5-1_Pandas_indexing.ipynb

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    "# Lecture 5-1\n",
    "\n",
    "# Pandas: Indexing, Arithmetic, Missing Values\n",
    "\n",
    "## Week 5 Monday\n",
    "\n",
    "## Miles Chen, PhD\n",
    "\n",
    "Based on Wes McKinney's Python for Data Analysis and the Pandas Documentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
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     "slide_type": "slide"
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   "source": [
    "## Series that we will use as examples"
   ]
  },
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   "source": [
    "# note that the value after the decimal place corresponds to the letter position.\n",
    "# i.e. 1.4 corresponds to d, the fourth letter.\n",
    "original1 = pd.Series([1.4, 2.3, 3.1, 4.2], index = ['d','c','a','b'])\n",
    "original2 = pd.Series([2.2, 3.1, 1.3, 4.4], index = ['b','a','c','d'])"
   ]
  },
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   "execution_count": 3,
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     "data": {
      "text/plain": [
       "d    1.4\n",
       "c    2.3\n",
       "a    3.1\n",
       "b    4.2\n",
       "dtype: float64"
      ]
     },
     "execution_count": 3,
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     "output_type": "execute_result"
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   "source": [
    "original1 # when you create a series, the original order of the index is preserved"
   ]
  },
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   "execution_count": 4,
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       "      <th>d</th>\n",
       "      <td>1.4</td>\n",
       "      <td>2.8</td>\n",
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       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>2.3</td>\n",
       "      <td>4.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>3.1</td>\n",
       "      <td>6.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>4.2</td>\n",
       "      <td>8.4</td>\n",
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       "     x   x2\n",
       "d  1.4  2.8\n",
       "c  2.3  4.6\n",
       "a  3.1  6.2\n",
       "b  4.2  8.4"
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   "source": [
    "# making a DataFrame with multiple series with the same index preserves the index order\n",
    "pd.DataFrame({\"x\":original1, \"x2\": original1 * 2})"
   ]
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      "text/plain": [
       "b    2.2\n",
       "a    3.1\n",
       "c    1.3\n",
       "d    4.4\n",
       "dtype: float64"
      ]
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     "execution_count": 5,
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   "source": [
    "original2 # note that original1 and original2 have different index orders"
   ]
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   "execution_count": 6,
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       "      <td>1.3</td>\n",
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       "      <th>d</th>\n",
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       "     x    y\n",
       "a  3.1  3.1\n",
       "b  4.2  2.2\n",
       "c  2.3  1.3\n",
       "d  1.4  4.4"
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    "# because original1 and original2 have index in different order, Pandas will sort the index before putting them together\n",
    "df = pd.DataFrame({\"x\":original1, \"y\": original2})\n",
    "df"
   ]
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    {
     "data": {
      "text/plain": [
       "Index(['d', 'c', 'a', 'b'], dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "original1.index # the index of original1 is the letters d, c, a, b in a tuple-like object"
   ]
  },
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   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
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    {
     "data": {
      "text/plain": [
       "d    1.4\n",
       "c    2.3\n",
       "a    3.1\n",
       "dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "original1['d':'a'] # when slicing pandas uses the index order or original1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
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     "slide_type": "fragment"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['a', 'b', 'c', 'd'], dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index  # the index of df are the letters abcd in order"
   ]
  },
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   "execution_count": 10,
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       "     x    y\n",
       "a  3.1  3.1\n",
       "b  4.2  2.2\n",
       "c  2.3  1.3"
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    "df['a':'c'] # when slicing Pandas uses the index order of the DataFrame, which has been sorted"
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   "source": [
    "## Rearranging value\n",
    "\n",
    "Both Series and DataFrames have the `.sort_index()` and `.sort_values()` methods which can be used to rearrange the value."
   ]
  },
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   "cell_type": "code",
   "execution_count": 11,
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    {
     "data": {
      "text/plain": [
       "b    2.2\n",
       "a    3.1\n",
       "c    1.3\n",
       "d    4.4\n",
       "dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
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    "original2"
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   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
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    {
     "data": {
      "text/plain": [
       "a    3.1\n",
       "b    2.2\n",
       "c    1.3\n",
       "d    4.4\n",
       "dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "original2.sort_index()"
   ]
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   "execution_count": 13,
   "metadata": {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "c    1.3\n",
       "b    2.2\n",
       "a    3.1\n",
       "d    4.4\n",
       "dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "original2.sort_values()"
   ]
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   "execution_count": 14,
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       "     x    y\n",
       "a  3.1  3.1\n",
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    "df"
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       "     x    y\n",
       "b  4.2  2.2\n",
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       "d  1.4  4.4"
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    "df.sort_values(by = \"x\", ascending = False)"
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   "source": [
    "# Changing the Index"
   ]
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The index of a Pandas Series or Pandas DataFrame is immutable and cannot be modified.\n",
    "\n",
    "However, if you want to change the index of a series or dataframe, you can define a new index and replace the existing index of the series/DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
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   "source": [
    "original1.index = range(4) # I replace the index of the series with this range object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.4\n",
       "1    2.3\n",
       "2    3.1\n",
       "3    4.2\n",
       "dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "original1"
   ]
