docs/mar_univariate_example.ipynb

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    "A basic example of generate artificial missing data under Missing at Random (MAR) mechanism with mdatagen library with the Iris dataset from scikit-learn. The observed feature is \"petal length\" and the feature that will receive the missing values is \"petal width\". The simulated missing rate is 12%. The method to choose missing values is random."
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      "sepal length (cm)     0\n",
      "sepal width (cm)      0\n",
      "petal length (cm)    38\n",
      "petal width (cm)      0\n",
      "target                0\n",
      "dtype: int64\n"
     ]
    }
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   "source": [
    "# Import the libraries\n",
    "import pandas as pd\n",
    "from sklearn.datasets import load_iris\n",
    "from mdatagen.univariate.uMAR import uMAR\n",
    "\n",
    "# Load the data\n",
    "iris = load_iris()\n",
    "iris_df = pd.DataFrame(data=iris.data, columns=iris.feature_names)\n",
    "\n",
    "X = iris_df.copy()   # Features\n",
    "y = iris.target    # Label values\n",
    "\n",
    "# Create a instance with missing rate equal to 12% in dataset under MAR mechanism\n",
    "generator = uMAR(X=X, \n",
    "                 y=y, \n",
    "                 missing_rate=12, \n",
    "                 x_miss='petal width (cm)',\n",
    "                 x_obs='petal length (cm)')\n",
    "\n",
    "# Generate the missing data under MNAR mechanism\n",
    "generate_data = generator.random()\n",
    "print(generate_data.isna().sum())"
   ]
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