100_pandas_puzzle_1.ipynb

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        "<a href=\"https://colab.research.google.com/github/Junseokee/Study-Python/blob/main/100_pandas_puzzle_1.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",
      "source": [
        "#10 minutes to pandas\n",
        "####This is a short introduction to pandas, geared mainly for new users. You can see more complex recipes in the Cookbook.\n",
        "\n",
        "####Customarily, we import as follows:"
      ],
      "metadata": {
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    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd"
      ],
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      "execution_count": 3,
      "outputs": []
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      "source": [
        "#Object creation\n",
        "####See the Intro to data structures section.\n",
        "\n",
        "####Creating a Series by passing a list of values, letting pandas create a default integer index:"
      ],
      "metadata": {
        "id": "l13GOsNXyLQn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "s = pd.Series([1, 3, 5, np.nan, 6, 8])\n",
        "print(s)"
      ],
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          "text": [
            "0    1.0\n",
            "1    3.0\n",
            "2    5.0\n",
            "3    NaN\n",
            "4    6.0\n",
            "5    8.0\n",
            "dtype: float64\n"
          ]
        }
      ]
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    {
      "cell_type": "code",
      "source": [
        "#Creating a DataFrame by passing a NumPy array, with a datetime index and labeled columns:\n",
        "dates = pd.date_range(\"20130101\", periods = 6) #2013.01.01부터 날짜 6개 생성\n",
        "dates"
      ],
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      "execution_count": 9,
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            "text/plain": [
              "DatetimeIndex(['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04',\n",
              "               '2013-01-05', '2013-01-06'],\n",
              "              dtype='datetime64[ns]', freq='D')"
            ]
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          "execution_count": 9
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      ]
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      "cell_type": "code",
      "source": [
        "df = pd.DataFrame(np.random.randn(6,4), index=dates, columns=list(\"ABCD\")) #6행4열 데이터프레임을 날짜데이터로 랜덤으로 생성 칼럼명은 ABCD\n",
        "df"
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              "                   A         B         C         D\n",
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              "2013-01-04 -0.750983 -2.596451  1.257411 -0.420489\n",
              "2013-01-05  1.083972 -0.809843  1.861297 -0.963160\n",
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      "cell_type": "code",
      "source": [
        "#Creating a DataFrame by passing a dictionary of objects that can be converted into a series-like structure:\n",
        "df2 = pd.DataFrame(\n",
        "    {\n",
        "        \"A\" : 1.0,\n",
        "        \"B\" : pd.Timestamp(\"20130102\"),\n",
        "        \"C\" : pd.Series(1, index=list(range(4)), dtype=\"float32\"),\n",
        "        \"D\" : np.array([3] * 4, dtype = \"int32\"),\n",
        "        \"E\" : pd.Categorical([\"test\", \"train\", \"test\", \"train\"]),\n",
        "        \"F\" : \"foo\",\n",
        "    }\n",
        ")\n",
        "df2"
      ],
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          "height": 175
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        "id": "1pZIqG_Wzo6P",
        "outputId": "a46ea584-6b19-4d9a-d63b-e4ba2b04affe"
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              "     A          B    C  D      E    F\n",
              "0  1.0 2013-01-02  1.0  3   test  foo\n",
              "1  1.0 2013-01-02  1.0  3  train  foo\n",
              "2  1.0 2013-01-02  1.0  3   test  foo\n",
              "3  1.0 2013-01-02  1.0  3  train  foo"
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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",
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    {
      "cell_type": "code",
      "source": [
        "#The columns of the resulting DataFrame have different dtypes:\n",
        "df2.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RpxgQZQW0yhb",
        "outputId": "5c526f36-9815-4613-b73c-6c4d24f160a8"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "A           float64\n",
              "B    datetime64[ns]\n",
              "C           float32\n",
              "D             int32\n",
              "E          category\n",
              "F            object\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Viewing data\n",
        "See the Basics section.\n",
        "\n",
        "Here is how to view the top and bottom rows of the frame:"
      ],
      "metadata": {
        "id": "TF4JLkik2cTr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.head() #앞 5개"
      ],
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          "height": 206
        },
        "id": "_cujBzmO0HxY",
        "outputId": "ce62cdf3-4871-4a9a-db8f-5d1c213e07c6"
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              "                   A         B         C         D\n",
              "2013-01-01 -1.249748  0.697185  1.553600  0.859551\n",
