Assignement_3_Pandas.ipynb

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        "<a href=\"https://colab.research.google.com/github/pdivya-mca/Pandas-Task/blob/main/Assignement_3_Pandas.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
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      "source": [
        "# pandas \n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vVX_UOoHX2ki"
      },
      "source": [
        "## Importing pandas\n",
        "\n",
        "### Getting started and checking your pandas setup\n",
        "\n",
        "Difficulty: *easy* \n",
        "\n",
        "**1.** **Import pandas under the name `pd`.**"
      ]
    },
    {
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      "execution_count": 2,
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      "outputs": [],
      "source": [
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6pIYyWdxX2kj"
      },
      "source": [
        "**2.** **Print the version of pandas that has been imported.**"
      ]
    },
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            "text/plain": [
              "'1.3.5'"
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      "source": [
        "pd.__version__"
      ]
    },
    {
      "cell_type": "markdown",
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        "id": "0j249vJVX2kk"
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      "source": [
        "**3.** **Print out all the version information of the libraries that are required by the pandas library.**"
      ]
    },
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            "\n",
            "INSTALLED VERSIONS\n",
            "------------------\n",
            "commit           : 66e3805b8cabe977f40c05259cc3fcf7ead5687d\n",
            "python           : 3.8.15.final.0\n",
            "python-bits      : 64\n",
            "OS               : Linux\n",
            "OS-release       : 5.10.133+\n",
            "Version          : #1 SMP Fri Aug 26 08:44:51 UTC 2022\n",
            "machine          : x86_64\n",
            "processor        : x86_64\n",
            "byteorder        : little\n",
            "LC_ALL           : None\n",
            "LANG             : en_US.UTF-8\n",
            "LOCALE           : en_US.UTF-8\n",
            "\n",
            "pandas           : 1.3.5\n",
            "numpy            : 1.21.6\n",
            "pytz             : 2022.6\n",
            "dateutil         : 2.8.2\n",
            "pip              : 21.1.3\n",
            "setuptools       : 57.4.0\n",
            "Cython           : 0.29.32\n",
            "pytest           : 3.6.4\n",
            "hypothesis       : None\n",
            "sphinx           : 1.8.6\n",
            "blosc            : None\n",
            "feather          : 0.4.1\n",
            "xlsxwriter       : None\n",
            "lxml.etree       : 4.9.1\n",
            "html5lib         : 1.0.1\n",
            "pymysql          : None\n",
            "psycopg2         : 2.9.5 (dt dec pq3 ext lo64)\n",
            "jinja2           : 2.11.3\n",
            "IPython          : 7.9.0\n",
            "pandas_datareader: 0.9.0\n",
            "bs4              : 4.6.3\n",
            "bottleneck       : None\n",
            "fsspec           : 2022.11.0\n",
            "fastparquet      : None\n",
            "gcsfs            : None\n",
            "matplotlib       : 3.2.2\n",
            "numexpr          : 2.8.4\n",
            "odfpy            : None\n",
            "openpyxl         : 3.0.10\n",
            "pandas_gbq       : 0.17.9\n",
            "pyarrow          : 9.0.0\n",
            "pyxlsb           : None\n",
            "s3fs             : None\n",
            "scipy            : 1.7.3\n",
            "sqlalchemy       : 1.4.44\n",
            "tables           : 3.7.0\n",
            "tabulate         : 0.8.10\n",
            "xarray           : 0.20.2\n",
            "xlrd             : 1.1.0\n",
            "xlwt             : 1.3.0\n",
            "numba            : 0.56.4\n"
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        "pd.show_versions()"
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      "source": [
        "## DataFrame basics\n",
        "\n",
        "### **A few of the fundamental routines for selecting, sorting, adding and aggregating data in DataFrames**\n",
        "\n",
        "Difficulty: *easy*\n",
        "\n",
        "Note: remember to import numpy using:\n",
        "```python\n",
        "import numpy as np\n",
        "```\n",
        "\n",
        "Consider the following Python dictionary `data` and Python list `labels`:\n",
        "\n",
        "``` python\n",
        "data = {'animal': ['cat', 'cat', 'snake', 'dog', 'dog', 'cat', 'snake', 'cat', 'dog', 'dog'],\n",
        "        'age': [2.5, 3, 0.5, np.nan, 5, 2, 4.5, np.nan, 7, 3],\n",
        "        'visits': [1, 3, 2, 3, 2, 3, 1, 1, 2, 1],\n",
        "        'priority': ['yes', 'yes', 'no', 'yes', 'no', 'no', 'no', 'yes', 'no', 'no']}\n",
        "\n",
        "labels = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j']\n",
        "```\n",
        "(This is just some meaningless data I made up with the theme of animals and trips to a vet.)\n",
        "\n",
        "**4.** Create a DataFrame `df` from this dictionary `data` which has the index `labels`."
      ]
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              "  animal  age  visits priority\n",
              "a    cat  2.5       1      yes\n",
              "b    cat  3.0       3      yes\n",
              "c  snake  0.5       2       no\n",
              "d    dog  NaN       3      yes\n",
              "e    dog  5.0       2       no\n",
              "f    cat  2.0       3       no\n",
              "g  snake  4.5       1       no\n",
              "h    cat  NaN       1      yes\n",
              "i    dog  7.0       2       no\n",
              "j    dog  3.0       1       no"
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      "source": [
        "import numpy as np\n",
        "\n",
        "data = {'animal': ['cat', 'cat', 'snake', 'dog', 'dog', 'cat', 'snake', 'cat', 'dog', 'dog'],\n",
        "        'age': [2.5, 3, 0.5, np.nan, 5, 2, 4.5, np.nan, 7, 3],\n",
        "        'visits': [1, 3, 2, 3, 2, 3, 1, 1, 2, 1],\n",
        "        'priority': ['yes', 'yes', 'no', 'yes', 'no', 'no', 'no', 'yes', 'no', 'no']}\n",
        "\n",
        "labels = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j']\n",
        "\n",
        "df=pd.DataFrame(data,index=labels)\n",
        "\n",
        "df"
      ]
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        "**5.** **Display a summary of the basic information about this DataFrame and its data.**"
      ]
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      "outputs": [
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 10 entries, a to j\n",
            "Data columns (total 4 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   animal    10 non-null     object \n",
            " 1   age       8 non-null      float64\n",
            " 2   visits    10 non-null     int64  \n",
            " 3   priority  10 non-null     object \n",
            "dtypes: float64(1), int64(1), object(2)\n",
            "memory usage: 400.0+ bytes\n"
          ]
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      "source": [
        "df.info()"
      ]
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      "source": [
        "**6.** **Return the first 3 rows of the DataFrame `df`.**"
      ]
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              "  animal  age  visits priority\n",
              "a    cat  2.5       1      yes\n",
              "b    cat  3.0       3      yes\n",
              "c  snake  0.5       2       no"
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              "                                                     [key], {});\n",
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              "  "
            ]
          },
          "metadata": {},
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      ],
      "source": [
        "df.head(3)"
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    {
      "cell_type": "markdown",
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      "source": [
        "**7.** **Select just the 'animal' and 'age' columns from the DataFrame `df`.**"
      ]
    },
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            "text/plain": [
              "  animal  age\n",
              "a    cat  2.5\n",
              "b    cat  3.0\n",
              "c  snake  0.5\n",
              "d    dog  NaN\n",
              "e    dog  5.0\n",
              "f    cat  2.0\n",
              "g  snake  4.5\n",
              "h    cat  NaN\n",
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              "      <th></th>\n",
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              "                                                     [key], {});\n",
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          "metadata": {},
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      ],
      "source": [
        "df[['animal','age']]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6ISIfWs_X2ko"
      },
      "source": [
        "8. Select the data in rows `[3, 4, 8]` *and* in columns `['animal', 'age'] "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  animal  age\n",
              "d    dog  NaN\n",
              "e    dog  5.0\n",
              "i    dog  7.0"
            ],
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              "\n",
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              "                                                     [key], {});\n",
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          "metadata": {},
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      ],
      "source": [
        "df.iloc[[3,4,8]][['animal','age']]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-AvIB3u4X2kq"
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      "source": [
        "**9.** **Select only the rows where the number of visits is greater than 3.**"
      ]
    },
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      "execution_count": null,
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        "id": "Rv7eor0iX2kr",
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        "outputId": "9c1497fe-7fab-463e-cc1b-5f3459cce85d"
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      "outputs": [
        {
          "output_type": "execute_result",
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            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [animal, age, visits, priority]\n",
              "Index: []"
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              "      <th>visits</th>\n",
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              "          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",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "df[df.visits>3]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "32RlQA95X2kr"
      },
      "source": [
        "**10.** **Select the rows where the age is missing, i.e. is `NaN`.**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Td9stC-eX2kr",
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          "height": 112
        },
        "outputId": "36681b5e-19ed-4ca2-d1ba-4ab149aff21a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  animal  age  visits priority\n",
              "d    dog  NaN       3      yes\n",
              "h    cat  NaN       1      yes"
            ],
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              "\n",
              "  <div id=\"df-2abe123a-f900-491c-94f6-9e7b0b4de77b\">\n",
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              "      <td>yes</td>\n",
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              "      <td>cat</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>yes</td>\n",
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              "                                                     [key], {});\n",
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              "\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",
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          "metadata": {},
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      ],
      "source": [
        "df[df['age'].isnull()]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Cqtph49XX2kr"
      },
      "source": [
        "**11. Select the rows where the animal is a cat and the age is less than 3.** "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ytuk3FDZX2kr",
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          "height": 112
        },
        "outputId": "43d68e6c-a3df-4b73-91e6-426a7a495198"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  animal  age  visits priority\n",
              "a    cat  2.5       1      yes\n",
              "f    cat  2.0       3       no"
            ],
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              "\n",
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              "      <th>age</th>\n",
              "      <th>visits</th>\n",
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              "      <td>2.0</td>\n",
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              "      <td>no</td>\n",
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              "  </tbody>\n",
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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",
              "            '<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": 11
        }
      ],
      "source": [
        "df[(df.animal=='cat') & (df.age < 3)]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sVAURTtYX2kr"
      },
      "source": [
        "**12.** **Select the rows the age is between 2 and 4 (inclusive).**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IaLGlp26X2ks",
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          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "outputId": "a6d2d28f-5e56-46c2-ea3c-20ff64cf42db"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  animal  age  visits priority\n",
              "a    cat  2.5       1      yes\n",
              "b    cat  3.0       3      yes\n",
              "f    cat  2.0       3       no\n",
              "j    dog  3.0       1       no"
            ],
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              "            + ' to learn more about interactive tables.';\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "df[df.age.between(2,4)]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AsPXgRrHX2ks"
      },
      "source": [
        "**13.** **Change the age in row 'f' to 1.5.**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wpC71UBTX2ks",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "041e3b1b-268f-4657-b59e-4140b279f45e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2.0"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "df.loc['f','age']"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.loc['f','age']=1.5"
      ],
      "metadata": {
        "id": "Jbyb-gn46hmU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.loc['f']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UPuSPbnx6lvr",
        "outputId": "915281e2-8400-40e0-ae82-173a6eeee2ab"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "animal      cat\n",
              "age         1.5\n",
              "visits        3\n",
              "priority     no\n",
              "Name: f, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-cYOypkKX2ks"
      },
      "source": [
        "**14.** **Calculate the sum of all visits (the total number of visits).**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "T1ZNyDYsX2ks",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e66ed689-a901-462e-c755-cdeedc1881a4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "19"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "df['visits'].sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mv6ip0KAX2ks"
      },
      "source": [