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  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=4, step=1)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "original1.index # We can see this has automatically become a RangeIndex object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.3"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "original1[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.3"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "original1.loc[1] # behaves the same as above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.3"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "original1.iloc[1] # behaves the same as above because the range index starts at 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
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   "outputs": [],
   "source": [
    "original1.index = range(1,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    1.4\n",
       "2    2.3\n",
       "3    3.1\n",
       "4    4.2\n",
       "dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "original1"
   ]
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   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.4"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "original1[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
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     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.4"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "original1.loc[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.3"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "original1.iloc[1] # behavior is different because range index starts at 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": [
     "raises-exception"
    ]
   },
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'a'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Input \u001b[1;32mIn [27]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43moriginal1\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43ma\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\n",
      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\series.py:958\u001b[0m, in \u001b[0;36mSeries.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m    955\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_values[key]\n\u001b[0;32m    957\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m key_is_scalar:\n\u001b[1;32m--> 958\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_value\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    960\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_hashable(key):\n\u001b[0;32m    961\u001b[0m     \u001b[38;5;66;03m# Otherwise index.get_value will raise InvalidIndexError\u001b[39;00m\n\u001b[0;32m    962\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m    963\u001b[0m         \u001b[38;5;66;03m# For labels that don't resolve as scalars like tuples and frozensets\u001b[39;00m\n",
      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\series.py:1069\u001b[0m, in \u001b[0;36mSeries._get_value\u001b[1;34m(self, label, takeable)\u001b[0m\n\u001b[0;32m   1066\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_values[label]\n\u001b[0;32m   1068\u001b[0m \u001b[38;5;66;03m# Similar to Index.get_value, but we do not fall back to positional\u001b[39;00m\n\u001b[1;32m-> 1069\u001b[0m loc \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mindex\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlabel\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1070\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mindex\u001b[38;5;241m.\u001b[39m_get_values_for_loc(\u001b[38;5;28mself\u001b[39m, loc, label)\n",
      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\range.py:389\u001b[0m, in \u001b[0;36mRangeIndex.get_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m    387\u001b[0m             \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[0;32m    388\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n\u001b[1;32m--> 389\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key)\n\u001b[0;32m    390\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39mget_loc(key, method\u001b[38;5;241m=\u001b[39mmethod, tolerance\u001b[38;5;241m=\u001b[39mtolerance)\n",
      "\u001b[1;31mKeyError\u001b[0m: 'a'"
     ]
    }
   ],
   "source": [
    "original1['a'] # throws an error because 'a' is no longer part of the index and cannot be used to select values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "original1.index = ['a','b','c','d'] # be careful as no restrictions regarding the meaning of the index is applied.\n",
    "# in the original 'a' was associated with 3.1. This index will associate it with 1.4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1.4\n",
       "b    2.3\n",
       "c    3.1\n",
       "d    4.2\n",
       "dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "original1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.4"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "original1['a']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.4"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "original1[0] # now that the index uses strings, you can index by position"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": [
     "raises-exception"
    ]
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Length mismatch: Expected axis has 4 elements, new values have 5 elements",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Input \u001b[1;32mIn [32]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[0m original1\u001b[38;5;241m.\u001b[39mindex \u001b[38;5;241m=\u001b[39m [\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m2\u001b[39m, \u001b[38;5;241m3\u001b[39m, \u001b[38;5;241m4\u001b[39m, \u001b[38;5;241m5\u001b[39m]\n",
      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\generic.py:5588\u001b[0m, in \u001b[0;36mNDFrame.__setattr__\u001b[1;34m(self, name, value)\u001b[0m\n\u001b[0;32m   5586\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m   5587\u001b[0m     \u001b[38;5;28mobject\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__getattribute__\u001b[39m(\u001b[38;5;28mself\u001b[39m, name)\n\u001b[1;32m-> 5588\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mobject\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__setattr__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   5589\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m:\n\u001b[0;32m   5590\u001b[0m     \u001b[38;5;28;01mpass\u001b[39;00m\n",
      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\_libs\\properties.pyx:70\u001b[0m, in \u001b[0;36mpandas._libs.properties.AxisProperty.__set__\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\series.py:572\u001b[0m, in \u001b[0;36mSeries._set_axis\u001b[1;34m(self, axis, labels, fastpath)\u001b[0m\n\u001b[0;32m    568\u001b[0m             \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[0;32m    570\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m fastpath:\n\u001b[0;32m    571\u001b[0m     \u001b[38;5;66;03m# The ensure_index call above ensures we have an Index object\u001b[39;00m\n\u001b[1;32m--> 572\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_mgr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mset_axis\u001b[49m\u001b[43m(\u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlabels\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\internals\\managers.py:214\u001b[0m, in \u001b[0;36mBaseBlockManager.set_axis\u001b[1;34m(self, axis, new_labels)\u001b[0m\n\u001b[0;32m    212\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mset_axis\u001b[39m(\u001b[38;5;28mself\u001b[39m, axis: \u001b[38;5;28mint\u001b[39m, new_labels: Index) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    213\u001b[0m     \u001b[38;5;66;03m# Caller is responsible for ensuring we have an Index object.\u001b[39;00m\n\u001b[1;32m--> 214\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_set_axis\u001b[49m\u001b[43m(\u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnew_labels\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    215\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxes[axis] \u001b[38;5;241m=\u001b[39m new_labels\n",
