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          "data": {
            "text/plain": [
              "   Degree of freedom 1  Degree of freedom 2  F-statistic   p-value\n",
              "0                    8                   29     0.251063  0.023463"
            ],
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              "      <th>Degree of freedom 1</th>\n",
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              "      <th>F-statistic</th>\n",
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              "                                                    [key], {});\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 1,\n  \"fields\": [\n    {\n      \"column\": \"Degree of freedom 1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 8,\n        \"max\": 8,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Degree of freedom 2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 29,\n        \"max\": 29,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          29\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"F-statistic\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0.25106326202802354,\n        \"max\": 0.25106326202802354,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.25106326202802354\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"p-value\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0.023462662623980567,\n        \"max\": 0.023462662623980567,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.023462662623980567\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 1
        }
      ],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import scipy.stats as stats\n",
        "\n",
        "# Create synthetic data for testing.\n",
        "group1 = np.random.normal(0.0, 1.0, size=9)\n",
        "group2 = np.random.normal(10.0, 2.0, size=30)\n",
        "\n",
        "# Calculate the F-statistic.\n",
        "variance1 = np.var(group1, ddof=1)\n",
        "variance2 = np.var(group2, ddof=1)\n",
        "f_value = variance1 / variance2\n",
        "\n",
        "# Calculate the p-value.\n",
        "df1 = len(group1) - 1\n",
        "df2 = len(group2) - 1\n",
        "p_value = stats.f.cdf(f_value, df1, df2)\n",
        "\n",
        "# Collect results.\n",
        "scores = {\n",
        "    \"Degree of freedom 1\": df1,\n",
        "    \"Degree of freedom 2\": df2,\n",
        "    \"F-statistic\": f_value,\n",
        "    \"p-value\": p_value,\n",
        "}\n",
        "pd.DataFrame(scores, index=[0])"
      ]
    }
  ]
}