Pandas/data_management.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create Series Object \n",
    "series2 = pd.Series([1, 2, 3, 4, 5, 6], index = pd.date_range('20190102', periods=6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2019-01-02    1\n",
       "2019-01-03    2\n",
       "2019-01-04    3\n",
       "2019-01-05    4\n",
       "2019-01-06    5\n",
       "2019-01-07    6\n",
       "Freq: D, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Display Series\n",
    "series2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Set a new column labeled F with the values contained in series2\n",
    "\n",
    "data_frame['F'] = series2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Set row1 value in column A to equal zero\n",
    "data_frame.at[dates[0], 'A'] =0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Set value in row 1, column 2 to zero\n",
    "data_frame.iat[0,1] = 0 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Set all rows in column D to numpy array 5*len(data_frame)\n",
    "data_frame.loc[:, 'D'] = np.array([5]*len(data_frame))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
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   "outputs": [
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       "    <tr>\n",
       "      <th>2019-01-05</th>\n",
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       "      <th>2019-01-06</th>\n",
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      "text/plain": [
       "                   A         B         C  D\n",
       "2019-01-01  0.000000  0.000000  0.893566  5\n",
       "2019-01-02  1.244767 -1.265866 -0.402773  5\n",
       "2019-01-03  0.092322 -0.379152  0.577408  5\n",
       "2019-01-04 -1.511079 -0.176840 -3.416331  5\n",
       "2019-01-05 -0.400668  1.216437  0.056223  5\n",
       "2019-01-06  1.768543 -0.399415 -0.738520  5"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Display DataFrame\n",
    "data_frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Copy DataFrame object into DataFrame2 object\n",
    "data_frame2 = data_frame.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Set all values greater than zero in dataframe2 object to (- data_frame2)\n",
    "data_frame2[data_frame2>0] = -data_frame2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
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       "      <th>2019-01-03</th>\n",
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       "      <td>-0.577408</td>\n",
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       "      <th>2019-01-04</th>\n",
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       "    <tr>\n",
       "      <th>2019-01-05</th>\n",
       "      <td>-0.400668</td>\n",
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       "      <td>-0.056223</td>\n",
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       "      <th>2019-01-06</th>\n",
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      "text/plain": [
       "                   A         B         C  D\n",
       "2019-01-01  0.000000  0.000000 -0.893566 -5\n",
       "2019-01-02 -1.244767 -1.265866 -0.402773 -5\n",
       "2019-01-03 -0.092322 -0.379152 -0.577408 -5\n",
       "2019-01-04 -1.511079 -0.176840 -3.416331 -5\n",
       "2019-01-05 -0.400668 -1.216437 -0.056223 -5\n",
       "2019-01-06 -1.768543 -0.399415 -0.738520 -5"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Display DataFrame2 object\n",
    "data_frame2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Reindex data_frame1 with dates from rows 1 - 5, the existing columns, plus new column E\n",
    "data_frame1 = data_frame.reindex(index=dates[0:4], columns=list(data_frame.columns)+['E'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Set the first and second row values in column E to one\n",
    "data_frame1.loc[dates[0]:dates[1], 'E'] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "                   A         B         C  D    E\n",
       "2019-01-01  0.000000  0.000000  0.893566  5  1.0\n",
       "2019-01-02  1.244767 -1.265866 -0.402773  5  1.0\n",
       "2019-01-03  0.092322 -0.379152  0.577408  5  NaN\n",
       "2019-01-04 -1.511079 -0.176840 -3.416331  5  NaN"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_frame1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>2019-01-02</th>\n",
       "      <td>1.244767</td>\n",
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      "text/plain": [
       "                   A         B         C  D    E\n",
       "2019-01-01  0.000000  0.000000  0.893566  5  1.0\n",
       "2019-01-02  1.244767 -1.265866 -0.402773  5  1.0"
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     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Drop rows in dataframe1 containing missing values in any of the columns\n",
    "data_frame1.dropna(how='any')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
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       "      <th>2019-01-01</th>\n",
       "      <td>0.000000</td>\n",
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       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>2019-01-02</th>\n",
       "      <td>1.244767</td>\n",
       "      <td>-1.265866</td>\n",
       "      <td>-0.402773</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>2019-01-03</th>\n",
       "      <td>0.092322</td>\n",
