Pandas_full.ipynb

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      "source": [
        "import pandas as pd\n",
        "import numpy as np"
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              "0    4\n",
              "1    7\n",
              "2   -5\n",
              "3    3\n",
              "dtype: int64"
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          "metadata": {},
          "execution_count": 3
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      "source": [
        "df =pd.Series([4, 7, -5, 3])\n",
        "df"
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        "df.values"
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              "array([ 4,  7, -5,  3])"
            ]
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          "metadata": {},
          "execution_count": 4
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        "df.index"
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            "text/plain": [
              "RangeIndex(start=0, stop=4, step=1)"
            ]
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          "metadata": {},
          "execution_count": 5
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      ]
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      "source": [
        "df2 = pd.Series([4, 7, -5, 3], index=['d', 'b', 'a', 'c'])\n",
        "df2"
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            "text/plain": [
              "d    4\n",
              "b    7\n",
              "a   -5\n",
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          "data": {
            "text/plain": [
              "a   -5\n",
              "b    7\n",
              "c    3\n",
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          "execution_count": 7
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        "df3=df2[df2>0]\n",
        "df3"
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              "d     8\n",
              "b    14\n",
              "c     6\n",
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        "'d' in df3"
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          "data": {
            "text/plain": [
              "True"
            ]
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          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_dict = {'Ohio': 35000, 'Texas': 71000, 'Oregon': 16000, 'Utah': 5000}\n",
        "df4 = pd.Series(new_dict)\n",
        "df4\n"
      ],
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              "Ohio      35000\n",
              "Texas     71000\n",
              "Oregon    16000\n",
              "Utah       5000\n",
              "dtype: int64"
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        "data = {'state': ['Ohio', 'Ohio', 'Ohio', 'Nevada', 'Nevada'],\n",
        " 'year': [2000, 2001, 2002, 2001, 2002],\n",
        " 'pop': [1.5, 1.7, 3.6, 2.4, 2.9]}\n",
        "frame = pd.DataFrame(data)\n",
        "frame\n"
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            "text/plain": [
              "    state  year  pop\n",
              "0    Ohio  2000  1.5\n",
              "1    Ohio  2001  1.7\n",
              "2    Ohio  2002  3.6\n",
              "3  Nevada  2001  2.4\n",
              "4  Nevada  2002  2.9"
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        "frame_2=pd.DataFrame(data, columns=['year', 'state', 'pop'])\n",
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              "   year   state  pop\n",
              "0  2000    Ohio  1.5\n",
              "1  2001    Ohio  1.7\n",
              "2  2002    Ohio  3.6\n",
              "3  2001  Nevada  2.4\n",
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      "execution_count": 18,
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            "text/plain": [
              "0      Ohio\n",
              "1      Ohio\n",
              "2      Ohio\n",
              "3    Nevada\n",
              "4    Nevada\n",
              "Name: state, dtype: object"
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      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      Ohio\n",
              "1      Ohio\n",
              "2      Ohio\n",
              "3    Nevada\n",
              "4    Nevada\n",
              "Name: state, dtype: object"
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        "outputId": "d5137084-407d-4d30-c8ff-752b9eabda04"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "year     2000\n",
              "state    Ohio\n",
              "pop       1.5\n",
              "Name: 0, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
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        "frame_2['debt'] = range(0,len(frame_2))\n",
        "frame_2"
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      "execution_count": 30,
      "outputs": [
        {
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            "text/plain": [
              "   year   state  pop  debt\n",
              "0  2000    Ohio  1.5     0\n",
              "1  2001    Ohio  1.7     1\n",
              "2  2002    Ohio  3.6     2\n",
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        "values = pd.Series([-1.2, -1.5, -1.7], index=[0,1,3])\n",
        "frame_2['debt'] = values\n",
        "frame_2"
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        {
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            "text/plain": [
              "   year   state  pop  debt\n",
              "0  2000    Ohio  1.5  -1.2\n",
              "1  2001    Ohio  1.7  -1.5\n",
              "2  2002    Ohio  3.6   NaN\n",
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    {
      "cell_type": "code",
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        "del frame_2['debt']\n",
        "frame_2"
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      "execution_count": 35,
      "outputs": [
        {
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            "text/plain": [