        "**15.** **Calculate the mean age for each different animal in `df`.**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1Pii6PuXX2kt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a033b71f-7a9f-4fec-f199-c25e00526b9f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "animal\n",
              "cat      2.333333\n",
              "dog      5.000000\n",
              "snake    2.500000\n",
              "Name: age, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "df.groupby('animal')['age'].mean()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4Ax_XHV6X2kt"
      },
      "source": [
        "**16.** **Append a new row 'k' to `df` with your choice of values for each column. Then delete that row to return the original DataFrame.**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IOsyzSj5X2kt",
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          "height": 394
        },
        "outputId": "44e26fc2-7d47-4a7e-856b-314c6669d008"
      },
      "outputs": [
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              "   animal  age  visits priority\n",
              "a     cat  2.5       1      yes\n",
              "b     cat  3.0       3      yes\n",
              "c   snake  0.5       2       no\n",
              "d     dog  NaN       3      yes\n",
              "e     dog  5.0       2       no\n",
              "f     cat  1.5       3       no\n",
              "g   snake  4.5       1       no\n",
              "h     cat  NaN       1      yes\n",
              "i     dog  7.0       2       no\n",
              "j     dog  3.0       1       no\n",
              "k  parrot  2.0       2      yes"
            ],
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        "df.loc['k']={'animal': 'parrot', 'age': 2, 'visits':2, 'priority':'yes'}\n",
        "df"
      ]
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              "  animal  age  visits priority\n",
              "a    cat  2.5       1      yes\n",
              "b    cat  3.0       3      yes\n",
              "c  snake  0.5       2       no\n",
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              "g  snake  4.5       1       no\n",
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              "  "
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          },
          "metadata": {},
          "execution_count": 19
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      ]
    },
    {
      "cell_type": "markdown",
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      "source": [
        "**17.** **Count the number of each type of animal in `df`.**"
      ]
    },
    {
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      "execution_count": null,
      "metadata": {
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      "outputs": [
        {
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          "data": {
            "text/plain": [
              "cat      4\n",
              "dog      4\n",
              "snake    2\n",
              "Name: animal, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "df['animal'].value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k1Qp52OVX2kt"
      },
      "source": [
        "**18.** **Sort `df` first by the values in the 'age' in decending order, then by the value in the 'visit' column in ascending order.**"
      ]
    },
    {
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              "  animal  age  visits priority\n",
              "i    dog  7.0       2       no\n",
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      "source": [
        "df.sort_values(by=['age','visits'],ascending=[False,True],inplace=True)\n",
        "df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ejuNI7U5X2ku"
      },
      "source": [
        "**19.** **The 'priority' column contains the values 'yes' and 'no'. Replace this column with a column of boolean values: 'yes' should be `True` and 'no' should be `False`.**"
      ]
    },
    {
      "cell_type": "code",
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              "  animal  age  visits  priority\n",
              "i    dog  7.0       2     False\n",
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      ],
      "source": [
        "df['priority']=df['priority'].map({'yes':True,'no':False})\n",
        "df"
      ]
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      "cell_type": "markdown",
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        "id": "TYzQlteUX2ku"
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      "source": [
        "**20.** **In the 'animal' column, change the 'snake' entries to 'python'.**"
      ]
    },
    {
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        {
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            "text/plain": [
              "   animal  age  visits  priority\n",
              "i     dog  7.0       2     False\n",
              "e     dog  5.0       2     False\n",
              "g  python  4.5       1     False\n",
              "j     dog  3.0       1     False\n",
              "b     cat  3.0       3      True\n",
              "a     cat  2.5       1      True\n",
              "f     cat  1.5       3     False\n",
              "c  python  0.5       2     False\n",
              "h     cat  NaN       1      True\n",
              "d     dog  NaN       3      True"
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      ],
      "source": [
        "df['animal'] = df['animal'].replace('snake', 'python')\n",
        "df"
      ]
    },
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        "**21.** **For each animal type and each number of visits, find the mean age. In other words, each row is an animal, each column is a number of visits and the values are the mean ages (hint: use a pivot table).**"
      ]
    },
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              "visits    1    2     3\n",
              "animal                \n",
              "cat     2.5  NaN  2.25\n",
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      ],
      "source": [
        "df.pivot_table(index='animal', columns='visits', values='age', aggfunc='mean')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vHtwfOpIX2kv"
      },
      "source": [
        "## DataFrames: beyond the basics\n",
        "\n",
        "### Slightly trickier: you may need to combine two or more methods to get the right answer\n",
        "\n",
        "Difficulty: *medium*\n",
        "\n",
        "The previous section was tour through some basic but essential DataFrame operations. Below are some ways that you might need to cut your data, but for which there is no single \"out of the box\" method."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uoSzi0rCX2kv"
      },
      "source": [
        "**22.** You have a DataFrame `df` with a column 'A' of integers. For example:\n",
        "```python\n",
        "df = pd.DataFrame({'A': [1, 2, 2, 3, 4, 5, 5, 5, 6, 7, 7]})\n",
        "```\n",
        "\n",
        "How do you filter out rows which contain the same integer as the row immediately above?"
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              "    A\n",
              "0   1\n",
              "1   2\n",
              "2   2\n",
              "3   3\n",
              "4   4\n",
              "5   5\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "df = pd.DataFrame({'A': [1, 2, 2, 3, 4, 5, 5, 5, 6, 7, 7]})\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
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        "df1 = pd.DataFrame()\n",
        "for i in range(1,len(df)):\n",
        "    if (df.loc[i,'A'] == df.loc[i-1,'A']):\n",
        "        df1.loc[i,'A'] =df.loc[i,'A']\n",
        "\n",
        "df1"
      ],
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        "colab": {
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          "height": 175
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        "id": "E1dbNgdg_Lsx",
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      A\n",
              "2   2.0\n",
              "6   5.0\n",
              "7   5.0\n",
              "10  7.0"
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              "      <td>5.0</td>\n",
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              "    <tr>\n",
              "      <th>10</th>\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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              "  "
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C4-wLaJcX2kv"
      },
      "source": [
        "**23.** Given a DataFrame of numeric values, say\n",
        "```python\n",
        "df = pd.DataFrame(np.random.random(size=(5, 3))) # a 5x3 frame of float values\n",
        "```\n",
        "\n",
        "how do you subtract the row mean from each element in the row?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1T5xr0UQX2kv",
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          "height": 206
        },
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              "          0         1         2\n",
              "0  0.782021  0.750987  0.357363\n",
              "1  0.841958  0.969982  0.397539\n",
              "2  0.424412  0.869885  0.508638\n",
              "3  0.322205  0.439916  0.983884\n",
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              "      <td>0.969982</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.424412</td>\n",
              "      <td>0.869885</td>\n",
              "      <td>0.508638</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
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              "      <td>0.983884</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.047654</td>\n",
              "      <td>0.727470</td>\n",
              "      <td>0.710018</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "\n",
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              "\n",
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              "      background-color: #434B5C;\n",
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              "  </style>\n",
              "\n",
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              "          const element = document.querySelector('#df-55b47275-4e3a-4246-bff2-71407c3a4332');\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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              "  </div>\n",
              "  "
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          },
          "metadata": {},
          "execution_count": 44
        }
      ],
      "source": [
        "df = pd.DataFrame(np.random.random(size=(5, 3)))\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#find mean\n",
        "df['row_mean']=df.mean(axis=1)\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "ndP67hHyQAee",
        "outputId": "ae568340-a401-4ee4-e2f0-459cab31be04"
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      "execution_count": null,
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              "          0         1         2  row_mean\n",
              "0  0.782021  0.750987  0.357363  0.630124\n",
              "1  0.841958  0.969982  0.397539  0.736493\n",
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              "3  0.322205  0.439916  0.983884  0.582002\n",
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              "          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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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#subtracting mean and deleting mean column fron df\n",
        "for i in df.index:\n",
        "  df.loc[i]=df.loc[i]-df.loc[i,'row_mean']\n",
        "df.drop('row_mean',axis=1,inplace=True)\n",
        "df"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
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        "id": "yUKqtmVlQYZd",
        "outputId": "347c1eac-a40d-4148-ed4d-8fb9039b7006"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          0         1         2\n",
              "0  0.151898  0.120863 -0.272760\n",
              "1  0.105465  0.233489 -0.338954\n",
              "2 -0.176566  0.268907 -0.092341\n",
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              "          const element = document.querySelector('#df-fe665522-2e70-4038-813b-f28d49f2dd1c');\n",
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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",
              "            '<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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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3va5f5ddX2kv"
      },
      "source": [
        "**24.** Suppose you have DataFrame with 10 columns of real numbers, for example:\n",
        "\n",
        "```python\n",
        "df = pd.DataFrame(np.random.random(size=(5, 10)), columns=list('abcdefghij'))\n",
        "```\n",
        "Which column of numbers has the smallest sum? (Find that column's label.)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "y8FWU3n0X2kw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "outputId": "e39ac4c8-c6db-4585-ab4b-a6a117d83f7b"
      },
      "outputs": [
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              "          a         b         c         d         e         f         g  \\\n",
              "0  0.928372  0.729102  0.410218  0.143764  0.552675  0.298534  0.343190   \n",
              "1  0.122412  0.917694  0.279712  0.953718  0.838164  0.399582  0.814883   \n",
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              "3  0.221996  0.678273  0.230301  0.507444  0.889222  0.770927  0.723023   \n",
              "4  0.710367  0.754709  0.295233  0.567401  0.701542  0.471410  0.573058   \n",
              "\n",
              "          h         i         j  \n",
              "0  0.195434  0.385959  0.431334  \n",
              "1  0.723787  0.229481  0.335788  \n",
              "2  0.865317  0.587614  0.614772  \n",
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              "      <td>0.928372</td>\n",
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              "  </style>\n",
              "\n",
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              "          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",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ],
      "source": [
        "df = pd.DataFrame(np.random.random(size=(5, 10)), columns=list('abcdefghij'))\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#find sum of all column\n",
        "df.loc['column_sum']=df.sum()\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "id": "jA1u53zgRTFs",
        "outputId": "50aefe4c-174a-45a3-d62f-26ae65abf2f5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   a         b         c         d         e         f  \\\n",
              "0           0.928372  0.729102  0.410218  0.143764  0.552675  0.298534   \n",
              "1           0.122412  0.917694  0.279712  0.953718  0.838164  0.399582   \n",
              "2           0.653719  0.401682  0.668477  0.227028  0.427270  0.475342   \n",
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              "4           0.710367  0.754709  0.295233  0.567401  0.701542  0.471410   \n",
              "column_sum  2.636867  3.481460  1.883942  2.399355  3.408873  2.415796   \n",
              "\n",
              "                   g         h         i         j  \n",
              "0           0.343190  0.195434  0.385959  0.431334  \n",
              "1           0.814883  0.723787  0.229481  0.335788  \n",
              "2           0.955419  0.865317  0.587614  0.614772  \n",
              "3           0.723023  0.530737  0.542187  0.640217  \n",
              "4           0.573058  0.203549  0.292069  0.795013  \n",
              "column_sum  3.409573  2.518824  2.037309  2.817125  "
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              "      <th>e</th>\n",
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              "      <th>g</th>\n",
              "      <th>h</th>\n",
              "      <th>i</th>\n",