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       "    <tr>\n",
       "      <th>2019-01-04</th>\n",
       "      <td>-1.511079</td>\n",
       "      <td>-0.176840</td>\n",
       "      <td>-3.416331</td>\n",
       "      <td>5</td>\n",
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      "text/plain": [
       "                   A         B         C  D    E\n",
       "2019-01-01  0.000000  0.000000  0.893566  5  1.0\n",
       "2019-01-02  1.244767 -1.265866 -0.402773  5  1.0\n",
       "2019-01-03  0.092322 -0.379152  0.577408  5  5.0\n",
       "2019-01-04 -1.511079 -0.176840 -3.416331  5  5.0"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Recode missing values to value of 5\n",
    "data_frame1.fillna(value=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
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       "      <th>2019-01-01</th>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>2019-01-02</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-03</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>2019-01-04</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
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      "text/plain": [
       "                A      B      C      D      E\n",
       "2019-01-01  False  False  False  False  False\n",
       "2019-01-02  False  False  False  False  False\n",
       "2019-01-03  False  False  False  False   True\n",
       "2019-01-04  False  False  False  False   True"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check for missing values and return True or False\n",
    "pd.isna(data_frame1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    0.198981\n",
       "B   -0.167473\n",
       "C   -0.505071\n",
       "D    5.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Return the mean value in all columns\n",
    "data_frame.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2019-01-01    1.473391\n",
       "2019-01-02    1.144032\n",
       "2019-01-03    1.322645\n",
       "2019-01-04   -0.026063\n",
       "2019-01-05    1.467998\n",
       "2019-01-06    1.407652\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Return the mean value in column A\n",
    "data_frame.mean(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create a new series using numpy to fill in missing values, indexed by dates shifting the index by two periods\n",
    "new_series = pd.Series([1,3,5, np.nan, 6,8], index=dates).shift(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-02</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-03</th>\n",
       "      <td>-0.907678</td>\n",
       "      <td>-1.379152</td>\n",
       "      <td>-0.422592</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-04</th>\n",
       "      <td>-4.511079</td>\n",
       "      <td>-3.176840</td>\n",
       "      <td>-6.416331</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-05</th>\n",
       "      <td>-5.400668</td>\n",
       "      <td>-3.783563</td>\n",
       "      <td>-4.943777</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-06</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C    D\n",
       "2019-01-01       NaN       NaN       NaN  NaN\n",
       "2019-01-02       NaN       NaN       NaN  NaN\n",
       "2019-01-03 -0.907678 -1.379152 -0.422592  4.0\n",
       "2019-01-04 -4.511079 -3.176840 -6.416331  2.0\n",
       "2019-01-05 -5.400668 -3.783563 -4.943777  0.0\n",
       "2019-01-06       NaN       NaN       NaN  NaN"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#subtract new_series values from the dataframe object\n",
    "data_frame.sub(new_series, axis='index')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-01</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.893566</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-02</th>\n",
       "      <td>1.244767</td>\n",
       "      <td>-1.265866</td>\n",
       "      <td>0.490793</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-03</th>\n",
       "      <td>1.337089</td>\n",
       "      <td>-1.645018</td>\n",
       "      <td>1.068202</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-04</th>\n",
       "      <td>-0.173990</td>\n",
       "      <td>-1.821858</td>\n",
       "      <td>-2.348130</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-05</th>\n",
       "      <td>-0.574659</td>\n",
       "      <td>-0.605421</td>\n",
       "      <td>-2.291907</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-06</th>\n",
       "      <td>1.193884</td>\n",
       "      <td>-1.004837</td>\n",
       "      <td>-3.030427</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C   D\n",
       "2019-01-01  0.000000  0.000000  0.893566   5\n",
       "2019-01-02  1.244767 -1.265866  0.490793  10\n",
       "2019-01-03  1.337089 -1.645018  1.068202  15\n",
       "2019-01-04 -0.173990 -1.821858 -2.348130  20\n",
       "2019-01-05 -0.574659 -0.605421 -2.291907  25\n",
       "2019-01-06  1.193884 -1.004837 -3.030427  30"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#use numpy and apply function to calculate the cumulative sum of each column\n",
    "data_frame.apply(np.cumsum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    3.279622\n",
       "B    2.482303\n",
       "C    4.309897\n",
       "D    0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#use lambda and apply function to calculate the difference between the max and min of x for each column\n",
