sitc/ml21/preprocessing/04_Unknown_Values.ipynb

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   "source": [
    "# Course Notes for Learning Intelligent Systems"
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    "Department of Telematic Engineering Systems, Universidad Politécnica de Madrid, © Carlos A. Iglesias"
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   "source": [
    "## [Introduction to  Preprocessing](00_Intro_Preprocessing.ipynb)"
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    "# Unknown values\n",
    "\n",
    "Two possible approaches are **remove** these rows or **fill** them. It depends on every case."
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   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
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    "## Filling NaN values\n",
    "If we need to fill errors or blanks, we can use the methods **fillna()** or **dropna()**.\n",
    "\n",
    "* For **string** fields, we can fill NaN with **' '**.\n",
    "\n",
    "* For **numbers**, we can fill with the **mean** or **median** value. \n"
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    "# Fill NaN with ' '\n",
    "df['col'] = df['col'].fillna(' ')\n",
    "# Fill NaN with 99\n",
    "df['col'] = df['col'].fillna(99)\n",
    "# Fill NaN with the mean of the column\n",
    "df['col'] = df['col'].fillna(df['col'].mean())"
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    "## Propagate non-null values forward or backward\n",
    "You can also **propagate** non-null values with these methods:\n",
    "\n",
    "* **ffill**: Fill values by propagating the last valid observation to the next valid.\n",
    "* **bfill**:  Fill values using the following valid observation to fill the gap.\n",
    "* **interpolate**:  Fill NaN values using interpolation.\n",
    "\n",
    "It will fill the next value in the dataframe with the previous non-NaN value. \n",
    "\n",
    "You may want to fill in one value (**limit=1**) or all the values. You can also indicate inplace=True to fill in-place."
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    "df = pd.DataFrame(data={'col1':[np.nan, np.nan, 2,3,4, np.nan, np.nan]})"
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       "   col1\n",
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    "We fill forward the value 4.0 and fill the next one (limit = 1)"
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       "   col1\n",
       "0   NaN\n",
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    " df.ffill(limit = 1)"
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    "We can also backfilling with **bfill**. Since we do not include *limit*, we fill all the values."
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       "   col1\n",
       "0   2.0\n",
       "1   2.0\n",
       "2   2.0\n",
       "3   3.0\n",
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    "## Removing NaN values\n",
    "We can remove them by row or column (use inplace=True if you want to modify the DataFrame)."
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   "source": [
    "# Drop any rows which have any nans\n",
    "df1 = df.dropna()\n",
    "# Drop columns that have any nans (axis = 1 -> drop columns, axis = 0 -> drop rows)\n",
    "df2 = df.dropna(axis=1)\n",
    "# Only drop columns which have at least 90% non-NaNs \n",
    "df3 = df.dropna(thresh=int(df.shape[0] * .9), axis=1)\n",
    "df1"
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   "source": [
    "# References\n",
    "* [Cleaning and Prepping Data with Python for Data Science — Best Practices and Helpful Packages](https://medium.com/@rrfd/cleaning-and-prepping-data-with-python-for-data-science-best-practices-and-helpful-packages-af1edfbe2a3), DeFilippi, 2019, \n",
    "* [Data Preprocessing for Machine learning in Python, GeeksForGeeks](https://www.geeksforgeeks.org/data-preprocessing-machine-learning-python/)"
   ]
  },
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   },
   "source": [
    "## Licence\n",
    "The notebook is freely licensed under under the [Creative Commons Attribution Share-Alike license](https://creativecommons.org/licenses/by/2.0/).  \n",
    "\n",
    "© Carlos A. Iglesias, Universidad Politécnica de Madrid."
   ]
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