              "      <th>j</th>\n",
              "    </tr>\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>0.928372</td>\n",
              "      <td>0.729102</td>\n",
              "      <td>0.410218</td>\n",
              "      <td>0.143764</td>\n",
              "      <td>0.552675</td>\n",
              "      <td>0.298534</td>\n",
              "      <td>0.343190</td>\n",
              "      <td>0.195434</td>\n",
              "      <td>0.385959</td>\n",
              "      <td>0.431334</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.122412</td>\n",
              "      <td>0.917694</td>\n",
              "      <td>0.279712</td>\n",
              "      <td>0.953718</td>\n",
              "      <td>0.838164</td>\n",
              "      <td>0.399582</td>\n",
              "      <td>0.814883</td>\n",
              "      <td>0.723787</td>\n",
              "      <td>0.229481</td>\n",
              "      <td>0.335788</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.653719</td>\n",
              "      <td>0.401682</td>\n",
              "      <td>0.668477</td>\n",
              "      <td>0.227028</td>\n",
              "      <td>0.427270</td>\n",
              "      <td>0.475342</td>\n",
              "      <td>0.955419</td>\n",
              "      <td>0.865317</td>\n",
              "      <td>0.587614</td>\n",
              "      <td>0.614772</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.221996</td>\n",
              "      <td>0.678273</td>\n",
              "      <td>0.230301</td>\n",
              "      <td>0.507444</td>\n",
              "      <td>0.889222</td>\n",
              "      <td>0.770927</td>\n",
              "      <td>0.723023</td>\n",
              "      <td>0.530737</td>\n",
              "      <td>0.542187</td>\n",
              "      <td>0.640217</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.710367</td>\n",
              "      <td>0.754709</td>\n",
              "      <td>0.295233</td>\n",
              "      <td>0.567401</td>\n",
              "      <td>0.701542</td>\n",
              "      <td>0.471410</td>\n",
              "      <td>0.573058</td>\n",
              "      <td>0.203549</td>\n",
              "      <td>0.292069</td>\n",
              "      <td>0.795013</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>column_sum</th>\n",
              "      <td>2.636867</td>\n",
              "      <td>3.481460</td>\n",
              "      <td>1.883942</td>\n",
              "      <td>2.399355</td>\n",
              "      <td>3.408873</td>\n",
              "      <td>2.415796</td>\n",
              "      <td>3.409573</td>\n",
              "      <td>2.518824</td>\n",
              "      <td>2.037309</td>\n",
              "      <td>2.817125</td>\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": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#find min sum value\n",
        "z=df.loc['column_sum'].min()\n",
        "z"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UHgPuysRRcPI",
        "outputId": "3078637b-e7c7-4090-f99e-6a4131dc8601"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.8839418805845702"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#find the column name\n",
        "df.loc['column_sum'][df.loc['column_sum']==z].index"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZoLg6a1mRraf",
        "outputId": "9eca401b-4b32-4882-838c-15b9127df17a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['c'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xl1H1oyYX2kw"
      },
      "source": [
        "**25.** How do you count how many unique rows a DataFrame has (i.e. ignore all rows that are duplicates)?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uo6hDlC2X2kw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "23b3d2e1-ece1-4890-f51e-36c2bb8a0f69"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ],
      "source": [
        "df[~df.duplicated()]['a'].count()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UwqnFrZOX2kw"
      },
      "source": [
        "The next three puzzles are slightly harder...\n",
        "\n",
        "**26.** You have a DataFrame that consists of 10 columns of floating--point numbers. Suppose that exactly 5 entries in each row are NaN values. For each row of the DataFrame, find the *column* which contains the *third* NaN value.\n",
        "\n",
        "(You should return a Series of column labels.)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lUderf34X2kw",
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          "height": 206
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        "outputId": "09b3fdc2-3038-43b1-dd73-b9d20a62fdbc"
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        {
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              "    a  b   c  d  e   f   g  h   i  j\n",
              "0   2  5   9  8  8   5  10  1  10  2\n",
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              "                                                     [key], {});\n",
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          },
          "metadata": {},
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        }
      ],
      "source": [
        "x = np.random.randint(1,11,(5,10))\n",
        "df =pd.DataFrame(x, columns =['a','b','c','d','e','f','g','h','i','j'])\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.loc[0,['a','c','h','i','b']] = np.NaN\n",
        "df.loc[1,['b','c','e','i','g']] = np.NaN\n",
        "df.loc[2,['a','d','f','i','b']] = np.NaN\n",
        "df.loc[3,['e','d','h','g','b']] = np.NaN\n",
        "df.loc[4,['j','f','h','i','b']] = np.NaN\n",
        "df"
      ],
      "metadata": {
        "colab": {
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          "height": 206
        },
        "id": "Eldjr0DhTU6U",
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      "execution_count": null,
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        {
          "output_type": "execute_result",
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            "text/plain": [
              "      a   b     c    d    e     f     g    h    i    j\n",
              "0   NaN NaN   NaN  8.0  8.0   5.0  10.0  NaN  NaN  2.0\n",
              "1  10.0 NaN   NaN  6.0  NaN  10.0   NaN  6.0  NaN  6.0\n",
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              "3   3.0 NaN   2.0  NaN  NaN   3.0   NaN  NaN  1.0  9.0\n",
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              "                                                     [key], {});\n",
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              "\n",
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            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "s = pd.Series()\n",
        "for i in df.index:\n",
        "    count=0\n",
        "    for j in df.columns:\n",
        "        if (np.isnan(df.loc[i,j])):\n",
        "            count +=1\n",
        "            if(count == 3):\n",
        "                s.at[i] = j\n",
        "                break\n",
        "s"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8lrw67D0Tets",
        "outputId": "fca4bf14-feea-42b5-d503-e320d1740d6f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-56-d98b61a62520>:1: DeprecationWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
            "  s = pd.Series()\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    c\n",
              "1    e\n",
              "2    d\n",
              "3    e\n",
              "4    h\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9c5Jx7eUX2kw"
      },
      "source": [
        "**27.** A DataFrame has a column of groups 'grps' and and column of numbers 'vals'. For example: \n",
        "\n",
        "```python\n",
        "df = pd.DataFrame({'grps': list('aaabbcaabcccbbc'), \n",
        "                   'vals': [12,345,3,1,45,14,4,52,54,23,235,21,57,3,87]})\n",
        "```\n",
        "For each *group*, find the sum of the three greatest values."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WfUHtifEX2kw",
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          "height": 520
        },
        "outputId": "2b979c4b-f057-470a-f765-3dc5f1599f4d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   grps  vals\n",
              "0     a    12\n",
              "1     a   345\n",
              "2     a     3\n",
              "3     b     1\n",
              "4     b    45\n",
              "5     c    14\n",
              "6     a     4\n",
              "7     a    52\n",
              "8     b    54\n",
              "9     c    23\n",
              "10    c   235\n",
              "11    c    21\n",
              "12    b    57\n",
              "13    b     3\n",
              "14    c    87"
            ],
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              "                                                     [key], {});\n",
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              "\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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            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ],
      "source": [
        "df = pd.DataFrame({'grps': list('aaabbcaabcccbbc'), \n",
        "                   'vals': [12,345,3,1,45,14,4,52,54,23,235,21,57,3,87]})\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "c = df.groupby('grps')['vals'].nlargest(3)\n",
        "print(c)\n",
        "print(c.loc['a'].sum(), c.loc['b'].sum(), c.loc['c'].sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1MeRFtXFTvuV",
        "outputId": "92ef2cd6-7bac-4777-9737-d73794b02e32"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "grps    \n",
            "a     1     345\n",
            "      7      52\n",
            "      0      12\n",
            "b     12     57\n",
            "      8      54\n",
            "      4      45\n",
            "c     10    235\n",
            "      14     87\n",
            "      9      23\n",
            "Name: vals, dtype: int64\n",
            "409 156 345\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G273QftuX2kw"
      },
      "source": [
        "**28.** A DataFrame has two integer columns 'A' and 'B'. The values in 'A' are between 1 and 100 (inclusive). For each group of 10 consecutive integers in 'A' (i.e. `(0, 10]`, `(10, 20]`, ...), calculate the sum of the corresponding values in column 'B'."
      ]
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      "metadata": {
        "id": "-ASnGv6_X2kx",
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          "height": 424
        },
        "outputId": "5d40cc40-e046-4721-fa30-2c6a6071e337"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      A  B\n",
              "0     1  3\n",
              "1     2  8\n",
              "2     3  3\n",
              "3     4  1\n",
              "4     5  1\n",
              "..  ... ..\n",
              "95   96  6\n",
              "96   97  2\n",
              "97   98  1\n",
              "98   99  2\n",
              "99  100  8\n",
              "\n",
              "[100 rows x 2 columns]"
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              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>96</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96</th>\n",
              "      <td>97</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>98</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>99</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>100</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>100 rows × 2 columns</p>\n",
              "</div>\n",
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              "          const element = document.querySelector('#df-c32287cc-5d9f-492b-b6ae-6ce0c4ddbc54');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\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",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ],
      "source": [
        "x = np.arange(1,101)\n",
        "df= pd.DataFrame(data ={'A': x} )\n",
        "df['B'] = np.random.randint(1,10,size=100)\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "A_bins = pd.cut(df['A'], bins=[0,10,20,30,40,50,60,70,80,90,100])\n",
        "df.groupby(A_bins)['B'].sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kW9-kztxUBot",
        "outputId": "8995f75e-d67e-47e5-9e48-430822298c32"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "A\n",
              "(0, 10]      38\n",
              "(10, 20]     47\n",
              "(20, 30]     51\n",
              "(30, 40]     50\n",
              "(40, 50]     54\n",
              "(50, 60]     51\n",
              "(60, 70]     61\n",
              "(70, 80]     49\n",
              "(80, 90]     50\n",
              "(90, 100]    41\n",
              "Name: B, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QM8UR3r6X2kx"
      },
      "source": [
        "## DataFrames: harder problems \n",
        "\n",
        "### These might require a bit of thinking outside the box...\n",
        "\n",
        "...but all are solvable using just the usual pandas/NumPy methods (and so avoid using explicit `for` loops).\n",
        "\n",
        "Difficulty: *hard*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "scFqJ2xJX2kx"
      },
      "source": [
        "**29.** Consider a DataFrame `df` where there is an integer column 'X':\n",
        "```python\n",
        "df = pd.DataFrame({'X': [7, 2, 0, 3, 4, 2, 5, 0, 3, 4]})\n",
        "```\n",
        "For each value, count the difference back to the previous zero (or the start of the Series, whichever is closer). These values should therefore be `[1, 2, 0, 1, 2, 3, 4, 0, 1, 2]`. Make this a new column 'Y'."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7S1IjX_OX2kx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "112c39fa-fd74-4122-9868-dc2ce2746ba7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0     True\n",
              "1     True\n",
              "2    False\n",
              "3     True\n",
              "4     True\n",
              "5     True\n",
              "6     True\n",
              "7    False\n",
              "8     True\n",
              "9     True\n",
              "Name: Y, dtype: bool"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ],
      "source": [
        "df = pd.DataFrame({'X': [7, 2, 0, 3, 4, 2, 5, 0, 3, 4]})\n",
        "df = df.assign(Y=df.loc[df.X==0])\n",
        "nul = df['Y'].isnull()\n",
        "nul\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.assign(Y=nul.groupby((nul.diff() == 1).cumsum()).cumsum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
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        "id": "kd8O9NtLWAQ9",
        "outputId": "c7a2b63e-3688-4672-90e5-91df3daf764b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   X  Y\n",
              "0  7  1\n",
              "1  2  2\n",
              "2  0  0\n",
              "3  3  1\n",
              "4  4  2\n",
              "5  2  3\n",
              "6  5  4\n",
              "7  0  0\n",
              "8  3  1\n",
              "9  4  2"
            ],
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              "\n",
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              "          const element = document.querySelector('#df-6821bbb6-6564-4258-9e1e-5f1ce6498553');\n",
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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",
              "            '<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",
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              "  "
            ]
          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oLNMGTh-X2kx"
      },
      "source": [
        "Here's an alternative approach based on a [cookbook recipe](http://pandas.pydata.org/pandas-docs/stable/cookbook.html#grouping):"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "KH-hgFhzX2kx"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B0I6_-2JX2kx"
      },
      "source": [
        "**30.** Consider a DataFrame containing rows and columns of purely numerical data. Create a list of the row-column index locations of the 3 largest values."