              "   year   state  pop\n",
              "0  2000    Ohio  1.5\n",
              "1  2001    Ohio  1.7\n",
              "2  2002    Ohio  3.6\n",
              "3  2001  Nevada  2.4\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d2d8ff77-237e-4140-bb2a-0f13f4a93057');\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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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
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        "frame_2.columns.name = 'state'\n",
        "frame_2.index.name = 'Index'\n",
        "frame_2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "nMQTZptEp1-k",
        "outputId": "836e5757-7469-414d-f3f8-ea977f7ced4a"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "state  year   state  pop\n",
              "Index                   \n",
              "0      2000    Ohio  1.5\n",
              "1      2001    Ohio  1.7\n",
              "2      2002    Ohio  3.6\n",
              "3      2001  Nevada  2.4\n",
              "4      2002  Nevada  2.9"
            ],
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              "    <tr>\n",
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              "      <td>2002</td>\n",
              "      <td>Nevada</td>\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",
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              "            + ' to learn more about interactive tables.';\n",
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              "  "
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          "metadata": {},
          "execution_count": 37
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    {
      "cell_type": "code",
      "source": [
        "frame_2.values\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "klwKDL76qvMW",
        "outputId": "65cedf24-3437-4aca-cf1b-af8a10cefb85"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[2000, 'Ohio', 1.5],\n",
              "       [2001, 'Ohio', 1.7],\n",
              "       [2002, 'Ohio', 3.6],\n",
              "       [2001, 'Nevada', 2.4],\n",
              "       [2002, 'Nevada', 2.9]], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.Series(range(3), index=['a', 'b', 'c'])\n",
        "df.index"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XbmNkp4xrJwG",
        "outputId": "a1390f7c-88de-44dc-ac6b-40b44f0ab680"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['a', 'b', 'c'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "index = pd.Index(np.arange(3))\n",
        "df2 = pd.Series([1.5, -2.5, 0], index=index)\n",
        "df2.index"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ibztlXbdy2Mc",
        "outputId": "324b036d-40b4-4522-da79-1c1680e50388"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Int64Index([0, 1, 2], dtype='int64')"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "3 in df2.index"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jZhtAKlv0er3",
        "outputId": "31450b31-3a38-40d7-86d6-7c32d68439fb"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_1=pd.Series([4.5, 7.2, -5.3, 3.6], index=['d', 'b', 'a', 'c'])\n",
        "df_2 = df.reindex(['a', 'b', 'c', 'd', 'e'],fill_value='missing')\n",
        "df_2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tC5OJwQb2Ueo",
        "outputId": "2fa0a0b7-303e-42a2-bd3e-0dd0fae5b30e"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "a       -5.3\n",
              "b        7.2\n",
              "c        3.6\n",
              "d        4.5\n",
              "e    missing\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_3 =pd.DataFrame(np.arange(9).reshape((3, 3)), index=['a', 'c', 'd'],columns=['Ohio', 'Texas', 'California'])\n",
        "df_3['states'] = ['Texas', 'Utah', 'California']\n",
        "df_3.reindex(columns=df_3['states'].values)\n",
        "df_3"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "2jYfaHpk_83-",
        "outputId": "99499dd0-303c-4b9f-afaa-bcc0fdf47fcf"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Ohio  Texas  California      states\n",
              "a     0      1           2       Texas\n",
              "c     3      4           5        Utah\n",
              "d     6      7           8  California"
            ],
            "text/html": [
              "\n",
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              "    <div class=\"colab-df-container\">\n",
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              "      <th></th>\n",
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              "      <th>states</th>\n",
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              "      <td>Utah</td>\n",
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              "      <th>d</th>\n",
              "      <td>6</td>\n",
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              "      <td>California</td>\n",
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              "\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-90eea494-b7b8-4bb1-9e66-cba37159fabc');\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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              "  "
            ]
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          "metadata": {},
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        }
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    {
      "cell_type": "code",
      "source": [
        "df =pd.Series(np.arange(5.), index=['a', 'b', 'c', 'd', 'e'])\n",
        "new_df =df.drop('c')\n",
        "new_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MaOYtTa_EDKR",
        "outputId": "e8c409e1-050a-4b65-dfb9-8095fc0974dd"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "a    0.0\n",
              "b    1.0\n",
              "d    3.0\n",
              "e    4.0\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
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    {
      "cell_type": "code",
      "source": [
        "new_df=new_df.drop(['d', 'a'])\n",
