Pandas/Data_Create.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create a series; np.nan = no value\n",
    "new_series = pd.Series([1,2,3,np.nan,4,5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "1    2.0\n",
       "2    3.0\n",
       "3    NaN\n",
       "4    4.0\n",
       "5    5.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#display datatype and contents\n",
    "new_series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#create datetime indexes of six days starting at jan1,2019\n",
    "dates = pd.date_range('20190101', periods=6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2019-01-01', '2019-01-02', '2019-01-03', '2019-01-04',\n",
       "               '2019-01-05', '2019-01-06'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Display datetime index\n",
    "dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create dataframe object using numpy array, indexed by datetime index, label columns\n",
    "data_frame = pd.DataFrame(np.random.randn(6,4), index=dates, columns=list('ABCD'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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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",
       "      <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>0.023244</td>\n",
       "      <td>0.444771</td>\n",
       "      <td>1.630457</td>\n",
       "      <td>-1.878056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-02</th>\n",
       "      <td>0.550677</td>\n",
       "      <td>0.154209</td>\n",
       "      <td>0.480038</td>\n",
       "      <td>-1.840289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-03</th>\n",
       "      <td>-0.983206</td>\n",
       "      <td>1.317816</td>\n",
       "      <td>-0.600999</td>\n",
       "      <td>0.351172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-04</th>\n",
       "      <td>0.194104</td>\n",
       "      <td>-0.116287</td>\n",
       "      <td>-1.755096</td>\n",
       "      <td>0.596257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-05</th>\n",
       "      <td>0.044678</td>\n",
       "      <td>-2.096094</td>\n",
       "      <td>-0.421191</td>\n",
       "      <td>0.354385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-06</th>\n",
       "      <td>1.200713</td>\n",
       "      <td>-0.485956</td>\n",
       "      <td>-0.104726</td>\n",
       "      <td>-0.350438</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2019-01-01  0.023244  0.444771  1.630457 -1.878056\n",
       "2019-01-02  0.550677  0.154209  0.480038 -1.840289\n",
       "2019-01-03 -0.983206  1.317816 -0.600999  0.351172\n",
       "2019-01-04  0.194104 -0.116287 -1.755096  0.596257\n",
       "2019-01-05  0.044678 -2.096094 -0.421191  0.354385\n",
       "2019-01-06  1.200713 -0.485956 -0.104726 -0.350438"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#display dataframe object\n",
    "data_frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#create dataframe object with dictionary passing column names through dictionary keys\n",
    "data_frame2 = pd.DataFrame({'A': 1.,\n",
    "                            'B': pd.Timestamp('20190102'),\n",
    "                            'C': pd.Series(1, index = list(range(4))),\n",
    "                            'D': np.array([3]*4, dtype='int32'),\n",
    "                            'E': 'Yes'\n",
    "                           })\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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",
       "      <th>E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2019-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2019-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2019-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2019-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A          B  C  D    E\n",
       "0  1.0 2019-01-02  1  3  Yes\n",
       "1  1.0 2019-01-02  1  3  Yes\n",
       "2  1.0 2019-01-02  1  3  Yes\n",
       "3  1.0 2019-01-02  1  3  Yes"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#display dataframe object\n",
    "data_frame2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A           float64\n",
       "B    datetime64[ns]\n",
       "C             int64\n",
       "D             int32\n",
       "E            object\n",
       "dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#display datatypes within each column of the dataframe dictionary\n",
    "data_frame2.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    <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",
       "      <th>E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2019-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A          B  C  D    E\n",
       "0  1.0 2019-01-02  1  3  Yes"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#View Data indicating rows from the top\n",
    "data_frame2.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2019-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "     A          B  C  D    E\n",
       "3  1.0 2019-01-02  1  3  Yes"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#View Data indicating rows from the bottom\n",
    "data_frame2.tail(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([0, 1, 2, 3], dtype='int64')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Look up the index and datatype\n",