      ]
    },
    {
      "cell_type": "code",
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              "    0   1   2   3   4   5   6\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ],
      "source": [
        "x = np.random.randint(1,100,(5,7))\n",
        "df= pd.DataFrame(x)\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a = df.stack().nlargest(3).index \n",
        "a"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hYdGyGqvXHtg",
        "outputId": "f2d7c0f9-cb69-442e-8215-ecd2f9277224"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MultiIndex([(4, 2),\n",
              "            (4, 5),\n",
              "            (2, 4)],\n",
              "           )"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yQ_y0_9UX2kx"
      },
      "source": [
        "**31.** Given a DataFrame with a column of group IDs, 'grps', and a column of corresponding integer values, 'vals', replace any negative values in 'vals' with the group mean."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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          "height": 520
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      },
      "outputs": [
        {
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            "text/plain": [
              "   grps  vals\n",
              "0     a    12\n",
              "1     a  -345\n",
              "2     a     3\n",
              "3     b     1\n",
              "4     b    45\n",
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              "7     a    52\n",
              "8     b   -54\n",
              "9     c    23\n",
              "10    c   235\n",
              "11    c    21\n",
              "12    b    57\n",
              "13    b     3\n",
              "14    c    87"
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      "source": [
        "df = pd.DataFrame({'grps': list('aaabbcaabcccbbc'), \n",
        "                   'vals': [12,-345,3,1,45,14,4,52,-54,23,235,21,57,3,87]})\n",
        "df\n"
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        "df['vals'] = df.groupby('grps')['vals'].transform(lambda x: np.where(x < 0, x.mean(), x))\n",
        "df"
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      "execution_count": null,
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              "   grps   vals\n",
              "0     a   12.0\n",
              "1     a  -54.8\n",
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              "3     b    1.0\n",
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              "6     a    4.0\n",
              "7     a   52.0\n",
              "8     b   10.4\n",
              "9     c   23.0\n",
              "10    c  235.0\n",
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              "14    c   87.0"
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              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-022637bd-4357-4790-91dc-ac097cc8cb6b');\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",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "c = df.groupby('grps')['vals'].mean()\n",
        "c"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6WVkXpbqXZkr",
        "outputId": "1b28edc6-74bc-446d-c3c2-b447d2c71f8c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "grps\n",
              "a     3.24\n",
              "b    23.28\n",
              "c    76.00\n",
              "Name: vals, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ozWOYi0DX2ky"
      },
      "source": [
        "**32.** Implement a rolling mean over groups with window size 3, which ignores NaN value. For example consider the following DataFrame:\n",
        "\n",
        "```python\n",
        ">>> df = pd.DataFrame({'group': list('aabbabbbabab'),\n",
        "                       'value': [1, 2, 3, np.nan, 2, 3, \n",
        "                                 np.nan, 1, 7, 3, np.nan, 8]})\n",
        ">>> df\n",
        "   group  value\n",
        "0      a    1.0\n",
        "1      a    2.0\n",
        "2      b    3.0\n",
        "3      b    NaN\n",
        "4      a    2.0\n",
        "5      b    3.0\n",
        "6      b    NaN\n",
        "7      b    1.0\n",
        "8      a    7.0\n",
        "9      b    3.0\n",
        "10     a    NaN\n",
        "11     b    8.0\n",
        "```\n",
        "The goal is to compute the Series:\n",
        "\n",
        "```\n",
        "0     1.000000\n",
        "1     1.500000\n",
        "2     3.000000\n",
        "3     3.000000\n",
        "4     1.666667\n",
        "5     3.000000\n",
        "6     3.000000\n",
        "7     2.000000\n",
        "8     3.666667\n",
        "9     2.000000\n",
        "10    4.500000\n",
        "11    4.000000\n",
        "```\n",
        "E.g. the first window of size three for group 'b' has values 3.0, NaN and 3.0 and occurs at row index 5. Instead of being NaN the value in the new column at this row index should be 3.0 (just the two non-NaN values are used to compute the mean (3+3)/2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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          "height": 426
        },
        "outputId": "5678a458-47ad-459b-8e05-21791f47f2e4"
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   group  value\n",
              "0      a    1.0\n",
              "1      a    2.0\n",
              "2      b    3.0\n",
              "3      b    NaN\n",
              "4      a    2.0\n",
              "5      b    3.0\n",
              "6      b    NaN\n",
              "7      b    1.0\n",
              "8      a    7.0\n",
              "9      b    3.0\n",
              "10     a    NaN\n",
              "11     b    8.0"
            ],
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              "\n",
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              "          document.querySelector('#df-295453f6-3a92-434a-8879-38ada6336a6e button.colab-df-convert');\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-295453f6-3a92-434a-8879-38ada6336a6e');\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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ],
      "source": [
        "df = pd.DataFrame({'group': list('aabbabbbabab'),\n",
        "                       'value': [1, 2, 3, np.nan, 2, 3, \n",
        "                                 np.nan, 1, 7, 3, np.nan, 8]})\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "g1 = df.groupby(['group'])['value']\n",
        "g2 = df.fillna(0).groupby(['group'])['value']\n",
        "\n",
        "s = g2.rolling(3, min_periods=1).sum() / g1.rolling(3, min_periods=1).count()\n",
        "\n",
        "s.reset_index(level=0, drop=True).sort_index() "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5oj_1HIWXmtY",
        "outputId": "aba451e4-a508-4245-aad0-1b234d9e2062"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0     1.000000\n",
              "1     1.500000\n",
              "2     3.000000\n",
              "3     3.000000\n",
              "4     1.666667\n",
              "5     3.000000\n",
              "6     3.000000\n",
              "7     2.000000\n",
              "8     3.666667\n",
              "9     2.000000\n",
              "10    4.500000\n",
              "11    4.000000\n",
              "Name: value, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qdTJJgm4X2ky"
      },
      "source": [
        "## Series and DatetimeIndex\n",
        "\n",
        "### Exercises for creating and manipulating Series with datetime data\n",
        "\n",
        "Difficulty: *easy/medium*\n",
        "\n",
        "pandas is fantastic for working with dates and times. These puzzles explore some of this functionality.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2a2MlJyfX2ky"
      },
      "source": [
        "**33.** Create a DatetimeIndex that contains each business day of 2015 and use it to index a Series of random numbers. Let's call this Series `s`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0Ovgw2VZX2ky",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "195cee94-6dc9-476e-d47b-113f8faaa80d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2015-01-01    0.702485\n",
              "2015-01-02    0.951101\n",
              "2015-01-05    0.409449\n",
              "2015-01-06    0.968160\n",
              "2015-01-07    0.485176\n",
              "                ...   \n",
              "2015-12-25    0.695051\n",
              "2015-12-28    0.307430\n",
              "2015-12-29    0.807294\n",
              "2015-12-30    0.887753\n",
              "2015-12-31    0.045674\n",
              "Freq: B, Length: 261, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "b_days = pd.date_range(start='01/01/2015', end='31/12/2015', freq='B')\n",
        "s = pd.Series(data = np.random.rand(len(b_days)), index = b_days)\n",
        "s"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WGbzmOnXX2ky"
      },
      "source": [
        "**34.** Find the sum of the values in `s` for every Wednesday."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "O79OS-5WX2ky",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7439cf77-f198-453e-8129-d55e45b882d5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "26.486295002121444"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "source": [
        "\n",
        "s[s.index.weekday == 2].sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jL6GQD7OX2ky"
      },
      "source": [
        "**35.** For each calendar month in `s`, find the mean of values."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qnWR7nb9X2kz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f3d44ff6-1ab1-42b2-9a6e-d7220a09cc7b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1     0.552709\n",
              "2     0.451215\n",
              "3     0.605660\n",
              "4     0.434339\n",
              "5     0.513784\n",
              "6     0.487763\n",
              "7     0.487604\n",
              "8     0.505034\n",
              "9     0.595060\n",
              "10    0.511380\n",
              "11    0.434493\n",
              "12    0.517331\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ],
      "source": [
        "s.groupby(s.index.month).mean()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vJcO_FUfX2kz"
      },
      "source": [
        "**36.** For each group of four consecutive calendar months in `s`, find the date on which the highest value occurred."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "n_5NfJEIX2kz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d6a05da4-729c-4cd1-90f1-571f9c2f0b09"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2015-01-31    0.993719\n",
              "2015-05-31    0.997737\n",
              "2015-09-30    0.997979\n",
              "2016-01-31    0.979839\n",
              "Freq: 4M, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ],
      "source": [
        "s.groupby(pd.Grouper(freq='4M')).max()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "s.resample('4M').max()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lXPkkyDy_-l6",
        "outputId": "1df6e4c7-18fb-4e71-880a-7a2a282dedca"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2015-01-31    0.993719\n",
              "2015-05-31    0.997737\n",
              "2015-09-30    0.997979\n",
              "2016-01-31    0.979839\n",
              "Freq: 4M, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mj-KqaGBX2kz"
      },
      "source": [
        "**37.** Create a DateTimeIndex consisting of the third Thursday in each month for the years 2015 and 2016."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Gc22ieshX2kz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "715ec315-eed0-4600-c071-33febcd67f39"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatetimeIndex(['2015-01-15', '2015-02-19', '2015-03-19', '2015-04-16',\n",
              "               '2015-05-21', '2015-06-18', '2015-07-16', '2015-08-20',\n",
              "               '2015-09-17', '2015-10-15', '2015-11-19', '2015-12-17',\n",
              "               '2016-01-21', '2016-02-18', '2016-03-17', '2016-04-21',\n",
              "               '2016-05-19', '2016-06-16', '2016-07-21', '2016-08-18',\n",
              "               '2016-09-15', '2016-10-20', '2016-11-17', '2016-12-15'],\n",
              "              dtype='datetime64[ns]', freq='WOM-3THU')"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ],
      "source": [
        "x = pd.date_range(start='01/01/2015', end='31/12/2016', freq='WOM-3THU')\n",
        "x"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pcHy9Vv8X2kz"
      },
      "source": [
        "## Cleaning Data\n",
        "\n",
        "### Making a DataFrame easier to work with\n",
        "\n",
        "Difficulty: *easy/medium*\n",
        "\n",
        "It happens all the time: someone gives you data containing malformed strings, Python, lists and missing data. How do you tidy it up so you can get on with the analysis?\n",
        "\n",
        "Take this monstrosity as the DataFrame to use in the following puzzles:\n",
        "\n",
        "```python\n",
        "df = pd.DataFrame({'From_To': ['LoNDon_paris', 'MAdrid_miLAN', 'londON_StockhOlm', \n",
        "                               'Budapest_PaRis', 'Brussels_londOn'],\n",
        "              'FlightNumber': [10045, np.nan, 10065, np.nan, 10085],\n",
        "              'RecentDelays': [[23, 47], [], [24, 43, 87], [13], [67, 32]],\n",
        "                   'Airline': ['KLM(!)', '<Air France> (12)', '(British Airways. )', \n",
        "                               '12. Air France', '\"Swiss Air\"']})\n",
        "```\n",
        "(It's some flight data I made up; it's not meant to be accurate in any way.)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FtbbxBSGX2kz"
      },
      "source": [
