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   "source": [
    "![](images/EscUpmPolit_p.gif \"UPM\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "# Course Notes for Learning Intelligent Systems"
   ]
  },
  {
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   "source": [
    "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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   "source": [
    "# Duplicated values\n",
    "\n",
    "There are two possible approaches: **remove** these rows or **filling** them. It depends on every case.\n",
    "\n",
    "\n"
   ]
  },
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   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
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   "source": [
    "## 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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   "source": [
    "# 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())"
   ]
  },
  {
   "cell_type": "markdown",
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     "slide_type": "slide"
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   "source": [
    "## Propagate non-null values forward or backwards\n",
    "You can also propagate non-null values forward or backwards by putting\n",
    "method=’pad’ as the method argument. It will fill the next value in the\n",
    "dataframe with the previous non-NaN value. Maybe you just want to fill one\n",
    "value ( limit=1 )or you want to fill all the values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
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   "outputs": [],
   "source": [
    "df = pd.DataFrame(data={'col1':[np.nan, np.nan, 2,3,4, np.nan, np.nan]})"
   ]
  },
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   "execution_count": 7,
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       "   col1\n",
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    "df"
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       "   col1\n",
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   "source": [
    "# We fill forward the value 4.0 and fill the next one (limit = 1)\n",
    "df.fillna(method='pad', limit=1)"
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    "We can also backfilling with **bfill**."
   ]
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   "execution_count": 10,
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       "   col1\n",
       "0   2.0\n",
       "1   2.0\n",
       "2   2.0\n",
       "3   3.0\n",
       "4   4.0\n",
       "5   NaN\n",
       "6   NaN"
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    "# Fill the first two NaN values with the first available value\n",
    "df.fillna(method='bfill')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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    }
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   "source": [
    "## Removing NaN values\n",
    "We can remove them by row or column."
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
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     "slide_type": "fragment"
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   "source": [
    "/# Drop any rows which have any nans\n",
    "df.dropna()\n",
    "/# Drop columns that have any nans\n",
    "df.dropna(axis=1)\n",
    "/# Only drop columns which have at least 90% non-NaNs\n",
    "df.dropna(thresh=int(df.shape[0] * .9), axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "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/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "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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