    "data_frame2.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['A', 'B', 'C', 'D', 'E'], dtype='object')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#List column names in dataframe object\n",
    "data_frame2.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.02324356,  0.44477072,  1.63045662, -1.87805552],\n",
       "       [ 0.55067703,  0.15420882,  0.48003806, -1.84028918],\n",
       "       [-0.98320592,  1.31781616, -0.60099917,  0.35117209],\n",
       "       [ 0.19410396, -0.11628734, -1.75509642,  0.59625741],\n",
       "       [ 0.0446778 , -2.09609406, -0.42119076,  0.35438516],\n",
       "       [ 1.20071321, -0.48595568, -0.10472559, -0.35043783]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#List contents of dataframe using to_numpy() dependent on pandas update\n",
    "data_frame.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.0, Timestamp('2019-01-02 00:00:00'), 1, 3, 'Yes'],\n",
       "       [1.0, Timestamp('2019-01-02 00:00:00'), 1, 3, 'Yes'],\n",
       "       [1.0, Timestamp('2019-01-02 00:00:00'), 1, 3, 'Yes'],\n",
       "       [1.0, Timestamp('2019-01-02 00:00:00'), 1, 3, 'Yes']], dtype=object)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#List contents of dataframe using to_numpy() dependent on pandas update\n",
    "data_frame2.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>count</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.171702</td>\n",
       "      <td>-0.130257</td>\n",
       "      <td>-0.128586</td>\n",
       "      <td>-0.461161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.717632</td>\n",
       "      <td>1.140151</td>\n",
       "      <td>1.133098</td>\n",
       "      <td>1.128271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-0.983206</td>\n",
       "      <td>-2.096094</td>\n",
       "      <td>-1.755096</td>\n",
       "      <td>-1.878056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.028602</td>\n",
       "      <td>-0.393539</td>\n",
       "      <td>-0.556047</td>\n",
       "      <td>-1.467826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.119391</td>\n",
       "      <td>0.018961</td>\n",
       "      <td>-0.262958</td>\n",
       "      <td>0.000367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.461534</td>\n",
       "      <td>0.372130</td>\n",
       "      <td>0.333847</td>\n",
       "      <td>0.353582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.200713</td>\n",
       "      <td>1.317816</td>\n",
       "      <td>1.630457</td>\n",
       "      <td>0.596257</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              A         B         C         D\n",
       "count  6.000000  6.000000  6.000000  6.000000\n",
       "mean   0.171702 -0.130257 -0.128586 -0.461161\n",
       "std    0.717632  1.140151  1.133098  1.128271\n",
       "min   -0.983206 -2.096094 -1.755096 -1.878056\n",
       "25%    0.028602 -0.393539 -0.556047 -1.467826\n",
       "50%    0.119391  0.018961 -0.262958  0.000367\n",
       "75%    0.461534  0.372130  0.333847  0.353582\n",
       "max    1.200713  1.317816  1.630457  0.596257"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Tabulate detailed summary statistics of each column in the data frame\n",
    "data_frame.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>A</th>\n",
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       "      <th>D</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         A    C    D\n",
       "count  4.0  4.0  4.0\n",
       "mean   1.0  1.0  3.0\n",
       "std    0.0  0.0  0.0\n",
       "min    1.0  1.0  3.0\n",
       "25%    1.0  1.0  3.0\n",
       "50%    1.0  1.0  3.0\n",
       "75%    1.0  1.0  3.0\n",
       "max    1.0  1.0  3.0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Tabulate detailed summary statistics of each column in the data frame\n",
    "data_frame2.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2019-01-01 00:00:00</th>\n",
       "      <th>2019-01-02 00:00:00</th>\n",
       "      <th>2019-01-03 00:00:00</th>\n",
       "      <th>2019-01-04 00:00:00</th>\n",
       "      <th>2019-01-05 00:00:00</th>\n",
       "      <th>2019-01-06 00:00:00</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>0.023244</td>\n",
       "      <td>0.550677</td>\n",
       "      <td>-0.983206</td>\n",
       "      <td>0.194104</td>\n",
       "      <td>0.044678</td>\n",
       "      <td>1.200713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>0.444771</td>\n",
       "      <td>0.154209</td>\n",
       "      <td>1.317816</td>\n",
       "      <td>-0.116287</td>\n",
       "      <td>-2.096094</td>\n",
       "      <td>-0.485956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>1.630457</td>\n",
       "      <td>0.480038</td>\n",
       "      <td>-0.600999</td>\n",
       "      <td>-1.755096</td>\n",
       "      <td>-0.421191</td>\n",
       "      <td>-0.104726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>-1.878056</td>\n",
       "      <td>-1.840289</td>\n",
       "      <td>0.351172</td>\n",
       "      <td>0.596257</td>\n",
       "      <td>0.354385</td>\n",