        "new_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8tpOBv8oTGeW",
        "outputId": "032245f3-6480-4523-bbe7-648f669fabdb"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "b    1.0\n",
              "e    4.0\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df =pd.DataFrame(np.arange(16).reshape((4, 4)),index=['Ohio', 'Colorado', 'Utah', 'New York'], columns=['one', 'two', 'three', 'four'])\n",
        "df=df.drop(['one','two'],axis=1)\n",
        "df"
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      "execution_count": 81,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "          three  four\n",
              "Ohio          2     3\n",
              "Colorado      6     7\n",
              "Utah         10    11\n",
              "New York     14    15"
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    {
      "cell_type": "code",
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        "df =pd.Series(np.arange(4.), index=['a', 'b', 'c', 'd'])\n",
        "df['b']"
      ],
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        "colab": {
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      "outputs": [
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              "2.0"
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          "metadata": {},
          "execution_count": 86
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      "execution_count": 87,
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              "b    1.0\n",
              "c    2.0\n",
              "dtype: float64"
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              "a    0.0\n",
              "b    1.0\n",
              "c    2.0\n",
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      "execution_count": 91,
      "outputs": [
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              "c    2.0\n",
              "d    3.0\n",
              "dtype: float64"
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      "source": [
        "df =pd.DataFrame(np.arange(16).reshape((4, 4)),index=['Ohio', 'Colorado', 'Utah', 'New York'], columns=['one', 'two', 'three', 'four'])\n",
        "df.loc['Utah',['one','two']]"
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      "execution_count": 96,
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            "text/plain": [
              "one    8\n",
              "two    9\n",
              "Name: Utah, dtype: int64"
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        "df2 = pd.DataFrame(np.arange(20.).reshape((4, 5)), columns=list('abcde'))\n",
        "df1+df2"
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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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        "df1.add(df2, fill_value=0)"
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e058f117-3786-4629-8603-c8b7a2531ccb');\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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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1.reindex(columns=df2.columns, fill_value=0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "3udblHsGmDyQ",
        "outputId": "5620ceca-2823-4580-d288-26890b5ba877"
      },
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     a    b     c     d  e\n",
              "0  0.0  1.0   2.0   3.0  0\n",
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              "\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d4528637-0dda-4b48-bdfc-13c2552a742e 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-d4528637-0dda-4b48-bdfc-13c2552a742e');\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",
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              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df=pd.DataFrame(np.random.randn(4, 3), columns=list('bde'),index=['Utah', 'Ohio', 'Texas', 'Oregon'])\n",
        "print(df)\n",
        "df.apply(lambda x: x.max())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DySj0liSmKhw",
        "outputId": "0b34a921-128b-4d4d-de25-40b231c6270e"
      },
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "               b         d         e\n",
            "Utah    2.032509  0.759756 -0.185594\n",
            "Ohio   -0.553399  1.276492 -0.583562\n",
            "Texas  -2.226230 -0.375013 -0.753695\n",
            "Oregon -0.506315  0.700892 -1.279878\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "b    2.032509\n",
              "d    1.276492\n",
              "e   -0.185594\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df)\n",
        "df.apply(lambda x: x.max(), axis=1)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YrCZLmKFqhR6",
        "outputId": "c27f33fd-9e83-48aa-fd02-069faa7ae0b2"
      },
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "               b         d         e\n",
            "Utah    2.032509  0.759756 -0.185594\n",
            "Ohio   -0.553399  1.276492 -0.583562\n",
            "Texas  -2.226230 -0.375013 -0.753695\n",
            "Oregon -0.506315  0.700892 -1.279878\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Utah      2.032509\n",
              "Ohio      1.276492\n",
              "Texas    -0.375013\n",
              "Oregon    0.700892\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 109
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def f(x):\n",
        "  return pd.Series([x.min(), x.max()], index=['min', 'max'])\n",
        "df.apply(f)  "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "I8CdfZgYr96j",
        "outputId": "ec268053-6c3e-40d3-bf40-799523ffc3da"
      },
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            b         d         e\n",
              "min -2.226230 -0.375013 -1.279878\n",
              "max  2.032509  1.276492 -0.185594"
            ],
            "text/html": [
              "\n",
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              "\n",
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              "      <th>b</th>\n",
