Pandas-Missing-Values.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Handling Missing Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Trade-Offs\n",
    "\n",
    "- There are multiple schemes to indicate missing data in a table or DataFrame. They use one of two strategies: using a *mask* that globally indicates missing values, or choosing a *sentinel value* that indicates a missing entry.\n",
    "\n",
    "- A mask might be an entirely separate Boolean array, or it may involve appropriation of one bit in the data representation to locally indicate the null status of a value.\n",
    "\n",
    "- A sentinel value could be a data-specific convention, such as indicating a missing integer value with -9999 or some rare bit pattern, or it could be a more global convention, such as indicating a missing floating-point value with NaN (Not a Number).\n",
    "\n",
    "- Using a separate mask array requires allocation of an additional Boolean array; a sentinel value reduces the range of valid values that can be represented and may require extra (often non-optimized) logic in CPU and GPU arithmetic. Common special values like NaN are not available for all data types."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Missing Data in Pandas\n",
    "\n",
    "- Pandas is constrained by its reliance on NumPy, which does not have a built-in notion of NA values for non-floating-point data types.\n",
    "\n",
    "- Pandas could have specified bit patterns for each individual data type to indicate nullness, but NumPy supports (for example) *fourteen* basic integer types once you account for available precisions, signedness, and endianness of the encoding.\n",
    "\n",
    "- NumPy supports masked arrays – that is, arrays that have a separate Boolean mask array attached for marking data as \"good\" or \"bad.\" Pandas could have used this approach, but it used too much overhead.\n",
    "\n",
    "- Therefore Pandas chose to use sentinels for missing data and to use two already-existing Python null values: the special floating-point ``NaN`` value, and the Python ``None`` object."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ``None``: Pythonic missing data\n",
    "\n",
    "- ``None`` is a singleton object that is often used for missing data in Python code. Because it is a Python object, ``None`` cannot be used in any arbitrary NumPy/Pandas array, but only in arrays with data type ``'object'`` (i.e., arrays of Python objects):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, None, 3, 4], dtype=object)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vals1 = np.array([1, None, 3, 4])\n",
    "vals1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- While an object array is useful for some purposes, any operations on the data will be done at the Python level, with much more overhead."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dtype = object\n",
      "48.4 ms ± 953 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "\n",
      "dtype = int\n",
      "1.45 ms ± 16.3 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for dtype in ['object', 'int']:\n",
    "    print(\"dtype =\", dtype)\n",
    "    %timeit np.arange(1E6, dtype=dtype).sum()\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The use of Python objects in an array also means that if you perform aggregations like ``sum()`` or ``min()`` across an array with a ``None`` value, you will generally get an error:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "unsupported operand type(s) for +: 'int' and 'NoneType'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-4-749fd8ae6030>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mvals1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/numpy/core/_methods.py\u001b[0m in \u001b[0;36m_sum\u001b[0;34m(a, axis, dtype, out, keepdims)\u001b[0m\n\u001b[1;32m     30\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     31\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_sum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeepdims\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 32\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mumr_sum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeepdims\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     33\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     34\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_prod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeepdims\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for +: 'int' and 'NoneType'"
     ]
    }
   ],
   "source": [
    "vals1.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ``NaN``: Missing numerical data\n",
    "\n",
    "- ``NaN`` (acronym for *Not a Number*), is different; it is a special floating-point value recognized by all systems that use the standard IEEE format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vals2 = np.array([1, np.nan, 3, 4]) \n",
    "vals2.dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Be aware that ``NaN`` is a bit like a data virus–it infects any other object it touches. Regardless of the operation, the result of arithmetic with ``NaN`` will be another ``NaN``:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "nan"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1 + np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "nan"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "0 *  np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(nan, nan, nan)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vals2.sum(), vals2.min(), vals2.max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- NumPy provides some special aggregations that will ignore these missing values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8.0, 1.0, 4.0)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.nansum(vals2), np.nanmin(vals2), np.nanmax(vals2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### NaN and None in Pandas\n",
    "\n",
    "- Pandas is built to handle both ``NaN`` and ``None`` interchangeably, converting between them where appropriate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "1    NaN\n",
       "2    2.0\n",
       "3    NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series([1, np.nan, 2, None])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- For types that don't have an available sentinel value, Pandas automatically type-casts when NA values are present. For example, if we set a value in an integer array to ``np.nan``, it will automatically be upcast to a floating-point type to accommodate the NA."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = pd.Series(range(2), dtype=int)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    NaN\n",
       "1    1.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[0] = None\n",
    "x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Upcasting conventions in Pandas when NA values are introduced:\n",
    "\n",
    "|Typeclass     | Conversion When Storing NAs | NA Sentinel Value      |\n",
    "|--------------|-----------------------------|------------------------|\n",
    "| ``floating`` | No change                   | ``np.nan``             |\n",
    "| ``object``   | No change                   | ``None`` or ``np.nan`` |\n",
    "| ``integer``  | Cast to ``float64``         | ``np.nan``             |\n",
    "| ``boolean``  | Cast to ``object``          | ``None`` or ``np.nan`` |\n",
    "\n",
    "Keep in mind that in Pandas, string data is always stored with an ``object`` dtype."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Operating on Null Values\n",