       "      <td>-0.350438</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   2019-01-01  2019-01-02  2019-01-03  2019-01-04  2019-01-05  2019-01-06\n",
       "A    0.023244    0.550677   -0.983206    0.194104    0.044678    1.200713\n",
       "B    0.444771    0.154209    1.317816   -0.116287   -2.096094   -0.485956\n",
       "C    1.630457    0.480038   -0.600999   -1.755096   -0.421191   -0.104726\n",
       "D   -1.878056   -1.840289    0.351172    0.596257    0.354385   -0.350438"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Transpose Data\n",
    "data_frame.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <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>A</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>2019-01-02 00:00:00</td>\n",
       "      <td>2019-01-02 00:00:00</td>\n",
       "      <td>2019-01-02 00:00:00</td>\n",
       "      <td>2019-01-02 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     0                    1                    2  \\\n",
       "A                    1                    1                    1   \n",
       "B  2019-01-02 00:00:00  2019-01-02 00:00:00  2019-01-02 00:00:00   \n",
       "C                    1                    1                    1   \n",
       "D                    3                    3                    3   \n",
       "E                  Yes                  Yes                  Yes   \n",
       "\n",
       "                     3  \n",
       "A                    1  \n",
       "B  2019-01-02 00:00:00  \n",
       "C                    1  \n",
       "D                    3  \n",
       "E                  Yes  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Transpose Data\n",
    "data_frame2.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>D</th>\n",
       "      <th>C</th>\n",
       "      <th>B</th>\n",
       "      <th>A</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-01</th>\n",
       "      <td>-1.878056</td>\n",
       "      <td>1.630457</td>\n",
       "      <td>0.444771</td>\n",
       "      <td>0.023244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-02</th>\n",
       "      <td>-1.840289</td>\n",
       "      <td>0.480038</td>\n",
       "      <td>0.154209</td>\n",
       "      <td>0.550677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-03</th>\n",
       "      <td>0.351172</td>\n",
       "      <td>-0.600999</td>\n",
       "      <td>1.317816</td>\n",
       "      <td>-0.983206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-04</th>\n",
       "      <td>0.596257</td>\n",
       "      <td>-1.755096</td>\n",
       "      <td>-0.116287</td>\n",
       "      <td>0.194104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-05</th>\n",
       "      <td>0.354385</td>\n",
       "      <td>-0.421191</td>\n",
       "      <td>-2.096094</td>\n",
       "      <td>0.044678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-06</th>\n",
       "      <td>-0.350438</td>\n",
       "      <td>-0.104726</td>\n",
       "      <td>-0.485956</td>\n",
       "      <td>1.200713</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   D         C         B         A\n",
       "2019-01-01 -1.878056  1.630457  0.444771  0.023244\n",
       "2019-01-02 -1.840289  0.480038  0.154209  0.550677\n",
       "2019-01-03  0.351172 -0.600999  1.317816 -0.983206\n",
       "2019-01-04  0.596257 -1.755096 -0.116287  0.194104\n",
       "2019-01-05  0.354385 -0.421191 -2.096094  0.044678\n",
       "2019-01-06 -0.350438 -0.104726 -0.485956  1.200713"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Sort data frame column order (by axis) with sort_index \n",
    "data_frame.sort_index(axis=1, ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>2019-01-05</th>\n",
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       "      <td>0.354385</td>\n",
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       "    <tr>\n",
       "      <th>2019-01-06</th>\n",
       "      <td>1.200713</td>\n",
       "      <td>-0.485956</td>\n",
       "      <td>-0.104726</td>\n",
       "      <td>-0.350438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-04</th>\n",
       "      <td>0.194104</td>\n",
       "      <td>-0.116287</td>\n",
       "      <td>-1.755096</td>\n",
       "      <td>0.596257</td>\n",
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       "    <tr>\n",
       "      <th>2019-01-02</th>\n",
       "      <td>0.550677</td>\n",
       "      <td>0.154209</td>\n",
       "      <td>0.480038</td>\n",
       "      <td>-1.840289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01</th>\n",
       "      <td>0.023244</td>\n",
       "      <td>0.444771</td>\n",
       "      <td>1.630457</td>\n",
       "      <td>-1.878056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-03</th>\n",
       "      <td>-0.983206</td>\n",
       "      <td>1.317816</td>\n",
       "      <td>-0.600999</td>\n",
       "      <td>0.351172</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2019-01-05  0.044678 -2.096094 -0.421191  0.354385\n",
       "2019-01-06  1.200713 -0.485956 -0.104726 -0.350438\n",
       "2019-01-04  0.194104 -0.116287 -1.755096  0.596257\n",
       "2019-01-02  0.550677  0.154209  0.480038 -1.840289\n",
       "2019-01-01  0.023244  0.444771  1.630457 -1.878056\n",