      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\internals\\base.py:69\u001b[0m, in \u001b[0;36mDataManager._validate_set_axis\u001b[1;34m(self, axis, new_labels)\u001b[0m\n\u001b[0;32m     66\u001b[0m     \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[0;32m     68\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m new_len \u001b[38;5;241m!=\u001b[39m old_len:\n\u001b[1;32m---> 69\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m     70\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mLength mismatch: Expected axis has \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mold_len\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m elements, new \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m     71\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvalues have \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnew_len\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m elements\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m     72\u001b[0m     )\n",
      "\u001b[1;31mValueError\u001b[0m: Length mismatch: Expected axis has 4 elements, new values have 5 elements"
     ]
    }
   ],
   "source": [
    "original1.index = [1, 2, 3, 4, 5] # if the object you provide is of a different length, you get a value error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "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>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>j</th>\n",
       "      <td>3.1</td>\n",
       "      <td>3.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>k</th>\n",
       "      <td>4.2</td>\n",
       "      <td>2.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>l</th>\n",
       "      <td>2.3</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>m</th>\n",
       "      <td>1.4</td>\n",
       "      <td>4.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     x    y\n",
       "j  3.1  3.1\n",
       "k  4.2  2.2\n",
       "l  2.3  1.3\n",
       "m  1.4  4.4"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# similarly you can change the index of a DataFrame by defining a new object and assigning it to the index.\n",
    "df.index = ['j','k','l','m']\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Reindexing\n",
    "\n",
    "Reindexing is different from just defining a new index.\n",
    "\n",
    "Reindexing takes a current Pandas object and creates a *new* Pandas object that *conforms* to the specified index.\n",
    "\n",
    "**Do not confuse reindexing with creating a new index for a dataframe object.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "original = pd.Series([1.4, 2.3, 3.1, 4.2], index = ['d','c','a','b'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "d    1.4\n",
       "c    2.3\n",
       "a    3.1\n",
       "b    4.2\n",
       "dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "original"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "newobj = original.reindex(['a','b','c','d','e']) # note this has an index value that doesn't exist in the original series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    3.1\n",
       "b    4.2\n",
       "c    2.3\n",
       "d    1.4\n",
       "e    NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newobj  # takes the data in orignal and moves it so it conforms to the specified index\n",
    "# values that do not exist for the new index get NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    3.1\n",
       "b    4.2\n",
       "c    2.3\n",
       "d    1.4\n",
       "e    0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# if you don't want NaN, you can specify a fill_value\n",
    "newobj2 = original.reindex(['a','b','c','d','e'], fill_value = 0)\n",
    "newobj2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "For ordered data like a time series, it might be desirable to fill values when reindexing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      blue\n",
       "3    purple\n",
       "6    yellow\n",
       "dtype: object"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj3 = pd.Series(['blue', 'purple', 'yellow'], index=[0, 3, 6])\n",
    "obj3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      blue\n",
       "1       NaN\n",
       "2       NaN\n",
       "3    purple\n",
       "4       NaN\n",
       "5       NaN\n",
       "6    yellow\n",
       "7       NaN\n",
       "8       NaN\n",
       "dtype: object"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj3.reindex(range(9))  # without any optional arguments, lots of missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      blue\n",
       "1      blue\n",
       "2      blue\n",
       "3    purple\n",
       "4    purple\n",
       "5    purple\n",
       "6    yellow\n",
       "7    yellow\n",
       "8    yellow\n",
       "dtype: object"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj3.reindex(range(9), method='ffill')\n",
    "# forward-fill pushes values 'forward' until a new value is encountered"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      blue\n",
       "1    purple\n",
       "2    purple\n",
       "3    purple\n",
       "4    yellow\n",
       "5    yellow\n",
       "6    yellow\n",
       "7       NaN\n",
       "8       NaN\n",
       "dtype: object"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj3.reindex(range(9), method='bfill')  \n",
    "# back-fill works in the opposite direction\n",
    "# there was no value at index 8 so, NaNs get filled in"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "## Date Ranges as Index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2010-01-01', '2010-01-02', '2010-01-03', '2010-01-04',\n",
       "               '2010-01-05', '2010-01-06'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we specify the creation of a date_index using the date_range function\n",
    "# freq = 'D' creates Daily values \n",
    "date_index = pd.date_range('1/1/2010', periods=6, freq='D')\n",
    "date_index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th>prices</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-01</th>\n",
       "      <td>100.0</td>\n",
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       "    <tr>\n",
       "      <th>2010-01-02</th>\n",
       "      <td>101.0</td>\n",
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       "    <tr>\n",
       "      <th>2010-01-03</th>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2010-01-04</th>\n",
       "      <td>100.0</td>\n",
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       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>89.0</td>\n",
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       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>88.0</td>\n",
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       "            prices\n",
       "2010-01-01   100.0\n",
       "2010-01-02   101.0\n",
       "2010-01-03     NaN\n",
       "2010-01-04   100.0\n",
       "2010-01-05    89.0\n",
       "2010-01-06    88.0"
      ]
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     "execution_count": 44,
     "metadata": {},
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   ],
   "source": [
    "# we create a DataFrame with the date index\n",
    "df2 = pd.DataFrame({\"prices\": [100, 101, np.nan, 100, 89, 88]}, index=date_index)\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
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    "tags": []
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   "outputs": [
    {
     "data": {
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       "  </thead>\n",
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       "      <th>2010-01-01</th>\n",
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       "      <th>2010-01-03</th>\n",
       "      <td>NaN</td>\n",
