpaIDsOFtqCxUOxJNsSWmy/cdxyI1KrRDp1OmPISd7cxf08ca/3CY9jwky+LwlgZ0DibIx0hpdT1bj5aqHY6myV++Bmw6XExFbQMRAd70jdPQ9vDWfHYD/PQnyFyidiSaMjQhDH9vAwUVtew8JtvFVXd8n/ROa4XZYmXFPluCI6PF7sho0Nvb+Nj+L0XrJMHRgGWN7RnG9tDg9vCWbIv/ZG7cgbdRz8juEYD0ilFdTSn8dzi80F3aM7SwPbuU4qp6Ak1GBnUJUTuck7OY4eBKWPy07NxsYWyycqxZLgnOSUmCowG2Bcaa3B7ekq1GReZiOUNuYWxyUyl1oaIDy5Xeab6h4BemdjSaYitlMLpHBF5a2x7ems9nwcoXmuoZCaDpWLPtaIl0Fz8JN/gN92x5ZU3dwzW5Pbyl+OHg5Q+VxyF3m9rRaMq4xv+/zYeLKZfu4uqxjS7aknFht3yfG6y/sdEblMXGICPGLcQG+9IzWukuvipDGv22RRIcldnmUPt3Ctbm9vCWjN6QNE65LQcdB13C/UgI96PBYmVtpizCVoXV2rQ+TBIcB8WVdaRnlQAwTkvNfE/G9n+YIfVwWrJNU8k6nLZJgqOyFfuV7HusVutRtMa+DkfaNrQk01QqK9gHpVlgMEHXUWpHoykrMwqwWiElJpCYYI1uD2/JdqzJ2QoVUim8OVuSqmz7l52brXFJgjN37lwSEhLw8fEhNTWVDRvaLr/99ttvM2bMGEJDQwkNDSUtLe2Ex990003odDqHy5QpU5z9Ntqd2WJlVeOCVPdKcBrPqo5tgboqdWPRGNs0oxx0VGIbVew6Erz91I1FY1ZouXpxWwKiIHaAclt2bjoYmhCKj5eevLJa9uaVqx2OJjk9wfnss8+YPXs2Tz75JJs3b2bAgAFMnjyZ/PzWs/Fly5Zx7bXXsnTpUtauXUt8fDyTJk0iOzvb4XFTpkwhJyfHfvnkk0+c/Vba3Y5mOxo01/DuZEIT4Pqv4OH98iHSwohu4XgZdGQVVXOoUJI/l7ONKsr0lAOrtal7uFudTAF0kxHj1vh4GRiRpDT6lbYNrXN6gvPiiy9y++23c/PNN9O7d2/eeOMN/Pz8mDdvXquP/+ijj7j77rsZOHAgKSkpvPPOO1gsFhYvdvzlNplMxMTE2C+hoaHOfivtznZGNbJ7uHvsaGiuexqYAtWOQnP8TUaGdlV27sjcuArGPQKjZ0PPqWpHoil788rJK6vFx0vPkK5udqy0TVPl7VA3Dg2yJatSmqJ1Tv1UraurY9OmTaSlNZ1N6fV60tLSWLt27Wk9R1VVFfX19YSFOW73XLZsGVFRUfTs2ZO77rqLwsK2F3XW1tZSVlbmcNGCFe56RiVOyn7QkQTH9boMh7QnIbyb2pFoysp9ylq/4Unh+HgZVI7mDHUeBn9YAXeuVjsSzbFNN248WExlrdQKasmpCU5BQQFms5no6GiH+6Ojo8nNzT2t53j00UeJi4tzSJKmTJnCBx98wOLFi3nuuedYvnw5U6dOxWw2t/oczz77LMHBwfZLfHz82b+pdlJWU8/mIyWAm2wPb82Kf8Hb58PxvWpHoim23Q1rDxRS1yC1goT67CdT7nisMXor63Ck5cYJEiP86RzqS53ZwroDsnOzJU3/xvzzn//k008/5ZtvvsHHp2nV/4wZM7jkkkvo168f06dPZ8GCBWzcuJFly5a1+jxz5syhtLTUfsnKynLRO2jbmoxCzBYrSRH+xIe56TqWQ6sge5Ms/muhV0wQEQEmqurM/H5YKum6zLJ/wt6foaFW7Ug0pbrOzPqDyu+hLfl2W7Jw34FOp7OP4siI8YmcmuBERERgMBjIy8tzuD8vL4+YmJiTfu8LL7zAP//5T3799Vf69+9/0scmJSURERFBRkZGq183mUwEBQU5XNRmO6Ma08ONDziy+K9Ver2OsT2klLpLlRyBZc/CpzOhvlrtaDRlw6Ei6hosxAX70C0yQO1wzk59NXxzF7zcH+oq1Y5GU8ZJaYo2OTXB8fb2ZsiQIQ4LhG0LhkeMGNHm9z3//PM8/fTTLFy4kKFDh57ydY4ePUphYSGxsbHtErezWa1NDe/cev2NrcrooVVy1txC0zocqTLqErZRxM5DwTdE1VC0xnasGdMjEp1O473u2mL0gcOrlET20Cq1o9GUkd0jMOp1HCqs4ojs3HTg9Cmq2bNn8/bbb/P++++ze/du7rrrLiorK7n55psBmDVrFnPmzLE//rnnnuPxxx9n3rx5JCQkkJubS25uLhUVFQBUVFTw8MMPs27dOg4dOsTixYu59NJL6d69O5MnT3b222kXBwsqOVpcjZdBx/DGbX5uKboP+EdBQzUcWad2NJoypkcEOh3szikjv7xG7XA8ny3BsY0qCjuPOJnS6WTEuA0BJiODG3fGyW4qR05PcK655hpeeOEFnnjiCQYOHEh6ejoLFy60Lzw+cuQIOTk59se//vrr1NXVceWVVxIbG2u/vPDCCwAYDAa2bdvGJZdcQnJyMrfeeitDhgxh5cqVmEwmZ7+ddmE74AztGoa/yahyNOdAp2saxZF1OA7CA0z0jQsGmnawCCexmOHAMuW27fdRAJBTWs3+/Ar0OhjV3Y1PpkCONSdhmxJfKQmOA5d8ut57773ce++9rX6t5cLgQ4cOnfS5fH19+eWXX9opMnW4ZXuGtnQ7H7Z9qhx0Lvib2tFoytjkCLZnl7Ji/3GuGNJZ7XA817EtUFMKpmCIG6R2NJpiS677dw4hxM8Net2dTOJY0BmgcL8yVRXSRe2INGNMj0he+HUfazIKaTBbMLpbXTUnkZ+Ci9U2mO2NGN1+RwNAtwngFw4RPcAsdRias23JXbW/AItFdn84je2MPmksGNx4RNQJlntSrS3fEGWNFcg0VQt9OwUT4udFeW0DW4+WqB2OZkiC42KbDhVTXW8mIsBErxj1d3Ods4Ao+FMGXDlPPlxaGNQlFH9vA4WVdezK0UZxSY9kq3Ar01MOzBYrqzMaR4vdebdmc7Z1ODJN5cCg1zGqu627uEyJ20iC42K26akxPSLQ6910R0NLUoCrVd5GvX0R+aoMOeg4zVXvw32bofd0tSPRlO3ZpZS4Y6+7k+k+EaL6QHRftSPRHFmHcyL5ZHKxVRkeUP+mNVYr5O+RQlwtjJGDjvPpdEprBr+wUz+2A2ne685j1mR0Hgp3r4Hxj6odieaMbpwST88qobS6XuVotMFDfuvdQ2FFLTuylamK0d09KMGxWOC18+C/qZC/S+1oNGVMs14x1XWttxIR50AS6ja5bfdwcVY6hfiSFOmPxYp9nWdHJwmOC61u/KVLiQkkKsjnFI92I3o9hCYot2Xxn4OkCH86hSi9YtYflINOu3v7fPjsBmVXjbAr94RedydTXw1ZG9WOQnNs/9cyYqyQBMeFVu7z0OkpkBoVbdDpdPbRulX7ZR1Ouyo+BMc2w54fwSdE7Wg0ZW2m0usuIdzPfXvdtaWyEJ5LgHmToLpE7Wg0pWlKXI41IAmOy1itVvtC0zGeeEbVvXF3w+E10guohTHJctBxisylynX8MPDxgB2J7ch2rBntiSdT/uEQHA9WCxxaqXY0mjI8KRwvg44jRVUcLpSeXZLguEjm8QpySmvwNuoZluiBiyEjkiGoE5hr4fBqtaPRlFHdlLYNe/PKySuTtg3txt6eQbaHt7RqvwefTIFSfwtkxLgFf5ORwV1sbRvkhEoSHBexnb0PSwjDx8ugcjROoNM1O+gsVTcWjQn196ZfJ6Vtg0xTtRNzAxxcrtyWBMfB0eIqDhRUYtDrGNHNzdsztCVJjjVtsS0qXyndxSXBcRVbguORQ8Y2ctBpk2wXb2e29gw+0p6hJVsSPaBzMEE+XipH4yQJo5W2DcUHlbVYws52rFmbWUi92aJyNOqSBMcF6hosrDug7KDxyAXGNkkTYPg9cMFTakeiObapglUZ0rahXdjbM4wHvQeOiJ6DlZ681s/GJwg6n6fclhMqB33iggm1tW3IKlE7HFVJguMCm48UU1VnJtzf2zPaM7TFPxymPAM90tSORHMGdwnFz9tAQUUde3LL1Q7H/QV3hs7DoPsFakeiKc3bM3j0yRQ0TU0ekASnOYe2DR18SlwSHBdY1Wx6ymPaM4gz0rxtg0xTtYNBM+G2RTD4BrUj0ZSdx5T2DAEmIwM8pT1DW3pfApP+AeP/T+1INEemxBWS4LiA7ZfMo6oXt8XcoAwZL/unVJltQWpUCGez/W6N6BaOl6e0Z2hLVC8YeS9EpagdiebY2jZs7eBtGzz8L0B9JVV1bMsuBTx8TtzG0gCfzIBlz8LxPWpHoym2BGfDoSJq6qVtw1nL3SEF3tqwan8HmZ4SJ9UpxJekCGnbIAmOk63OKMRqhR5RAcQEe1B7hrZ4+UDXkcptWfznoFtkALHBPtQ1WNhwsEjtcNzXFzfB84lwYLnakWhKVV0Dvx9Wfq86xGgxKDvptnwEy59XOxLNse3YtTV47ogkwXGypu7hHWD0xsa2XVwW/zlwaNuQIdNUZ6X0KBTuV27HDlA3Fo1Zf7CIerOVTiG+JEb4qx2Oa1QWwHd3KwlObYXa0WiKtIiRBMeprFYrK/Z1wCFjW8G/Q6ugoVbdWDRmtKzDOTe2UcFOQ8A3RNVQtKb59JRO10E2M4QlQUhXsNRLBfUWhncLx6DXcaiwiqyiKrXDUYUkOE50qLCK7JJqvAw6UpM8sD1DW6L6gH8k1FdB1ga1o9EU2/bN3TllHC+X5O+M2UYFbaOEws6+maEjnUxJBfU2Bfl4MbBxJ11HHTGWBMeJbL9USg0Uo8rRuJBeL9NUbYgIMNE7VqmFtCazYx50zprFAgeWKbe7SYLTXF5ZDfvyKtDplN5nHUqS9KVqS0efppIEx4lW7betv+lgBxxo+gA6tkXdODRIpqnOUt52qCoE74CmKrYCaPoA69cpmFB/b5WjcbHEsaDTQ8FeKM1WOxpNsX32rM4swNwBK6hLguMkDWYLaxq3543uSAuMbXpOhTtXwcyv1I5Ec5qfVVmlVtDps01BdB0FBg/tsXSWbKPFHWb3VHN+YU39yGwjfAKAAfEhBJiMlFTVs/NYqdrhuJwkOE6yLbuU8poGgn297J2kOxTfUIjpp0xXCQfDEsPwNurJLash83il2uG4j35XwkUvwXm3qR2Jplit1qYEpyOOFkPTNFVhhrpxaIyXoXkF9Y43YiyfPk5iGzIe2biSXQgbHy8D5yWEAk3TmOI0BHeGoTdD8iS1I9GUvXnlHC+vxdfLwJCuoWqHo47UO+HhTEh7Uu1INMc2TdUR1+FIguMktjOqUR1xyNimLAe+vgPeuUDaNrQwuntTd3EhzoXtg2tYYhgmYwftrB4QCf4d+Fh7ErZRvU2Hi6mu61gV1CXBcYLK2ga2HCkGOugCYxtTIOz4Go5ugOKDakejKbbfi3UHiqg3W1SOxg2kfwLr35JFpK3o0OtvWiMnUw6SIvyJC/ahzmxhw6GOVUFdEhwnWH+wkHqzlfgwX7qGd5CKoq0xBUD8MOW21Khw0Ds2iFA/LypqG0jPKlE7HO1bNxd+fhgOr1E7Ek2pbTCz/kBje4aOfDIFcHQTvHchfHKt2pFoik6na2rb0MGmxCXBcQLbYi7bNESHJvVwWqXX6xjZXbaLn5aK45C7XbmdNF7VULRm8+ESquvNRASYSIkJVDscdXn5wOFVyk6q+hq1o9EU207ejnaskQTHCaSjbzO2ejgHV4ClY83/nsqY7h3zrOqMHWxsqhndT1lrIexsve5Gdw/vOO0Z2hLVGwKioaEastarHY2mjOqm7KTak1veoSqoS4LTzvLKatifr1QUHdG4Pa9Dix0IpmCl6++xdLWj0RTbsPHWo6WU1dSrHI2G2aY3u41XNQwtWpWh1Nrq0JsZbHS6phE+qYfjIDzARJ84pYL66g60scElCc7cuXNJSEjAx8eH1NRUNmw4eX+iL774gpSUFHx8fOjXrx8//fSTw9etVitPPPEEsbGx+Pr6kpaWxv79+535Fk5bh64o2hqDERLHKLcPSCn15jqH+pEY4Y/ZYmVdY1FI0YLVKv2n2lBaVc/2oyUAjOmIxURbI1PibeqIFdSdnuB89tlnzJ49myeffJLNmzczYMAAJk+eTH5+fquPX7NmDddeey233norW7ZsYfr06UyfPp0dO3bYH/P888/zyiuv8MYbb7B+/Xr8/f2ZPHkyNTXqz7vKjoZWdDtfKfrnJz+TluxVjTvQWdUZKcyAsmwwmKDrSLWj0ZQ1mQVYrNA9KoCYYB+1w9GGpHHK9bF0qOpYO4ZOZYy9NMXxDlNB3ekJzosvvsjtt9/OzTffTO/evXnjjTfw8/Nj3rx5rT7+5ZdfZsqUKTz88MP06tWLp59+msGDB/Paa68ByujNSy+9xF/+8hcuvfRS+vfvzwcffMCxY8f49ttvnf12TkoqirZh6C1K24ahN6sdieaM7sBFuE5L3k7QG6FLKnj5qh2NpqyUk6kTBcVBZApgVdb9CbuhCaF4G/XkldWSebxC7XBcwqkJTl1dHZs2bSItLa3pBfV60tLSWLt2bavfs3btWofHA0yePNn++IMHD5Kbm+vwmODgYFJTU9t8ztraWsrKyhwuziAVRdvQ0Rc/nsSIbuHodXCgoJLskmq1w9GePtPh0UNw8StqR6I5qyXBaV3KRZA8RWkXI+x8vAwMSwgDOs40lVMTnIKCAsxmM9HR0Q73R0dHk5ub2+r35ObmnvTxtuszec5nn32W4OBg+yU+Pv6s3s+pSEXRU6irggLpFdNckI8XA+JDANlN1SZTIIQlqh2FpmQVVXG4sAqjXsfwbrKZwcHEx+G6z5qmq4RdRxsx7hC7qObMmUNpaan9kpWV5ZTXGdEtnLvHd+PywZ2c8vxu7cByeK4rfD5L7Ug0x75dPEMWGjvoIOsEzobtDHxQF6VbtBCnwzbat+5AYYeooO7UBCciIgKDwUBeXp7D/Xl5ecTExLT6PTExMSd9vO36TJ7TZDIRFBTkcHGGPnHBPDIlhUsHSoJzgui+YK6H/J1Qnnfqx3cgtiJcqzMKsFjkQ91uxQvw1njY/qXakWhOU/0b2T3VppIjcHyf2lFoSu/YIML8vamsM3eICupOTXC8vb0ZMmQIixcvtt9nsVhYvHgxI0aMaPV7RowY4fB4gEWLFtkfn5iYSExMjMNjysrKWL9+fZvPKTTAPxxi+yu3pUaFg0FdQvD3NlBUWceuHOesD3NLmYvh2Bao6xgLIk+X2WJlTWNZAdnM0IZ1b8BL/WDJU2pHoil6vY6RjVOaHWEdjtOnqGbPns3bb7/N+++/z+7du7nrrruorKzk5puVHTWzZs1izpw59sc/8MADLFy4kH//+9/s2bOHv/71r/z+++/ce++9gNJX48EHH+Tvf/8733//Pdu3b2fWrFnExcUxffp0Z78dcS6kRkWrvAx6hjcWhZTt4o1qy+HoRuW2tGdwsCO7lJKqegJNRgZ0DlY7HG3qNES5lgrqJxjTgfpSOT3Bueaaa3jhhRd44oknGDhwIOnp6SxcuNC+SPjIkSPk5OTYHz9y5Eg+/vhj3nrrLQYMGMCXX37Jt99+S9++fe2PeeSRR7jvvvu44447OO+886ioqGDhwoX4+EgtCE2ztW04sEzWV7QwqnvHWvx3SodWgaUBQhMhNEHtaDTFlgQP7xaO0dAhllGeubhBUkG9DbYp8Y5QQd0lq9Puvfde+whMS8uWLTvhvquuuoqrrrqqzefT6XQ89dRTPPWUDD+6lfjhYPSB8hw4vheiUtSOSDNsZ1UbDhVRU2/Gx6uD78Kzt2eQ6sUtSa+702CroL5ngVJBvfMQtSPSjE4hviRG+HOwoJJ1mYVM6tP62lVPIOm/cB0vH+jSuE5KpqkcdI8KIDrIRF2Dhd8PFasdjvps67SkPYOD6jozmw4rvx9S/+YUbMlx5jJVw9CijlJBXRIc4VpDb4HJzyqFuISdTqez74hZmeH5c+MnVZoNBXtBp2/qYyYAWH+wkDqzxX4WLk7ClhxnrYdaWajeXEephyMJjnCt3pfAiLulcFsrxnSQg84pNdRA3yug+wVSjbYF2+/G6O4R6KRC+MmFJUFIF7DUw+E1akejKR2lgrpUiBJCI0Z2V3ZS7TxWRlFlHWEdtRt9eDe4svVedR2d9Lo7AzodTPgLGE1KLzNhZ6ugvuVICav3F3D1ec6p7q82GcERrleeB5v/Bzu/UTsSTYkK9CElJhBo6jMkhE1+eQ17csuBpl134hQGXKP0M/OR7fQt2Sqor/TgY40kOML1MhbB9/fCmtfUjkRzRnf07eIV+ZC/W8oItMKW9PaJC+q4o3ui3XSECuqS4AjXsy3+O7YZqmXHUHP2xX8ZBVg74of81k/hv8Ph6zvUjkRzbJVnZXrqDOVuh2XPQcbiUz+2A+kIFdQlwRGuF9wJIpLBaoGDK9WORlOGJYbhbdCTXVLNwYJKtcNxvcwlynXcIHXj0Bir1dpU/0b6T52ZHV/Bsmdg2+dqR6IpzSuoe2rbBklwhDqkbUOr/LyNDO4aAnh+jYoT1NfAkbXKbSnw52B/fgX55bWYjHqGJsjOsjOSJBXU22LfuemhpSkkwRHqsBfhkgSnpTGNc+OeelbVpiNrlS3igbEQKVWum7P9LgxLDJMq12eqy3Aw+kJFrrK+S9jZ1uFsPFRMTb3n9eySBEeoI2E06I1QfBCKD6kdjabYzqrWZRZSb7aoHI0L2UbzkiYoW3yFna0xolQvPgtGE3Qdqdy2TYEKALpF+hMb7ENdg4UNB4vUDqfdSYIj1GEKhM7nATo4tkXtaDSlT1wwIX5elNc2sDWrRO1wXEf6T7WqrsHC+sYPH1lgfJa6yZR4a5QK6o3bxT2wu7gkOEI9F70EjxyAPpepHYmmGPQ6e52TDjNNVXEccrcpt5PGqxqK1mw5UkxVnZlwf296xQSpHY57sq3DObQaGmrVjUVjxiR77pS4JDhCPVEp4BemdhSaNKaDNMOz8wmC67+CtL9BQJTa0WiK7XdgVPcI9HqZujsr0X3APwoMXlCYoXY0mjKqm7KTak9uOcfLPSv5kwRHCA2yTUWkZ5VQVlOvcjQuYDRB9zQY/aDakWjOCql/c+50OrhlITxyUEl2hF14gIk+ccrIoKdVUJcER6hr13fw3jRY/bLakWhK51A/kiL8MVusrM0sVDscoZKSqjq2Hy0Bmhafi7MU3g0M0n6xNbbkeYWHrcORBEeoq/I4HF4N+35VOxLNsR10PHHxn4OiA/Dr43BwhdqRaM6azEIsVugRFUBssK/a4XgOSwfanXgabMUjV+33rArqkuAIddkW/2Wth9oKdWPRGFs9HI/vS7XvV1jzCqx4Qe1INMeW3Np+F8Q5WvlveKk/7PpW7Ug0ZWhCKCajnvzyWvbne85xWBIcoa6wJAjpApZ6ZSRH2A1PCsOg13GosIqsoiq1w3Ee29bdbuerG4fGWK1WVuxrbM8g01Pto6oISg5LPZwWfLwMDEtUNnx40m4qSXCEunS6pg82Oeg4CPTxYnCXEMCzDjoOzPVwaJVyW+rfODhUWEV2STVeBh2pSbLbsF00r6DuQVMx7WGMB06JS4Ij1CcJTptG2+bGPbRXDEc3Ql0F+EVAdD+1o9EU2wfN0K5h+HnL4th20WUkGExQdhQK9qsdjabYjjXrDxRR2+AZbRskwRHqSxwLOj0U7IOSLLWj0ZQxycpZ1eqMQswWDzzjtFUvThoHejkcNWefnkqW6al24+0HXUcot+WEykFKTCARASaq681sOlysdjjtQo4oQn2+oUr12p7ToK5S7Wg0pX+nYAJ9jJRW17M9u1TtcNpf5mLluttEdePQmHqzhXUHlPIAth0uop3IiHGr9Hodo7srRf88ZWODJDhCG274Bq79RKluLOyMBj0ju9kOOh42TVVfA2U5ym1Zf+MgPauEitoGQv287EXYRDuxt21YBQ116saiMWM9rG2DJDhCaJxti/AKDzno2Hn5wOxdcM8GCIpTOxpNWbmvsXt4j0hpz9DeovtCfCoMvFZZ/yXsbI03dxwrpbDC/ds2SIIjtKX4EJTnqR2Fpth2N2w+XExFbYPK0bQznQ4ie6odhebYklnZHu4Eej3c+itc+G/phddCVJAPKTGBWK2e0QdPEhyhHQtmw8sDYMsHakeiKV3D/eka7keDxco6T2nbYLVKNdk2lFbVs03aMwiVjGucprItcndnkuAI7bA1wbPtrBF2YzytV0zBPvh3Mnx3r9QjaWFNZgEWK3SX9gzOZa6Hw2ugxgMX758D25T4yv3H3b5tgyQ4Qjtsuxuy1kNtubqxaMxY2zqcfR6S4GQuUfqQlR5VpqmEnUxPuch7U5VLxmK1I9GUoQmh+HgpbRv25rn3cVgSHKEdYYkQmgiWhqbqtgKAEd3CMTa2bThS6AFtG2wfKtKewYHVarUX+Bsr/aecKz5VuZbt4g58vAykJio7N1e6+TSVJDhCW7o31kORg46DQB8vBncNBWC5u09TNdQ2a88gCU5zhwqrOFqstGew9QYSTmJv27BEpklbsG0Xd/cpcacmOEVFRcycOZOgoCBCQkK49dZbqahoe1teUVER9913Hz179sTX15cuXbpw//33U1rqOEeq0+lOuHz66afOfCvCVaQIV5vG2tbhuPs01ZG10FANAdFN664E0PR/O7RrGP4mac/gVPa2DdnKmjBhZzvWrD9YRE29+7ZtcGqCM3PmTHbu3MmiRYtYsGABK1as4I477mjz8ceOHePYsWO88MIL7Nixg/nz57Nw4UJuvfXWEx773nvvkZOTY79Mnz7die9EuEzCGNAZoDADig+rHY2m2M6q1mYWUm924x1ItuS12/my/qYFW4Jj+78WTuTtB11HKrczflM3Fo3pHhVATJAPdQ0WNhwsUjucs+a0U4Tdu3ezcOFCNm7cyNChQwF49dVXmTZtGi+88AJxcScW9urbty9fffWV/d/dunXjH//4B9dffz0NDQ0YjU3hhoSEEBMT46zwhVp8giDtSQhLAn85yDfXNy6YMH9viirr2HKkxH2nMDKaJTjCrrbBzJrGMgDjJMFxje4T4cBSZU3YiHvUjkYzdDodY5Mj+Pz3o6zYd9xtE26njeCsXbuWkJAQe3IDkJaWhl6vZ/369af9PKWlpQQFBTkkNwD33HMPERERDBs2jHnz5p10O1ttbS1lZWUOF6Fhox6AXhcrZ1jCTukV4+bTVBYL9EiDmP5NJfMFAJsOFVNdbyYy0ESv2EC1w+kYuqcp14dXQ321urFoTNN2cfddaOy0BCc3N5eoqCiH+4xGI2FhYeTm5p7WcxQUFPD000+fMK311FNP8fnnn7No0SKuuOIK7r77bl599dU2n+fZZ58lODjYfomPjz/zNySEBrj94j+9HtL+CneuhAD3PCt0luX7mnZP6WTqzjUiU2DS3+Hmn5X1OMJudPcIdDrYm1dObmmN2uGclTNOcB577LFWF/k2v+zZs+ecAysrK+PCCy+kd+/e/PWvf3X42uOPP86oUaMYNGgQjz76KI888gj/+te/2nyuOXPmUFpaar9kZWWdc3zCybI3w9JnIGer2pFoiq02yvbsUooqpVGgJ7EnOMlS/8ZldDoYeR90Gqwk38Iu1N+b/p2CAeylC9zNGa/Beeihh7jppptO+pikpCRiYmLIz893uL+hoYGioqJTrp0pLy9nypQpBAYG8s033+Dl5XXSx6empvL0009TW1uLyXRiFm4ymVq9X2jY2rmw40uwmCF2gNrRaEZ0Y6+YPbnlrNx/nEsHdlI7pNNnsSgLjLuOAG9/taPRlLyyGvbklqPTNU0NCKG2scmRbD1ayor9BVw11P1mPs44ZY2MjCQlJeWkF29vb0aMGEFJSQmbNm2yf++SJUuwWCykpqa2+fxlZWVMmjQJb29vvv/+e3x8fE4ZU3p6OqGhoZLEeBLb3HimVBltyTZN5XZz43nb4aMr4D99lMRV2NnWVPXvpCwkFy626zv49h4okdH95mzJ9qr9xzFb3K9WkNPG5Hr16sWUKVO4/fbb2bBhA6tXr+bee+9lxowZ9h1U2dnZpKSksGHDBqApuamsrOTdd9+lrKyM3NxccnNzMZuVA+IPP/zAO++8w44dO8jIyOD111/nmWee4b777nPWWxFqsO2wObYFKtxzeNRZxrprrxjbVtz44aA3qBuLxtjaM7jrbhW3t3YupH8oJ1QtDOoSQqDJSHFVPduz3a9nl1MnHT/66CNSUlKYOHEi06ZNY/To0bz11lv2r9fX17N3716qqpTS85s3b2b9+vVs376d7t27Exsba7/Y1s14eXkxd+5cRowYwcCBA3nzzTd58cUXefLJJ535VoSrBUYrO21A2cYp7Gy9YvLK3KxXzP7GBKdHmrpxaIzZ0tSeQbaHq6RbYwV1qYfjwMugZ1Tjzs3le93vRNOppTLDwsL4+OOP2/x6QkKCwxno+PHjT3lGOmXKFKZMmdJuMQoN654GuduUg07/q9WORjNsvWKW7zvOin3HSYkJUjukU6spVZqoQtP0owCUBeMlVfUE+hgZGB+idjgdU/c0WPYMHFgB5gYwSBVpm/E9I1m4M5dl+/J5IK2H2uGcEVk2LrTL9kGYsVhZoCrsbGf6y9zlrOrAcrCaIbwHhCaoHY2m2M6MR3WLwGiQQ7Iq4gaCbyjUlkL272pHoynjeirHmq1ZJRS72c5N+WsS2hU/DLwDle7ipUfUjkZTxjcedDYeKqKitkHlaE5DxiLlWkZvTmCraWT7IBEq0Bua1v3JNJWD2GBfekYHYrHCygz32tggCY7QLoMX3LEMHjkgZ/0tJEb40yXMj3qzlTVaP+hYrcooHEiC00JpVT1bjhQDssBYdfZ1OLLQuCVb8u1u63AkwRHaFtFddty0QqfT2UdxlrtD24ZZ38HkZyFhlNqRaMrqzAIsVqW5YacQX7XD6di6NyY4DTXQUKtuLBozPrnpWGNxo+3ikuAI92C1yjqcFmwJzrK9Gt8urtNBRA8YcTd4yYd4cyuatWcQKguMgYf2wd1rwSg11ZobkhCKn7eBgopaduW4Ty9HSXCE9i1/Hl7qB3t+UDsSTRmRFIG3UU92STWZxyvUDkecIavVytK9SrV3WX+jEYHRakegSSajgZHdGreLu8OIcSNJcIT2VRdDaZYs/mvB19tAamIYoOHdVLUV8MXNsPl/Ur24hd055eSV1eLjpbf/PwqNqK+WEeMW3HEdjiQ4Qvu6N1v8p+WpGBWM7xkFaDjBObQSdn4NK/4FOjncNLdsnzJ6M6pbBD5ess5MM768FZ5LgJwtakeiKbZ1OJuOFFNWU69yNKdHjjhC+7qOAqMPlGVD/m61o9EU2zqcDQeLqNTidnHbqFuPC5S1OMJu2R4lKR2fEqVyJMKBuVZZaLx/kdqRaEp8mB9Jkf6YLVZWu0kfPElwhPZ5+ULCaOV2hhx0mkuK8Cc+zJc6s4W1mYVqh+PIam36kJDt4Q5Kq+rZ1Lg9fLxsD9eWHpOU6/2/qhuHBo1PVpJxd1mHIwmOcA89JivX++Sg05xOp7MfdGxTHppRmAklh8HgDQlj1I5GU1ZmKN2Zu0cFEB/mp3Y4ornuFyjX2Zul0W8L49xl52YjSXCEe+jReNA5slbpayTsNLtd3DY91WUEmALUjUVjljZOT02Q3VPaExQLMf0Aq3QXbyE1MQwfLz25ZTXsy9P+zk1JcIR7CEtUzqyG3Q71NWpHoykjuoXjbdBztLiazOOVaofTZP8vyrVMTzmwWKwsbxxtm9BT1t9okn2aSqbEm/PxMjA8KRyAZXs1NmLcCklwhPu4/kuY+pzUqmjBz9tIapJtu7hGDjoWC5jrAR0kT1Y7Gk3ZeayMgoo6/L0NDE2Q7eGaZEtwMn6T8gYt2JLyJXs0cqw5CUlwhPAA45I11rZBr4ebFsDDmRCRrHY0mmIr7je6h1KoUWhQp6GQPBXG/gnM7tVB29nOb9z19/vhYkqrtL1dXP66hHtpqIODK6D4sNqRaIqtHs76A0VU1Wlou7h/uGwPb8GW4IyX6SntMhjhuk9h5H3SXqSF+DA/ekQFYLZYWbFfIydUbZAER7iXb++C9y+G9I/VjkRTukU2bRdfnaHydnGrFapL1I1Bo4oq60jPKgGaFocL4W5sozhLNT5NJQmOcC9J45VrqVHhQKfTMTFFWZu0eHeeusHkbIXnk+Cjq6XydAsr9h3HaoWUmEBig2VkQPMqjkP6J7JdvAV7grM3H7OGu4tLgiPci227+LHNUKHtswdXsx10luzJx6LmQWf/r2A1g94o01Mt2BaBT5Dqxe7hkxnw7Z2wb6HakWjKkK6hBPkYKa6qt49IapEkOMK9BMZA7ADltjTfdJCaFIaft4H88lp2HitTL5B9jdvDkyepF4MGmS1W+yJw2R7uJmwnVDJi7MBo0DO2cWPDkj0qjxifhCQ4wv3Yqxr/om4cGmMyGhjTIwKAxWoddCoLIHuTcruHJDjNpWeVUFxVT6CPkcFdQtQOR5wOW4KTubSx7IGwmdjLNmKs3ek7SXCE+7HVVclcIgedFib2UtbhqFajYv8iwAox/SEoTp0YNMq2NmpcciRGgxx63ULsIPCPhLpyOLxG7Wg0ZVxyFDod7M4p41hJtdrhtEr+yoT7iRsEfuFQWwZZG9SORlNsUx/bjpaSX6ZCxWfbWgUp7neCxbuVpDOtlxSqdBt6fbMRY1mH01yYvzeDu4QCTaUPtEYSHOF+9Aa45FX4wwroOlLtaDQlMtDEgPgQQIWDjrleGVWDpg8FAUBWURV788ox6HWyPdzd9JyqXO/9SXYFtmDf2LBbEhwh2k/KhcpiY9mlc4KJjQedxa4+6FjMcMHfoO+V0Gmwa19b435rnJ4a2jWUED9vlaMRZ6TbBDCYlOKixYfUjkZTbAnO6swCauq119JCEhwhPIztoLMqw8UHHS8fGHoLXPmuMsom7GR6yo15+8O1n8Cf9itNf4WdUs/Jh5p6C2szVS4w2gpJcIT7OrwGvv4DbJqvdiSa0icuiOggE1V1ZtYd0N5Bp6Mpq6m3/z+k9ZYExy11nwgBMrXYkk6ns9d00mLzTUlwhPvK2QbbPoVtX6gdiabodDrOT3HxbqqSI7D+TSjJcs3ruZEV+47TYLGSFOlPYoS/2uEI0a4mNktwrBpboyQJjnBftsV/R9ZAVZG6sWhMWq+mdTguOejs+g5+fgS+u9v5r+VmZHrKQ2z/EuZfBFs/UzsSTRnZLQKTUU92STW7c8rVDseBJDjCfYV2hei+YLVIpdEWmh909uVVOP8F9/ykXPe80Pmv5UYazBb7bjZJcNxcwX44tBL2/KB2JJri621gTA9l+m7RLm1VNZYER7i35ls4hZ2vt4FR3ZWqxr85u/lmZSFkrVNup0xz7mu5mU2HiympqifEz0uqF7s727EmYwnUq1BjSsMmNa4tW7Q7V+VIHDk1wSkqKmLmzJkEBQUREhLCrbfeSkXFyc8mx48fj06nc7jceeedDo85cuQIF154IX5+fkRFRfHwww/T0NDgzLcitKpn4wdqxmJoqFU3Fo2x7aZyeoKzb6EyihbTD0K6OPe13MzixjVQE3pGSfVidxc7AALjoL4SDq1SOxpNmdgrCr0OdmSXka2hqsZO/YubOXMmO3fuZNGiRSxYsIAVK1Zwxx13nPL7br/9dnJycuyX559/3v41s9nMhRdeSF1dHWvWrOH9999n/vz5PPHEE858K0KrYgdCYCzUVcDBlWpHoykXNJ5VbTlS4tyqxntleqottuRSpqc8gE4HPacot2XE2EF4gIkhXZWqxr9paJrKaQnO7t27WbhwIe+88w6pqamMHj2aV199lU8//ZRjx46d9Hv9/PyIiYmxX4KCguxf+/XXX9m1axcffvghAwcOZOrUqTz99NPMnTuXuro6Z70doVV6PSRPUdbiWKQvVXPRQT4MbKxq/KuzDjp1VcroGcj0VAsHjldw4HglXgYdY5Mj1A5HtIfkxmmqfQulqnELthOqX3dpZ5rKaQnO2rVrCQkJYejQofb70tLS0Ov1rF+//qTf+9FHHxEREUHfvn2ZM2cOVVVVDs/br18/oqObzogmT55MWVkZO3fubPX5amtrKSsrc7gIDzLtX3DX6qY5cmE3uU8M4MQEJ28HWBogOF5psCnsbLunUhPDCfTxUjka0S4Sx4KXH5RlQ+42taPRlAt6K8ea9QeKKK3Wxsmm0xKc3NxcoqKiHO4zGo2EhYWRm9t2hnfdddfx4YcfsnTpUubMmcP//vc/rr/+eofnbZ7cAPZ/t/W8zz77LMHBwfZLfHz82b4toUUG+fBoy6Q+yt/G2swCymqccNCJHwaPHIAZH0nbjBYW2aenok7xSOE2vHyUdX89JiutSYRdYoQ/PaICaLBYWaaR5ptnnOA89thjJywCbnnZs2fPWQd0xx13MHnyZPr168fMmTP54IMP+Oabb8jMzDzr55wzZw6lpaX2S1aWFCPzSHWVylZOYdctMoBukf7Um60sdVbRP58gZQGmsCuoqOX3Q0ptJqle7GGueAdmfi791lphO6H6dac21uGccYLz0EMPsXv37pNekpKSiImJIT/f8YDa0NBAUVERMTExp/16qampAGRkZAAQExNDXp7jD8/277ae12QyERQU5HARHubAMng+Cb64We1INMdp01QWS/s+nwf5bVceFiv06xRM51A/tcMR7UlGKttkm6Zatjef2gb1R7jOOMGJjIwkJSXlpBdvb29GjBhBSUkJmzZtsn/vkiVLsFgs9qTldKSnpwMQGxsLwIgRI9i+fbtD8rRo0SKCgoLo3bv3mb4d4Smi+4G5DvK2K20DhN2kxgRn2Z789m2+ufiv8OY42PNj+z2nh1i4U5kun9L39E/mhJspOQLH0tWOQlP6dwomOshEZZ2ZNRpovum0NTi9evViypQp3H777WzYsIHVq1dz7733MmPGDOLi4gDIzs4mJSWFDRs2AJCZmcnTTz/Npk2bOHToEN9//z2zZs1i7Nix9O+vLGCcNGkSvXv35oYbbmDr1q388ssv/OUvf+Gee+7BZDI56+0IrfMPh/jhym35wHXQ/KDTbh1/rVbYvQBy0pXEUtiV1dSzOqMAaBo9Ex5m2xfwUj+lPYmw0+t19pIIWqhq7NQ6OB999BEpKSlMnDiRadOmMXr0aN566y371+vr69m7d699l5S3tze//fYbkyZNIiUlhYceeogrrriCH35oKo1tMBhYsGABBoOBESNGcP311zNr1iyeeuopZ74V4Q56X6Jc7/pe3Tg0Rq/XMalx6PiXne20hbNgHxRlgsEbuqe1z3N6iKV78qk3W+keFUD3qAC1wxHOkDBKuc5aD+Xa2RatBbYR49925WGxqLuV3ujMJw8LC+Pjjz9u8+sJCQkOjQDj4+NZvnz5KZ+3a9eu/PSTFFoSLfS6GBY+BkfWQnkeBMriTptJfaL537rD/LY7D7PFikF/jusIbKNkiWPBFHjuAXqQhTsap6dk9MZzBcVB5/Pg6EbYswDOu03tiDRjeFIYASYj+eW1bD1awqAuoarFIrXDhecI7gydhgBWaYjXglKLxUhBRR1bjhSf+xPubvz59pTifs1V15lZtvc4IOtvPF6vi5Xr3XKsac5kNDCupzaab0qCIzxL70uVa5mmcuBt1DOxsTfVOU9TFR+GY5tBp286yAsAVuw/TnW9mU4hvvSJk92aHi3lIuX64EqoKlI3Fo2Z1DsaXy8D9WZ1d1pKgiM8S5/L4IKn4eKX1Y5EcyY12y5uPZcy87sbk8euoyBAitg198uOpt1TOtlO7NnCuyktYqxm2Puz2tFoypS+MWx54gL+fKG6O5slwRGeJaQLjLofwhLVjkRzxiVH4m3Uc7iwij255Wf/RDH9lUSy/zXtF5wHqGuw2JtryvRUB2EbwZTmmw5MRgM+Xga1w3DuImMhhHb4m4yM7RHJb7vz+Gl7Dr1iz3IKJWmcchEO1h0opKymgYgAE4NVXFgpXKj/NRCRDD0uUDsS0QoZwRGex2KB9E/g05lQXaJ2NJpy8QClYOaP23LObZpKnMBW3O+C3tHnvktNuIewROh7uewk1ChJcITn0eth9UvK9s19C9WORlMm9orG26jnQEElu3POYppq0/uQf/a95jyV2WK199+R6SkhtEESHOGZZDdVqwJMRiY0buH8cfuxM/vm0mz44X74b6pSZ0jYbT5STEFFLYE+RkYkhasdjnCl+hpY/i94d7JyW2iGJDjCM/VqrGqc8RvUnsOCWg90YX+lVcoZT1PZdk/FD5ciii0s2Kokixc0jpCJDsTgDZveg6x1kLlY7WhEM/KXKDxTdB8ISwJzLez/Ve1oNGViShQmo55DhVXsPFZ2+t+46zvlus90p8TlrhrMFn7cngPAxQPiVI5GuJxe3zRivONrdWMRDiTBEZ5Jp5Npqjb4m4yc31j0b8G2nNP7prIcOLJOuW0bHRMArD9YREFFHSF+XozuEaF2OEINfa9Urvf+BHWV6sYi7CTBEZ7LluDs+wVqK9SNRWMu7N+4m2r7sdObptr9A2CFzsMguJNzg3MzPzROT03tG4uXQQ6pHVKnwRCaAPVVUvRPQ+SvUXiu2IEQ3h3iBkGFLIpt7vyUKHy9DGQVVbM9u/TU3yDTU62qa7Dwc2P1YtsWfNEB6XTQ9wrltkxTaYYkOMJz6XTwh5Vwy89KWXVh5+dt5PxeyjTVj6eapqqrhPxdym2ZnnKwcv9xSqvriQw0kZoou6c6NNs0VcYiqb+lEZLgCM/m7ad2BJp1UT9lxGHBqXZTefvDQ3tg1vcQEu+i6NyDbXrqwn6xUtyvo4vuDZ2GKlPjsnNTE6RVg+gYKguhMh+ieqkdiWaM7xmFn7eB7JJqth4tZWB8SNsPNpqkPUML1XVmFu1Spj4vGSi7pwRw22/KyLHQBBnBEZ5v13fw72RY8Ee1I9EUX28DE3sp9WxsdVxOUF8D0tKhVUv35lNZZ6ZTiC+DTpYcio5DkhtNkQRHeL5OQ8FihiNroSRL7Wg05cLGaaofth3DbGklkVn5b3hlEGz/0sWRad/36UpSePGAOHTywSZsrFbI2wlZG9WOpMOTBEd4vuBOkDBaub3jK3Vj0ZgJKZEE+3qRV1bL2sxCxy9arbD9cyg+qE5wGlZeU8+SvfmA7J4SLWz5H7w+EhY9rnYkHZ4kOKJj6Ne4w0FGIhyYjAYuaqyJ8/Xmo45fPLoRig+Blz/0nOr64DRs0a486hosJEX60zs2SO1whJZ0TwN0MmKsAZLgiI6h1yWg94K87ZC/W+1oNOXywZ0B+HlHLpW1DU1f2PaZct3rYmUnlbCz7Z66RKanREtBcdB1lHJ75zfqxtLBSYIjOga/MOhxgXJbRnEcDO4SQkK4H9X1Zn7ZqRStw1zfVLCs/9XqBadB+eU1rNhfAEjvKdGGvpcr19s/VzeODk4SHNFx2Kapdn0rO4Oa0el0XDZIGcX5enO2cmfGYqguAv8oSJTt4c19t0VZkD2oSwjdIgPUDkdoUZ/LlC7juduVi1CFJDii40ieChf+G275RbZztnDZIKW/1OrMAnJKq5ump/pdCQYpl2VjtVr5YpOyruLKIZ1VjkZoll9Y07q1LR+pG0sHJgmO6Di8/eC828BfOj631CXcj2EJYVit8O2WY8rPadD1MGCG2qFpyvbsUvblVWAy6rmov0xPiZMYOFO5PrBMRoxVIgmO6LjkoOPg8sHKKM7Xm49i7ToSLp0LsQNUjkpbvvhd2Wk2uU8Mwb5eKkcjNK3bRLj2U7hzpYwYq0QSHNHx7Poe3rkAtn6qdiSaMrVfLN5GPfvzK9h5rEztcDSnpt7M9427p2R6SpySwahMUxkkEVaLJDii4zm+F45uUApyCbtgXy9mdDfzlPE9Vq1aonY4mvPb7jxKq+uJDfZhVHeZ5hRnwGJR2p4Il5IER3Q8A68DnR4Or4bCTLWj0ZRbfFcwy7iIAXv+Q73ZonY4mvLlJmV66vLBnaRzuDh9m/8HL/eHDW+pHUmHIwmO6HiCOynz4yCjOM2Z6+ma9S0AH9SOZ9ne4+rGoyG5pTWs2Kf8PK4cEq9yNMKtWBqgNAvSP5J1fy4mCY7omAbfoFynfwLmhpM/tqPY9wu6ijwqjKH8ZhnCJxuOqB2RZnyzJRuLFYZ2DSUxQqo6izPQ93Iw+sLxPZC9We1oOhSnJjhFRUXMnDmToKAgQkJCuPXWW6moqGjz8YcOHUKn07V6+eKLL+yPa+3rn34qC0bFGUieCn4RUJELGb+pHY02bH4fgIZ+11KPkaV78zlaXKVyUOprXvvmqqGyuFicIZ9gpd0JQPqH6sbSwTg1wZk5cyY7d+5k0aJFLFiwgBUrVnDHHXe0+fj4+HhycnIcLn/7298ICAhg6lTHZn/vvfeew+OmT5/uzLciPI3Ru6nGi0xTQelRe6IXMvo2RnUPx2qFTzdIs8AtWSUcOF6Jj5eeaf2kc7g4C4Maa+Js/wrqq9WNpQNxWoKze/duFi5cyDvvvENqaiqjR4/m1Vdf5dNPP+XYsWOtfo/BYCAmJsbh8s0333D11VcTEOBYEj0kJMThcT4+Ps56K8JTDboBup0P/a9ROxL1bfkQrBZIGAPh3ZiZ2hWATzdmdfjFxh+vV6bqpvWNJdBHtvyKs5AwFoK7QG0p7P5B7Wg6DKclOGvXriUkJIShQ4fa70tLS0Ov17N+/frTeo5NmzaRnp7OrbfeesLX7rnnHiIiIhg2bBjz5s3DepLFW7W1tZSVlTlchCAqBW74BnpfonYk6jMFQUA0DL4RgAt6RxMZaKKgopZFu/JUDk49RZV19to314/oqnI0wm3p9U3r/mQ3lcs4LcHJzc0lKirK4T6j0UhYWBi5ubmn9RzvvvsuvXr1YuTIkQ73P/XUU3z++ecsWrSIK664grvvvptXX321zed59tlnCQ4Otl/i42UXhBAORtwNf9wJfaYD4GXQc81Q5e/ko/WHVQxMXZ9tzKKuwULfTkEMig9ROxzhzgbfCAOvh2n/UjuSDuOME5zHHnuszYXAtsuePXvOObDq6mo+/vjjVkdvHn/8cUaNGsWgQYN49NFHeeSRR/jXv9r+pZkzZw6lpaX2S1aWrCsQzVTkw7J/wqFVakeiLoOXQ9XVGcPi0elgdUYhB463vTnAU5ktVj5cpyR3s0YkoJNy++JcBEbD9LkQN0jtSDqMM24T/NBDD3HTTTed9DFJSUnExMSQn5/vcH9DQwNFRUXExMSc8nW+/PJLqqqqmDVr1ikfm5qaytNPP01tbS0mk+mEr5tMplbvFwKAlf+G9W9Aj0mQMFrtaFyrIh+OpUP3iaA3OHypc6gfE3pGsWRPPp9sOMKfL+ytTowqWbInn+ySakL8vLhkgDTWFMLdnHGCExkZSWRk5CkfN2LECEpKSti0aRNDhgwBYMmSJVgsFlJTU0/5/e+++y6XXHLJab1Weno6oaGhksSIszPsDlj/Juz/FQoyIKK72hG5zsZ3YPlzyjbWa07cwjoztQtL9uTzxaajPDSpJz5ehlaexDN9sPYQANecF9+h3rdwsvw9sPY1iO4Dw+9SOxqP5rQ1OL169WLKlCncfvvtbNiwgdWrV3PvvfcyY8YM4uKUs6Hs7GxSUlLYsGGDw/dmZGSwYsUKbrvtthOe94cffuCdd95hx44dZGRk8Prrr/PMM89w3333OeutCE8X3k0ZvQHY8Ka6sbhSfbWS4AD0ubzVh4zvGUVcsA8lVfX8vCPHhcGpK/N4BSv3F6DTwfWpsrhYtKPsTUppirVzwWJWOxqP5tQ6OB999BEpKSlMnDiRadOmMXr0aN56q2kFeX19PXv37qWqyrGY2Lx58+jcuTOTJk064Tm9vLyYO3cuI0aMYODAgbz55pu8+OKLPPnkk858K8LTDb9TuU7/GGpK1Y3FVbZ9BlWFyvbVXq3vJDPodVw7rAsA/1vbcRYb297rxJRo4sP8VI5GeJS+l4NvmNK+Ye/Pakfj0XTWk+2v9lBlZWUEBwdTWlpKUFCQ2uEILbBa4b/DlXLqk5+BEfeoHZFzWSzK+y3Ye8r3m19Ww6jnllBvtvLVXSMY0jXMhYG6XkVtAyOeWUx5bQMf3DKMscmnniYX4oz89ldY9R9IHAc3fq92NG7lTD6/pReVEAA6HaT+Qbm9/k3PHzrOXKwkN96BSsHDk4gK8uGyQZ0AeHP5AVdEp6pvtmRTXttAUoQ/o7tHqB2O8ERDbwGdHg4uh+N71Y7GY0mCI4RN/xkQGAdJ46DOw7dFr31NuR5yI/icehTzjrFJACzanUdGvuf+bKxWKx+sOQTADSO6otfL1nDhBCFdoOc05bYU/nMaSXCEsPH2gwe2wiWvKg3yPFVdpbLOSKdvGrU6he5RgaT1isZqhXdWeu4ozuLd+ezPr8Df28AVQ6SxpnCiYbcr1+kfQ2WhurF4KElwhGjO6K12BM7n7Q+3L4W71ylnkqfpznHKKM7Xm7PJL6txVnSqsVqtvLo0A4AbRiQQJH2nhDMljoOkCTB6dsc47qhAEhwhWpOzFda9oXYUzqPTQWTPM/qWoQlhDOkaSp3ZwnuN0zieZHVGIVuzSvDx0nPbmES1wxGeTqeDWd/CuIfBFKh2NB5JEhwhWio6AG+OhV/mQMF+taNpX0fWndM2+D80rsX5cN1hymvq2ysqTXhtqfJ/PeO8LkQESNFQIdydJDhCtBSWBMlTwWqBFR7UGK+6BD6+Bl7sDbk7zuop0npFkxTpT3lNA59sONK+8ano90NFrDtQhJdBxx8ap+KEcAmrFfb9Ah9eAbXlakfjUSTBEaI14x9Trrd/4TmjOGvnQk0JBMdDVK+zegq9XmcfxXl31UHqGiztGKB6Xmtce3PlkM7EBvuqHI3oUKwWWDgHMn6D3+epHU370Eh5PUlwhGhN3EBlG6enjOJUFsK6/yq3J/zfCY01z8T0QZ2ICjSRV1bL15uPtlOA6tl+tJRle4+j18Gd47qpHY7oaPQGGPOQcnvNq0oLFXf39R3w40NKM18VSYIjRFvGPapce8Iozur/KLV9YgcojTXPgclosNfFeem3/VTXuXdRxLmNozeXDuxE13B/laMRHVL/q5WWKZXHYfMHakdzbrI3w/bPYeO7kuAIoVmeMopTngsb3lZun/+4snvjHF0/vCudQnzJLathvhvvqNqXV87CnbkA3D1eRm+ESgxeMPpB5fbql6GhVtVwzsm+X5Tr/ldDTF9VQ5EER4iTGf8YBMZC5/PUjuTsrXgBGmogPhW6p7XLU/p4GZh9QTIA/12WQUlVXbs8r6u9+Os+AKb2jaFHtGzVFSoadL1yrCnLVrqNu6sJc+DmhXD+X9SORBIcIU4qdgA8uL2p6qi7sVoBK+gM7TZ6YzN9UCdSYgIpr2ngv8sy2+15XWVtZiELd+Zi0Ov4Y2OyJoRqjKamtThLn4GaMnXjORddR5xREVFnkQRHiFMxuHFFW50OLvw3PLgNEse061Mb9DoenZoCwPw1h8gucZ/FkWaLlacW7AJgZmoXkmX0RmjBkJsgeYrSLsbdiv9lb1KmwzVEEhwhTofVCju+hs9ngcUNt0YHO6ev0vjkSIYnhVHXYOE/i/Y55TWc4Yvfs9idU0aQj5EH02T0RmiEwQuu+wxSLmzX0VanM9fDV7fBywPhwHK1o7GTBEeI01FZAN/fB7u+g62fqB3NqVmtsOhJyN/j1JfR6XQ8NlWpqfPV5qPsydX+sHp5TT0v/LoXgAfSkgnzlz5AQqPcpfDf5veVCvCmQOg0RO1o7CTBEeJ0BETCuEeU24ueUKoCa9mOr2D1S/BO2jm1ZjgdA+NDmNYvBqsV/vnzHqwaKfLVlrlLMymoqCMpwp8bhndVOxwhWvf7PHipH+z/Te1ITq66BJY9p9we9wiYAlQNpzlJcIQ4Xal3QUQyVBUoiwC1qroYFjZWYh71APgEO/0lH56cglGvY9ne4/y0XVvz8M0dLqxk3qqDAPzlol54G+UQKDSqMFP5W/5ljjIFpFW//gUq8yG8Owy+Ue1oHMhftxCny+gN0xrr4Wx8G3K2qRtPW377q1IwLCIZRt3vkpdMjPDn7gndAXjiux0UVmizjsezP+2hzmxhTI8IJvSMUjscIdo29mHwC4eCfUrRPC3KXNK0pf2SV5VjpIZIgiPEmUgaD30uU4r//fSwZnqu2B1ZB5vmK7cveknZeuoi907oTs/oQAor6/jrD7tc9rqna+GOHBbuzEWvg8cv6o3OnRZxio7HN6SplsyyZ6BUY21Raivg+weU28PugK4j1Y2nFZLgCHGmJv0DvPwhax0cWqV2NE0a6uCHB5Xbg66HhFEufXlvo55/XdUfg17HD1uP8ctO7UxV5ZbW8NjX2wH4w7husi1cuIfBN0LcYGUd3Td3gkVDbVEMXjDgGghLgolPqh1NqyTBEeJMBXeCC1+AG39o99oy5yT9Qzi+WxnWvuBpVULo3znE3qfqz9/s0ESFY4vFyp++2EpJVT19OwXxR9kWLtyF3gBXvKOcUB1aqbRx0AqjSRlhumutphYWNycJjhBnY+B1kDhW7SgcDbpBSWymPg9+YaqF8cDEHnSPCqCgopanNDBVNW/1QVZlFODjpeflGYNkYbFwL+HdYNrzgE5puaK2hlowNzT928tHvVhOQf7ShThXRQdh1X/UjkIZMh51P/S7UtUwfLwMPH9lf3Q6+HpLtqpTVbuOlfH8QqXmzeMX9aZbpDbPNIU4qYEz4a41MOH/1I4EfvkzvHM+5O5QO5JTkgRHiHNRXQxvjVd2Lm39zPWvb66Hlf+GuirXv/ZJDO4Sym2jEwH442fp7Dzm3Fo8rampN/PgZ1uoM1tI6xXNdcPU740jxFnR6SC6d9O/1VqLs+Htxh2kW7W36LkVkuAIcS58QyH1TuX2ggfh+F7Xvv4vf4bFT8FHV2luR9cjU1IY1T2cqjozt87/ndxS1w2vWyxW/vLtDvblVRARYOK5K/rJrinhGQoz4Z2Jrj+hyvgNfn5UuT3xSeg5xbWvfxYkwRHiXI17BBLHQX0VfH6j66ocp38MG95Ubo+4R3O9a7wMev47cwg9ogLILavh1vc3UlnbcOpvPEdWq5Wnf9zFl5uOotfBv68eQHiA67bLC+FU2z6HY1vgu7thz0+uec383fDFzWA1w4DrYPQfXfO650gSHCHOlW2nQ0C0sovpg0ugqsi5r5m1oWlL+LjHIGWac1/vLAX7ejHvpvOICPBm57Ey7v9kC2aLc0ea/rNoH++tPgTA81cOYFxypFNfTwiXGvcI9LsaLA3wxY2Qsdi5r1dxHD6+GmrLoOsouPhlzZ1MtUUSHCHaQ0AUXP81+EUo89PzL1IadDrDvl/gg0vBXAvJU2Dco855nXYSH+bH27OGYjLqWbwnn7/9sNNp/areXJ7JK0syAPjbJX24cohzuqgLoRq9Aaa/Dr0uAXMdfDoTDq123ustexZKjij1bq75UHPVik9GEhwh2ktMX7jpR2UkxzcEvPza/zV2fAWfXKtMh3WbCFe8C3rt/xkP6hLKS9cMBOCDtYe5+6PN7T5d9dH6wzz7s9I9/eHJPblxZEK7Pr8QmmEwKn/7PSZBQ7UywnL0d+e81vjHILofXPe5quUnzobOqvXWv05QVlZGcHAwpaWlBAUFqR2O8DRFB8A/EkxOqJZbmAnvXgA9JsMlryhbw93I579n8edvtlNvtpIcHcBbNwwlIcL/nJ6zus7Mcwv3MH/NIQDuGt+NR6ektEO0QmhcfQ18fBUcXAE9p8G1n7TP8xbsh4geTf82NyhJlQacyee30079/vGPfzBy5Ej8/PwICQk5re+xWq088cQTxMbG4uvrS1paGvv373d4TFFRETNnziQoKIiQkBBuvfVWKioqnPAOhDhLYUmOyc23d8Pa/zoWxzoTVUVgsSi3w7vBHctg+n/dLrkBuHpoPJ/eMYKoQBP78iq45LVVLNubf9bPtzWrhAtfXWlPbv4wNolHJvdsp2iF0DgvH7j2U0ieClP+ee7PZ7Uq1ZLnDoOtnzbdr5Hk5kw5LcGpq6vjqquu4q677jrt73n++ed55ZVXeOONN1i/fj3+/v5MnjyZmpqm7aUzZ85k586dLFq0iAULFrBixQruuOMOZ7wFIc7d4TWQ/hH8MgfeHHNmc+W15bD8eXh5AOz6pun+kC5us8ivNUO6hvLDfaMZ1CWEspoGbp6/kTlfb2NfXvlpP0e92cJ/Fu3j8tfXcOB4JdFBJt6/ZRhzpvWS7eCiY/H2h+s+hdCuTff99jfY/uXpl46wWJS1ffMvgkVPKM2Eszc7J14XcvoU1fz583nwwQcpKSk56eOsVitxcXE89NBD/OlPfwKgtLSU6Oho5s+fz4wZM9i9eze9e/dm48aNDB06FICFCxcybdo0jh49Slxc3GnFJFNUwmUsFtjygVIIsLpYua/PZZByEcQPg+B4x2TFYlYKaO1ZoBTwqypU7u91CVzzP5eH70y1DWae/G4nn27Mst83unsEN49KYELPKPR6x0SlrsHCmswCftmZy6JdeRRUKH2uLh4Qx9OX9iHEz30WPwrhNIfXwHtTldvR/aDnVKVmTeygE9fr1VcrIzXr/gsF+5T79EZlNOi82zR5InUmn9+aSXAOHDhAt27d2LJlCwMHDrTfP27cOAYOHMjLL7/MvHnzeOihhyguLrZ/vaGhAR8fH7744gsuu+yyVp+7traW2tpa+7/LysqIj4+XBEe4TlWRUpBv03yg2Z/c/ekQplT85ZPrlGJa5qbfVcK6wfl/ht6XucVi4rOx4WAR81Yd5Nddudh2kIf4eRHm502gj5EAHyPeBj2/HyqmvNnC5DB/b/56SR8uGXB6JzZCdAi26uYrXgBLfdP9ATHQabBSpC+qcY3amtfg1z8rt01BMORGpXBpsHZ3H55JgqOZibXcXKVfTXR0tMP90dHR9q/l5uYSFRXl8HWj0UhYWJj9Ma159tln+dvf/tbOEQtxBvzC