       "2019-01-03 -0.983206  1.317816 -0.600999  0.351172"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Sort values by values in column B\n",
    "data_frame.sort_values(by='B')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2019-01-01    0.023244\n",
       "2019-01-02    0.550677\n",
       "2019-01-03   -0.983206\n",
       "2019-01-04    0.194104\n",
       "2019-01-05    0.044678\n",
       "2019-01-06    1.200713\n",
       "Freq: D, Name: A, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Display Column A from data_frame object along with dateindex\n",
    "data_frame['A']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>D</th>\n",
       "      <th>E</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2019-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2019-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2019-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A          B  C  D    E\n",
       "0  1.0 2019-01-02  1  3  Yes\n",
       "1  1.0 2019-01-02  1  3  Yes\n",
       "2  1.0 2019-01-02  1  3  Yes"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Display the first three rows in data_frame2 object (slice rows)\n",
    "data_frame2[0:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <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>0.023244</td>\n",
       "      <td>0.444771</td>\n",
       "      <td>1.630457</td>\n",
       "      <td>-1.878056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-02</th>\n",
       "      <td>0.550677</td>\n",
       "      <td>0.154209</td>\n",
       "      <td>0.480038</td>\n",
       "      <td>-1.840289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-03</th>\n",
       "      <td>-0.983206</td>\n",
       "      <td>1.317816</td>\n",
       "      <td>-0.600999</td>\n",
       "      <td>0.351172</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2019-01-01  0.023244  0.444771  1.630457 -1.878056\n",
       "2019-01-02  0.550677  0.154209  0.480038 -1.840289\n",
       "2019-01-03 -0.983206  1.317816 -0.600999  0.351172"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#List rows corresponding to to specific date indexes in obj data_frame (slice rows)\n",
    "data_frame['20190101':'20190103']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    0.023244\n",
       "B    0.444771\n",
       "C    1.630457\n",
       "D   -1.878056\n",
       "Name: 2019-01-01 00:00:00, dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#List dataframe values in all columns corresponding to date index 0 (the first row, date index)\n",
    "data_frame.loc[dates[0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-01</th>\n",
       "      <td>0.023244</td>\n",
       "      <td>0.444771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-02</th>\n",
       "      <td>0.550677</td>\n",
       "      <td>0.154209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-03</th>\n",
       "      <td>-0.983206</td>\n",
       "      <td>1.317816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-04</th>\n",
       "      <td>0.194104</td>\n",
       "      <td>-0.116287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-05</th>\n",
       "      <td>0.044678</td>\n",
       "      <td>-2.096094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-06</th>\n",
       "      <td>1.200713</td>\n",
       "      <td>-0.485956</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B\n",
       "2019-01-01  0.023244  0.444771\n",
       "2019-01-02  0.550677  0.154209\n",
       "2019-01-03 -0.983206  1.317816\n",
       "2019-01-04  0.194104 -0.116287\n",
       "2019-01-05  0.044678 -2.096094\n",
       "2019-01-06  1.200713 -0.485956"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Display all rows in columns A and B\n",
    "data_frame.loc[:,['A', 'B']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-01</th>\n",
       "      <td>0.020301</td>\n",
       "      <td>-0.224572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-02</th>\n",
       "      <td>0.171315</td>\n",
       "      <td>1.784813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-03</th>\n",
       "      <td>1.347328</td>\n",
       "      <td>-0.821988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-04</th>\n",
       "      <td>-1.363023</td>\n",
       "      <td>0.203241</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B\n",
       "2019-01-01  0.020301 -0.224572\n",
       "2019-01-02  0.171315  1.784813\n",
       "2019-01-03  1.347328 -0.821988\n",
       "2019-01-04 -1.363023  0.203241"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Display all rows corresponding to specific dates in columns A & B\n",
    "data_frame.loc['20190101': '20190104', ['A', 'B']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    0.023244\n",
       "B    0.444771\n",
       "Name: 2019-01-01 00:00:00, dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#List values in columns A and B corresponding to the first row, date index Jan 1 2019\n",
    "data_frame.loc['20190101', ['A', 'B']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.02324356257387112"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Display the scalar value in the first row in column A\n",