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       "      <th>2010-01-04</th>\n",
       "      <td>100.0</td>\n",
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       "      <th>2010-01-05</th>\n",
       "      <td>89.0</td>\n",
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       "      <th>2010-01-06</th>\n",
       "      <td>88.0</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "            prices\n",
       "2010-01-01   100.0\n",
       "2010-01-02   101.0\n",
       "2010-01-03     NaN\n",
       "2010-01-04   100.0\n",
       "2010-01-05    89.0\n",
       "2010-01-06    88.0"
      ]
     },
     "execution_count": 45,
     "metadata": {},
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   ],
   "source": [
    "# we create a DataFrame with the date index\n",
    "df2 = pd.DataFrame({\"prices\": [100, 101, np.nan, 100, 89, 88]}, index=date_index)\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
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     "slide_type": "fragment"
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   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2009-12-29</th>\n",
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       "      <th>2009-12-30</th>\n",
       "      <td>NaN</td>\n",
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       "      <th>2009-12-31</th>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2010-01-01</th>\n",
       "      <td>100.0</td>\n",
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       "      <th>2010-01-02</th>\n",
       "      <td>101.0</td>\n",
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       "      <th>2010-01-03</th>\n",
       "      <td>NaN</td>\n",
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       "      <th>2010-01-04</th>\n",
       "      <td>100.0</td>\n",
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       "      <th>2010-01-05</th>\n",
       "      <td>89.0</td>\n",
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       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>88.0</td>\n",
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       "    <tr>\n",
       "      <th>2010-01-07</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "</div>"
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      "text/plain": [
       "            prices\n",
       "2009-12-29     NaN\n",
       "2009-12-30     NaN\n",
       "2009-12-31     NaN\n",
       "2010-01-01   100.0\n",
       "2010-01-02   101.0\n",
       "2010-01-03     NaN\n",
       "2010-01-04   100.0\n",
       "2010-01-05    89.0\n",
       "2010-01-06    88.0\n",
       "2010-01-07     NaN"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date_index2 = pd.date_range('12/29/2009', periods=10, freq='D')  # a new date index\n",
    "df2.reindex(date_index2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
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       "      <th>2009-12-29</th>\n",
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       "      <th>2009-12-30</th>\n",
       "      <td>100.0</td>\n",
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       "      <th>2010-01-01</th>\n",
       "      <td>100.0</td>\n",
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       "    <tr>\n",
       "      <th>2010-01-02</th>\n",
       "      <td>101.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-03</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-04</th>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>89.0</td>\n",
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       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>88.0</td>\n",
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       "    <tr>\n",
       "      <th>2010-01-07</th>\n",
       "      <td>NaN</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            prices\n",
       "2009-12-29   100.0\n",
       "2009-12-30   100.0\n",
       "2009-12-31   100.0\n",
       "2010-01-01   100.0\n",
       "2010-01-02   101.0\n",
       "2010-01-03     NaN\n",
       "2010-01-04   100.0\n",
       "2010-01-05    89.0\n",
       "2010-01-06    88.0\n",
       "2010-01-07     NaN"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.reindex(date_index2, method = 'bfill') \n",
    "# The value for Jan 3 isn't filled in because that NaN was not created by the reindexing process\n",
    "# The NaN already existed in the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## `.reindex()` vs `.loc()`\n",
    "\n",
    "If you don't need to fill in any missing info, then `.reindex()` and `.loc()` work very similarly.\n",
    "If the new index will have values that don't exist in the current index, you need to use `reindex`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>val</th>\n",
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      "text/plain": [
       "   val\n",
       "d  1.4\n",
       "c  2.3\n",
       "a  3.1\n",
       "b  4.2"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj5 = pd.DataFrame({'val':[1.4, 2.3, 3.1, 4.2]}, index = ['d','c','a','b'])\n",
    "obj5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
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       "      <td>3.1</td>\n",
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       "   val\n",
       "a  3.1\n",
       "b  4.2\n",
       "c  2.3\n",
       "d  1.4"
      ]
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     "metadata": {},
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   "source": [
    "obj5.reindex(['a','b','c','d'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>3.1</td>\n",
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       "      <td>4.2</td>\n",
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       "   val\n",
       "a  3.1\n",
       "b  4.2\n",
       "c  2.3\n",
       "d  1.4"
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     "execution_count": 50,
     "metadata": {},
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   "source": [
    "obj5.loc[['a','b','c','d']] # works the same as reindex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
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   "outputs": [
    {
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       "        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>val</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>3.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>4.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>1.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   val\n",
       "a  3.1\n",
       "b  4.2\n",
       "c  2.3\n",
       "d  1.4\n",
       "e  NaN"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj5.reindex(['a','b','c','d','e'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": [
     "raises-exception"
    ]
   },
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "\"['e'] not in index\"",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Input \u001b[1;32mIn [52]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mobj5\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mloc\u001b[49m\u001b[43m[\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43ma\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mb\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mc\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43md\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43me\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m]\u001b[49m\n",