              "2013-01-02  1.756773  0.744990  0.917294 -0.804845\n",
              "2013-01-03  0.306445  0.319013  0.762304  0.003968\n",
              "2013-01-04 -0.750983 -2.596451  1.257411 -0.420489\n",
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              "      <th>2013-01-01</th>\n",
              "      <td>-1.249748</td>\n",
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              "      <td>1.553600</td>\n",
              "      <td>0.859551</td>\n",
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              "    <tr>\n",
              "      <th>2013-01-02</th>\n",
              "      <td>1.756773</td>\n",
              "      <td>0.744990</td>\n",
              "      <td>0.917294</td>\n",
              "      <td>-0.804845</td>\n",
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              "    <tr>\n",
              "      <th>2013-01-03</th>\n",
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              "      <td>0.762304</td>\n",
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              "      <th>2013-01-04</th>\n",
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              "      <td>1.257411</td>\n",
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    {
      "cell_type": "code",
      "source": [
        "df.tail(3) #뒤 3개"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "2ylcyyKozpvX",
        "outputId": "0655f9d6-60e0-49c2-8c78-f248772753ee"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   A         B         C         D\n",
              "2013-01-04 -0.750983 -2.596451  1.257411 -0.420489\n",
              "2013-01-05  1.083972 -0.809843  1.861297 -0.963160\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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              "  "
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          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.index #인덱스 값"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kEQ_F6oq2-uw",
        "outputId": "f78eaea6-51e8-4d5a-c19c-dc8c1a6b0081"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatetimeIndex(['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04',\n",
              "               '2013-01-05', '2013-01-06'],\n",
              "              dtype='datetime64[ns]', freq='D')"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns #컬럼의 값"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ox0e-XgQ2-sY",
        "outputId": "49d5ec3b-a734-4a4b-a6d7-ca12544dc511"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['A', 'B', 'C', 'D'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "DataFrame.to_numpy() gives a NumPy representation of the underlying data. Note that this can be an expensive operation when your DataFrame has columns with different data types, which comes down to a fundamental difference between pandas and NumPy: NumPy arrays have one dtype for the entire array, while pandas DataFrames have one dtype per column. When you call DataFrame.to_numpy(), pandas will find the NumPy dtype that can hold all of the dtypes in the DataFrame. This may end up being object, which requires casting every value to a Python object.\n",
        "\n",
        "Note.\n",
        "\n",
        "DataFrame.to_numpy() does not include the index or column labels in the output."
      ],
      "metadata": {
        "id": "JI4clxKe32PB"
      }
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    {
      "cell_type": "code",
      "source": [
        "#For df, our DataFrame of all floating-point values, DataFrame.to_numpy() is fast and doesn’t require copying data:\n",
        "df.to_numpy()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wYJcD4au2-py",
        "outputId": "820fc463-052d-4c30-e3c7-e805586e1c4c"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-1.24974818,  0.69718468,  1.5535997 ,  0.85955125],\n",
              "       [ 1.75677265,  0.74499049,  0.91729363, -0.80484472],\n",
              "       [ 0.30644534,  0.31901337,  0.76230352,  0.00396843],\n",
              "       [-0.75098328, -2.59645087,  1.25741068, -0.42048934],\n",
              "       [ 1.08397196, -0.8098426 ,  1.86129722, -0.96315993],\n",
              "       [-0.98981734, -2.48163248, -1.15865206, -1.49404761]])"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#For df2, the DataFrame with multiple dtypes, DataFrame.to_numpy() is relatively expensive:\n",
        "df2.to_numpy() "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GKvRVsKO2-jJ",
        "outputId": "9b4f797a-230e-4537-f47d-931a74bbd128"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1.0, Timestamp('2013-01-02 00:00:00'), 1.0, 3, 'test', 'foo'],\n",
              "       [1.0, Timestamp('2013-01-02 00:00:00'), 1.0, 3, 'train', 'foo'],\n",
              "       [1.0, Timestamp('2013-01-02 00:00:00'), 1.0, 3, 'test', 'foo'],\n",
              "       [1.0, Timestamp('2013-01-02 00:00:00'), 1.0, 3, 'train', 'foo']],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#describe() shows a quick statistic summary of your data:\n",
        "df.describe() #요약 R의 summary랑 비슷"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "xM9FyKuH2-aj",
        "outputId": "ad7e7b11-3d4f-472e-88dc-e319423c84be"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              A         B         C         D\n",
              "count  6.000000  6.000000  6.000000  6.000000\n",
              "mean   0.026107 -0.687790  0.865542 -0.469837\n",