    "data_frame.loc[dates[0], 'A']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.02324356257387112"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Display the scalar value in the first row in column A\n",
    "data_frame.at[dates[0], 'A']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    0.194104\n",
       "B   -0.116287\n",
       "C   -1.755096\n",
       "D    0.596257\n",
       "Name: 2019-01-04 00:00:00, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#List values in DataFrame from row 3 from all columns\n",
    "data_frame.iloc[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>A</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-02</th>\n",
       "      <td>0.550677</td>\n",
       "      <td>0.480038</td>\n",
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       "    <tr>\n",
       "      <th>2019-01-03</th>\n",
       "      <td>-0.983206</td>\n",
       "      <td>-0.600999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-05</th>\n",
       "      <td>0.044678</td>\n",
       "      <td>-0.421191</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         C\n",
       "2019-01-02  0.550677  0.480038\n",
       "2019-01-03 -0.983206 -0.600999\n",
       "2019-01-05  0.044678 -0.421191"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Display values in rows 1, 2, 4 and column 0 and 2 \n",
    "data_frame.iloc[[1, 2, 4], [0,2]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>E</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2019-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2019-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2019-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "     A          B  C  D    E\n",
       "1  1.0 2019-01-02  1  3  Yes\n",
       "2  1.0 2019-01-02  1  3  Yes\n",
       "3  1.0 2019-01-02  1  3  Yes"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Display rows 2 - 4 (1 - 3) and all columns\n",
    "data_frame2.iloc[1:4, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th>E</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A    E\n",
       "0  1.0  Yes\n",
       "1  1.0  Yes\n",
       "2  1.0  Yes\n",
       "3  1.0  Yes"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Display all dataframe rows, the first column and the fifth column\n",
    "data_frame2.iloc[:, [0,4]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.15420881798036798"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Display the scalar value in the second row and second column\n",
    "data_frame.iloc[1,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Yes'"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Display the string in dataframe2 in the third row and fifth column using .iat method\n",
    "data_frame2.iat[2,4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2019-01-01</th>\n",
       "      <td>0.023244</td>\n",
       "      <td>0.444771</td>\n",
       "      <td>1.630457</td>\n",
       "      <td>-1.878056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-02</th>\n",
       "      <td>0.550677</td>\n",
       "      <td>0.154209</td>\n",
       "      <td>0.480038</td>\n",
       "      <td>-1.840289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-04</th>\n",
       "      <td>0.194104</td>\n",
       "      <td>-0.116287</td>\n",
       "      <td>-1.755096</td>\n",
       "      <td>0.596257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-05</th>\n",
       "      <td>0.044678</td>\n",
       "      <td>-2.096094</td>\n",
       "      <td>-0.421191</td>\n",
       "      <td>0.354385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-06</th>\n",
       "      <td>1.200713</td>\n",
       "      <td>-0.485956</td>\n",
       "      <td>-0.104726</td>\n",
       "      <td>-0.350438</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2019-01-01  0.023244  0.444771  1.630457 -1.878056\n",
       "2019-01-02  0.550677  0.154209  0.480038 -1.840289\n",
       "2019-01-04  0.194104 -0.116287 -1.755096  0.596257\n",
       "2019-01-05  0.044678 -2.096094 -0.421191  0.354385\n",
       "2019-01-06  1.200713 -0.485956 -0.104726 -0.350438"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Filter data_frame by values in column A greather than zero\n",
    "data_frame[data_frame.A>0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2019-01-01</th>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2019-01-02</th>\n",
       "      <td>0.550677</td>\n",
       "      <td>0.154209</td>\n",
       "      <td>0.480038</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-03</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.317816</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.351172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-04</th>\n",
       "      <td>0.194104</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.596257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-05</th>\n",
       "      <td>0.044678</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.354385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-06</th>\n",
       "      <td>1.200713</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  0.023244  0.444771  1.630457       NaN\n",
       "2019-01-02  0.550677  0.154209  0.480038       NaN\n",
       "2019-01-03       NaN  1.317816       NaN  0.351172\n",
       "2019-01-04  0.194104       NaN       NaN  0.596257\n",