      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:967\u001b[0m, in \u001b[0;36m_LocationIndexer.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m    964\u001b[0m axis \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxis \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;241m0\u001b[39m\n\u001b[0;32m    966\u001b[0m maybe_callable \u001b[38;5;241m=\u001b[39m com\u001b[38;5;241m.\u001b[39mapply_if_callable(key, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj)\n\u001b[1;32m--> 967\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_getitem_axis\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmaybe_callable\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:1194\u001b[0m, in \u001b[0;36m_LocIndexer._getitem_axis\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m   1191\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(key, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mndim\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m key\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m   1192\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot index with multidimensional key\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m-> 1194\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_getitem_iterable\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1196\u001b[0m \u001b[38;5;66;03m# nested tuple slicing\u001b[39;00m\n\u001b[0;32m   1197\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_nested_tuple(key, labels):\n",
      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:1132\u001b[0m, in \u001b[0;36m_LocIndexer._getitem_iterable\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m   1129\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_key(key, axis)\n\u001b[0;32m   1131\u001b[0m \u001b[38;5;66;03m# A collection of keys\u001b[39;00m\n\u001b[1;32m-> 1132\u001b[0m keyarr, indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_listlike_indexer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1133\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj\u001b[38;5;241m.\u001b[39m_reindex_with_indexers(\n\u001b[0;32m   1134\u001b[0m     {axis: [keyarr, indexer]}, copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, allow_dups\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m   1135\u001b[0m )\n",
      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:1330\u001b[0m, in \u001b[0;36m_LocIndexer._get_listlike_indexer\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m   1327\u001b[0m ax \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj\u001b[38;5;241m.\u001b[39m_get_axis(axis)\n\u001b[0;32m   1328\u001b[0m axis_name \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj\u001b[38;5;241m.\u001b[39m_get_axis_name(axis)\n\u001b[1;32m-> 1330\u001b[0m keyarr, indexer \u001b[38;5;241m=\u001b[39m \u001b[43max\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_indexer_strict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis_name\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1332\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m keyarr, indexer\n",
      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py:5796\u001b[0m, in \u001b[0;36mIndex._get_indexer_strict\u001b[1;34m(self, key, axis_name)\u001b[0m\n\u001b[0;32m   5793\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m   5794\u001b[0m     keyarr, indexer, new_indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reindex_non_unique(keyarr)\n\u001b[1;32m-> 5796\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_raise_if_missing\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkeyarr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindexer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis_name\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   5798\u001b[0m keyarr \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtake(indexer)\n\u001b[0;32m   5799\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(key, Index):\n\u001b[0;32m   5800\u001b[0m     \u001b[38;5;66;03m# GH 42790 - Preserve name from an Index\u001b[39;00m\n",
      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py:5859\u001b[0m, in \u001b[0;36mIndex._raise_if_missing\u001b[1;34m(self, key, indexer, axis_name)\u001b[0m\n\u001b[0;32m   5856\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNone of [\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mkey\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m] are in the [\u001b[39m\u001b[38;5;132;01m{\u001b[39;00maxis_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m]\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m   5858\u001b[0m not_found \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(ensure_index(key)[missing_mask\u001b[38;5;241m.\u001b[39mnonzero()[\u001b[38;5;241m0\u001b[39m]]\u001b[38;5;241m.\u001b[39munique())\n\u001b[1;32m-> 5859\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnot_found\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m not in index\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "\u001b[1;31mKeyError\u001b[0m: \"['e'] not in index\""
     ]
    }
   ],
   "source": [
    "obj5.loc[['a','b','c','d','e']]  # .loc() returns a warning or error if you give an entry in the index that doesn't exist"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Dropping rows or columns\n",
    "\n",
    "you can use `df.drop()` to remove rows (default) or columns (specify axis = 1) at certain index locations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>z</th>\n",
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       "   A  B   C   D\n",
       "x  0  1   2   3\n",
       "y  4  5   6   7\n",
       "z  8  9  10  11"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(np.arange(12).reshape(3,4), columns=['A', 'B', 'C', 'D'], index = ['x','y','z'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>y</th>\n",
       "      <td>4</td>\n",
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      "text/plain": [
       "   A  B  C  D\n",
       "y  4  5  6  7"
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   "source": [
    "# drop rows\n",
    "df.drop(['x', 'z'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "   A   D\n",
       "x  0   3\n",
       "y  4   7\n",
       "z  8  11"
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     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop columns\n",
    "df.drop(['B', 'C'], axis = 1) # we must specify axis = 1 otherwise Pandas will look for \"B\" and \"C\" in the row names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>y</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>z</th>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   A  B   C   D\n",
       "x  0  1   2   3\n",
       "y  4  5   6   7\n",
       "z  8  9  10  11"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df.drop returns a new object and leaves df unchanged\n",
    "# you can change this behavior with the argument inplace = True\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Data Alignment\n",
    "\n",
    "When performing element-wise arithmetic, Pandas will align the index values before doing the computation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    7.3\n",
       "c   -2.5\n",
       "d    3.4\n",
       "e    1.5\n",
       "dtype: float64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1 = pd.Series([7.3, -2.5, 3.4, 1.5], index=['a', 'c', 'd', 'e'])\n",
    "s1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a   -2.1\n",
       "c    3.6\n",
       "e   -1.5\n",
       "f    4.0\n",
       "g    3.1\n",
       "dtype: float64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s2 = pd.Series([-2.1, 3.6, -1.5, 4, 3.1],\n",
    "               index=['a', 'c', 'e', 'f', 'g'])\n",
    "s2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>s1</th>\n",
       "      <th>s2</th>\n",
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       "      <th>a</th>\n",
       "      <td>7.3</td>\n",
       "      <td>-2.1</td>\n",
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       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>-2.5</td>\n",
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       "      <th>d</th>\n",
       "      <td>3.4</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>e</th>\n",
       "      <td>1.5</td>\n",