    "data_frame.apply(lambda x: x.max() - x.min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create a new series of 10 indexed values (starting at zero) between 0 and 7 (6)\n",
    "new_series = pd.Series(np.random.randint(0,7, size=10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    3\n",
       "1    0\n",
       "2    6\n",
       "3    6\n",
       "4    1\n",
       "5    4\n",
       "6    0\n",
       "7    5\n",
       "8    2\n",
       "9    5\n",
       "dtype: int32"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create a series containing strings\n",
    "new_series = pd.Series(['A', 'B', 'C', 'Aaba', 'Baca', np.nan, 'CABA', 'dog', 'cat'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       A\n",
       "1       B\n",
       "2       C\n",
       "3    Aaba\n",
       "4    Baca\n",
       "5     NaN\n",
       "6    Caba\n",
       "7     Dog\n",
       "8     Cat\n",
       "dtype: object"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Title Case\n",
    "new_series.str.title()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       A\n",
       "1       B\n",
       "2       C\n",
       "3    AABA\n",
       "4    BACA\n",
       "5     NaN\n",
       "6    CABA\n",
       "7     DOG\n",
       "8     CAT\n",
       "dtype: object"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Upper Case\n",
    "new_series.str.upper()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       a\n",
       "1       b\n",
       "2       c\n",
       "3    aaba\n",
       "4    baca\n",
       "5     NaN\n",
       "6    caba\n",
       "7     dog\n",
       "8     cat\n",
       "dtype: object"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Lower Case\n",
    "new_series.str.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Generate new dataframe with random numpy integers\n",
    "data_frame = pd.DataFrame(np.random.randn(10, 4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th>0</th>\n",
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       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.329079</td>\n",
       "      <td>1.182510</td>\n",
       "      <td>0.432781</td>\n",
       "      <td>-0.555050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.162578</td>\n",
       "      <td>-0.775229</td>\n",
       "      <td>-0.888423</td>\n",
       "      <td>0.150560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.518460</td>\n",
       "      <td>-0.179861</td>\n",
       "      <td>0.529662</td>\n",
       "      <td>-2.020166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.254087</td>\n",
       "      <td>-0.236411</td>\n",
       "      <td>0.303745</td>\n",
       "      <td>0.339692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.904049</td>\n",
       "      <td>0.237649</td>\n",
       "      <td>-0.195946</td>\n",
       "      <td>1.212039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.935007</td>\n",
       "      <td>-0.940444</td>\n",
       "      <td>-0.396582</td>\n",
       "      <td>1.193070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.267587</td>\n",
       "      <td>-0.654678</td>\n",
       "      <td>-0.106629</td>\n",
       "      <td>0.612476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-0.597486</td>\n",
       "      <td>0.547825</td>\n",
       "      <td>-1.392847</td>\n",
       "      <td>0.321578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-0.351057</td>\n",
       "      <td>0.911293</td>\n",
       "      <td>-0.143490</td>\n",
       "      <td>-1.488609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.623595</td>\n",
       "      <td>0.115610</td>\n",
       "      <td>-0.078414</td>\n",
       "      <td>0.262129</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3\n",
       "0 -0.329079  1.182510  0.432781 -0.555050\n",
       "1  0.162578 -0.775229 -0.888423  0.150560\n",
       "2  1.518460 -0.179861  0.529662 -2.020166\n",
       "3 -1.254087 -0.236411  0.303745  0.339692\n",
       "4 -0.904049  0.237649 -0.195946  1.212039\n",
       "5  0.935007 -0.940444 -0.396582  1.193070\n",
       "6 -0.267587 -0.654678 -0.106629  0.612476\n",
       "7 -0.597486  0.547825 -1.392847  0.321578\n",
       "8 -0.351057  0.911293 -0.143490 -1.488609\n",
       "9  0.623595  0.115610 -0.078414  0.262129"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Generate an object containing parts of dataframe\n",
    "parts = [data_frame[:3],  data_frame[3:7], data_frame[7:]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.329079</td>\n",
       "      <td>1.182510</td>\n",
       "      <td>0.432781</td>\n",
       "      <td>-0.555050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.162578</td>\n",
       "      <td>-0.775229</td>\n",
       "      <td>-0.888423</td>\n",
       "      <td>0.150560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.518460</td>\n",
       "      <td>-0.179861</td>\n",
       "      <td>0.529662</td>\n",
       "      <td>-2.020166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.254087</td>\n",
       "      <td>-0.236411</td>\n",
       "      <td>0.303745</td>\n",
       "      <td>0.339692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.904049</td>\n",
       "      <td>0.237649</td>\n",
       "      <td>-0.195946</td>\n",
       "      <td>1.212039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.935007</td>\n",