4OKXlAPI1k+VOjbluRCa0PQYnU5JbgzeENETht2u9KBx0/nv0zUsMYxhiWFkFVXxv3WH+XTDEUqq6impqj/hsZGBJib3iWZynxiGJ4XjZfDMpE+Is2bwUnY+nXc77P8V9v4EmUugIle5PeSmpgTHLwyCu8DwO5VmvT6edcJ/RkfOxx57jOeee+6kj9m9ezcpKdrawTBnzhxmz55t/7dtBEcIl4sbpFxAWXTcfAg47W8w6e/KGhu9QZ34VBQf5sf/TevF7AuSOXC8koraBspr6imvaaCyroGUmEAGxYeeMHUlhGiFfzgMvFa5NNTCoVXKLsyI5KbH9J4O/a/x2OPNGSU4Dz30EDfddNNJH5OUlHRWgcTExACQl5dHbGys/f68vDz7lFVMTAz5+Y47LhoaGigqKrJ/f2tMJhMmk5RqFxrTcmQmors6cWiMj5eB3nGedSYphKqMJug+Ubk05+2EWl0ackYJTmRkJJGRzil7npiYSExMDIsXL7YnNGVlZaxfv96+E2vEiBGUlJSwadMmhgwZAsCSJUuwWCykpqY6JS4hhBBCuB+nTWAfOXKE9PR0jhw5gtlsJj09nfT0dIeaNSkpKXzzjbL9VafT8eCDD/L3v/+d77//nu3btzNr1izi4uKYPn06AL169WLKlCncfvvtbNiwgdWrV3PvvfcyY8aM095BJYQQQgjP57TVi0888QTvv/++/d+DBinrDpYuXcr48eMB2Lt3L6WlpfbHPPLII1RWVnLHHXdQUlLC6NGjWbhwIT4+PvbHfPTRR9x7771MnDgRvV7PFVdcwSuvvOKstyGEEEIINyStGmSbuBBCCOEWNNGqQQghhBBCLZLgCCGEEMLjSIIjhBBCCI8jCY4QQgghPI4kOEIIIYTwOJLgCCGEEMLjSIIjhBBCCI8jCY4QQgghPI4kOEIIIYTwOE5r1aBltuLNZWVlKkcihBBCiNNl+9w+nSYMHTLBKS8vByA+Pl7lSIQQQghxpsrLywkODj7pYzpkLyqLxcKxY8cIDAxEp9O163OXlZURHx9PVlaW9LlyIvk5u4b8nF1Dfs6uIT9n13HWz9pqtVJeXk5cXBx6/clX2XTIERy9Xk/nzp2d+hpBQUHyB+QC8nN2Dfk5u4b8nF1Dfs6u44yf9alGbmxkkbEQQgghPI4kOEIIIYTwOJLgtDOTycSTTz6JyWRSOxSPJj9n15Cfs2vIz9k15OfsOlr4WXfIRcZCCCGE8GwygiOEEEIIjyMJjhBCCCE8jiQ4QgghhPA4kuAIIYQQwuNIgtOO5s6dS0JCAj4+PqSmprJhwwa1Q/Iozz77LOeddx6BgYFERUUxffp09u7dq3ZYHu+f//wnOp2OBx98UO1QPFJ2djbXX3894eHh+Pr60q9fP37//Xe1w/IoZrOZxx9/nMTERHx9fenWrRtPP/30afUzEm1bsWIFF198MXFxceh0Or799luHr1utVp544gliY2Px9fUlLS2N/fv3uyw+SXDayWeffcbs2bN58skn2bx5MwMGDGDy5Mnk5+erHZrHWL58Offccw/r1q1j0aJF1NfXM2nSJCorK9UOzWNt3LiRN998k/79+6sdikcqLi5m1KhReHl58fPPP7Nr1y7+/e9/ExoaqnZoHuW5557j9ddf57XXXmP37t0899xzPP/887z66qtqh+bWKisrGTBgAHPnzm31688//zyvvPIKb7zxBuvXr8ff35/JkydTU1PjmgCtol0MGzbMes8999j/bTabrXFxcdZnn31Wxag8W35+vhWwLl++XO1QPFJ5ebm1R48e1kWLFlnHjRtnfeCBB9QOyeM8+uij1tGjR6sdhse78MILrbfccovDfZdffrl15syZKkXkeQDrN998Y/+3xWKxxsTEWP/1r3/Z7yspKbGaTCbrJ5984pKYZASnHdTV1bFp0ybS0tLs9+n1etLS0li7dq2KkXm20tJSAMLCwlSOxDPdc889XHjhhQ6/16J9ff/99wwdOpSrrrqKqKgoBg0axNtvv612WB5n5MiRLF68mH379gGwdetWVq1axdSpU1WOzHMdPHiQ3Nxch+NHcHAwqampLvtc7JDNNttbQUEBZrOZ6Ohoh/ujo6PZs2ePSlF5NovFwoMPPsioUaPo27ev2uF4nE8//ZTNmzezceNGtUPxaAcOHOD1119n9uzZ/N///R8bN27k/vvvx9vbmxtvvFHt8DzGY489RllZGSkpKRgMBsxmM//4xz+YOXOm2qF5rNzcXIBWPxdtX3M2SXCEW7rnnnvYsWMHq1atUjsUj5OVlcUDDzzAokWL8PHxUTscj2axWBg6dCjPPPMMAIMGDWLHjh288cYbkuC0o88//5yPPvqIjz/+mD59+pCens6DDz5IXFyc/Jw9mExRtYOIiAgMBgN5eXkO9+fl5RETE6NSVJ7r3nvvZcGCBSxdupTOnTurHY7H2bRpE/n5+QwePBij0YjRaGT58uW88sorGI1GzGaz2iF6jNjYWHr37u1wX69evThy5IhKEXmmhx9+mMcee4wZM2bQr18/brjhBv74xz/y7LPPqh2ax7J99qn5uSgJTjvw9vZmyJAhLF682H6fxWJh8eLFjBgxQsXIPIvVauXee+/lm2++YcmSJSQmJqodkkeaOHEi27dvJz093X4ZOnQoM2fOJD09HYPBoHaIHmPUqFEnlDrYt28fXbt2VSkiz1RVVYVe7/hxZzAYsFgsKkXk+RITE4mJiXH4XCwrK2P9+vUu+1yUKap2Mnv2bG688UaGDh3KsGHDeOmll6isrOTmm29WOzSPcc899/Dxxx/z3XffERgYaJ/HDQ4OxtfXV+XoPEdgYOAJ65r8/f0JDw+X9U7t7I9//CMjR47kmWee4eqrr2bDhg289dZbvPXWW2qH5lEuvvhi/vGPf9ClSxf69OnDli1bePHFF7nlllvUDs2tVVRUkJGRYf/3wYMHSU9PJywsjC5duvDggw/y97//nR49epCYmMjjjz9OXFwc06dPd02ALtmr1UG8+uqr1i5duli9vb2tw4YNs65bt07tkDwK0OrlvffeUzs0jyfbxJ3nhx9+sPbt29dqMpmsKSkp1rfeekvtkDxOWVmZ9YEHHrB26dLF6uPjY01KSrL++c9/ttbW1qodmltbunRpq8fkG2+80Wq1KlvFH3/8cWt0dLTVZDJZJ06caN27d6/L4tNZrVLKUQghhBCeRdbgCCGEEMLjSIIjhBBCCI8jCY4QQgghPI4kOEIIIYTwOJLgCCGEEMLjSIIjhBBCCI8jCY4QQgghPI4kOEIIIYTwOJLgCCGEEMLjSIIjhBBCCI8jCY4QQgghPI4kOEIIIYTwOP8P9JO6hnCBJz4AAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import numpy as np\n",
        "\n",
        "# Creating data\n",
        "x = np.linspace(0, 10, 100)\n",
        "y1 = np.sin(x)\n",
        "y2 = np.cos(x)\n",
        "\n",
        "# Creating plot\n",
        "fig, ax = plt.subplots()\n",
        "\n",
        "# Plotting data on the Axes\n",
        "ax.plot(x, y1, '-')\n",
        "ax.plot(x, y2, '--')\n",
        "\n",
        "\n",
        "# Always include the following line in your notebooks to make sure your plot is displayed.\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "id": "eZlPJGKYLXGz"
      },
      "outputs": [],
      "source": [
        "#elaborate on plt.show\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VIgXkYmSLXGz"
      },
      "source": [
        "## Adding Titles, Labels and Legends\n",
        "Let's now add a title to our plot, as well as labels for the x and y axes. These features improve the readability and usefulness of our plots."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "nNOQ2uoBLXGz",
        "outputId": "319b6c87-d8e0-46b4-97e3-090b272808ca"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Creating plot\n",
        "fig, ax = plt.subplots()\n",
        "\n",
        "# Plotting data on the Axes\n",
        "ax.plot(x, y, label='sin(x)')\n",
        "\n",
        "# Adding title and labels\n",
        "ax.set_title('A Simple Sin Plot')\n",
        "ax.set_xlabel('x')\n",
        "ax.set_ylabel('y')\n",
        "\n",
        "# Adding legend\n",
        "ax.legend()\n",
        "\n",
        "# Displaying the plot\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "id": "ZBoCPG5LLXGz"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "id": "a_kHdhEyLXGz"
      },
      "outputs": [],
      "source": [
        "#elaborate on legend function"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AHSA-0GJLXGz"
      },
      "source": [
        "## Customizing Plot Appearance\n",
        "Matplotlib offers extensive customization options to make your plots visually appealing and convey information effectively. Let's explore a few customization techniques."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "BDLzUo8YLXGz",
        "outputId": "6c225dde-b1e2-43be-90ef-0e6d2f041ca3"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "#you can change options, such as the font size(in points) or the figure size(in inches) \n",
        "plt.rcParams.update({'font.size': 12, 'figure.figsize': (5, 4)}) # set font and plot size to be larger\n",
        "\n",
        "# Creating plot\n",
        "fig, ax = plt.subplots()\n",
        "\n",
        "# Plotting data on the Axes\n",
        "ax.plot(x, y, label='sin(x)', color='blue', linestyle='--', linewidth=2)\n",
        "\n",
        "# Adding title and labels\n",
        "ax.set_title('A Customized Sin Plot')\n",
        "ax.set_xlabel('x')\n",
        "ax.set_ylabel('y')\n",
        "\n",
        "# Adding legend\n",
        "ax.legend()\n",
        "\n",
        "\n",
        "\n",
        "# Displaying the plot\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qSelhngILXGz"
      },
      "source": [
        "In the code above, we have customized our plot by specifying the color, line style, and line width using the corresponding arguments in the plot() method. We can also customize other elements such as markers, gridlines, and axis limits."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0e9VzpuqLXGz"
      },
      "source": [
        "## Multiple Plots in the Same Figure\n",
        "Matplotlib allows us to create multiple plots within the same Figure, enabling side-by-side comparison or visualization of multiple datasets."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 400
        },
        "id": "tq-dGo2kLXGz",
        "outputId": "bbcb470a-3060-4ef0-c52b-0927dfb05759"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Creating data for the second plot\n",
        "y2 = np.cos(x)\n",
        "\n",
        "# Creating plot\n",
        "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 4))\n",
        "\n",
        "# Plotting data on the first Axes\n",
        "ax1.plot(x, y, label='sin(x)', color='blue')\n",
        "ax1.set_title('Sin Plot')\n",
        "ax1.legend()\n",
        "\n",
        "# Plotting data on the second Axes\n",
        "ax2.plot(x, y2, label='cos(x)', color='red')\n",
        "ax2.set_title('Cos Plot')\n",
        "ax2.legend()\n",
        "\n",
        "# Adjusting spacing between subplots\n",
        "fig.tight_layout()\n",
        "\n",
        "# Displaying the plot\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hyir7HPcLXG0"
      },
      "source": [
        "In the code above, we use the subplots() function to create two Axes objects side by side. Each Axes is assigned to a separate variable (ax1 and ax2), allowing us to customize and plot data independently."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3d2b-60wLXG0"
      },
      "source": [
        "## Saving Plots\n",
        "Matplotlib provides the ability to save your plots as image files in various formats, such as PNG, JPEG, PDF, and SVG. Let's see how to save our plot as a PNG file.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "metadata": {
        "id": "EdZuO7q0LXG0"
      },
      "outputs": [],
      "source": [
        "# Saving the plot as a PNG file\n",
        "fig.savefig('my_plot.png', dpi=300)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sbw_jw47LXG0"
      },
      "source": [
        "## Plot Types\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nBDj7hg0LXG0"