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       "      <th>f</th>\n",
       "      <td>NaN</td>\n",
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       "      <th>g</th>\n",
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      "text/plain": [
       "    s1   s2\n",
       "a  7.3 -2.1\n",
       "c -2.5  3.6\n",
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       "e  1.5 -1.5\n",
       "f  NaN  4.0\n",
       "g  NaN  3.1"
      ]
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     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "pd.DataFrame({'s1':s1,'s2':s2}) # for reference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    5.2\n",
       "c    1.1\n",
       "d    NaN\n",
       "e    0.0\n",
       "f    NaN\n",
       "g    NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1 + s2  # returns a new series, where the indexes are the union of the indexes of s1 and s2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    5.2\n",
       "c    1.1\n",
       "d    NaN\n",
       "e    0.0\n",
       "f    NaN\n",
       "g    NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1.add(s2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    s1   s2\n",
       "a  7.3 -2.1\n",
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       "e  1.5 -1.5\n",
       "f  NaN  4.0\n",
       "g  NaN  3.1"
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     "execution_count": 62,
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    "pd.DataFrame({'s1':s1,'s2':s2})"
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  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    9.4\n",
       "c   -6.1\n",
       "d    3.4\n",
       "e    3.0\n",
       "f   -4.0\n",
       "g   -3.1\n",
       "dtype: float64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1.sub(s2, fill_value = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a   -9.4\n",
       "c    6.1\n",
       "d   -3.4\n",
       "e   -3.0\n",
       "f    4.0\n",
       "g    3.1\n",
       "dtype: float64"
      ]
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     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1.rsub(s2, fill_value = 0) # .rsub means 'right hand subtract' sets the series in the argument as the base"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a   -15.33\n",
       "c    -9.00\n",
       "d      NaN\n",
       "e    -2.25\n",
       "f      NaN\n",
       "g      NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1 * s2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a   -15.33\n",
       "c    -9.00\n",
       "d     3.40\n",
       "e    -2.25\n",
       "f     4.00\n",
       "g     3.10\n",
       "dtype: float64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1.multiply(s2, fill_value = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "For data frames with different columns, the rows and columns will be aligned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th>d</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>3.0</td>\n",
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       "    <tr>\n",
       "      <th>Colorado</th>\n",
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       "            b    c    d\n",
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       "Colorado  6.0  7.0  8.0"
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    "df1 = pd.DataFrame(np.arange(9.).reshape((3, 3)), columns=list('bcd'),\n",
    "                   index=['Ohio', 'Texas', 'Colorado'])\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
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    {
     "data": {
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       "      <th>Utah</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
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       "      <th>Ohio</th>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
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       "      <th>Texas</th>\n",
       "      <td>6.0</td>\n",
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       "      <td>8.0</td>\n",
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       "    <tr>\n",
       "      <th>Oregon</th>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "          b     d     e\n",
       "Utah    0.0   1.0   2.0\n",
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       "Texas   6.0   7.0   8.0\n",
       "Oregon  9.0  10.0  11.0"
      ]
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     "execution_count": 68,
     "metadata": {},
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   "source": [
    "df2 = pd.DataFrame(np.arange(12.).reshape((4, 3)), columns=list('bde'),\n",
    "                   index=['Utah', 'Ohio', 'Texas', 'Oregon'])\n",
    "df2"
   ]
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  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
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   "outputs": [
    {
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       "      <td>NaN</td>\n",
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       "      <th>Ohio</th>\n",
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       "      <th>Oregon</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "Oregon    NaN NaN   NaN NaN\n",
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     "execution_count": 69,
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    "df1 + df2 \n",
    "# c is in df1, but not df2\n",
    "# e is in df2, but not df1\n",
    "# the result returns the union of columns, but will fill in NaN for elements that do not exist in both"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
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   "outputs": [
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       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>9.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Utah</th>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            b    c     d     e\n",
       "Colorado  6.0  7.0   8.0   NaN\n",
       "Ohio      3.0  1.0   6.0   5.0\n",
       "Oregon    9.0  NaN  10.0  11.0\n",
       "Texas     9.0  4.0  12.0   8.0\n",
       "Utah      0.0  NaN   1.0   2.0"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# if you want to fill in values that are missing, you can use df.add() and specify the fill_value\n",
    "# this will perform the above operation, but instead of using NaN when it can't find a value \n",
    "# (which will return NaN),\n",
    "# it will use the fill_value\n",
    "df1.add(df2, fill_value = 0)\n",
    "# you still get NaN if the value does not exist in either DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "Arithmetic operations that can be called on DataFrames and Series are:\n",
    "\n",
    "- `.add()`, `.radd()` and `.sub()`, `.rsub()`\n",
    "- `.mul()`, `.rmul()` and `.div()`, `.rdiv()` \n",
    "- `.floordiv()`, `.rfloordiv()` (floor division `//`)\n",
    "- `.pow()`, `.rpow()` (exponentiation `**`)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Summary Stats of a DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1.5</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>6.0</td>\n",
       "      <td>-4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>1.5</td>\n",
       "      <td>-1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f</th>\n",
       "      <td>6.0</td>\n",
       "      <td>-4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>g</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "   one  two\n",
       "a  1.5  NaN\n",
       "b  6.0 -4.5\n",
       "c  NaN  NaN\n",
       "d  1.5 -1.5\n",
       "e  4.0  0.0\n",
       "f  6.0 -4.5\n",
       "g  NaN  4.0"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'one':[1.5,6.0,np.nan, 1.5,4,6, np.nan],\n",
    "                   'two':[np.nan, -4.5, np.nan, -1.5, 0, -4.5, 4]},\n",