        "**38.** Some values in the the FlightNumber column are missing. These numbers are meant to increase by 10 with each row so 10055 and 10075 need to be put in place. Fill in these missing numbers and make the column an integer column (instead of a float column)."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.DataFrame({'From_To': ['LoNDon_paris', 'MAdrid_miLAN', 'londON_StockhOlm', \n",
        "                               'Budapest_PaRis', 'Brussels_londOn'],\n",
        "              'FlightNumber': [10045, np.nan, 10065, np.nan, 10085],\n",
        "              'RecentDelays': [[23, 47], [], [24, 43, 87], [13], [67, 32]],\n",
        "                   'Airline': ['KLM(!)', '<Air France> (12)', '(British Airways. )', \n",
        "                               '12. Air France', '\"Swiss Air\"']})"
      ],
      "metadata": {
        "id": "Njq4DCFhAW5i"
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      "execution_count": null,
      "outputs": []
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        "df"
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        "id": "m1-PLSB5AZOK",
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      "execution_count": null,
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        {
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          "data": {
            "text/plain": [
              "            From_To  FlightNumber  RecentDelays              Airline\n",
              "0      LoNDon_paris       10045.0      [23, 47]               KLM(!)\n",
              "1      MAdrid_miLAN           NaN            []    <Air France> (12)\n",
              "2  londON_StockhOlm       10065.0  [24, 43, 87]  (British Airways. )\n",
              "3    Budapest_PaRis           NaN          [13]       12. Air France\n",
              "4   Brussels_londOn       10085.0      [67, 32]          \"Swiss Air\""
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              "                                                     [key], {});\n",
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      "execution_count": null,
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        "outputId": "687ac2df-19cb-4938-e8d9-40f73e795d4f"
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 5 entries, 0 to 4\n",
            "Data columns (total 4 columns):\n",
            " #   Column        Non-Null Count  Dtype  \n",
            "---  ------        --------------  -----  \n",
            " 0   From_To       5 non-null      object \n",
            " 1   FlightNumber  3 non-null      float64\n",
            " 2   RecentDelays  5 non-null      object \n",
            " 3   Airline       5 non-null      object \n",
            "dtypes: float64(1), object(3)\n",
            "memory usage: 288.0+ bytes\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['FlightNumber'].interpolate(inplace = True)\n",
        "df['FlightNumber'] = df['FlightNumber'].astype(int)\n",
        "df"
      ],
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        "colab": {
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        "id": "9uMeG9ljALmx",
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      "execution_count": null,
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        {
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            "text/plain": [
              "            From_To  FlightNumber  RecentDelays              Airline\n",
              "0      LoNDon_paris         10045      [23, 47]               KLM(!)\n",
              "1      MAdrid_miLAN         10055            []    <Air France> (12)\n",
              "2  londON_StockhOlm         10065  [24, 43, 87]  (British Airways. )\n",
              "3    Budapest_PaRis         10075          [13]       12. Air France\n",
              "4   Brussels_londOn         10085      [67, 32]          \"Swiss Air\""
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [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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    {
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      "metadata": {
        "id": "7J2DpArVX2k0"
      },
      "source": [
        "**39.** The From\\_To column would be better as two separate columns! Split each string on the underscore delimiter `_` to give a new temporary DataFrame with the correct values. Assign the correct column names to this temporary DataFrame. "
      ]
    },
    {
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      "execution_count": null,
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            "text/plain": [
              "   FlightNumber  RecentDelays              Airline      From         To\n",
              "0         10045      [23, 47]               KLM(!)    LoNDon      paris\n",
              "1         10055            []    <Air France> (12)    MAdrid      miLAN\n",
              "2         10065  [24, 43, 87]  (British Airways. )    londON  StockhOlm\n",
              "3         10075          [13]       12. Air France  Budapest      PaRis\n",
              "4         10085      [67, 32]          \"Swiss Air\"  Brussels     londOn"
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ],
      "source": [
        "temp = df.copy()\n",
        "temp['From'] = df['From_To'].apply(lambda x:x.split('_')[0])\n",
        "temp['To'] = df['From_To'].apply(lambda x:x.split('_')[1])\n",
        "temp.drop('From_To', axis=1, inplace=True)\n",
        "temp"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "07kQHn5QX2k0"
      },
      "source": [
        "**40.** Notice how the capitalisation of the city names is all mixed up in this temporary DataFrame. Standardise the strings so that only the first letter is uppercase (e.g. \"londON\" should become \"London\".)"
      ]
    },
    {
      "cell_type": "code",
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   FlightNumber  RecentDelays              Airline      From         To\n",
              "0         10045      [23, 47]               KLM(!)    London      Paris\n",
              "1         10055            []    <Air France> (12)    Madrid      Milan\n",
              "2         10065  [24, 43, 87]  (British Airways. )    London  Stockholm\n",
              "3         10075          [13]       12. Air France  Budapest      Paris\n",
              "4         10085      [67, 32]          \"Swiss Air\"  Brussels     London"
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\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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          "metadata": {},
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        }
      ],
      "source": [
        "temp['From'] = temp['From'].str.capitalize()\n",
        "temp['To'] = temp['To'].str.capitalize()\n",
        "temp"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FOXWMV9wX2k0"
      },
      "source": [
        "**41.** Delete the From_To column from `df` and attach the temporary DataFrame from the previous questions."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "id": "_Kh98wQWX2k0",
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        },
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   FlightNumber  RecentDelays              Airline      From         To\n",
              "0         10045      [23, 47]               KLM(!)    London      Paris\n",
              "1         10055            []    <Air France> (12)    Madrid      Milan\n",
              "2         10065  [24, 43, 87]  (British Airways. )    London  Stockholm\n",
              "3         10075          [13]       12. Air France  Budapest      Paris\n",
              "4         10085      [67, 32]          \"Swiss Air\"  Brussels     London"
            ],
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              "  </style>\n",
              "\n",
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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",
              "            '<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",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ],
      "source": [
        "df.drop('From_To', axis=1, inplace=True)\n",
        "df = pd.concat([df, temp[['From', 'To']]], axis=1)\n",
        "df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Noxdc5bLX2k0"
      },
      "source": [
        "**42**. In the Airline column, you can see some extra puctuation and symbols have appeared around the airline names. Pull out just the airline name. E.g. `'(British Airways. )'` should become `'British Airways'`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "id": "NZlZM9X7X2k0",
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      },
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        {
          "output_type": "execute_result",
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            "text/plain": [
              "   FlightNumber  RecentDelays          Airline      From         To\n",
              "0         10045      [23, 47]              KLM    London      Paris\n",
              "1         10055            []       Air France    Madrid      Milan\n",
              "2         10065  [24, 43, 87]  British Airways    London  Stockholm\n",
              "3         10075          [13]       Air France  Budapest      Paris\n",
              "4         10085      [67, 32]        Swiss Air  Brussels     London"
            ],
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              "      <th>FlightNumber</th>\n",
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              "      <th>To</th>\n",
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          "metadata": {},
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      ],
      "source": [
        "df['Airline'] = df['Airline'].str.extract('([a-zA-Z\\s]+)', expand=False).str.strip()\n",
        "df"
      ]
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    {
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        "**43**. In the RecentDelays column, the values have been entered into the DataFrame as a list. We would like each first value in its own column, each second value in its own column, and so on. If there isn't an Nth value, the value should be NaN.\n",
        "\n",
        "Expand the Series of lists into a DataFrame named `delays`, rename the columns `delay_1`, `delay_2`, etc. and replace the unwanted RecentDelays column in `df` with `delays`."
      ]
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            "text/plain": [
              "   delay_1  delay_2  delay_3\n",
              "0     23.0     47.0      NaN\n",
              "1      NaN      NaN      NaN\n",
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      ],
      "source": [
        "delays = df['RecentDelays'].apply(pd.Series)\n",
        "delays.columns = ['delay_'+str(n) for n in range(1,len(delays.columns)+1)]\n",
        "delays"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop('RecentDelays', axis=1).join(delays)"
      ],
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        "id": "gSAmBjIlBEhv",
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      },
      "execution_count": null,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "   FlightNumber          Airline      From         To  delay_1  delay_2  \\\n",
              "0         10045              KLM    London      Paris     23.0     47.0   \n",
              "1         10055       Air France    Madrid      Milan      NaN      NaN   \n",
              "2         10065  British Airways    London  Stockholm     24.0     43.0   \n",
              "3         10075       Air France  Budapest      Paris     13.0      NaN   \n",
              "4         10085        Swiss Air  Brussels     London     67.0     32.0   \n",
              "\n",
              "   delay_3  \n",
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              "          document.querySelector('#df-d26e8cf0-a6e1-4eeb-ac14-42c77c2ef82e button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d26e8cf0-a6e1-4eeb-ac14-42c77c2ef82e');\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",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nwbZvoLXX2k1"
      },
      "source": [
        "The DataFrame should look much better now."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": true,
        "id": "FqmzWkT-X2k1"
      },
      "source": [
        "## Using MultiIndexes\n",
        "\n",
        "### Go beyond flat DataFrames with additional index levels\n",
        "\n",
        "Difficulty: *medium*\n",
        "\n",
        "Previous exercises have seen us analysing data from DataFrames equipped with a single index level. However, pandas also gives you the possibilty of indexing your data using *multiple* levels. This is very much like adding new dimensions to a Series or a DataFrame. For example, a Series is 1D, but by using a MultiIndex with 2 levels we gain of much the same functionality as a 2D DataFrame.\n",
        "\n",
        "The set of puzzles below explores how you might use multiple index levels to enhance data analysis.\n",
        "\n",
        "To warm up, we'll look make a Series with two index levels. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "otQwEvsnX2k1"
      },
      "source": [
        "**44**. Given the lists `letters = ['A', 'B', 'C']` and `numbers = list(range(10))`, construct a MultiIndex object from the product of the two lists. Use it to index a Series of random numbers. Call this Series `s`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zoJN-KXBX2k1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5b934881-2eb1-41fa-aa31-13e001602f3b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "A  0    0.985689\n",
              "   1    0.381902\n",
              "   2    0.300864\n",
              "   3    0.397485\n",
              "   4    0.262409\n",
              "   5    0.714168\n",
              "   6    0.723636\n",
              "   7    0.019058\n",
              "   8    0.662151\n",
              "   9    0.417798\n",
              "B  0    0.757792\n",