      },
      "source": [
        "### Line Plot\n",
        "\n",
        "#### Necessary Inputs:\n",
        "\n",
        "- `x, y`: Arrays or lists of numeric values representing the coordinates of the points in the plot.\n",
        "\n",
        "#### Optional Inputs:\n",
        "\n",
        "- `color`: A string specifying the color of the line.\n",
        "- `linewidth`: A number specifying the width of the line.\n",
        "- `linestyle`: A string specifying the style of the line (e.g. '-', '--', '-.', ':').\n",
        "- `marker`: A string specifying the marker style (e.g. 'o', 'v', '^', '<', '>', 's', '*', 'h', 'H', 'D', 'd', 'P', 'X').\n",
        "- `label`: A string specifying the label for the line, which will be shown in the legend.\n",
        "\"\"\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "metadata": {
        "id": "rKuhiAxwBVdN",
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 372
        },
        "outputId": "02f080e7-cc18-4eea-9384-8d25e03eaeb1"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "x = np.linspace(0, 10, 100)\n",
        "y = np.sin(x)\n",
        "\n",
        "fig, ax = plt.subplots()\n",
        "ax.plot(x, y, color='red', linewidth=2, linestyle='-', marker='o', label='My Line')\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pt8lmOVSBVdN"
      },
      "source": [
        "### Scatter Plot\n",
        "\n",
        "#### Necessary Inputs:\n",
        "\n",
        "- `x, y`: Arrays or lists of numeric values representing the coordinates of the points in the plot.\n",
        "\n",
        "#### Optional Inputs:\n",
        "\n",
        "- `s`: A scalar or an array of the same length as `x` and `y` to specify the size of the markers.\n",
        "- `c`: A color or sequence of colors for the markers.\n",
        "- `marker`: A string specifying the marker style.\n",
        "- `alpha`: A number between 0 (transparent) and 1 (opaque) specifying the transparency of the markers.\n",
        "\"\"\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 372
        },
        "id": "Dm_VX5d_LXG0",
        "outputId": "52818123-af8e-4c4a-a511-95884cfdbe40"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "x = np.random.randn(100)\n",
        "y = np.random.randn(100)\n",
        "\n",
        "fig, ax = plt.subplots()\n",
        "ax.scatter(x, y, s=30, c='red', marker='o', alpha=0.5)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J3SY_-2_LXG0"
      },
      "source": [
        "### Histogram\n",
        "\n",
        "#### Necessary Inputs:\n",
        "\n",
        "- `x`: An array or list of numeric values for which the histogram will be computed.\n",
        "\n",
        "#### Optional Inputs:\n",
        "\n",
        "- `bins`: An integer or sequence or 'auto', optional. If an integer is given, `bins + 1` bin edges are calculated and returned.\n",
        "- `color`: A string specifying the color of the histogram.\n",
        "- `alpha`: A number between 0 (transparent) and 1 (opaque) specifying the transparency of the histogram.\n",
        "- `density`: A boolean, if `True`, the first element of the return tuple will be the counts normalized to form a probability density.\n",
        "\"\"\"\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 372
        },
        "id": "uVHlRAUpLXG0",
        "outputId": "d253bc02-8409-4ea0-9c36-3c714b7db42d"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x400 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "x = np.random.randn(1000)\n",
        "\n",
        "fig, ax = plt.subplots()\n",
        "ax.hist(x, bins=30, color='red', alpha=0.7, density=True)\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5-Lv8caoLXG0"
      },
      "source": [
        "### Bar Chart\n",
        "\n",
        "#### Necessary Inputs:\n",
        "\n",
        "- `x`: A sequence of scalars representing the x coordinates of the bars. See also `align` for the alignment of the bars to the coordinates.\n",
        "- `height`: A scalar or sequence of scalars, the heights of the bars.\n",
        "\n",
        "#### Optional Inputs:\n",
        "\n",
        "- `width`: A scalar or array-like (optional). The width(s) of the bars.\n",
        "- `color`: A string specifying the color of the bars.\n",
        "- `edgecolor`: A string specifying the color of the bar borders.\n",
        "- `align`: `{center, edge}`, optional, default: `center`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 372
        },
        "id": "URwIVBr0LXG0",
        "outputId": "d384e24e-ad7f-4eab-9206-77ddbaebe8fc"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "categories = ['A', 'B', 'C', 'D', 'E']\n",
        "values = [7, 12, 5, 8, 4]\n",
        "\n",
        "fig, ax = plt.subplots()\n",
        "ax.bar(categories, values, width=0.5, color='blue', edgecolor='black', align='center')\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zAS0Zm7jLXG1"
      },
      "source": [
        "### Box Plot\n",
        "\n",
        "#### Necessary Inputs:\n",
        "\n",
        "- `x`: The input data. This can be a 1-D sequence or array or a sequence of sequences if multiple data sets are being plotted.\n",
        "\n",
        "#### Optional Inputs:\n",
        "\n",
        "- `notch`: A boolean, if `True`, will produce a notched box plot.\n",
        "- `vert`: A boolean, if `True` (default), makes the boxes vertical. If `False`, everything is drawn horizontally.\n",
        "- `patch_artist`: A boolean, if `True`, boxes will be filled. (Default is `False`).\n",
        "- `widths`: The widths of the boxes.\n",
        "- `showcaps`: A boolean, if `True`, show the caps on the ends of whiskers.\n",
        "\n",
        "Please note that these are only a subset of possible parameters for each function. Refer to the Matplotlib documentation for a complete list."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 372
        },
        "id": "KKHoOfspLXG1",
        "outputId": "e811fe4b-caff-4b62-8d2a-e5c0b165f6ed"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x400 with 1 Axes>"
            ],
            "image/png": 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D8w3TmauG74KjoqLEDUK3KyyAQ+A4KNx7iZ1EsurLiwAATz75pMhJpCsvLw9Dhw4VO0aLiL39bXFZLV26FM7Ozpg/f36z54mNjUV0dHTjY41GAy8vr5YODQ8PDwBAdnY2QkJCbvt9dnb2LdOZK29vbwBAYmIifH19xQ1DjcaMG48brv6w6tJd7CiSZunUFQCwf/9+PPDAAyKnkRa1Wo2oqKjGddyciL39bVFZ/fbbb9ixYwc2bNiA/Pz8xudrampQV1eHvLw8ODo6wtnZ+Zb5FAoFFApFm8OGhYXB29sbq1atQkpKyi0fRfV6PeLj4+Hj44OwsLA2jyUmW1tbAICvry8CAwNFTkMNrK0VqLHvzK8A70FmaQ3g5vv3wQcfFDeMRDWs4+ZE7O1viw6wuHLlCvR6PRYsWAAfH5/Gn8zMTOTm5sLHxwcrVqwwSlAAsLCwwLp163D06FFMnDjxlqNRJk6ciKNHj2Lt2rU834qIyMDE3v626JOVv78/Pv7449ueX7p0KSoqKrBx40b07NnTYOHuJCIiAklJSYiJiUFoaGjj8z4+PkhKSuJ5VkRERiLm9rdFZeXi4oKJEyfe9nzDuVZ3+p0xRERE4PHHH+cVLIiITEys7a/Z3krUwsLCrA9PJ2o1Xd3N/xUEcXNQhyXG9tcgV11PTU1tPBKEiIyn5rIa146sBQCUfb0N9eXFIiciMg3eIkSCVCoVTp8+DZVKJXYUkgh9bQ2uHduBov2vwMLJE5+4R6OgqAxFu+ei4synEASefGsOuG63HstKgpRKJQIDA6FUKsWOQhJQc/EcivfMhzb7S6xbuxbfHf8Bj7+wHGez1Xh6xlO49uUWXP1wKequm/fJ8B0B1+3WY1kRSZReW4XSLzaj6IPXMNCvF7J/Pofo6OjGHdmOjo7Yvn07jh07hi7QoGj3PGhOfgJBz2tjUvvDsiKSoOoLp1G8ex50uWnYvHkz0r5LRe/eve847ciRI/FL9s948YXnUPbtTpQceBV1pZdMnJjIuFhWRBKiq6lE6acbUPzRcoQGBeCX89l48cUXb7tw6P+yt7fHpk2bkJaWBg+behS+/xLKf/iIn7Ko3WBZEUlE1W+ZKN49F0JeJnbu3Imvj33V4mvIDRs2DNnnfkLMopegSd+LksTFqC3+0ziBiUzIbM+zIhJD9e8/Qld5zeDL1d+4juoLpzFm7Fj8e8cOdOvWrdXLsrW1xdtvv43IyEj8Y+Ys/PafRbDpE9p4zT5j01e37hZARHfDsiJqpuhFC3Hggw8AVBp82XI7a8yL24vp06dDJpMZZJmDBw/GT2eysHLlSnzx1TEAtQZZ7j0p5bh/4BO84joZlEwQTH8avEajgZOTE8rLy+Ho6Gjq4YmISCKa2wfcZ0VERJLHsiIiIsljWRERkeSxrIiISPJYVkREJHksKyIikjyWFRERSR7LioiIJI9lRUREkseyIiIiyWNZERGR5LGsiIhI8lhWREQkeSwrIiKSPJYVERFJHsuKiIgkj2VFRESSx7IiIiLJY1kREZHksayIiEjyWFZERCR5LCsiIpI8lhUREUkey4qIiCSPZUVERJLHsiIiIsljWRERkeSxrIiISPIsxQ5AZAzLli/HoUPJYsdo5OnhgWVLX8dDDz1kkvEuX76MlStXIu374yYZ73+tXBGHiIgIUcam9kkmCIJg6kE1Gg2cnJxQXl4OR0dHUw9PHYCrmzs0lp2g8OwjdhQAQH2+GtUFv2PEIyOx6q2VCAkJMco4RUVFiI+Px5at2wArGyh6D4HMwsooYzWl6rdMjB8xBCkpKSYdl8xTc/uAn6yo3bLtNQidQqeJHQMAIAgCqn/7ARnH92HIkCEYO3YcVq58EwMGDDDI8ktLS/H2229j07sJqBdksBscCceBj0OuUBpk+S1RX1Zo8jGp/WNZEZmATCaDss8Q2PYORpU6Hd9kHEBgYCAmRUTgzRUr0Ldv31Ytt7y8HO+88w7WrlsHbZ0OysAJcBk8CRY29gb+C4jExbIiMiGZTA47v4egVA3DjfPf4tNvP0BKQACmPfEE4v75T/Tu3btZy7lx4wbeffddxK9egxtV1bB7cCzcQyJhoXQy8l9AJA4eDUgkApncAvYBf0PXZ7ai86g5OHT0S6h8ffHMM8/g4sWLTc5XU1ODDRs2oIe3D15fugxCz6Fwf3YHOj/yDIuK2jWWFZGIZBZWcBgwFl1nb4fTQ08j8eDH6NWrN+bOnYv8/PzG6Wpra7F161Z4398T0TGLofUcAI9nd8B51BxYOnQR8S8gMg1+DUgkAXIrBRwHPQ77/qNRkXUU/35/L3bueg9zX5wDX19frFj5Fi5f+gt2fg/BY/xyWDl3EzsykUmxrFqgqqoKOTk5UKlUUCpNf5QVtX9ya1s4hUyGw4Cx0JxMwc5/b0cv+xrUOAfDY9YrsHbtIXZEagfMcVvGrwFbICcnB0FBQcjJyRE7CrVzcoUdOg2bjqHPvoGs5+0xeMKTLCoyGHPclrGsiCRMrrATOwKRJLSorM6fP4/Jkyfj/vvvh1KphIuLC4YPH44jR44YKx8REVHL9lldvHgRFRUVmDFjBjw9PVFVVYVDhw5hwoQJ2L59O5577jlj5SQiog6sRWU1duxYjB079pbn5s2bh6CgIKxfv55lRURERtHmfVYWFhbw8vJCWVmZAeIQERHdrlWHrt+4cQPV1dUoLy/H4cOH8dlnn2Hq1KlNTq/VaqHVahsfazSa1gwruurqagCAWq0WOQndS31dHXSVpdDX1UBuZSN2nA5F0OtQVlaGrKwssaNQExq2YQ3bNHPQqrKKiYnB9u3bAQByuRwRERFISEhocvr4+HjExcW1LqGE5OXlAQCioqLEDULNc+Yz2Pd7FAr3XmIn6VCEuhp8910WgoKCxI5C95CXl4ehQ4eKHaNZWlVWCxcuRGRkJPLz83Hw4EHodDrU1tY2OX1sbCyio6MbH2s0Gnh5ebVmaFF5e3sDABITE+Hr6ytuGLqrkSNHod4nGFZduosdpcORWdngoYcewvr168WOQk1Qq9WIiopq3KaZg1aVlUqlgkqlAgD84x//wOjRoxEeHo7MzEzIZLLbplcoFFAoFG1LKgG2trYAAF9fXwQGBoqchu7G0soKgn0XfgUoApncAp06deI6YgYatmnmwCAnBUdGRuLkyZPIzc01xOKIiIhuYZCyathJV15ebojFERER3aJFZVVcXHzbc3V1dfjPf/4DW1tb+Pn5GSwYERFRgxbts3r++eeh0WgwfPhwdOvWDYWFhdi3bx9ycnKwbt062NvzVtpERGR4LSqrqVOnYteuXdi6dStKS0vh4OCAoKAgrFmzBhMmTDBWRqIOSV9bg8rcY8BAoF5TAjj4iB2JSDQtKqtp06Zh2rRpxspCRACE+lpUnP0MNzKT4N7pBjDQBldT4lHqMgJOQ6fB0tFN7IhEJsdbhLSASqXC6dOnGw/bJzIkQVeHirOfoXDncyhPfQ/TJ0/ER9+eRc2MLzEz5k1YX8lCwb+fx7WvtqG+8prYccmMmeO2jGXVAkqlEoGBgWZzZ00yD4Jeh8qfv0bRrhdx/cstiBg7CupffsF7772HHr18YeMTjPnRS3Ax70+sXBEH/J6Owh3P4vq370FXxSNwqeXMcVvGsiISiSDocUOdhuLd81D66TsY83AIzp07hwP796NPnz63TW9vb4/Y2Fj8dTEPr8cuQf0vX6Jwx7MoS9sLfU2lCH8BkemwrIhMTBAEVP32A4r3vISrh9/Gw4P8cerUKXycnAx/f/97zt+pUyfExcXh4p9/YtGCuag5cxgFO2aj7MQH0GurTPAXEJkey4rIRARBQPWF0yhJjEZJ8koE+3rj+PHj+OzTT1t10VcXFxe8/fbbyPvzAubMnoWqHw6i8N/PojwzGfq6GiP8BUTiadW1AYnMgl4PQa8TOwUAQHv5PCq+34eqS+cRHDIE8YnbMWLECIMs293dHZs2bcLLL7+MN998E+/t3o2q0ymwC54M+36jILOwMsg4zSboTTsedQgyQRAEUw+q0Wjg5OSE8vJyODo6mnp46gB8evZG3oXfxY5xi/4DArF61Vt49NFH73jBZ0O5cOEC4uJWIDFxL/R6cYojKioKe/fuFWVsMi/N7QOWFbVLubm5yMjIEDtGI3d3d4wePdqoJfW/cnJykJmZabLx/tuECRPQuXNnUcYm88KyIiIiyWtuH/AACyIikjyWFRERSR7LioiIJI9lRUREkseyIiIiyWNZERGR5LGsiIhI8lhWREQkeSwrIiKSPJYVERFJHsuKiIgkj2VFRESSx7IiIiLJY1kREZHksayIiEjyWFZERCR5LCsiIpI8lhUREUkey4qIiCSPZUVERJLHsiIiIsljWRERkeSxrIiISPJYVkREJHksKyIikjyWFRERSR7LioiIJM9S7ABE5uKnn35CTU2N0Zbv6+sLR0dHgy9XEAScO3fOqNn/l5OTE1QqlcnGo/aPZUXUDIcOHUJkZKRRx3D37IadO7Zj3LhxBltmQUEB5rz4Ij5JSTHYMptDLpfjxIkTCA4ONum41H6xrIia4fLly7CwsobbU+uNsnyhTgvN8f0YP348nnjySWzauBEuLi6tX54g4P3338dLCxdBK8jhEr4YVq7ehgt8F/qaShTtfxUFBQUmGY86BpYVUXPJ5LA24gbfJfKfuHH+GySl7MQXX3yJLZsTMGXKFMhkshYtJy8vD7NnP4uvvz4GO/9H4PbIs7CwdTBS6tvpqspNNhZ1HDzAgkgiZDIZ7P1HouvTW6B1fQDTpk3DhMcfR35+frPm1+v12LRpE/z69sX3p87BLfKfcBkXbdKiIjIWlhWRxFjYdYbL47Fwnfgavkz9Hg/4+mLXrl0QBKHJedRqNUKHDsNLL70ES9UIuM1KgG3PgSZMTWRcLCsiiVI+EAq3WVuAHoMxe/ZsPDLyb7hw4cIt09TV1WHVqlXo3/9B/PTbX+j65Go4j5oDuUIpUmoi42BZEUmYha0DuoxdCLfJccg4k42+/gHYsGEDdDodzpw5g6CBg7B06TLYBobDdcYm2Hj5ix2ZyChYVu1QVVUVsrKyUFVVJXYUMhDb+4PgNjMBVr6PYNGiRQjq74dnw4NxuVSDrk+tQ+eHZ0FupRA7JhkB1+ebWFbtUE5ODoKCgpCTkyN2FDIguUIJ51EvoOv0NbARqnBqti2GTF8AhUdvsaOREXF9vollRWRmbLr3RZfwV24+kPPsE+oYWFZE5qiF514RmbsWldXJkycxb9489O3bF3Z2drjvvvswZcoU5ObmGisfERFRy65gsWbNGhw/fhyTJ09Gv379UFhYiISEBAQGBuKHH36Avz+PRCIiIsNrUVlFR0dj//79sLa2bnxu6tSpCAgIwOrVq5GYmGjwgERERC0qq9DQ0Nue6927N/r27Qu1Wm2wUERERP+tzYcSCYKAoqIi9O3bt8lptFottFpt42ONRtPWYekuqqurAYD/gDCgS5cuQdDroa+rgdzKRuw4ZuGPP/5AVlaW2DHMXsN63LBed1RtLqt9+/bhypUrWLFiRZPTxMfHIy4urq1DUTPl5eUBAKKiosQN0g7VlV6Gwr2X2DHMwuLFi8WO0K7k5eVh6NChYscQTZvKKicnB3PnzsWQIUMwY8aMJqeLjY1FdHR042ONRgMvL6+2DE134e3tDQBITEyEr6+vuGHaif379+Odje/Cqkt3saOYjbVr12LEiBFixzB7arUaUVFRjet1R9XqsiosLMS4cePg5OSEpKQkWFhYNDmtQqGAQsFLwZiKra0tgJu3SQ8MDBQ5TfuQnp4OmVzOrwBboGfPnnz/GVDDet1RtaqsysvLMWbMGJSVlSE9PR2enp6GzkVERNSoxWVVU1OD8PBw5Obm4tixY/Dz8zNGLiIiokYtKiudToepU6ciIyMDn3zyCYYMGWKsXERERI1aVFYxMTE4fPgwwsPDce3atdtOAubRZ0SmoavSANwNTB1Ii8rq7NmzAIAjR47gyJEjt/2eZUVkXIKuHpofk3H1wofAMwrUV5QC9j5ixyIyuhZdyDY1NRWCIDT5Q0TGU1v0B0r2LYbm+0RMnDAeAHDt8BpoTh2GoNeJnI7IuHgznHZIpVLh9OnTUKlUYkchAxDqa1F24gNUZB6Cr68v9hzOxMB+frjx11kMLdmDU1t2oObXdHR+dAGsXHj+YnvD9fkm3s+qHVIqlQgMDIRSqRQ7CrVRzWU1iv/zEqpOfoy4fy7HmazTGDhwIGCthF2vUGzYvB1paWnwsKlH4Z4FKD/xIQRdvdixyYC4Pt/EsiKSIH1tNa4d246i/a/A39sDZ8+ewbJly26540GDsLAwZJ/7CS/HRENzYj9KEmOgLfxdhNRExsOyIpKY6j/PoPj9+ag9fwzr163DDxkn7nqhaODm1Q1Wr16NHzMz0dPVDkV7Y3D9u/ch1NeaKDWRcbGsiCRCV1OJ0k83oPjgMgT3U+F89s9YtGjRXS9l9r+CgoKQdfoU3lwRh+rTh1G8ZwFqLp83Ymoi0+ABFkTNJOjqUZF11CjL1tdpUXX6E1ihDjt27MDs2bMhk8latSwrKyu8/vrrmDRpEmbOehon978K+36PwtrN27Chm6CvrTHJONSxsKyImiEkJARKpS0qUt8z2hijRo3C9u3b0L27Ya7s7ufnh4wTx5GQkIBlb7yBil++Mchym8PVrSsCAgJMNh61fzJBhBOkNBoNnJycUF5eDkdHR1MPT0REEtHcPuA+KyIikjyWFRERSR7LioiIJI9lRUREkseyIiIiyWNZERGR5LGsiIhI8lhWREQkeSwrIiKSPJYVERFJHsuKiIgkj2VFRESSx7IiIiLJY1kREZHksayIiEjyWFZERCR5LCsiIpI8lhUREUkey4qIiCSPZUVERJLHsiIiIsljWRERkeSxrIiISPJYVkREJHksKyIikjyWFRERSR7LioiIJI9lRUREkmcpdgAiYxMEAWvXroVarRY7Crp3746FCxfC2dnZ6GOVlJRgw4YNKCgoMPpY/83R0RFvvfUW7OzsTDoutW8yQRAEUw+q0Wjg5OSE8vJyODo6mnp46mDy8vLg4+MDhet9sLARdwNaW5wHW4U1lr7+GubPnw9bW1uDj3Hjxg288847iF+zBrX1eli79jD4GE0SBFRdzsHevXsRFRVlunHJbDW3D/jJijoMp0eeg633g6Jm0N24jvITHyD2tdewfsNGvPXmCsyYMQOWlm1fFevq6rBz5068sfyfuHb9OuweHAv3IVNgoXQyQPLmEerr8Ne6SSYbjzoO7rMiMiELu85wHjUH7s9sRWWnXpg9ezb6+gfgk08+QWu/5BAEAR999BH6qHzx4ty5qHHrC4/Z2+A88lmTFhWRMbGsiERg1dkTLhNegfuMDbistcHEiRMxJHQovv/++xYt55tvvsHAQYMxZcoUFMuc4TFzE7qMj4GlU1cjJScSB8uKSEQK915wnboSblPexLmLJQgLC8P48eE4f/78Xec7e/YsRo1+FCNHjoS6sAJdn1gF18jlsHbzMVFyItNiWRFJgK3PALg+tR4u4S/j2InT6NevH2bNmoVLly7dMt2ff/6JJ6dPx4ABA/D9mV/gMjEWrtPXwua+fiIlJzINHmBBJBEymRx2fg9B+UAoKs5+jv1JH2Lf/gNYMH8ennvuOWzevBlbtmyFXOkI50fnwT7gb5BZcBWmjoHvdAmqqqpCTk4OVCoVlEql2HHIxGQWVnAMCoe9/0hoTn6M7du24Jt97+D3SlvYhz4Bh6AJkFvbiB2TJKAjbSv4NaAE5eTkICgoCDk5OWJHIRHJFUp0GjYdw2YvRdbz9hg6eymchkxhUVGjjrStYFkRSZzc9uaJknIbe5GTEImnxWVVWVmJ5cuX47HHHoOzszNkMhnef/99I0QjIiK6qcVldfXqVaxYsQJqtRr9+/c3RiYiIqJbtPgACw8PDxQUFMDd3R2nTp3CoEGDjJGLiIioUYs/WSkUCri7uxsjCxER0R2Z5NB1rVYLrVbb+Fij0ZhiWLNVXV0NAJK4pUV7kJ+fDwCou5YPRTcV5FY8ms7Y8vLykJWVJXaMdq9hG9GwzWjPTFJW8fHxiIuLM8VQ7UJeXh4A8BYLBnb9qy1QePaBwr2X2FHavWXLlmHZsmVix+gw8vLyMHToULFjGJVJyio2NhbR0dGNjzUaDby8vEwxtFny9vYGACQmJsLX11fcMO1Afn4+wsPD0XnUi7Dq0l3sOB3Cm2++ibFjx4odo91Tq9WIiopq3Ga0ZyYpK4VCAYVCYYqh2oWGG/L5+voiMDBQ5DTmr+GuvFbOnvwK0ES8vb353jUhY9zEU2p4UjAREUkey4qIiCSPZUVERJLXqn1WCQkJKCsrazwk+MiRI7h8+TIAYP78+XBy4q20iYjIcFpVVmvXrsXFixcbHycnJyM5ORnAzcOtWVZEhqO7cQ1QALqqcqD970cnuqNWlVXDeUBEZDy6ag00GR/hasGnwDMKlH68Ete7hcMp+O+8Ajt1OLz5ogSpVCqcPn0aKpVK7CgkAn1dDSpOH0Hlj4dgLRcQ8fLLKJk0HOG6rxG/7l0UnvsC9sGT4RA4DjJLa7Hjkog60raCZSVBSqWS56h0QIJeh8pzX6Ey4wPoq8oxZ84LWLp0Kdzc3AAAb/QbhWdffAlxcXH4986dqDpzBPah02HX92HI5BYipycxdKRtBY8GJBKZIAioyj2B4t3zcO2LBESMG4Vff83Bpk2bGouqgYeHB7Zt2wb1L79g3MgwlH76Dor3vISqP05CEASR/gIi42NZEYmo5lI2Sva9jJKPV2HYAF9kZWXhwP79uP/+++86X58+fXAoKQmZmZkYpOqBkqQ4XP0gFtor7f/25tQxsayIRFBbkoeSQ3Eo2v8qHnBT4tixY/jqyy8wYMCAFi1n8ODB+C71W3z22WfwdpShMHExrn78FupKLxkpOZE4WFZEJlSvKcbV//MOCnbPh5v+Gj788EOcPnUSI0eObPUyZTIZHnvsMfz801ns3bsXnaqvoGDXXJR+/i7qK64aMD2ReHiABbV7MpkMAFD+5WZUKsQ9UUl79TI6d+6Ef23ejNmzZ8PKyspgy5bL5YiKisLkyZOxdetWxK14E4XqVCicuxlsjHv6f/vNGl5zIkORCSLsldVoNHByckJ5eTkcHR1NPTx1QFu3bpXEzSy9vLwwZ84c2Nsb/zyp8vJybNmyBQUFBUYf6785Ojpi6dKlsLHhFe7p3prbBywrIiISTXP7gPusiIhI8lhWREQkeSwrIiKSPJYVERFJHsuKiIgkj2VFRESSx7IiIiLJY1kREZHksayIiEjyWFZERCR5LCsiIpI8lhUREUmeKLcIabh2rkajEWN4IiKSiIYeuNc11UUpq4qKCgA3b5dARERUUVEBJyenJn8vyi1C9Ho98vPz4eDg0OybtGk0Gnh5eeHSpUsd9rYifA34GgB8DRrwdWgfr4EgCKioqICnpyfk8qb3TInyyUoul6N79+6tmtfR0dFs/08xFL4GfA0AvgYN+DqY/2twt09UDXiABRERSR7LioiIJM9sykqhUGD58uVQKBRiRxENXwO+BgBfgwZ8HTrWayDKARZEREQtYTafrIiIqONiWRERkeSxrIiISPJYVkREJHksKyIikjyzLKu0tDRMmDABXl5esLGxgbu7Ox577DEcP35c7Ggm9fXXX+Ppp59Gnz59oFQqcf/992P27NkoKCgQO5rJFBQU4NVXX8WIESMaL9+Vmpoqdiyj0Gq1WLJkCTw9PWFra4vg4GB89dVXYscyqcrKSixfvhyPPfYYnJ2dIZPJ8P7774sdy6ROnjyJefPmoW/fvrCzs8N9992HKVOmIDc3V+xoRmWWZZWbmwu5XI4XXngBmzdvxuLFi1FYWIjhw4fj888/FzueySxZsgSpqamYNGkSNm3ahGnTpuHgwYMYMGAACgsLxY5nEr/++ivWrFmDK1euICAgQOw4RjVz5kysX78e06dPx8aNG2FhYYGxY8fi+++/FzuayVy9ehUrVqyAWq1G//79xY4jijVr1uDQoUMYOXIkNm7ciOeeew5paWkIDAxEdna22PGMR2gnbty4IXTt2lV49NFHxY5iMt99952g0+luew6A8Prrr4uUyrQ0Go1QWloqCIIgfPTRRwIA4dtvvxU3lBFkZmYKAIR//etfjc9VV1cLPXv2FIYMGSJiMtOqqakRCgoKBEEQhJMnTwoAhN27d4sbysSOHz8uaLXaW57Lzc0VFAqFMH36dJFSGZ9ZfrK6E6VSCVdXV5SVlYkdxWSGDx9+21WKhw8fDmdnZ6jVapFSmZaDgwOcnZ3FjmF0SUlJsLCwwHPPPdf4nI2NDZ555hlkZGTg0qVLIqYzHYVCAXd3d7FjiCo0NBTW1ta3PNe7d2/07du3Xa/3Zl1WGo0GV69eRU5ODl577TVkZ2dj5MiRYscSVWVlJSorK+Hi4iJ2FDKgM2fOoE+fPrddWXvw4MEAgLNnz4qQiqRCEAQUFRW16/VelFuEGMqUKVPwxRdfAACsra3x/PPPY9myZSKnEteGDRtQW1uLqVOnih2FDKigoAAeHh63Pd/wXH5+vqkjkYTs27cPV65cwYoVK8SOYjSil5Ver0dtbW2zplUoFLfcrHH16tWIiYnBpUuXsGfPHtTW1qK+vt5YUY2qLa9Dg7S0NMTFxWHKlCl45JFHDB3R6AzxGrRX1dXVd7xYqY2NTePvqWPKycnB3LlzMWTIEMyYMUPsOEYjelmlpaVhxIgRzZpWrVZDpVI1Pn7wwQcb/zsqKgqBgYGYOXMmkpKSDB3T6NryOgA337CTJk2Cv78/du7caYyIRtfW16A9s7W1hVarve35mpqaxt9Tx1NYWIhx48bBycmpcb9meyV6WalUKuzevbtZ097pa5AG1tbWmDBhAlavXo3q6mqzW3nb8jpcunQJo0ePhpOTEz799FM4ODgYI6LRGeq90B55eHjgypUrtz3fcE6dp6enqSORyMrLyzFmzBiUlZUhPT293b8HRC8rd3d3zJw50yDLqq6uhiAIqKioMLuyau3rUFpaitGjR0Or1eLrr7826424Id8L7c2DDz6Ib7/9FhqN5paDLDIzMxt/Tx1HTU0NwsPDkZubi2PHjsHPz0/sSEZnlkcDFhcX3/ZcWVkZDh06BC8vL7i5uYmQyvRu3LiBsWPH4sqVK/j000/Ru3dvsSORkURGRkKn02HHjh2Nz2m1WuzevRvBwcHw8vISMR2Zkk6nw9SpU5GRkYGPPvoIQ4YMETuSSYj+yao1xowZg+7duyM4OBhubm7466+/sHv3buTn5+PDDz8UO57JTJ8+HT/++COefvppqNXqW86xsLe3x8SJE8ULZ0IrV64EAJw/fx4AsHfv3sarOixdulS0XIYUHByMyZMnIzY2FsXFxejVqxf27NmDvLw87Nq1S+x4JpWQkICysrLGIyCPHDmCy5cvAwDmz58PJycnMeMZXUxMDA4fPozw8HBcu3YNiYmJt/w+KipKpGRGJvZZya2RkJAgDBs2THBxcREsLS0FV1dXITw8XEhLSxM7mkn16NFDAHDHnx49eogdz2Saeg3M9O3dpOrqamHx4sWCu7u7oFAohEGDBgmff/652LFM7m7v+z///FPseEb30EMPdZj3/H/jbe2JiEjyzHKfFRERdSwsKyIikjyWFRERSR7LioiIJI9lRUREkseyIiIiyWNZERGR5LGsiIhI8lhWREQkeSwrIiKSPJYVERFJHsuKiIgk7/8Ch3t5YYnh7lcAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "data = [np.random.randn(100) for _ in range(5)]\n",
