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imputing-missing-not-at-random.ipynb
{
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"<a href=\"https://colab.research.google.com/gist/rwcitek/ba1f0eabd82245be17f36e09d8f34397/imputing-missing-not-at-random.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"id": "lIYdn1woOS1n"
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n"
]
},
{
"cell_type": "code",
"source": [
"# 1. Create a sample dataset to simulate your data\n",
"np.random.seed(42)\n",
"df = pd.DataFrame()\n",
"df['Target'] = np.random.randint(0, 2, 10)\n",
"df['Feature_A'] = np.random.rand(10)\n",
"df['Beta_Feature'] = np.array([0.1, 0.2, np.nan, 0.7, 0.9, np.nan, 0.3, 0.4, np.nan, 0.8])\n",
"df\n"
],
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{
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"data": {
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" Target Feature_A Beta_Feature\n",
"0 0 0.155995 0.1\n",
"1 1 0.058084 0.2\n",
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"9 1 0.181825 0.8"
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"metadata": {},
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]
},
{
"cell_type": "code",
"source": [
"# Define the column to impute and the arbitrary value\n",
"COLUMN_NAME = 'Beta_Feature'\n",
"ARBITRARY_VALUE = -999 # Safe choice for a 0-1 range feature\n"
],
"metadata": {
"id": "dgRuOYQblemm"
},
"execution_count": 19,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Create the new binary indicator column\n",
"indicator_column_name = f'{COLUMN_NAME}_has_value'\n",
"df[indicator_column_name] = df[COLUMN_NAME].notnull().astype(\"int8\")\n",
"df\n"
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"execution_count": 20,
"outputs": [
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" Target Feature_A Beta_Feature Beta_Feature_has_value\n",
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"2 0 0.866176 NaN 0\n",
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"type": "dataframe",
"variable_name": "df",
"summary": "{\n \"name\": \"df\",\n \"rows\": 10,\n \"fields\": [\n {\n \"column\": \"Target\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 1,\n \"num_unique_values\": 2,\n \"samples\": [\n 1,\n 0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Feature_A\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.3698211542093973,\n \"min\": 0.020584494295802447,\n \"max\": 0.9699098521619943,\n \"num_unique_values\": 10,\n \"samples\": [\n 0.21233911067827616,\n 0.05808361216819946\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Beta_Feature\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.3132015933791495,\n \"min\": 0.1,\n \"max\": 0.9,\n \"num_unique_values\": 7,\n \"samples\": [\n 0.1,\n 0.2\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Beta_Feature_has_value\",\n \"properties\": {\n \"dtype\": \"int8\",\n \"num_unique_values\": 2,\n \"samples\": [\n 0,\n 1\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
}
},
"metadata": {},
"execution_count": 20
}
]
},
{
"cell_type": "code",
"source": [
"# Impute the missing values with the arbitrary value\n",
"df.fillna({COLUMN_NAME: ARBITRARY_VALUE}, inplace=True)\n",
"df"
],
"metadata": {
"id": "85xK9wzslsDO",
"outputId": "35f2fbac-7dbc-4468-d4c7-5f45a0d2fea8",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 363
}
},
"execution_count": 21,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" Target Feature_A Beta_Feature Beta_Feature_has_value\n",
"0 0 0.155995 0.1 1\n",
"1 1 0.058084 0.2 1\n",
"2 0 0.866176 -999.0 0\n",
"3 0 0.601115 0.7 1\n",
"4 0 0.708073 0.9 1\n",
"5 1 0.020584 -999.0 0\n",
"6 0 0.969910 0.3 1\n",
"7 0 0.832443 0.4 1\n",
"8 0 0.212339 -999.0 0\n",
"9 1 0.181825 0.8 1"
],
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"\n",
" .colab-df-quickchart-complete:disabled,\n",
" .colab-df-quickchart-complete:disabled:hover {\n",
" background-color: var(--disabled-bg-color);\n",
" fill: var(--disabled-fill-color);\n",
" box-shadow: none;\n",
" }\n",
"\n",
" .colab-df-spinner {\n",
" border: 2px solid var(--fill-color);\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" animation:\n",
" spin 1s steps(1) infinite;\n",
" }\n",
"\n",
" @keyframes spin {\n",
" 0% {\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" border-left-color: var(--fill-color);\n",
" }\n",
" 20% {\n",
" border-color: transparent;\n",
" border-left-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" }\n",
" 30% {\n",
" border-color: transparent;\n",
" border-left-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" border-right-color: var(--fill-color);\n",
" }\n",
" 40% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" }\n",
" 60% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" }\n",
" 80% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" border-bottom-color: var(--fill-color);\n",
" }\n",
" 90% {\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" }\n",
" }\n",
"</style>\n",
"\n",
" <script>\n",
" async function quickchart(key) {\n",
" const quickchartButtonEl =\n",
" document.querySelector('#' + key + ' button');\n",
" quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n",
" quickchartButtonEl.classList.add('colab-df-spinner');\n",
" try {\n",
" const charts = await google.colab.kernel.invokeFunction(\n",
" 'suggestCharts', [key], {});\n",
" } catch (error) {\n",
" console.error('Error during call to suggestCharts:', error);\n",
" }\n",
" quickchartButtonEl.classList.remove('colab-df-spinner');\n",
" quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
" }\n",
" (() => {\n",
" let quickchartButtonEl =\n",
" document.querySelector('#df-0b30a286-d761-4ea9-b0a4-b28caba8b623 button');\n",
" quickchartButtonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
" })();\n",
" </script>\n",
" </div>\n",
"\n",
" <div id=\"id_923dd2d0-803f-40b8-b268-34c74cc9e9df\">\n",
" <style>\n",
" .colab-df-generate {\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-generate: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-generate {\n",
" background-color: #3B4455;\n",
" fill: #D2E3FC;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-generate: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",
" <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
" title=\"Generate code using this dataframe.\"\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=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
" </svg>\n",
" </button>\n",
" <script>\n",
" (() => {\n",
" const buttonEl =\n",
" document.querySelector('#id_923dd2d0-803f-40b8-b268-34c74cc9e9df button.colab-df-generate');\n",
" buttonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
"\n",
" buttonEl.onclick = () => {\n",
" google.colab.notebook.generateWithVariable('df');\n",
" }\n",
" })();\n",
" </script>\n",
" </div>\n",
"\n",
" </div>\n",
" </div>\n"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "dataframe",
"variable_name": "df",
"summary": "{\n \"name\": \"df\",\n \"rows\": 10,\n \"fields\": [\n {\n \"column\": \"Target\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 1,\n \"num_unique_values\": 2,\n \"samples\": [\n 1,\n 0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Feature_A\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.3698211542093973,\n \"min\": 0.020584494295802447,\n \"max\": 0.9699098521619943,\n \"num_unique_values\": 10,\n \"samples\": [\n 0.21233911067827616,\n 0.05808361216819946\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Beta_Feature\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 482.7975356652563,\n \"min\": -999.0,\n \"max\": 0.9,\n \"num_unique_values\": 8,\n \"samples\": [\n 0.2,\n 0.3\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Beta_Feature_has_value\",\n \"properties\": {\n \"dtype\": \"int8\",\n \"num_unique_values\": 2,\n \"samples\": [\n 0,\n 1\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
}
},
"metadata": {},
"execution_count": 21
}
]
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "F2xb2pBnmg2o"
},
"execution_count": 21,
"outputs": []
}
],
"metadata": {
"colab": {
"provenance": [],
"include_colab_link": true
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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