       "      <td>-0.940444</td>\n",
       "      <td>-0.396582</td>\n",
       "      <td>1.193070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.267587</td>\n",
       "      <td>-0.654678</td>\n",
       "      <td>-0.106629</td>\n",
       "      <td>0.612476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-0.597486</td>\n",
       "      <td>0.547825</td>\n",
       "      <td>-1.392847</td>\n",
       "      <td>0.321578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-0.351057</td>\n",
       "      <td>0.911293</td>\n",
       "      <td>-0.143490</td>\n",
       "      <td>-1.488609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.623595</td>\n",
       "      <td>0.115610</td>\n",
       "      <td>-0.078414</td>\n",
       "      <td>0.262129</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3\n",
       "0 -0.329079  1.182510  0.432781 -0.555050\n",
       "1  0.162578 -0.775229 -0.888423  0.150560\n",
       "2  1.518460 -0.179861  0.529662 -2.020166\n",
       "3 -1.254087 -0.236411  0.303745  0.339692\n",
       "4 -0.904049  0.237649 -0.195946  1.212039\n",
       "5  0.935007 -0.940444 -0.396582  1.193070\n",
       "6 -0.267587 -0.654678 -0.106629  0.612476\n",
       "7 -0.597486  0.547825 -1.392847  0.321578\n",
       "8 -0.351057  0.911293 -0.143490 -1.488609\n",
       "9  0.623595  0.115610 -0.078414  0.262129"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Concat range object\n",
    "pd.concat(parts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "parts = [data_frame[:3],  data_frame[7:]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1.182510</td>\n",
       "      <td>0.432781</td>\n",
       "      <td>-0.555050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.162578</td>\n",
       "      <td>-0.775229</td>\n",
       "      <td>-0.888423</td>\n",
       "      <td>0.150560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.518460</td>\n",
       "      <td>-0.179861</td>\n",
       "      <td>0.529662</td>\n",
       "      <td>-2.020166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-0.597486</td>\n",
       "      <td>0.547825</td>\n",
       "      <td>-1.392847</td>\n",
       "      <td>0.321578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-0.351057</td>\n",
       "      <td>0.911293</td>\n",
       "      <td>-0.143490</td>\n",
       "      <td>-1.488609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.623595</td>\n",
       "      <td>0.115610</td>\n",
       "      <td>-0.078414</td>\n",
       "      <td>0.262129</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3\n",
       "0 -0.329079  1.182510  0.432781 -0.555050\n",
       "1  0.162578 -0.775229 -0.888423  0.150560\n",
       "2  1.518460 -0.179861  0.529662 -2.020166\n",
       "7 -0.597486  0.547825 -1.392847  0.321578\n",
       "8 -0.351057  0.911293 -0.143490 -1.488609\n",
       "9  0.623595  0.115610 -0.078414  0.262129"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Rows 3 - 6 are missing\n",
    "pd.concat(pieces)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "#left object dictionary dataframe with values 1,2 with the same dict key\n",
    "left = pd.DataFrame({'key': ['foo', 'foo'], 'lval': [1,2]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "#right object dictionary dataframe with values 4,5 with the same dict key\n",
    "right = pd.DataFrame({'key': ['foo', 'foo'], 'rval':[4,5]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "   key  lval\n",
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     "execution_count": 93,
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   "source": [
    "left"
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  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      "text/plain": [
       "   key  rval\n",
       "0  foo     4\n",
       "1  foo     5"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "right"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr>\n",
       "      <th>3</th>\n",
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      "text/plain": [
       "   key  lval  rval\n",
       "0  foo     1     4\n",
       "1  foo     1     5\n",
       "2  foo     2     4\n",
       "3  foo     2     5"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Merge both the left right objects together, matching on key\n",
    "pd.merge(left, right, on='key')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Define left2 object with different dictionary keys\n",
    "left2 = pd.DataFrame({'key': ['foo', 'bar'], 'lval': [1, 2]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Define right2 object with different dictionary keys\n",
    "right2 = pd.DataFrame({'key': ['foo', 'bar'], 'rval': [4,5]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      "text/plain": [
       "   key  lval\n",
       "0  foo     1\n",
       "1  bar     2"
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     "execution_count": 102,
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   "source": [
    "left"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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      "text/plain": [
       "   key  rval\n",
       "0  foo     4\n",
       "1  bar     5"
      ]
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     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "right"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th>lval</th>\n",