    "                  index=['a', 'b', 'c', 'd','e','f','g'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "one    19.0\n",
       "two    -6.5\n",
       "dtype: float64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sum()  # default behavior returns column sums and skips missing values\n",
    "# default behavior sums across axis 0 (sums the row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "      <td>NaN</td>\n",
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       "      <th>d</th>\n",
       "      <td>1.5</td>\n",
       "      <td>-1.5</td>\n",
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       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
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       "</table>\n",
       "</div>"
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      "text/plain": [
       "   one  two\n",
       "a  1.5  NaN\n",
       "b  6.0 -4.5\n",
       "c  NaN  NaN\n",
       "d  1.5 -1.5\n",
       "e  4.0  0.0\n",
       "f  6.0 -4.5\n",
       "g  NaN  4.0"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df # for reference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1.5\n",
       "b    1.5\n",
       "c    0.0\n",
       "d    0.0\n",
       "e    4.0\n",
       "f    1.5\n",
       "g    4.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sum(axis = 1) # sum across axis=1, sum across the columns and give row sums"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "one   NaN\n",
       "two   NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sum(skipna = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "one    3.8\n",
       "two   -1.3\n",
       "dtype: float64"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1.50\n",
       "b    0.75\n",
       "c     NaN\n",
       "d    0.00\n",
       "e    2.00\n",
       "f    0.75\n",
       "g    4.00\n",
       "dtype: float64"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean(axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>1.5</td>\n",
       "      <td>-1.5</td>\n",
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       "      <th>e</th>\n",
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       "      <th>f</th>\n",
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       "      <th>g</th>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   one  two\n",
       "a  1.5  NaN\n",
       "b  6.0 -4.5\n",
       "c  NaN  NaN\n",
       "d  1.5 -1.5\n",
       "e  4.0  0.0\n",
       "f  6.0 -4.5\n",
       "g  NaN  4.0"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df # for reference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "one    1.5\n",
       "two   -4.5\n",
       "dtype: float64"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "one    a\n",
       "two    b\n",
       "dtype: object"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.idxmin()  # which row has the minimum value, also .idxmax()\n",
    "# returns the first minimum, if there are multiple\n",
    "# you can also specify axis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "## Summary stats available for dataframes and series\n",
    "\n",
    "- `count()` - number of non NA values\n",
    "- `quantile()`\n",
    "- `sum()`\n",
    "- `mean()`\n",
    "- `median()`\n",
    "- `mad()` - mean absolute deviation\n",
    "- `prod()`\n",
    "- `var()`, `std()`\n",
    "\n",
    "https://pandas.pydata.org/pandas-docs/stable/reference/series.html#computations-descriptive-stats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "## Unique values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th>one</th>\n",
       "      <th>two</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1.5</td>\n",
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       "      <th>b</th>\n",
       "      <td>6.0</td>\n",
       "      <td>-4.5</td>\n",
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       "      <th>c</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>1.5</td>\n",
       "      <td>-1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>f</th>\n",
       "      <td>6.0</td>\n",
       "      <td>-4.5</td>\n",
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       "      <th>g</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
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       "</table>\n",
       "</div>"
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      "text/plain": [
       "   one  two\n",
       "a  1.5  NaN\n",
       "b  6.0 -4.5\n",
       "c  NaN  NaN\n",
       "d  1.5 -1.5\n",
       "e  4.0  0.0\n",
       "f  6.0 -4.5\n",
       "g  NaN  4.0"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df # for reference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.5, 6. , nan, 4. ])"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.one.unique()  # shows the unique values in the order observed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ nan, -4.5, -1.5,  0. ,  4. ])"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.two.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": [
     "raises-exception"
    ]
   },
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'DataFrame' object has no attribute 'unique'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Input \u001b[1;32mIn [84]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mdf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43munique\u001b[49m()\n",
      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\generic.py:5575\u001b[0m, in \u001b[0;36mNDFrame.__getattr__\u001b[1;34m(self, name)\u001b[0m\n\u001b[0;32m   5568\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[0;32m   5569\u001b[0m     name \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_internal_names_set\n\u001b[0;32m   5570\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m name \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_metadata\n\u001b[0;32m   5571\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m name \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_accessors\n\u001b[0;32m   5572\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_info_axis\u001b[38;5;241m.\u001b[39m_can_hold_identifiers_and_holds_name(name)\n\u001b[0;32m   5573\u001b[0m ):\n\u001b[0;32m   5574\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m[name]\n\u001b[1;32m-> 5575\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mobject\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__getattribute__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'unique'"
     ]
    }
   ],
   "source": [
    "df.unique()  # unique can only be applied to a series (a column in a dataframe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
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   "outputs": [
    {
     "data": {
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      "text/plain": [
       "   one  two\n",
       "a  1.5  NaN\n",
       "b  6.0 -4.5\n",
       "c  NaN  NaN\n",
       "d  1.5 -1.5\n",
       "e  4.0  0.0\n",
       "f  6.0 -4.5\n",
       "g  NaN  4.0"
      ]
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     "execution_count": 85,
     "metadata": {},
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    }
   ],
   "source": [
    "df # for reference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.one.nunique()  # number of non-missing unique values exist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.5    2\n",
       "6.0    2\n",
       "4.0    1\n",
       "Name: one, dtype: int64"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.one.value_counts()  # tally up counts of each value\n",
    "# returns a series. the index are the unique values observed, the values are the frequencies.\n",
    "# they appear in descending order of frequency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a     True\n",
       "b    False\n",
       "c    False\n",
       "d     True\n",
       "e     True\n",
       "f    False\n",
       "g    False\n",
       "Name: one, dtype: bool"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.one.isin([1.5, 4.0]) # checks to see if the value has membership in a particular list\n",
    "# returns a series with boolean values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a     True\n",
       "b    False\n",
       "c    False\n",
       "d     True\n",
       "e     True\n",
       "f    False\n",
       "g    False\n",
       "Name: one, dtype: bool"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df.one == 1.5) | (df.one == 4.0)  # must use bitwise or. .isin() is much prefered"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "   one  two\n",
       "a  1.5  NaN\n",
       "d  1.5 -1.5\n",
       "e  4.0  0.0"
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    "df.loc[  df.one.isin([1.5,4.0]),  ]  # can filter rows based on the .isin() membership"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# filtering out missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
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    {
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       "   one  two\n",
       "a  1.5  NaN\n",
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    "df"
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  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
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       "   one  two\n",
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    "df.dropna() # gets rid of any row that is not complete"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
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   "outputs": [
    {
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       "   one  two\n",
       "a  1.5  NaN\n",
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       "d  1.5 -1.5\n",
       "e  4.0  0.0\n",
       "f  6.0 -4.5\n",
       "g  NaN  4.0"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna(how = 'all')  # only drops rows that are entirely NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
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       "   one  two\n",
       "b  6.0 -4.5\n",
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       "e  4.0  0.0\n",
       "f  6.0 -4.5\n",
       "g  NaN  4.0"
      ]
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     "execution_count": 94,
     "metadata": {},
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    }
   ],
   "source": [
    "# you can also use .notnull(), which is True for values that are not missing\n",
    "df[df.two.notnull()]  # You can use this in conjuntion with specifying the column"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Filling in Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
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       "   one  two\n",
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       "c  NaN  NaN\n",
       "d  1.5 -1.5\n",
       "e  4.0  0.0\n",
       "f  6.0 -4.5\n",
       "g  NaN  4.0"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "df"
   ]
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  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
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     "slide_type": "fragment"
    }
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   "outputs": [
    {
     "data": {
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       "   one  two\n",
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     "execution_count": 96,
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   "source": [
    "df.fillna(0) # fill in missing values with a constant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
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   },
   "outputs": [
    {
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       "      one  two\n",
       "a     1.5  0.0\n",
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       "c  1000.0  0.0\n",
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   "source": [
    "df.fillna({'one': 1000, 'two': 0})  # use a dictionary to specify values to use for each column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
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       "   one  two\n",
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   "source": [
    "df.fillna(method = 'bfill')  # backfills. You can also use ffill"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "one    3.8\n",
       "two   -1.3\n",
       "dtype: float64"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
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       "   one  two\n",
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   "source": [
    "df.fillna(df.mean())  # fill na with df.mean() will fill in the column means"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "all of the above fillna methods have created new DataFrame objects. If you want to modify the current DataFrame, you can use the optional argument `inplace = True`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
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       "       a    b   c    d    e    f    g\n",
       "one  1.5  6.0 NaN  1.5  4.0  6.0  NaN\n",
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      ]
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    "df.T"
   ]
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  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "   one  two\n",
       "a  1.5  1.5\n",
       "b  6.0 -4.5\n",
       "c  NaN  NaN\n",
       "d  1.5 -1.5\n",
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       "f  6.0 -4.5\n",
       "g  4.0  4.0"
      ]
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     "execution_count": 102,
     "metadata": {},
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   ],
   "source": [
    "# apparently you can only fill missing values with dictionaries/series over a column \n",
    "# so we have to do some Transpose magic\n",
    "df.T.fillna(df.T.mean()).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# dealing with duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
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      "text/plain": [
       "   one  two\n",
       "a  1.5  NaN\n",
       "b  6.0 -4.5\n",
       "c  NaN  NaN\n",
       "d  1.5 -1.5\n",
       "e  4.0  0.0\n",
       "f  6.0 -4.5\n",
       "g  NaN  4.0"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    False\n",
       "b    False\n",
       "c    False\n",
       "d    False\n",
       "e    False\n",
       "f     True\n",
       "g    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated()  # sees if any of the rows are a duplicate of an earlier row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "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>one</th>\n",
       "      <th>two</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1.5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>6.0</td>\n",
       "      <td>-4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>1.5</td>\n",
       "      <td>-1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>g</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   one  two\n",
       "a  1.5  NaN\n",
       "b  6.0 -4.5\n",
       "c  NaN  NaN\n",
       "d  1.5 -1.5\n",
       "e  4.0  0.0\n",
       "g  NaN  4.0"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[~df.duplicated()]  # gets rid of the duplicated rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    False\n",
       "b    False\n",
       "c    False\n",
       "d     True\n",
       "e    False\n",
       "f     True\n",
       "g     True\n",
       "Name: one, dtype: bool"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.one.duplicated()"
   ]
  }
 ],
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