              "   1    0.838774\n",
              "   2    0.089245\n",
              "   3    0.346883\n",
              "   4    0.700130\n",
              "   5    0.257965\n",
              "   6    0.730779\n",
              "   7    0.083018\n",
              "   8    0.866804\n",
              "   9    0.909663\n",
              "C  0    0.341616\n",
              "   1    0.935221\n",
              "   2    0.770980\n",
              "   3    0.724450\n",
              "   4    0.169361\n",
              "   5    0.462121\n",
              "   6    0.524011\n",
              "   7    0.399106\n",
              "   8    0.846612\n",
              "   9    0.245370\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ],
      "source": [
        "letters = ['A', 'B', 'C']\n",
        "numbers = list(range(10))\n",
        "multi_ind = pd.MultiIndex.from_product([letters, numbers])\n",
        "s = pd.Series(np.random.rand(30), index=multi_ind)\n",
        "s"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GkKdPfyMX2k1"
      },
      "source": [
        "**45.** Check the index of `s` is lexicographically sorted (this is a necessary proprty for indexing to work correctly with a MultiIndex)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MOyob680X2k1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2bacf4c4-f47d-4165-f02b-f4f39952207c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-72-a3d445bd2394>:1: FutureWarning: MultiIndex.is_lexsorted is deprecated as a public function, users should use MultiIndex.is_monotonic_increasing instead.\n",
            "  multi_ind.is_lexsorted()\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ],
      "source": [
        "multi_ind.is_lexsorted()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lrneZh27X2k2"
      },
      "source": [
        "**46**. Select the labels `1`, `3` and `6` from the second level of the MultiIndexed Series."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "s_NeUZjwX2k2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8cf5cc57-d647-45bd-e07d-aba6189cc9b9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "A  1    0.381902\n",
              "   3    0.397485\n",
              "   6    0.723636\n",
              "B  1    0.838774\n",
              "   3    0.346883\n",
              "   6    0.730779\n",
              "C  1    0.935221\n",
              "   3    0.724450\n",
              "   6    0.524011\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ],
      "source": [
        "s.loc[:,[1,3,6]]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IWDWEEHEX2k2"
      },
      "source": [
        "**47**. Slice the Series `s`; slice up to label 'B' for the first level and from label 5 onwards for the second level."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zbEo-puvX2k2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "091ae18e-0787-4916-9a22-a883b91647c3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "A  5    0.714168\n",
              "   6    0.723636\n",
              "   7    0.019058\n",
              "   8    0.662151\n",
              "   9    0.417798\n",
              "B  5    0.257965\n",
              "   6    0.730779\n",
              "   7    0.083018\n",
              "   8    0.866804\n",
              "   9    0.909663\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ],
      "source": [
        "s.loc[pd.IndexSlice[:'B', 5:]]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zOGuzIttX2k2"
      },
      "source": [
        "**48**. Sum the values in `s` for each label in the first level (you should have Series giving you a total for labels A, B and C)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vhzWGugXX2k3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6f89926e-2c9d-4646-a99c-4751ce743f68"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-75-f29a8ec3f800>:1: FutureWarning: Using the level keyword in DataFrame and Series aggregations is deprecated and will be removed in a future version. Use groupby instead. df.sum(level=1) should use df.groupby(level=1).sum().\n",
            "  s.sum(level=0)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "A    4.865159\n",
              "B    5.581054\n",
              "C    5.418847\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ],
      "source": [
        "s.sum(level=0)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-8N52-uEX2k3"
      },
      "source": [
        "**49**. Suppose that `sum()` (and other methods) did not accept a `level` keyword argument. How else could you perform the equivalent of `s.sum(level=1)`?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VGHPVoVvX2k3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1a124894-582e-45a8-d125-a9997867dbb0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    2.085096\n",
              "1    2.155897\n",
              "2    1.161089\n",
              "3    1.468818\n",
              "4    1.131900\n",
              "5    1.434254\n",
              "6    1.978426\n",
              "7    0.501182\n",
              "8    2.375567\n",
              "9    1.572831\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ],
      "source": [
        "s.unstack().sum(axis=0)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TDzRSTz5X2k3"
      },
      "source": [
        "**50**. Exchange the levels of the MultiIndex so we have an index of the form (letters, numbers). Is this new Series properly lexsorted? If not, sort it."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "s.index = s.index.swaplevel(0,1)\n",
        "s.index.is_lexsorted()\n",
        "s.sort_index()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ejdvmRmUYi6V",
        "outputId": "129344fe-a59a-4795-b9b4-e4e4a3e0928d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-77-553473a7fa84>:2: FutureWarning: MultiIndex.is_lexsorted is deprecated as a public function, users should use MultiIndex.is_monotonic_increasing instead.\n",
            "  s.index.is_lexsorted()\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0  A    0.985689\n",
              "   B    0.757792\n",
              "   C    0.341616\n",
              "1  A    0.381902\n",
              "   B    0.838774\n",
              "   C    0.935221\n",
              "2  A    0.300864\n",
              "   B    0.089245\n",
              "   C    0.770980\n",
              "3  A    0.397485\n",
              "   B    0.346883\n",
              "   C    0.724450\n",
              "4  A    0.262409\n",
              "   B    0.700130\n",
              "   C    0.169361\n",
              "5  A    0.714168\n",
              "   B    0.257965\n",
              "   C    0.462121\n",
              "6  A    0.723636\n",
              "   B    0.730779\n",
              "   C    0.524011\n",
              "7  A    0.019058\n",
              "   B    0.083018\n",
              "   C    0.399106\n",
              "8  A    0.662151\n",
              "   B    0.866804\n",
              "   C    0.846612\n",
              "9  A    0.417798\n",
              "   B    0.909663\n",
              "   C    0.245370\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sixy4A_3X2k3"
      },
      "source": [
        "## Minesweeper\n",
        "\n",
        "### Generate the numbers for safe squares in a Minesweeper grid\n",
        "\n",
        "Difficulty: *medium* to *hard*\n",
        "\n",
        "If you've ever used an older version of Windows, there's a good chance you've played with [Minesweeper](https://en.wikipedia.org/wiki/Minesweeper_(video_game). If you're not familiar with the game, imagine a grid of squares: some of these squares conceal a mine. If you click on a mine, you lose instantly. If you click on a safe square, you reveal a number telling you how many mines are found in the squares that are immediately adjacent. The aim of the game is to uncover all squares in the grid that do not contain a mine.\n",
        "\n",
        "In this section, we'll make a DataFrame that contains the necessary data for a game of Minesweeper: coordinates of the squares, whether the square contains a mine and the number of mines found on adjacent squares."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lW4jbQcxX2k3"
      },
      "source": [
        "**51**. Let's suppose we're playing Minesweeper on a 5 by 4 grid, i.e.\n",
        "```\n",
        "X = 5\n",
        "Y = 4\n",
        "```\n",
        "To begin, generate a DataFrame `df` with two columns, `'x'` and `'y'` containing every coordinate for this grid. That is, the DataFrame should start:\n",
        "```\n",
        "   x  y\n",
        "0  0  0\n",
        "1  0  1\n",
        "2  0  2\n",
        "```"
      ]
    },
    {
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        "id": "JUi8RlrfX2k4",
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              "                                                     [key], {});\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ],
      "source": [
        "df = pd.DataFrame(columns=['x','y'])\n",
        "r = 5\n",
        "c = 4\n",
        "l = 0\n",
        "for i in range(r):\n",
        "    for j in range(c):\n",
        "        df.loc[l] = [i,j]\n",
        "        l +=1\n",
        "df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u2mHT-mxX2k4"
      },
      "source": [
        "**52**. For this DataFrame `df`, create a new column of zeros (safe) and ones (mine). The probability of a mine occuring at each location should be 0.4."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BZ5RlTPMX2k4",
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          "height": 677
        },
        "outputId": "f7530d87-5675-4a2f-d677-6166ead0789c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    x  y  mine\n",
              "0   0  0     0\n",
              "1   0  1     0\n",
              "2   0  2     0\n",
              "3   0  3     0\n",
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              "12  3  0     0\n",
              "13  3  1     0\n",
              "14  3  2     0\n",
              "15  3  3     0\n",
              "16  4  0     0\n",
              "17  4  1     1\n",
              "18  4  2     1\n",
              "19  4  3     0"
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              "                                                     [key], {});\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ],
      "source": [
        "df['mine'] = np.random.binomial(1, 0.4, 20)\n",
        "df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pxim26d6X2k4"
      },
      "source": [
        "**53**. Now create a new column for this DataFrame called `'adjacent'`. This column should contain the number of mines found on adjacent squares in the grid. \n",
        "\n",
        "(E.g. for the first row, which is the entry for the coordinate `(0, 0)`, count how many mines are found on the coordinates `(0, 1)`, `(1, 0)` and `(1, 1)`.)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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          "height": 206
        },
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      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "x  0  1  2  3  4\n",
              "y               \n",
              "0  1  1  2  2  1\n",
              "1  1  2  1  3  1\n",
              "2  0  1  1  3  1\n",
              "3  0  0  0  1  1"
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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",
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              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ],
      "source": [
        "from scipy.signal import convolve2d\n",
        "mine_grid = df.pivot_table(columns='x', index='y', values='mine')\n",
        "counts = convolve2d(mine_grid.astype(complex), np.ones((3, 3)), mode='same').real.astype(int)\n",
        "counts - mine_grid"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['adjacent'] = (counts - mine_grid).values.flatten()\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 677
        },
        "id": "q5k481LcZXM3",
        "outputId": "66f4d9cf-f95c-41da-f7c1-266109169303"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    x  y  mine  adjacent\n",
              "0   0  0     0         1\n",
              "1   0  1     0         1\n",
              "2   0  2     0         2\n",
              "3   0  3     0         2\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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            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fIWS1n1bX2k4"
      },
      "source": [
        "**54**. For rows of the DataFrame that contain a mine, set the value in the `'adjacent'` column to NaN."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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        },
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      },
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        {
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          "data": {
            "text/plain": [
              "    x  y  mine  adjacent\n",
              "0   0  0     0       1.0\n",
              "1   0  1     0       1.0\n",
              "2   0  2     0       2.0\n",
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              "4   1  0     1       NaN\n",
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              "                                                     [key], {});\n",
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              "\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",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 84
        }
      ],
      "source": [
        "df.loc[df['mine'] == 1, 'adjacent'] = np.nan\n",
        "df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mRlMnzsyX2k5"
      },
      "source": [
        "**55**. Finally, convert the DataFrame to grid of the adjacent mine counts: columns are the `x` coordinate, rows are the `y` coordinate."