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              "      <th>e</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-2.226230</td>\n",
              "      <td>-0.375013</td>\n",
              "      <td>-1.279878</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2.032509</td>\n",
              "      <td>1.276492</td>\n",
              "      <td>-0.185594</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "    }\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-3564c4a8-31e3-4289-9207-2226db99e221 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-3564c4a8-31e3-4289-9207-2226db99e221');\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": 110
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "format = lambda x: '%.2f' % x\n",
        "df.applymap(format)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "6lhrN_CLuhxN",
        "outputId": "f26aea24-e69e-4891-dd38-580c3adf8b0e"
      },
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            b      d      e\n",
              "Utah     2.03   0.76  -0.19\n",
              "Ohio    -0.55   1.28  -0.58\n",
              "Texas   -2.23  -0.38  -0.75\n",
              "Oregon  -0.51   0.70  -1.28"
            ],
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>b</th>\n",
              "      <th>d</th>\n",
              "      <th>e</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Utah</th>\n",
              "      <td>2.03</td>\n",
              "      <td>0.76</td>\n",
              "      <td>-0.19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ohio</th>\n",
              "      <td>-0.55</td>\n",
              "      <td>1.28</td>\n",
              "      <td>-0.58</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Texas</th>\n",
              "      <td>-2.23</td>\n",
              "      <td>-0.38</td>\n",
              "      <td>-0.75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Oregon</th>\n",
              "      <td>-0.51</td>\n",
              "      <td>0.70</td>\n",
              "      <td>-1.28</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cb2e9037-fbca-43e1-8cd7-0ceb1552b970')\"\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",
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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",
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              "    }\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-cb2e9037-fbca-43e1-8cd7-0ceb1552b970 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-cb2e9037-fbca-43e1-8cd7-0ceb1552b970');\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": 113
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "format = lambda x: '%.2f' % x\n",
        "df['e'].map(format)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9EoLHlvBzCiI",
        "outputId": "514d81c2-e4b2-49b1-bc48-78d42aa68754"
      },
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Utah      -0.19\n",
              "Ohio      -0.58\n",
              "Texas     -0.75\n",
              "Oregon    -1.28\n",
              "Name: e, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 114
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df=pd.Series(range(4), index=['d', 'a', 'b', 'c'])\n",
        "print(df)\n",
        "df.sort_index()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oNLpJMwhzsWI",
        "outputId": "fb600c45-cbd7-4d5f-ec65-91fe442b03a1"
      },
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "d    0\n",
            "a    1\n",
            "b    2\n",
            "c    3\n",
            "dtype: int64\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "a    1\n",
              "b    2\n",
              "c    3\n",
              "d    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 115
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df=pd.DataFrame(np.arange(8).reshape((2, 4)), index=['three', 'one'],columns=['d', 'a', 'b', 'c'])\n",
        "print(df)\n",
        "df.sort_index(axis=1,ascending=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 164
        },
        "id": "8EQ_qaFB0S3x",
        "outputId": "e3c03dd1-8c90-4f2c-9627-b9ebb8e219b1"
      },
      "execution_count": 117,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "       d  a  b  c\n",
            "three  0  1  2  3\n",
            "one    4  5  6  7\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       d  c  b  a\n",
              "three  0  3  2  1\n",
              "one    4  7  6  5"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-985a67fc-bbff-4e2b-9978-81ae99d61bff\">\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",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
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              "      <th>d</th>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>one</th>\n",
              "      <td>4</td>\n",
              "      <td>7</td>\n",
              "      <td>6</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-985a67fc-bbff-4e2b-9978-81ae99d61bff')\"\n",
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              "              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",
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              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\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-985a67fc-bbff-4e2b-9978-81ae99d61bff 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-985a67fc-bbff-4e2b-9978-81ae99d61bff');\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": 117
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df=pd.Series([4, np.nan, 7, np.nan, -3, 2])\n",
        "print(df)\n",
        "df.sort_values()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NjFmKkFR0yM5",
        "outputId": "2ff5f56a-ad16-4859-8f0a-2b982de1ee5b"
      },
      "execution_count": 119,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0    4.0\n",
            "1    NaN\n",
            "2    7.0\n",
            "3    NaN\n",
            "4   -3.0\n",
            "5    2.0\n",
            "dtype: float64\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4   -3.0\n",