       "      <th>rval</th>\n",
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      "text/plain": [
       "   key  lval  rval\n",
       "0  foo     1     4\n",
       "1  bar     2     5"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "#Merge the two tables together on key\n",
    "pd.merge(left, right, on='key')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>0.041786</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.629584</td>\n",
       "      <td>-0.356662</td>\n",
       "      <td>1.138358</td>\n",
       "      <td>0.439996</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.574882</td>\n",
       "      <td>-1.234643</td>\n",
       "      <td>0.491369</td>\n",
       "      <td>-0.906986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.788064</td>\n",
       "      <td>-0.855062</td>\n",
       "      <td>0.567493</td>\n",
       "      <td>-0.775903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.890111</td>\n",
       "      <td>-0.769811</td>\n",
       "      <td>-0.449902</td>\n",
       "      <td>-1.514317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.427082</td>\n",
       "      <td>2.201430</td>\n",
       "      <td>-0.243384</td>\n",
       "      <td>0.169364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.092758</td>\n",
       "      <td>-1.632013</td>\n",
       "      <td>-1.537818</td>\n",
       "      <td>-0.234461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-0.930874</td>\n",
       "      <td>2.688348</td>\n",
       "      <td>-0.300998</td>\n",
       "      <td>1.726515</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C         D\n",
       "0 -0.464150  1.159781  0.230530  0.041786\n",
       "1  0.629584 -0.356662  1.138358  0.439996\n",
       "2 -0.574882 -1.234643  0.491369 -0.906986\n",
       "3 -0.788064 -0.855062  0.567493 -0.775903\n",
       "4  0.890111 -0.769811 -0.449902 -1.514317\n",
       "5  0.427082  2.201430 -0.243384  0.169364\n",
       "6  0.092758 -1.632013 -1.537818 -0.234461\n",
       "7 -0.930874  2.688348 -0.300998  1.726515"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Create a 8 by 4 dataframe object from random numpy numbers and columns labeled A - D\n",
    "data_frame = pd.DataFrame(np.random.randn(8,4), columns=['A', 'B', 'C', 'D'])\n",
    "data_frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A   -0.788064\n",
       "B   -0.855062\n",
       "C    0.567493\n",
       "D   -0.775903\n",
       "Name: 3, dtype: float64"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#list values in third row in all columns\n",
    "data_frame.iloc[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bar</td>\n",
       "      <td>one</td>\n",
       "      <td>1.122632</td>\n",
       "      <td>0.941896</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>foo</td>\n",
       "      <td>two</td>\n",
       "      <td>-0.793202</td>\n",
       "      <td>1.866327</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bar</td>\n",
       "      <td>three</td>\n",
       "      <td>-0.883435</td>\n",
       "      <td>-1.038996</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>foo</td>\n",
       "      <td>two</td>\n",
       "      <td>1.249388</td>\n",
       "      <td>-0.755372</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>bar</td>\n",
       "      <td>two</td>\n",
       "      <td>0.400154</td>\n",
       "      <td>-2.235132</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>foo</td>\n",
       "      <td>one</td>\n",
       "      <td>-2.266596</td>\n",
       "      <td>-0.305542</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>foo</td>\n",
       "      <td>three</td>\n",
       "      <td>0.619397</td>\n",
       "      <td>-0.378573</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>C</td>\n",
       "      <td>Aaba</td>\n",
       "      <td>Baca</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CABA</td>\n",
       "      <td>dog</td>\n",
       "      <td>cat</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A      B         C         D    0    1    2     3     4   5     6    7  \\\n",
       "0  foo    one  0.410153  1.614064  NaN  NaN  NaN   NaN   NaN NaN   NaN  NaN   \n",
       "1  bar    one  1.122632  0.941896  NaN  NaN  NaN   NaN   NaN NaN   NaN  NaN   \n",
       "2  foo    two -0.793202  1.866327  NaN  NaN  NaN   NaN   NaN NaN   NaN  NaN   \n",
       "3  bar  three -0.883435 -1.038996  NaN  NaN  NaN   NaN   NaN NaN   NaN  NaN   \n",
       "4  foo    two  1.249388 -0.755372  NaN  NaN  NaN   NaN   NaN NaN   NaN  NaN   \n",
       "5  bar    two  0.400154 -2.235132  NaN  NaN  NaN   NaN   NaN NaN   NaN  NaN   \n",
       "6  foo    one -2.266596 -0.305542  NaN  NaN  NaN   NaN   NaN NaN   NaN  NaN   \n",
       "7  foo  three  0.619397 -0.378573  NaN  NaN  NaN   NaN   NaN NaN   NaN  NaN   \n",
       "8  NaN    NaN       NaN       NaN    A    B    C  Aaba  Baca NaN  CABA  dog   \n",
       "\n",
       "     8  \n",
       "0  NaN  \n",
       "1  NaN  \n",
       "2  NaN  \n",
       "3  NaN  \n",
       "4  NaN  \n",
       "5  NaN  \n",
       "6  NaN  \n",
       "7  NaN  \n",
       "8  cat  "
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Append new_series to data_frame ignoring index\n",
    "data_frame.append(new_series, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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