              "std    1.221200  1.540157  1.070440  0.824476\n",
              "min   -1.249748 -2.596451 -1.158652 -1.494048\n",
              "25%   -0.930109 -2.063685  0.801051 -0.923581\n",
              "50%   -0.222269 -0.245415  1.087352 -0.612667\n",
              "75%    0.889590  0.602642  1.479552 -0.102146\n",
              "max    1.756773  0.744990  1.861297  0.859551"
            ],
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              "\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>6.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>6.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.026107</td>\n",
              "      <td>-0.687790</td>\n",
              "      <td>0.865542</td>\n",
              "      <td>-0.469837</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.221200</td>\n",
              "      <td>1.540157</td>\n",
              "      <td>1.070440</td>\n",
              "      <td>0.824476</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-1.249748</td>\n",
              "      <td>-2.596451</td>\n",
              "      <td>-1.158652</td>\n",
              "      <td>-1.494048</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>-0.930109</td>\n",
              "      <td>-2.063685</td>\n",
              "      <td>0.801051</td>\n",
              "      <td>-0.923581</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>-0.222269</td>\n",
              "      <td>-0.245415</td>\n",
              "      <td>1.087352</td>\n",
              "      <td>-0.612667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.889590</td>\n",
              "      <td>0.602642</td>\n",
              "      <td>1.479552</td>\n",
              "      <td>-0.102146</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.756773</td>\n",
              "      <td>0.744990</td>\n",
              "      <td>1.861297</td>\n",
              "      <td>0.859551</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2958f17e-3145-4a94-8216-0056a6ace26b')\"\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-2958f17e-3145-4a94-8216-0056a6ace26b 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-2958f17e-3145-4a94-8216-0056a6ace26b');\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": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Transposing your data:\n",
        "df.T"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "HoxzQeoy40es",
        "outputId": "6d030f9f-ca2c-4713-e82d-f529b77b5a0d"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   2013-01-01  2013-01-02  2013-01-03  2013-01-04  2013-01-05  2013-01-06\n",
              "A   -1.249748    1.756773    0.306445   -0.750983    1.083972   -0.989817\n",
              "B    0.697185    0.744990    0.319013   -2.596451   -0.809843   -2.481632\n",
              "C    1.553600    0.917294    0.762304    1.257411    1.861297   -1.158652\n",
              "D    0.859551   -0.804845    0.003968   -0.420489   -0.963160   -1.494048"
            ],
            "text/html": [
              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>2013-01-01</th>\n",
              "      <th>2013-01-02</th>\n",
              "      <th>2013-01-03</th>\n",
              "      <th>2013-01-04</th>\n",
              "      <th>2013-01-05</th>\n",
              "      <th>2013-01-06</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>A</th>\n",
              "      <td>-1.249748</td>\n",
              "      <td>1.756773</td>\n",
              "      <td>0.306445</td>\n",
              "      <td>-0.750983</td>\n",
              "      <td>1.083972</td>\n",
              "      <td>-0.989817</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>B</th>\n",
              "      <td>0.697185</td>\n",
              "      <td>0.744990</td>\n",
              "      <td>0.319013</td>\n",
              "      <td>-2.596451</td>\n",
              "      <td>-0.809843</td>\n",
              "      <td>-2.481632</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C</th>\n",
              "      <td>1.553600</td>\n",
              "      <td>0.917294</td>\n",
              "      <td>0.762304</td>\n",
              "      <td>1.257411</td>\n",
              "      <td>1.861297</td>\n",
              "      <td>-1.158652</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>D</th>\n",
              "      <td>0.859551</td>\n",
              "      <td>-0.804845</td>\n",
              "      <td>0.003968</td>\n",
              "      <td>-0.420489</td>\n",
              "      <td>-0.963160</td>\n",
              "      <td>-1.494048</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c49cdf12-fc61-473b-8142-3c1ca763d32c')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "  </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",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
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              "\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-c49cdf12-fc61-473b-8142-3c1ca763d32c 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-c49cdf12-fc61-473b-8142-3c1ca763d32c');\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": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Sorting by an axis: 축기준\n",
        "df.sort_index(axis=1, ascending=False) #True 할 경우 ABCD"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "VMxlBnAC40hh",
        "outputId": "783292f2-2a32-4c31-fea3-e6e09c6d34b5"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   D         C         B         A\n",