        "\n",
        "fig, ax = plt.subplots()\n",
        "ax.boxplot(data, notch=True, vert=False, patch_artist=True, widths=0.5, showcaps=True)\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-O9RG79mLXG1"
      },
      "source": [
        "### Pie Chart\n",
        "\n",
        "#### Necessary Inputs:\n",
        "\n",
        "- `x`: Array-like. The wedge sizes.\n",
        "\n",
        "#### Optional Inputs:\n",
        "\n",
        "- `explode`: Array-like, optional, default: None. If not None, is a len(x) array which specifies the fraction of the radius with which to offset each wedge.\n",
        "- `labels`: List of str, optional. The labels for each wedge.\n",
        "- `colors`: Array-like, optional. A sequence of colors through which the pie chart will cycle.\n",
        "- `autopct`: None (default), str, or function, optional. If not None, is a string or function used to label the wedges with their numeric value.\n",
        "\"\"\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 346
        },
        "id": "bkhPUT4CLXG1",
        "outputId": "7ede041a-670e-4dac-81a0-17639c8e9c51"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "labels = 'Frogs', 'Hogs', 'Dogs', 'Logs'\n",
        "sizes = [15, 30, 45, 10]\n",
        "explode = (0, 0.1, 0, 0)  # only \"explode\" the 2nd slice (i.e. 'Hogs')\n",
        "\n",
        "fig, ax = plt.subplots()\n",
        "ax.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%', shadow=True, startangle=90)\n",
        "ax.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.\n",
        "\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OjeM1lJ-LXG1"
      },
      "source": [
        "### Area Plot\n",
        "\n",
        "#### Necessary Inputs:\n",
        "\n",
        "- `x`: Array-like or scalar. The x coordinates of the left sides of the bars\n",
        "\n",
        "#### Optional Inputs:\n",
        "\n",
        "- `y`: Array-like, optional. The heights of the bars.\n",
        "- `stacked`: In line and bar plots, this applies only to column-wise data and stacks the columns. In area plot, this stacks the areas in a single plot."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 393
        },
        "id": "xgjTdyJnLXG1",
        "outputId": "fbeff2bd-ca0d-475c-d6a9-a72280d057ac"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "df = pd.DataFrame({\n",
        "    'sales': [3, 2, 3, 9, 10, 6],\n",
        "    'signups': [5, 5, 6, 12, 14, 13],\n",
        "    'visits': [20, 42, 28, 62, 81, 50],\n",
        "}, index=pd.date_range(start='2018/01/01', end='2018/07/01',\n",
        "                       freq='M'))\n",
        "df.plot.area()\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f07Bpr97LXG1"
      },
      "source": [
        "### Violin Plot\n",
        "\n",
        "#### Necessary Inputs:\n",
        "\n",
        "- `dataset`: Array or sequence of vectors.\n",
        "\n",
        "#### Optional Inputs:\n",
        "\n",
        "- `vert`: If true (default), makes the violins vertical. If false, everything is drawn horizontally.\n",
        "- `widths`: Either a scalar or a vector that sets the maximal width of each violin.\n",
        "- `showmedians`: If True, will toggle rendering of the medians."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 372
        },
        "id": "CeYaBsL6LXG1",
        "outputId": "655860d0-e4e4-4afe-ea8d-4415af5888cb"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "data = [np.random.normal(0, std, 100) for std in range(1, 5)]\n",
        "\n",
        "fig, ax = plt.subplots()\n",
        "ax.violinplot(data)\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-spViJgcLXG1"
      },
      "source": [
        "markdown_text = \"\"\"\n",
        "### Quiver Plot\n",
        "\n",
        "#### Necessary Inputs:\n",
        "\n",
        "- `X, Y, U, V`: The x, y coordinates, and the horizontal and vertical vectors.\n",
        "\n",
        "#### Optional Inputs:\n",
        "\n",
        "- `C`: The colors of the arrows.\n",
        "- `angles`: The angles of the arrows. If set to 'xy', the arrows point from (x,y) to (x+u, y+v).\n",
        "- `scale`: If scale is None (default), the arrows are drawn in the unit specified in the xy coordinate system.\n",
        "- `width`: The width of the arrow shafts.\n",
        "\n",
        "\"\"\"\n",
        "\n",
        "print(markdown_text)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 63,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 372
        },
        "id": "kmvRehA0LXG1",
        "outputId": "74d2f098-b8b8-400e-eb26-8b55f0376c75"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "x = np.arange(0, 2.2, 0.2)\n",
        "y = np.arange(0, 2.2, 0.2)\n",
        "X, Y = np.meshgrid(x, y)\n",
        "U = np.cos(X)*Y\n",
        "V = np.sin(X)*Y\n",
        "\n",
        "fig, ax = plt.subplots()\n",
        "q = ax.quiver(X, Y, U, V, units='width')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cHZFTjjGLXG1"
      },
      "source": [
        "## Examples with additional libraries"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fYloVXDRLXG2"
      },
      "source": [
        "### 3D Plot\n",
        "\n",
        "#### Necessary Inputs:\n",
        "\n",
        "- `X, Y, Z`: 1D or 2D arrays representing the (X, Y) coordinates and Z values of the surface.\n",
        "\n",
        "#### Optional Inputs:\n",
        "\n",
        "- `color`: The color of the surface.\n",
        "- `cmap`: The colormap of the surface.\n",
        "- `edgecolor`: The color of the lines on the surface."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 64,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 362
        },
        "id": "YP1GqfSyLXG2",
        "outputId": "d6905633-f448-4327-94e0-c7cc3354ead6"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "from mpl_toolkits.mplot3d import Axes3D\n",
        "\n",
        "x = np.outer(np.linspace(-2, 2, 30), np.ones(30))\n",
        "y = x.copy().T\n",
        "z = np.cos(x ** 2 + y ** 2)\n",
        "\n",
        "fig = plt.figure()\n",
        "ax = plt.axes(projection='3d')\n",
        "ax.plot_surface(x, y, z, cmap='viridis')\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BemGV18DLXG2"
      },
      "source": [
        "### Heatmap\n",
        "\n",
        "#### Necessary Inputs:\n",
        "\n",
        "- `data`: 2D dataset that can be coerced into an ndarray. If a Pandas DataFrame is provided, the index/column information will be used to label the columns and rows.\n",
        "\n",
        "#### Optional Inputs:\n",
        "\n",
        "- `vmin, vmax`: Values to anchor the colormap, otherwise they are inferred from the data and other keyword arguments.\n",
        "- `cmap`: The mapping from data values to color space.\n",
        "- `center`: The value at which to center the colormap when plotting divergant data.\n",
        "\"\"\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 65,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 372
        },
        "id": "QSVYbDl-LXG2",
        "outputId": "40838d18-f8ec-4adb-a2a5-5a8985372f8b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import seaborn as sns\n",
        "data = np.random.rand(10, 12)\n",
        "ax = sns.heatmap(data, cmap='viridis')\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d4Wq895ILXG2"
      },
      "source": [
        "## Coloring in Data Visualization\n",
        "\n",
        "The way data is colored in a visualization can greatly impact the interpretation and effectiveness of the image. We generally have two types of data that we might want to color - continuous data and categorical (qualitative) data.\n",
        "\n",
        "#### Continuous Data:\n",
        "\n",
        "Continuous data are numerical values that can be measured on a scale and have a meaningful order or ranking. For example, temperature or height. When coloring continuous data:\n",
        "\n",
        "- The differences between your color steps should be significant enough to differentiate, but not so high that they skew perception of the data distribution.\n",
        "- Use Sequential palettes. These palettes have a natural progression of colors - either single hue or multi-hue. An example of a single hue palette is a grayscale, where color progresses from white to black. Multi-hue palettes transition between two or more colors.\n",
        "\n",
        "![Matplotlib Colormaps](https://matplotlib.org/stable/_images/sphx_glr_colormaps_001.png)\n",
        "![Matplotlib Colormaps](https://matplotlib.org/stable/_images/sphx_glr_colormaps_002.png)\n",
        "![Matplotlib Colormaps](https://matplotlib.org/stable/_images/sphx_glr_colormaps_003.png)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6__oLXMKLXG2"
      },
      "source": [