    "\n",
    "- There are several methods for detecting, removing, and replacing null values in Pandas data structures:\n",
    "\n",
    "- ``isnull()``: Generate a boolean mask indicating missing values\n",
    "- ``notnull()``: Opposite of ``isnull()``\n",
    "- ``dropna()``: Return a filtered version of the data\n",
    "- ``fillna()``: Return a copy of the data with missing values filled or imputed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Detecting null values\n",
    "- Pandas data structures have two methods for detecting null data: ``isnull()`` and ``notnull()``. Either one will return a Boolean mask over the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = pd.Series([1, np.nan, 'hello', None])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1     True\n",
       "2    False\n",
       "3     True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Boolean masks can be used directly as a ``Series`` or ``DataFrame`` index:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        1\n",
       "2    hello\n",
       "dtype: object"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data.notnull()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- ``isnull()`` and ``notnull()`` methods produce similar Boolean results for ``DataFrame``s."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dropping null values\n",
    "\n",
    "- More convenience methods are available:\n",
    "    - ``dropna()`` (which removes NA values)\n",
    "    - ``fillna()`` (which fills in NA values)\n",
    "- For a ``Series``, the result is straightforward."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        1\n",
       "2    hello\n",
       "dtype: object"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-``DataFrame``s have more options.\n",
    "\n",
    "- We cannot drop single values from a ``DataFrame`` - only full rows or full columns. Depending on the application, you might want one or the other, so ``dropna()`` gives a number of options.\n",
    "\n",
    "- By default, ``dropna()`` will drop all rows in which *any* null value is present:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
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   "source": [
    "df = pd.DataFrame([[1,      np.nan, 2],\n",
    "                   [2,      3,      5],\n",
    "                   [np.nan, 4,      6]])\n",
    "df"
   ]
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  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
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   "source": [
    "df.dropna()"
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Or you can drop NA values along a different axis; ``axis=1`` drops all columns containing a null value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
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       "0  2\n",
       "1  5\n",
       "2  6"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna(axis='columns')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- This drops good data as well; you might rather be interested in dropping rows or columns with *all* NA values, or a majority of NA values. This can be specified through the ``how`` or ``thresh`` parameters.\n",
    "\n",
    "- The default is ``how='any'``, such that any row or column (depending on the ``axis`` keyword) containing a null value will be dropped.\n",
    "- You can also specify ``how='all'``, which will only drop rows/columns that are *all* null values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe thead tr:only-child th {\n",
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       "\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "     0    1  2   3\n",
       "0  1.0  NaN  2 NaN\n",
       "1  2.0  3.0  5 NaN\n",
       "2  NaN  4.0  6 NaN"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[3] = np.nan\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
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       "      <td>6</td>\n",
       "    </tr>\n",
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       "</table>\n",
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      "text/plain": [
       "     0    1  2\n",
       "0  1.0  NaN  2\n",
       "1  2.0  3.0  5\n",
       "2  NaN  4.0  6"
      ]
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     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df.dropna(axis='columns', how='all')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The ``thresh`` parameter lets you specify a minimum number of non-null values for the row/column to be kept:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "</div>"
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      "text/plain": [
       "     0    1  2   3\n",
       "1  2.0  3.0  5 NaN"
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     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna(axis='rows', thresh=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The first and last row have been dropped because they contain only two non-null values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Filling null values\n",
    "\n",
    "- Sometimes you'd rather replace NA values with a valid value. This value might be a single number like zero, or it might be some sort of imputation or interpolation from the good values.\n",
    "\n",
    "- You could do this in-place using the ``isnull()`` method as a mask, but because it is such a common operation Pandas provides the ``fillna()`` method, which returns a copy of the array with the null values replaced."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1.0\n",
       "b    NaN\n",
       "c    2.0\n",
       "d    NaN\n",
       "e    3.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.Series([1, np.nan, 2, None, 3], index=list('abcde'))\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Fill NA entries with a single value, such as zero:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1.0\n",
       "b    0.0\n",
       "c    2.0\n",
       "d    0.0\n",
       "e    3.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.fillna(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Forward-fill to propagate the previous value forward:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1.0\n",
       "b    1.0\n",
       "c    2.0\n",
       "d    2.0\n",
       "e    3.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# forward-fill\n",
    "data.fillna(method='ffill')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Back-fill to propagate the next values backward:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1.0\n",
       "b    2.0\n",
       "c    2.0\n",
       "d    3.0\n",
       "e    3.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# back-fill\n",
    "data.fillna(method='bfill')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "- We can also specify an ``axis`` along which the fills take place in a ``DataFrame``."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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   "source": [
    "df"
   ]
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  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "     0    1    2    3\n",
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     "execution_count": 29,
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   "source": [
    "df.fillna(method='ffill', axis=1)"
   ]
  }
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