              "2013-01-01  0.859551  1.553600  0.697185 -1.249748\n",
              "2013-01-02 -0.804845  0.917294  0.744990  1.756773\n",
              "2013-01-03  0.003968  0.762304  0.319013  0.306445\n",
              "2013-01-04 -0.420489  1.257411 -2.596451 -0.750983\n",
              "2013-01-05 -0.963160  1.861297 -0.809843  1.083972\n",
              "2013-01-06 -1.494048 -1.158652 -2.481632 -0.989817"
            ],
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              "      <th></th>\n",
              "      <th>D</th>\n",
              "      <th>C</th>\n",
              "      <th>B</th>\n",
              "      <th>A</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2013-01-01</th>\n",
              "      <td>0.859551</td>\n",
              "      <td>1.553600</td>\n",
              "      <td>0.697185</td>\n",
              "      <td>-1.249748</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-01-02</th>\n",
              "      <td>-0.804845</td>\n",
              "      <td>0.917294</td>\n",
              "      <td>0.744990</td>\n",
              "      <td>1.756773</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-01-03</th>\n",
              "      <td>0.003968</td>\n",
              "      <td>0.762304</td>\n",
              "      <td>0.319013</td>\n",
              "      <td>0.306445</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-01-04</th>\n",
              "      <td>-0.420489</td>\n",
              "      <td>1.257411</td>\n",
              "      <td>-2.596451</td>\n",
              "      <td>-0.750983</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-01-05</th>\n",
              "      <td>-0.963160</td>\n",
              "      <td>1.861297</td>\n",
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              "      <td>1.083972</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-01-06</th>\n",
              "      <td>-1.494048</td>\n",
              "      <td>-1.158652</td>\n",
              "      <td>-2.481632</td>\n",
              "      <td>-0.989817</td>\n",
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              "  </svg>\n",
              "      </button>\n",
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              "    }\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-8faa92e7-a683-4147-85d7-a40394d232cc 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-8faa92e7-a683-4147-85d7-a40394d232cc');\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": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Sorting by values: 값기준\n",
        "df.sort_values(by=\"B\")  #B의 값이 큰순서로 정렬"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "GUI9xLf240j6",
        "outputId": "bf9a22f6-4c25-42ff-b7ac-d467aa6d878c"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   A         B         C         D\n",
              "2013-01-04 -0.750983 -2.596451  1.257411 -0.420489\n",
              "2013-01-06 -0.989817 -2.481632 -1.158652 -1.494048\n",
              "2013-01-05  1.083972 -0.809843  1.861297 -0.963160\n",
              "2013-01-03  0.306445  0.319013  0.762304  0.003968\n",
              "2013-01-01 -1.249748  0.697185  1.553600  0.859551\n",
              "2013-01-02  1.756773  0.744990  0.917294 -0.804845"
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              "    <tr>\n",
              "      <th>2013-01-04</th>\n",
              "      <td>-0.750983</td>\n",
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              "      <td>1.257411</td>\n",
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              "      <th>2013-01-06</th>\n",
              "      <td>-0.989817</td>\n",
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              "      <td>-1.158652</td>\n",
              "      <td>-1.494048</td>\n",
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              "      <td>1.861297</td>\n",
              "      <td>-0.963160</td>\n",
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              "      <td>0.762304</td>\n",
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              "      <th>2013-01-02</th>\n",
              "      <td>1.756773</td>\n",
              "      <td>0.744990</td>\n",
              "      <td>0.917294</td>\n",
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          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "8C5JD2JS40mS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "eY72jKgq40ok"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "PuRpB3I240rF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "jfwiHDO640tD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Importing pandas\n",
        "#Getting started and checking your pandas setup\n",
        "#Difficulty: easy\n"
      ],
      "metadata": {
        "id": "irQJwJpew7uP"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jrx3exBnuAYR"
      },
      "outputs": [],
      "source": [
        "#1. Import pandas under the alias pd.\n",
        "import pandas in pd\n",
        "\n",
        "#2. Print the version of pandas that has been imported.\n",
        "\n",
        "#3. Print out all the version information of the libraries that are required by the pandas library."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "Y0MPS1RUxDbp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "AeXVMdquw7Sp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "vrjs8SWIw7Vf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "jbSwREJ8w7Xf"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}