        "- Use Diverging palettes when your data has a critical midpoint like an average or zero. The colors diverge from the midpoint to extreme high and low values. This type of palette is best when the data's extremes are more interesting than the middle values.\n",
        "![Matplotlib Colormaps](https://matplotlib.org/stable/_images/sphx_glr_colormaps_004.png)\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c7-lZhdBLXG2"
      },
      "source": [
        "#### Cyclic Data:\n",
        "\n",
        "Cyclic data is a type of data that undergoes cycles in a predictably recurring manner. The data does not have a start or end but continuously cycles through the same sequence. Examples of this kind of data are the hours of a day, days of a week, months in a year, or angles in a circle. When coloring cyclic data:\n",
        "\n",
        "- Choose colors that can represent the cyclic nature of your data. You want to make sure each color is distinguishable and makes sense in a cyclic pattern.\n",
        "- Use Cyclic palettes which are designed to wrap around, perfect for representing data with a repeating nature. These palettes are best used when your data's high and low values meet and repeat, such as in a circular pattern.\n",
        "\n",
        "\n",
        "\n",
        "![Matplotlib Colormaps](https://matplotlib.org/stable/_images/sphx_glr_colormaps_005.png)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fsiTSHuHLXG2"
      },
      "source": [
        "#### Qualitative Data:\n",
        "\n",
        "Qualitative data is non-numerical data like categories, names, or labels. When coloring qualitative data:\n",
        "\n",
        "- Choose colors that go well together and attract the reader’s eye. You want to make sure each color is distinguishable, but also that they make sense together.\n",
        "- Use Qualitative palettes which are used to color nominal data - data without an intrinsic order or ranking.\n",
        "\n",
        "![Matplotlib Colormaps](https://matplotlib.org/stable/_images/sphx_glr_colormaps_006.png)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A75WRWHVLXG2"
      },
      "source": [
        "# Let's get back to our data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t0z1tM2PLXG2"
      },
      "source": [
        "### Plotting Directly from Pandas DataFrame\n",
        "\n",
        "Pandas DataFrame provides an in-built `.plot()` function that allows you to create a variety of visualizations directly. This function is essentially a wrapper around Matplotlib's plotting functionality. This means it leverages Matplotlib under the hood to create the plots. Because of this, many of the functions, techniques, and principles you've learned about Matplotlib can be applied when plotting directly from a DataFrame.\n",
        "\n",
        "How does it work?\n",
        "\n",
        "When you call `.plot()` on a DataFrame, pandas prepares a plot with Matplotlib using the data in the DataFrame. The `kind` parameter determines what kind of plot to create. For example, `kind='line'` creates a line plot, `kind='bar'` creates a bar plot, etc. Depending on the kind of plot, the `.plot()` function will make intelligent decisions about how to use the DataFrame's index and columns.\n",
        "\n",
        "How does it relate to what we've learned about Matplotlib?\n",
        "\n",
        "Since pandas uses Matplotlib for plotting, we can interact with the Matplotlib objects directly. After calling `.plot()`, you'll get a Matplotlib `AxesSubplot` object. You can use this to set labels, titles, modify the axes, etc., like you would do with any other Matplotlib plot.\n",
        "\n",
        "Furthermore, you can pass many of the same parameters to `.plot()` that you can pass to the equivalent Matplotlib function. For example, you can pass `color` to set the color, `linestyle` to set the line style, etc. This gives you the power and flexibility of Matplotlib with the simplicity and convenience of pandas.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pq1F0hG2LXG2"
      },
      "source": [
        "### Difference Between ax.scatter() and df.plot(kind='scatter')\n",
        "\n",
        "1. `ax.scatter()`: This method is a part of the Matplotlib's axes object. It creates a scatter plot, which is a type of plot using Cartesian coordinates to display values for typically two variables from a dataset. The data is displayed as a collection of points, each having the value of one variable determining the position on the horizontal axis and the value of the other variable determining the position on the vertical axis. When using `ax.scatter()`, you need to manually supply the x and y arrays.\n",
        "\n",
        "2. `df.plot(kind='scatter')`: This method is a part of the pandas DataFrame API. It is essentially a wrapper around Matplotlib's plotting functionality (including `ax.scatter()`) that takes the column names of the DataFrame for the x and y coordinates. This makes it easier and more convenient to plot data directly from DataFrames. Underneath, it uses Matplotlib to generate scatter plots. Therefore, many of the customizations that you can do in Matplotlib (like color, size, transparency, etc.) are available when plotting directly from a DataFrame.\n",
        "\n",
        "To summarize, `ax.scatter()` and `df.plot(kind='scatter')` both create scatter plots, but the former requires manual data extraction and is more flexible, while the latter works directly with pandas DataFrames and is easier for quick plots.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ReEPJXvmBVdN"
      },
      "source": [
        "### Scatterplot\n",
        "\n",
        "Let's plot the relationship between ratings and revenue. All we need to do is call `.plot()` on `movies_df` with some info about how to construct the plot:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 66,
      "metadata": {
        "id": "CyfcKj3BBVdN",
        "outputId": "e36a3abd-ec51-4371-c01b-c6f731df2ab3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "movies_df.plot(kind='scatter', x='rating', y='revenue_millions', title='Revenue (millions) vs Rating');"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HLe7AIqrBVdN"
      },
      "source": [
        "The semicolon is not a syntax error, just a way to hide extra output in Jupyter notebooks."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U_yMyTiHBVdN"
      },
      "source": [
        "### Histogram\n",
        "If we want to plot a simple Histogram based on a single column, we can call plot on a column:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 67,
      "metadata": {
        "id": "18bJWbvjBVdN",
        "outputId": "2a6e7382-4e52-4c60-fb2b-f60cf02c2f8a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 396
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "movies_df['rating'].plot(kind='hist', title='Rating');"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8RxfRSm-BVdN"
      },
      "source": [
        "### Boxplot\n",
        "Using a Boxplot we can visualize the rating quartiles of the n directors with the most movies:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 68,
      "metadata": {
        "id": "cBlyNTU8BVdN",
        "outputId": "e5f6dcc0-0987-47c7-e122-d8bcc96a287f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 569
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "n=10\n",
        "directors = movies_df['director'].value_counts()[:n].index.tolist()\n",
        "chart = movies_df[movies_df['director'].isin(directors)].boxplot(column=['rating'], by=\"director\")\n",
        "chart.set_xticklabels(chart.get_xticklabels(), rotation=90);"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nrBzu5otLXG3"
      },
      "source": [
        "## Your Turn!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xJUdsonWLXG3"
      },
      "source": [
        "### Histogram of Movie Runtimes\n",
        "\n",
        "A histogram is an accurate representation of the distribution of numerical data. Here, we will visualize the distribution of movie runtimes using a histogram.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 69,
      "metadata": {
        "id": "v1QS21HtLXG3"
      },
      "outputs": [],
      "source": [
        "#your code here"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "95AMql4GLXG3"
      },
      "source": [
        "### Scatter Plot: Movie Runtime vs Rating\n",
        "\n",
        "A scatter plot can be used to find the correlation between two variables. Here, we will plot the movie runtime against its rating to see if there's any relationship.\n"
      ]
    },
    {
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        "### Pie Chart of Movie Counts by Director\n",
        "\n",
        "A pie chart is useful for showing part-to-whole relationships. We'll visualize the distribution of movies directed by the top 5 directors.\n"
      ]
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