      ]
    },
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              "  adjacent                    \n",
              "x        0    1    2    3    4\n",
              "y                             \n",
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              "1      1.0  1.0  NaN  3.0  NaN\n",
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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",
              "            '<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": 85
        }
      ],
      "source": [
        "df.drop('mine', axis=1).set_index(['y', 'x']).unstack()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ca75v9D0X2k5"
      },
      "source": [
        "## Plotting\n",
        "\n",
        "### Visualize trends and patterns in data\n",
        "\n",
        "Difficulty: *medium*\n",
        "\n",
        "To really get a good understanding of the data contained in your DataFrame, it is often essential to create plots: if you're lucky, trends and anomalies will jump right out at you. This functionality is baked into pandas and the puzzles below explore some of what's possible with the library.\n",
        "\n",
        "**56.** Pandas is highly integrated with the plotting library matplotlib, and makes plotting DataFrames very user-friendly! Plotting in a notebook environment usually makes use of the following boilerplate:\n",
        "\n",
        "```python\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "plt.style.use('ggplot')\n",
        "```\n",
        "\n",
        "matplotlib is the plotting library which pandas' plotting functionality is built upon, and it is usually aliased to ```plt```.\n",
        "\n",
        "```%matplotlib inline``` tells the notebook to show plots inline, instead of creating them in a separate window.  \n",
        "\n",
        "```plt.style.use('ggplot')``` is a style theme that most people find agreeable, based upon the styling of R's ggplot package.\n",
        "\n",
        "For starters, make a scatter plot of this random data, but use black X's instead of the default markers. \n",
        "\n",
        "```df = pd.DataFrame({\"xs\":[1,5,2,8,1], \"ys\":[4,2,1,9,6]})```\n",
        "\n",
        "Consult the [documentation](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.plot.html) if you get stuck!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "collapsed": true,
        "id": "lXqIZLnbX2k5"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "plt.style.use('ggplot')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.DataFrame({\"xs\":[1,5,2,8,1], \"ys\":[4,2,1,9,6]})\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
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        "id": "iCX-QLgr_5jN",
        "outputId": "78301d76-9323-44d1-fa2a-24b4455c4c79"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   xs  ys\n",
              "0   1   4\n",
              "1   5   2\n",
              "2   2   1\n",
              "3   8   9\n",
              "4   1   6"
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              "                                                     [key], {});\n",
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          "metadata": {},
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        "plt.scatter(df['xs'], df['ys'], marker='x', color='black')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
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        "id": "YamTJ_AWACFE",
        "outputId": "95db104d-929f-4037-aded-ba964f0cc674"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qYYss-vIX2k5"
      },
      "source": [
        "**57.** Columns in your DataFrame can also be used to modify colors and sizes.  Bill has been keeping track of his performance at work over time, as well as how good he was feeling that day, and whether he had a cup of coffee in the morning.  Make a plot which incorporates all four features of this DataFrame.\n",
        "\n",
        "(Hint:  If you're having trouble seeing the plot, try multiplying the Series which you choose to represent size by 10 or more)\n",
        "\n",
        "*The chart doesn't have to be pretty: this isn't a course in data viz!*\n",
        "\n",
        "```\n",
        "df = pd.DataFrame({\"productivity\":[5,2,3,1,4,5,6,7,8,3,4,8,9],\n",
        "                   \"hours_in\"    :[1,9,6,5,3,9,2,9,1,7,4,2,2],\n",
        "                   \"happiness\"   :[2,1,3,2,3,1,2,3,1,2,2,1,3],\n",
        "                   \"caffienated\" :[0,0,1,1,0,0,0,0,1,1,0,1,0]})\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "collapsed": true,
        "id": "S7P5kEMdX2k5"
      },
      "outputs": [],
      "source": [
        "df = pd.DataFrame({\"productivity\":[5,2,3,1,4,5,6,7,8,3,4,8,9],\n",
        "                   \"hours_in\"    :[1,9,6,5,3,9,2,9,1,7,4,2,2],\n",
        "                   \"happiness\"   :[2,1,3,2,3,1,2,3,1,2,2,1,3],\n",
        "                   \"caffienated\" :[0,0,1,1,0,0,0,0,1,1,0,1,0]})"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "YD3nRNPbAMOT",
        "outputId": "22763309-1b30-44eb-9519-369e4e1e6fb6"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   productivity  hours_in  happiness  caffienated\n",
              "0             5         1          2            0\n",
              "1             2         9          1            0\n",
              "2             3         6          3            1\n",
              "3             1         5          2            1\n",
              "4             4         3          3            0"
            ],
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              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c6a80b59-ae92-4e9c-a6ff-e26cb5be7156 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c6a80b59-ae92-4e9c-a6ff-e26cb5be7156');\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",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(df[\"hours_in\"], df[\"productivity\"], s = df.happiness * 30, c = df.caffienated)\n",
        "plt.xlabel(\"Hours\")\n",
        "plt.ylabel(\"Productivity\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "jwKjr6p2ARDm",
        "outputId": "832d67b7-8235-4152-aa61-7960d0bd1610"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CHgjUDbAX2k5"
      },
      "source": [
        "**58.**  What if we want to plot multiple things?  Pandas allows you to pass in a matplotlib *Axis* object for plots, and plots will also return an Axis object.\n",
        "\n",
        "Make a bar plot of monthly revenue with a line plot of monthly advertising spending (numbers in millions)\n",
        "\n",
        "```\n",
        "df = pd.DataFrame({\"revenue\":[57,68,63,71,72,90,80,62,59,51,47,52],\n",
        "                   \"advertising\":[2.1,1.9,2.7,3.0,3.6,3.2,2.7,2.4,1.8,1.6,1.3,1.9],\n",
        "                   \"month\":range(12)\n",
        "                  })\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "collapsed": true,
        "id": "Rer3l6yTX2k5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 425
        },
        "outputId": "de818f1e-200e-4fa2-969c-19a45d2360d1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    revenue  advertising  month\n",
              "0        57          2.1      0\n",
              "1        68          1.9      1\n",
              "2        63          2.7      2\n",
              "3        71          3.0      3\n",
              "4        72          3.6      4\n",
              "5        90          3.2      5\n",
              "6        80          2.7      6\n",
              "7        62          2.4      7\n",
              "8        59          1.8      8\n",
              "9        51          1.6      9\n",
              "10       47          1.3     10\n",
              "11       52          1.9     11"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-36c77e77-9def-4b6b-90ea-ea8aff1f0307\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>revenue</th>\n",
              "      <th>advertising</th>\n",
              "      <th>month</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>57</td>\n",
              "      <td>2.1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>68</td>\n",
              "      <td>1.9</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>63</td>\n",
              "      <td>2.7</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>71</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>72</td>\n",
              "      <td>3.6</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>90</td>\n",
              "      <td>3.2</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>80</td>\n",
              "      <td>2.7</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>62</td>\n",
              "      <td>2.4</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>59</td>\n",
              "      <td>1.8</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>51</td>\n",
              "      <td>1.6</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>47</td>\n",
              "      <td>1.3</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>52</td>\n",
              "      <td>1.9</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-36c77e77-9def-4b6b-90ea-ea8aff1f0307')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-36c77e77-9def-4b6b-90ea-ea8aff1f0307 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-36c77e77-9def-4b6b-90ea-ea8aff1f0307');\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",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "df = pd.DataFrame({\"revenue\":[57,68,63,71,72,90,80,62,59,51,47,52],\n",
        "                   \"advertising\":[2.1,1.9,2.7,3.0,3.6,3.2,2.7,2.4,1.8,1.6,1.3,1.9],\n",
        "                   \"month\":range(12)\n",
        "                   \n",
        "                  })\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ax = df.plot.bar(\"month\", \"revenue\", color = \"green\")\n",
        "df.plot.line(\"month\", \"advertising\", secondary_y = True, ax = ax)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "coDlPLPzAgMG",
        "outputId": "b2ffaaa9-dcf8-4f03-9b85-de4534051275"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f9de67d4ac0>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, axes = plt.subplots(nrows=2, sharex=True)\n",
        "axes[0].bar(df.month, df.revenue)\n",
        "axes[1].plot(df.advertising)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "F-vhWN_PArJW",