       "2019-01-05  0.044678       NaN       NaN  0.354385\n",
       "2019-01-06  1.200713       NaN       NaN       NaN"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Filter data_frame by values in all columns greater than zero\n",
    "data_frame[data_frame>0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>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>-1.878056</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>-1.840289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-03</th>\n",
       "      <td>-0.983206</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.600999</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-04</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.116287</td>\n",
       "      <td>-1.755096</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-05</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.096094</td>\n",
       "      <td>-0.421191</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-06</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.485956</td>\n",
       "      <td>-0.104726</td>\n",
       "      <td>-0.350438</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2019-01-01       NaN       NaN       NaN -1.878056\n",
       "2019-01-02       NaN       NaN       NaN -1.840289\n",
       "2019-01-03 -0.983206       NaN -0.600999       NaN\n",
       "2019-01-04       NaN -0.116287 -1.755096       NaN\n",
       "2019-01-05       NaN -2.096094 -0.421191       NaN\n",
       "2019-01-06       NaN -0.485956 -0.104726 -0.350438"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Filter data_frame by values in column A less than than zero\n",
    "data_frame[data_frame<0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Copy DataFrame into object DataFrame2\n",
    "data_frame2 = data_frame.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create column E in dataframe2 and list values\n",
    "data_frame2['E'] = ['one', 'one', 'two', 'three', 'four', 'three']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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",
       "      <th>E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-01</th>\n",
       "      <td>0.020301</td>\n",
       "      <td>-0.224572</td>\n",
       "      <td>0.500634</td>\n",
       "      <td>-0.251051</td>\n",
       "      <td>one</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-02</th>\n",
       "      <td>0.171315</td>\n",
       "      <td>1.784813</td>\n",
       "      <td>-0.831449</td>\n",
       "      <td>-0.596673</td>\n",
       "      <td>one</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-03</th>\n",
       "      <td>1.347328</td>\n",
       "      <td>-0.821988</td>\n",
       "      <td>-0.428355</td>\n",
       "      <td>-0.610691</td>\n",
       "      <td>two</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-04</th>\n",
       "      <td>-1.363023</td>\n",
       "      <td>0.203241</td>\n",
       "      <td>3.178943</td>\n",
       "      <td>-0.156640</td>\n",
       "      <td>three</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-05</th>\n",
       "      <td>-0.336574</td>\n",
       "      <td>-1.776838</td>\n",
       "      <td>0.129316</td>\n",
       "      <td>-1.576318</td>\n",
       "      <td>four</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-06</th>\n",
       "      <td>-0.958720</td>\n",
       "      <td>0.719363</td>\n",
       "      <td>0.098674</td>\n",
       "      <td>-0.836064</td>\n",
       "      <td>three</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D      E\n",
       "2019-01-01  0.020301 -0.224572  0.500634 -0.251051    one\n",
       "2019-01-02  0.171315  1.784813 -0.831449 -0.596673    one\n",
       "2019-01-03  1.347328 -0.821988 -0.428355 -0.610691    two\n",
       "2019-01-04 -1.363023  0.203241  3.178943 -0.156640  three\n",
       "2019-01-05 -0.336574 -1.776838  0.129316 -1.576318   four\n",
       "2019-01-06 -0.958720  0.719363  0.098674 -0.836064  three"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Display DataFrame2\n",
    "data_frame2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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",
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       "</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",
       "      <th>E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-03</th>\n",
       "      <td>1.347328</td>\n",
       "      <td>-0.821988</td>\n",
       "      <td>-0.428355</td>\n",
       "      <td>-0.610691</td>\n",
       "      <td>two</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-05</th>\n",
       "      <td>-0.336574</td>\n",
       "      <td>-1.776838</td>\n",
       "      <td>0.129316</td>\n",
       "      <td>-1.576318</td>\n",
       "      <td>four</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D     E\n",
       "2019-01-03  1.347328 -0.821988 -0.428355 -0.610691   two\n",
       "2019-01-05 -0.336574 -1.776838  0.129316 -1.576318  four"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Filter DataFrame2 by values in column E equal to two and four\n",
    "data_frame2[data_frame2['E'].isin(['two', 'four'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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