How-to-avoid-SettingWithCopyWarning.ipynb

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   "metadata": {},
   "source": [
    "## Demonstration of when the Pandas SettingWithCopyWarning occurs and how to avoid it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.0.3'"
      ]
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     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "pd.__version__"
   ]
  },
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   "execution_count": 2,
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       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>0.1</td>\n",
       "      <td>x</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.4</td>\n",
       "      <td>z</td>\n",
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       "   A    B  C\n",
       "0  1  0.1  x\n",
       "1  2  0.2  y\n",
       "2  3  0.4  z"
      ]
     },
     "execution_count": 2,
     "metadata": {},
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   ],
   "source": [
    "# Create some random data\n",
    "data = {\n",
    "    'A': [1, 2, 3],\n",
    "    'B': [0.1, 0.2, 0.4],\n",
    "    'C': ['x', 'y', 'z']\n",
    "}\n",
    "\n",
    "# Make a Pandas dataframe\n",
    "df = pd.DataFrame(data)\n",
    "df"
   ]
  },
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   "execution_count": 3,
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "A      1\n",
       "B    0.1\n",
       "C      x\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {},
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    }
   ],
   "source": [
    "# Take a slice of the dataframe\n",
    "first = df.loc[0, :]\n",
    "first"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
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     "text": [
      "/var/folders/y3/28pc8qrx3dd36zwcys8z1rzc0000gn/T/ipykernel_15826/1080533129.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  first['B'] = 99\n"
     ]
    }
   ],
   "source": [
    "# Try to change one of the values in the slice\n",
    "first['B'] = 99"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "A     1\n",
       "B    99\n",
       "C     x\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# Even though there was a warning, it did work...\n",
    "first"
   ]
  },
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   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
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    {
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      "text/plain": [
       "   A    B  C\n",
       "0  1  0.1  x\n",
       "1  2  0.2  y\n",
       "2  3  0.4  z"
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   "source": [
    "# But the original dataframe was not affected\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Just to verify that the slice is a copy, let's change\n",
    "# a value in the original dataframe\n",
    "df.loc[0,'C'] = \"XXX\""
   ]
  },
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   "cell_type": "code",
   "execution_count": 8,
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       "   A    B    C\n",
       "0  1  0.1  XXX\n",
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   "source": [
    "df"
   ]
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  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "A     1\n",
       "B    99\n",
       "C     x\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
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   "source": [
    "first\n",
    "# It must be a copy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
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     "execution_count": 10,
     "metadata": {},
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   "source": [
    "# You can use this private attribute to check\n",
    "first._is_view"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To avoid the SettingWithCopyWarning, just make the fact that\n",
    "# you are making a copy explicit\n",
    "second = df.loc[1,:].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "second['B'] = 9.99\n",
    "# No warning was raised!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "jupyter": {
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A       2\n",
       "B    9.99\n",
       "C       y\n",
       "Name: 1, dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "second"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The details of when Pandas makes a copy or gives you a view are not clear. [See this discussion](https://stackoverflow.com/questions/23296282/what-rules-does-pandas-use-to-generate-a-view-vs-a-copy) and the examples below. They are an implementation detail intended to minimize memory usage and computation.\n",
    "\n",
    "Therefore, **you should never rely on the fact that you have a view or a copy in your code**. The only time it really matters is when you make changes to your view/copy. If you do not want to make changes to the original dataframe, you make sure it's a copy by using the copy method, as in the example above.\n",
    "\n",
    "If you do want to make changes to the original dataframe, you should explicitely write those changes to the original dataframe.  For example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
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   "outputs": [
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      "text/plain": [
       "   A     B    C\n",
       "0  1  9.99  XXX\n",
       "1  2  0.20    y\n",
       "2  3  0.40    z"
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   "source": [
    "# Instead of first['B'] = 9.99, use the loc method on the original dataframe\n",
    "df.loc[0, 'B'] = 9.99\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Mysteries of copy/view"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
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       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
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       "      <td>9</td>\n",
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      "text/plain": [
       "   A  B  C\n",
       "0  1  4  7\n",
       "1  2  5  8\n",
       "2  3  6  9"
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     "execution_count": 15,
     "metadata": {},
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   "source": [
    "df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9]})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
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    {
     "data": {
      "text/plain": [
       "True"
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     "execution_count": 16,
     "metadata": {},
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   "source": [
    "df['A']._is_view"
   ]
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   "cell_type": "code",
   "execution_count": 17,
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       "False"
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     "execution_count": 17,
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   "execution_count": 18,
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       "True"
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     "execution_count": 18,
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    "df.iloc[:, 0:2]._is_view"
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     "execution_count": 20,
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   "source": [
    "df.loc[[0, 1]]._is_view"
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     "execution_count": 23,
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     "execution_count": 24,
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   ]
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     "execution_count": 25,
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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       "   A  B  C\n",
       "0  1  4  7\n",
       "1  2  5  8\n",
       "2  3  6  9"
      ]
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     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "data = np.array([[1, 4, 7], [2, 5, 8], [3, 6, 9]])\n",
    "df = pd.DataFrame(data, columns=['A', 'B', 'C'])\n",
    "df"
   ]
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  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
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     },
     "execution_count": 27,
     "metadata": {},
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   "source": [
    "df['A']._is_view"
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  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df[['A', 'B']]._is_view"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df.iloc[:, 0:2]._is_view"
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  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
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     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df.loc[0]._is_view"
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  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df.loc[[0, 1]]._is_view"
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  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
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     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
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    "df.loc[0:2]._is_view"
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   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
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     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
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    "df.loc[0]._is_view"
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  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
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     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df.loc[0:2, 'A']._is_view"
   ]
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  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df.loc[0:2, ['A', 'B']]._is_view"
   ]
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  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df.iloc[0:2, 0:2]._is_view"
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   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
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       "      <td>8</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
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       "   A  B  C  C  D  E\n",
       "0  1  4  7  1  4  7\n",
       "1  2  5  8  2  5  8\n",
       "2  3  6  9  3  6  9"
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     "execution_count": 37,
     "metadata": {},
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    "df2 = pd.concat([df.copy(), df.copy().rename(columns={'A': 'C', 'B': 'D', 'C': 'E'})], axis=1)\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
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     "execution_count": 38,
     "metadata": {},
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   "source": [
    "df2['A']._is_view"
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   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
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     "execution_count": 39,
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     "output_type": "execute_result"
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   "source": [
    "df2[['A', 'B']]._is_view"
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   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
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       "False"
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     "execution_count": 40,
     "metadata": {},
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   "execution_count": 41,
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     "data": {
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     "execution_count": 41,
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    "df2.iloc[:, 0]._is_view"
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   "execution_count": 42,
   "metadata": {},
   "outputs": [
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     "data": {
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     "execution_count": 42,
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     "execution_count": 43,
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