              "5    2.0\n",
              "0    4.0\n",
              "2    7.0\n",
              "1    NaN\n",
              "3    NaN\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 119
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df= pd.DataFrame({'b': [4, 7, -3, 2], 'a': [0, 1, 0, 1]})\n",
        "print(df)\n",
        "df.sort_values(by=['a','b'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 262
        },
        "id": "s2_AGn6L1bpd",
        "outputId": "7c021c21-9563-4e1c-fa3a-7f8e59caa873"
      },
      "execution_count": 124,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   b  a\n",
            "0  4  0\n",
            "1  7  1\n",
            "2 -3  0\n",
            "3  2  1\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   b  a\n",
              "2 -3  0\n",
              "0  4  0\n",
              "3  2  1\n",
              "1  7  1"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9ca9f375-7a03-46ab-9832-b9251e75fb90\">\n",
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              "      <td>1</td>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "    }\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-9ca9f375-7a03-46ab-9832-b9251e75fb90 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-9ca9f375-7a03-46ab-9832-b9251e75fb90');\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": 124
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df=pd.Series([7, -5, 7, 4, 2, 0, 4])\n",
        "df.rank()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n_b6jo0o1-49",
        "outputId": "34b28ace-7cc6-4b6d-9b0d-22b9e9c9d999"
      },
      "execution_count": 129,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    6.5\n",
              "1    1.0\n",
              "2    6.5\n",
              "3    4.5\n",
              "4    3.0\n",
              "5    2.0\n",
              "6    4.5\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 129
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.rank(method='first')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yeICujGG24gL",
        "outputId": "03725c1b-08fd-406f-bbf9-cf919c29e770"
      },
      "execution_count": 130,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    6.0\n",
              "1    1.0\n",
              "2    7.0\n",
              "3    4.0\n",
              "4    3.0\n",
              "5    2.0\n",
              "6    5.0\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 130
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df=pd.DataFrame([[1.4, np.nan], [7.1, -4.5], [np.nan, np.nan], [0.75, -1.3]], index=['a', 'b', 'c', 'd'])\n",
        "print(df)\n",
        "df.sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x1CR4rW125R2",
        "outputId": "5bdcc3ae-22ff-4031-807d-a5002ba242e4"
      },
      "execution_count": 131,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "      0    1\n",
            "a  1.40  NaN\n",
            "b  7.10 -4.5\n",
            "c   NaN  NaN\n",
            "d  0.75 -1.3\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    9.25\n",
              "1   -5.80\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 131
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df=pd.DataFrame([[1.4, np.nan], [7.1, -4.5], [np.nan, np.nan], [0.75, -1.3]], index=['a', 'b', 'c', 'd'])\n",
        "print(df)\n",
        "df.sum(axis=1,skipna=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fJLzfPGE4aGF",
        "outputId": "a8472993-c3e7-4bc0-baa6-6b4db2d49a79"
      },
      "execution_count": 134,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "      0    1\n",
            "a  1.40  NaN\n",
            "b  7.10 -4.5\n",
            "c   NaN  NaN\n",
            "d  0.75 -1.3\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "a     NaN\n",
              "b    2.60\n",
              "c     NaN\n",
              "d   -0.55\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 134
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df)\n",
        "df.idxmax()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UpF580lI48Ew",
        "outputId": "146f7c68-b39d-4591-9a1c-0871a7be6fa1"
      },
      "execution_count": 135,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "      0    1\n",
            "a  1.40  NaN\n",
            "b  7.10 -4.5\n",
            "c   NaN  NaN\n",
            "d  0.75 -1.3\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    b\n",
              "1    d\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 135
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df)\n",
        "df.idxmin()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "idtzpLya5yWR",
        "outputId": "41ea0952-d877-4252-971b-e6e8f69df26e"
      },
      "execution_count": 137,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "      0    1\n",
            "a  1.40  NaN\n",
            "b  7.10 -4.5\n",
            "c   NaN  NaN\n",
            "d  0.75 -1.3\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    d\n",
              "1    b\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 137
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df)\n",
        "df.cumsum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 262
        },
        "id": "ywXidYCh55xG",
        "outputId": "a31d8e41-905d-405d-ce6b-b25a4d6c5026"
      },
      "execution_count": 138,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "      0    1\n",
            "a  1.40  NaN\n",
            "b  7.10 -4.5\n",
            "c   NaN  NaN\n",
            "d  0.75 -1.3\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      0    1\n",
              "a  1.40  NaN\n",
              "b  8.50 -4.5\n",
              "c   NaN  NaN\n",
              "d  9.25 -5.8"
            ],
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              "\n",
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              "    <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",
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              "\n",