        "outputId": "0cdc5bc2-5524-41b0-f89e-da068e94c363"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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Nzc2t7uHOKjk5mfz8/JAeI5yCOR576CD2nTexb78Bh4rgvDaYIaMwXXqFbb47kv58rLXYd/8auCVu/QY4N9yFubB9hd8fSWMJhmgaTzSNBaBFixanfT0o90KpX78+7du3Z/PmzeUWuISfzcvF/vU17PocKCmBjt1wBo+EC9tH9XRJeYwxmL6Dsee3xV0wBzfzXsyI8ZghI3UvFfGFKhf4wYMHiYmJoX79+pSUlLB161auvvrqYGaTarK7t+OufAU+fA9iYjA9B2CuuBrTQv/Ifp9peQHOrx7FPvtb7MvPYnd+jPPzqZgGjbyOJnJWVS7wwsJCsrOzcV0Xay29evXi8ssvD2Y2qQLrurBlQ6C4d30CcQ0wPxqNGTgc0zjB63gRy9SLg8nT4Z03sYsX4j4wDWfydEzqRV5HEzmjKhf4eeedx8MPPxzMLFINtuQ49u9vY1cuhbxcSGqKuXYS5geDMHXreR3PF4wxmAHDsOdfiPvkHNyHf4kZPREz6MoaPdUkkUv3A/c5e+gg9u03AguThw8GFiYnTQ/rwmS0MRe0xblvHu6iedjFfwhMqfz0Nkxcfa+jiZxEBe5T9t+5gWdDrl/13cLkkJHQtmYvTAaLqd8A5+Z7sX9din3pWdwvpuFMvhtzXmuvo4mcoAL3mVMWJnsNDCxMntPS62hRxxiDGTwSm9oO9/dzcTOmY675Babf/+gfSYkIKnAfsG4ZbNmIu+Jl2L3924XJ/8UMHKaFyTAwbS4OTKk8/Sj2+d/Bzo9h/BSvY4mowCOZLTnO0eWv4L7y/MkLk33SMHXqeh2vRjENG+HcOjNwM6xXX8B+vptvZmRA/cZeR5MaTAUeoew3JbiP3seh3dvh/LaB+1pfpoVJLxnHwQwbg21zMe5Tj1Bwx0RM1z6YwSMxrVK9jic1kAo8AllrsX/Mht3baTR1Focv6aI51whiLuqAM3Medde8xdEVr2L/sQYu7hTY3dr+Mv1ZSdiowCOQXfkK9r23MVdfR71+QzgSRfd0iBamUQINf3YbxYOuwq5dgV31Ou5j6ZByXuCMvHtfTGwtr2NKlNMNHyKM3boR+9KzgR/Nh13jdRwph4lrgPM/P8b5zVOYn90OgF00D/eXN+Aufwl79IjHCSWa6Qw8gtj/9znuU49Ay1TMxNv1o7iPmNhamN6DsL0Gwscf4q58BfvSs9g3lgSecDToKkxSE69jSpRRgUcIe/ggbvaDUKcuzs33YurU8TqSVIExBi7tQsylXbCf78auXIpd9Tp21euYbn0xg0dgWmkzkASHCjwC2NJS3AVzoHA/zp2zMYnJXkeSIDCtWmN+cQd25ATsqtewa1dqwVOCSgUeAezip2DHNszPp2Fat/M6jgSZSWqCGXM9dvg1p1nwHIHp/kMteEqVaBHTY+47b2LfeQszZBROrwFex5EQOnnBcyoAdtFj31vwPOxxQvEbnYF7yH6yJfA4rw5dMaPGex1HwiSw4DkQ22vAyQuey75d8EzTgqdUjArcIzbvK9wnH4ZmKTg33IlxtMOypjntgufq17GrX8d07YsZogVPOTsVuAfssaO4TzwIgHPLrwJPg5Ea7bQLnhvWQLuOOFddh2l7idcRJQJpDjzMrFsWuNY7LxfnxrsxTc/xOpJEEJPUBGfM9TgPL8SMnghff4mbeS/ue+94HU0ikAo8zOzL/xe2bcJcewOmXUev40iEMnENcIaMwrl/PrS5BLvwUdy/vup1LIkwKvAwctevxq54GdP/Rzj9h3odR3zA1IvDuX0WXN4bu2Qh7l+ewVrrdSyJECrwMLG7t2P/7xNwUQfMNTd4HUd8xNSqjTPpLkz/H2FXvIx95nFsWZnXsSQCaBEzDGzBPtz5D0FCcmDeO1a/7VI5xomB626EhvHY11/EHj6IM2m6brlQw+kMPMTs8eO42Q9ByfHAFScNGnkdSXzKGINz1VjMT26CbZtws+7DHjnkdSzxkAo8hKy12Gcegy/+FbjWu0UrryNJFHD6/whn8t3w2S7cOTOwBbpffE2lAg8h+8Zi7KZ3MT/+KaZjN6/jSBQxl/fGuT0dCvNx50zHfvWF15HEAyrwELEfrMe++gKm5wDM4JFex5EoZNp1xLnrISgtDZyJ/2uH15EkzFTgIWA//xfuwixIvQgz4WbdMlRCxrRqjXP3HIirj5v5K+y2972OJGGkAg8ye/AAbvZsiGuAc9MvMbVqex1Jopxpeg7OjDnQPAU3+0Hcv7/tdSQJk6gqcLvvawrTb8ddvQy7Py/8x//mG9zf/QYOF+Hcci8mPjHsGaRmMo0ScO58CNq2xz6dhbvyFa8jSRhE1wXJBwooy8/Dvvj7wG1azz0f07E7pnN3OK8Nxgndv1fWWuzz82HXJ5hJ0zHntQnZsUROx9SLw7ltFu7CTOyfF+EePID58URN4UWxKhd4fn4+2dnZHDhwAGMMaWlpDB3q7fZw0/YSkp94kX0fbcFu3YDdshG7/C/YN5dA4wRMx26Bq0Eu7hz0DRA25zXsulWY4dfgdOsT1M8WqShTqxbOpLuwLz6FXfEKHCyCCbdo81iUqvKfakxMDOPHjyc1NZVjx44xY8YMOnbsyLnnnhvMfFVimqdgmo+EwSOxRw4FFna2bMBuehf7t5VQqzZc3AnTqXug1Ks51WE/eh/750XQpRfmyrFBGoVI1QR2bU6GxvHYV18I7NqcPB1Tp67X0STIqlzgCQkJJCQkAFCvXj1SUlIoKCiIiAL/PlO/IaZnf+jZH1v6DXz6MXbrRuzmfwT+F+D8tphO3TCdegSmXSrxI6f96kvc38+FlPNwfj4tpNM0IhVljMEMvxa3YTz2+QW4j96Hc9tMTP2GXkeTIDI2CLc2y8vLY9asWWRmZhIXd/LDCXJycsjJyQEgIyODkpKS6h7urGJjYyktLS33+6y1lH7+L0o2vsvxje/yzc5/grU4TZpRp2sf6nTvQ+32l531KhL38EEKpt+Ae+QQSXMXEhOCe3tXdDx+EU3j8ctYiv/+DkWPziKmeQoJs7KISW522u/zy3gqIprGAlC79ul7qNoFXlxczKxZsxg1ahQ9evQo9/tzc3Orc7hyJScnk59f+a3FtqgwcEa+dSP880MoKYE69eDSywILoR26Yhp+dx8TW1YWeLL4px/j3Pkgpk1onphS1fFEqmgaj5/GYndsC1zeWjcOZ9qvMee0POV7/DSe8kTTWABatGhx2tertbJRWlpKZmYmffv2rVB5RzLTOAHTdzD0HYwtOQ7bt2K3fLsQ+v56rHGgdTtM5+6Yjt2x77wJn2zB/PTWkJW3SLCYizrg3PkQ7mPpuHNm4Nx6H6Z1O69j+Z49ftzTO0JW+QzcWkt2djYNGjRg4sSJFX5fpJ6Bn4l1Xfh897dlvgG+2HPi10za1TjXXB+0Y51OtJ1JRNN4/DgWu+9r3KyZUFSAc+MMTIeuJ37Nj+M5k1CPxVobuK3v++tx7s7AxDUI2bEgBGfgO3bsYO3atbRq1Yq77roLgLFjx9KlS5eqfmREMo4TWOQ8vy1c/RPs/n2BaZZDRZhhY7yOJ1IppklznBlzcB+7H/eJBzE/vQ2n90CvY/mKdcuwLzyJXbMc84NBgalWj1S5wNu1a8eSJUuCmcUXTFITzAA9Dk38K7Brczbu736DXTQP91ARzhDdcK0i7DcluH/IhA/+jvnRjzEjJ3i6UUrXvInUQKZeHM6tMzFd+2D/sgj3z08HpgvljOzRI7iP/TpQ3tdcjzPqp57vctX2LJEaytSqBTfcAQ0bYVcupWDHNtwOXTGdukOr1trT8D22qDBw1Vnu55hf3IHTo5/XkQAVuEiNZpwYGDsZWqZiNv0N+8afscsWQ+PEwOa2jt3h4o6Y2jX32Zs2Lxd3XjocPIBzy32YSyNnnU8FLlLDGWMwfQeTOPI69u35V+DWEFv+gf3HWuzaFVC7NlxyWeC2E526YRoleB05bOxnuwNn3tbFueNBzAUXeh3pJCpwETnBNGyE6TUAeg3AfvMNfPpRoMy3fHv7CWO+vfVE98BUS8p5ns8Dh4r9ZAvu/IcC9/af9mtM88i6TQiowEXkDEytWtD+Mkz7y7BjJ8OXe78r86XPYZc+B0lNvyvzC9tjYmt5HTso7KZ3cRc+Cs1ScG5PxyQkeR3ptFTgIlIuYwy0vADT8gIYfi32wH7s1k2BDW5/W4ldvQzqxWHad4FO3TEdLvftjbPct9/EvvgktG4XmPOuH9pNOtWhAheRSjPxSZgfDoEfDsEePw6fbA7cS2jLBtj0LtZxoM0lJ+7yaZqdfidhJLHWYl97EbvsT9CpO86kuyJ+8VYFLiLVYurUgc49MJ17BK4l37szMM2y5R/YPy8K3Cu/ecq3Uy09AvcUirBLFK1bhn3+Seza5ZgfpGHG34yJifE6VrlU4CISNMZxIPUiTOpFMHIcNv/fgTLfugGb83rgKUHntMRccTWm54DAPLvHTt5dORozcrxvFmZV4CISMia5GWbQcBg0HHvsKPbD97A5r2L/+AR26XOYgcMx/X/k2Xy5PXokcJvdTz/CXPMLnLSrPMlRVSpwEQkLUy8O03sgttcA2L4Vd8XLgatZ3voLps8VmEFXYpo0D1see6AgsDX+q8jaXVkZKnARCStjDFzciZiLO2G/3INd+Sr2nbewq9/AXN4bM3gk5oK2Ic1g83Jxs2bBoaLAPWHaXxbS44WKClxEPGPOvQDz86nYkeOxq17Hrl2O3fQuXHgpzuCR0OHyoC94fre70kbk7srKUIGLiOdMQhJm9ETssDGB68pXvYb7xAPfW/Dsf9bn01aU/WQLbvZD0KAhztT0iNxdWRkqcBGJGKZeHGbwCOzA4dj312FXvBy0BU9347vYpyN/d2VlqMBFJOKY2FhMj37Y7j8MLHiufCWw4PnmnwMLnmlXVWrB0337DeyLv4fWF+Pc8quI3l1ZGSpwEYlYJy947sWuXIpdsxz79psVWvAM7K58IXCLXJ/srqwMFbiI+II59/wzLHi2xxk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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H6iELCKDX2k5"
      },
      "source": [
        "Now we're finally ready to create a candlestick chart, which is a very common tool used to analyze stock price data.  A candlestick chart shows the opening, closing, highest, and lowest price for a stock during a time window.  The color of the \"candle\" (the thick part of the bar) is green if the stock closed above its opening price, or red if below.\n",
        "\n",
        "![Candlestick Example](img/candle.jpg)\n",
        "\n",
        "This was initially designed to be a pandas plotting challenge, but it just so happens that this type of plot is just not feasible using pandas' methods.  If you are unfamiliar with matplotlib, we have provided a function that will plot the chart for you so long as you can use pandas to get the data into the correct format.\n",
        "\n",
        "Your first step should be to get the data in the correct format using pandas' time-series grouping function.  We would like each candle to represent an hour's worth of data.  You can write your own aggregation function which returns the open/high/low/close, but pandas has a built-in which also does this."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1K0HZvaZX2k6"
      },
      "source": [
        "The below cell contains helper functions.  Call ```day_stock_data()``` to generate a DataFrame containing the prices a hypothetical stock sold for, and the time the sale occurred.  Call ```plot_candlestick(df)``` on your properly aggregated and formatted stock data to print the candlestick chart."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "collapsed": true,
        "id": "iAuC9H0lX2k6"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "def float_to_time(x):\n",
        "    return str(int(x)) + \":\" + str(int(x%1 * 60)).zfill(2) + \":\" + str(int(x*60 % 1 * 60)).zfill(2)\n",
        "\n",
        "def day_stock_data():\n",
        "    #NYSE is open from 9:30 to 4:00\n",
        "    time = 9.5\n",
        "    price = 100\n",
        "    results = [(float_to_time(time), price)]\n",
        "    while time < 16:\n",
        "        elapsed = np.random.exponential(.001)\n",
        "        time += elapsed\n",
        "        if time > 16:\n",
        "            break\n",
        "        price_diff = np.random.uniform(.999, 1.001)\n",
        "        price *= price_diff\n",
        "        results.append((float_to_time(time), price))\n",
        "    \n",
        "    \n",
        "    df = pd.DataFrame(results, columns = ['time','price'])\n",
        "    df.time = pd.to_datetime(df.time)\n",
        "    return df\n",
        "\n",
        "#Don't read me unless you get stuck!\n",
        "def plot_candlestick(agg):\n",
        "    \"\"\"\n",
        "    agg is a DataFrame which has a DatetimeIndex and five columns: [\"open\",\"high\",\"low\",\"close\",\"color\"]\n",
        "    \"\"\"\n",
        "    fig, ax = plt.subplots()\n",
        "    for time in agg.index:\n",
        "        ax.plot([time.hour] * 2, agg.loc[time, [\"high\",\"low\"]].values, color = \"black\")\n",
        "        ax.plot([time.hour] * 2, agg.loc[time, [\"open\",\"close\"]].values, color = agg.loc[time, \"color\"], linewidth = 10)\n",
        "\n",
        "    ax.set_xlim((8,16))\n",
        "    ax.set_ylabel(\"Price\")\n",
        "    ax.set_xlabel(\"Hour\")\n",
        "    ax.set_title(\"OHLC of Stock Value During Trading Day\")\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YvyzwHyxX2k6"
      },
      "source": [
        "**59.** Generate a day's worth of random stock data, and aggregate / reformat it so that it has hourly summaries of the opening, highest, lowest, and closing prices"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "collapsed": true,
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          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "10629b31-bcd3-4dd0-e400-eef58d7b44b4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 time       price\n",
              "0 2022-12-05 09:30:00  100.000000\n",
              "1 2022-12-05 09:30:00   99.942569\n",
              "2 2022-12-05 09:30:01   99.992603\n",
              "3 2022-12-05 09:30:01   99.990165\n",
              "4 2022-12-05 09:30:04   99.896058"
            ],
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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",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "df = day_stock_data()\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.tail()"
      ],
      "metadata": {
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      },
      "execution_count": 18,
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        {
          "output_type": "execute_result",
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            "text/plain": [
              "                    time      price\n",
              "6598 2022-12-05 15:59:32  99.089897\n",
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              "\n",
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    {
      "cell_type": "code",
      "source": [
        "df1 = df.resample('1H').ohlc(_method='ohlc')\n",
        "df1.columns = df1.columns.droplevel()\n",
        "df1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "FJOZTYRtA97N",
        "outputId": "bb6108b3-f112-495f-97a0-0c60554b7286"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
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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": 24
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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MfEFB-RpX2k7"
      },
      "source": [
        "**60.** Now that you have your properly-formatted data, try to plot it yourself as a candlestick chart.  Use the ```plot_candlestick(df)``` function above, or matplotlib's [```plot``` documentation](https://matplotlib.org/api/_as_gen/matplotlib.axes.Axes.plot.html) if you get stuck."
      ]
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    {
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-12-05 10:00:00</th>\n",
              "      <td>102.583125</td>\n",
              "      <td>104.025279</td>\n",
              "      <td>100.636553</td>\n",
              "      <td>103.403885</td>\n",
              "      <td>green</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-12-05 11:00:00</th>\n",
              "      <td>103.318290</td>\n",
              "      <td>105.032697</td>\n",
              "      <td>103.099079</td>\n",
              "      <td>104.650086</td>\n",
              "      <td>green</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-12-05 12:00:00</th>\n",
              "      <td>104.580423</td>\n",
              "      <td>105.015673</td>\n",
              "      <td>102.693541</td>\n",
              "      <td>104.747826</td>\n",
              "      <td>green</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-12-05 13:00:00</th>\n",
              "      <td>104.775688</td>\n",
              "      <td>105.122160</td>\n",
              "      <td>102.474197</td>\n",
              "      <td>103.571981</td>\n",
              "      <td>red</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-12-05 14:00:00</th>\n",
              "      <td>103.477893</td>\n",
              "      <td>103.997883</td>\n",
              "      <td>101.609218</td>\n",
              "      <td>102.064573</td>\n",
              "      <td>red</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-12-05 15:00:00</th>\n",
              "      <td>102.024015</td>\n",
              "      <td>102.446347</td>\n",
              "      <td>98.837215</td>\n",
              "      <td>99.144944</td>\n",
              "      <td>red</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d6a92be7-06ee-4a66-b873-c9dde93ded82')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d6a92be7-06ee-4a66-b873-c9dde93ded82 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d6a92be7-06ee-4a66-b873-c9dde93ded82');\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",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "df1['color'] = (df1.close > df1.open).map({True:\"green\", False:\"red\"})\n",
        "df1"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_candlestick(df1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "8k6fbKcKBUlF",
        "outputId": "e11c7f40-357e-4a49-8817-6b283cbf4343"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.3"
    },
    "colab": {
      "provenance": [],
      "collapsed_sections": [
        "vHtwfOpIX2kv",
        "QM8UR3r6X2kx",
        "qdTJJgm4X2ky",
        "pcHy9Vv8X2kz",
        "FqmzWkT-X2k1",
        "sixy4A_3X2k3",
        "ca75v9D0X2k5"
      ],
      "include_colab_link": true
    }
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