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              "        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>0</th>\n",
              "      <th>1</th>\n",
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              "  </thead>\n",
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              "      <td>1.40</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>b</th>\n",
              "      <td>8.50</td>\n",
              "      <td>-4.5</td>\n",
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              "      <th>c</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>d</th>\n",
              "      <td>9.25</td>\n",
              "      <td>-5.8</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8aabddc3-ad1b-4af4-ad0f-76194959fd64')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "    }\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",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-8aabddc3-ad1b-4af4-ad0f-76194959fd64 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-8aabddc3-ad1b-4af4-ad0f-76194959fd64');\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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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 138
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe().T"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "J2wP3hM86Omu",
        "outputId": "057271c2-5f05-40db-e247-b474a0ab3807"
      },
      "execution_count": 139,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   count      mean       std   min    25%  50%   75%  max\n",
              "0    3.0  3.083333  3.493685  0.75  1.075  1.4  4.25  7.1\n",
              "1    2.0 -2.900000  2.262742 -4.50 -3.700 -2.9 -2.10 -1.3"
            ],
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              "\n",
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "\n",
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              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3.0</td>\n",
              "      <td>3.083333</td>\n",
              "      <td>3.493685</td>\n",
              "      <td>0.75</td>\n",
              "      <td>1.075</td>\n",
              "      <td>1.4</td>\n",
              "      <td>4.25</td>\n",
              "      <td>7.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2.0</td>\n",
              "      <td>-2.900000</td>\n",
              "      <td>2.262742</td>\n",
              "      <td>-4.50</td>\n",
              "      <td>-3.700</td>\n",
              "      <td>-2.9</td>\n",
              "      <td>-2.10</td>\n",
              "      <td>-1.3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "       width=\"24px\">\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",
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              "      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-209bb703-d1d2-4b3c-96d7-d5ef8fa5c61a 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-209bb703-d1d2-4b3c-96d7-d5ef8fa5c61a');\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": 139
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df=pd.Series(['c', 'a', 'd', 'a', 'a', 'b', 'b', 'c', 'c'])\n",
        "print(df)\n",
        "df.unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c7eyA5cQ6bSL",
        "outputId": "966176eb-3fd3-4914-fa16-d9c4ff4a0232"
      },
      "execution_count": 140,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0    c\n",
            "1    a\n",
            "2    d\n",
            "3    a\n",
            "4    a\n",
            "5    b\n",
            "6    b\n",
            "7    c\n",
            "8    c\n",
            "dtype: object\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['c', 'a', 'd', 'b'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 140
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.value_counts(df.values,sort=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J0vn5VVg7eMb",
        "outputId": "2ea2e0ce-c199-4a7c-f1ef-3ca88f9523f5"
      },
      "execution_count": 152,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "c    3\n",
              "a    3\n",
              "d    1\n",
              "b    2\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 152
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df=pd.Series(['c', 'a', 'd', 'a', 'a', 'b', 'b', 'c', 'c'])\n",
        "print(df)\n",
        "df.isin(['c'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "latXPJzg7q7y",
        "outputId": "50ea38d9-f4e5-43aa-fcae-2dbd457dbf34"
      },
      "execution_count": 154,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0    c\n",
            "1    a\n",
            "2    d\n",
            "3    a\n",
            "4    a\n",
            "5    b\n",
            "6    b\n",
            "7    c\n",
            "8    c\n",
            "dtype: object\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0     True\n",
              "1    False\n",
              "2    False\n",
              "3    False\n",
              "4    False\n",
              "5    False\n",
              "6    False\n",
              "7     True\n",
              "8     True\n",
              "dtype: bool"
            ]
          },
          "metadata": {},
          "execution_count": 154
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df =pd.DataFrame({'Qu1': [1, 3, 4, 3, 4],\n",
        " 'Qu2': [2, 3, 1, 2, 3],\n",
        " 'Qu3': [1, 5, 2, 4, 4]})"
      ],
      "metadata": {
        "id": "TWv-kAY58pMD"
      },
      "execution_count": 155,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_QFp8SBd9p-6",
        "outputId": "2f709e06-fbe1-426d-c280-3c796b9b736e"
      },
      "execution_count": 156,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Qu1  Qu2  Qu3\n",
              "1    2    1      1\n",
              "3    2    4      1\n",
              "     3    5      1\n",
              "4    1    2      1\n",
              "     3    4      1\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 156
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "xHkZDu999sRN"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}