Brief-intro-to-pandas-by-example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Brief Intro to Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'3.6.8'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from platform import python_version\n",
    "python_version()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are basically three main types of data objects in Pandas\n",
    "1. Index\n",
    "2. Series\n",
    "3. Dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prepare some random data using numpy\n",
    "data = np.random.randn(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prepare a date range\n",
    "index = pd.date_range('2018-01-01', '2018-01-10')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a Series\n",
    "x = pd.Series(data, index=index, name='A')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2018-01-01   -1.055001\n",
       "2018-01-02    1.113949\n",
       "2018-01-03   -1.232003\n",
       "2018-01-04    0.238258\n",
       "2018-01-05    1.415033\n",
       "Freq: D, Name: A, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use the head method to see only the first 5 rows\n",
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2018-01-06   -0.073440\n",
       "2018-01-07    0.702295\n",
       "2018-01-08   -0.572126\n",
       "2018-01-09    0.655728\n",
       "2018-01-10   -0.542613\n",
       "Freq: D, Name: A, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The tail method shows only last 5 rows\n",
    "x.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show datatype usually inferred from data\n",
    "x.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'A'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1.05500115,  1.11394917, -1.23200331,  0.23825755,  1.41503329,\n",
       "       -0.07343993,  0.70229539, -0.57212584,  0.65572826, -0.54261343])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03', '2018-01-04',\n",
       "               '2018-01-05', '2018-01-06', '2018-01-07', '2018-01-08',\n",
       "               '2018-01-09', '2018-01-10'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The index here is a DatetimeIndex (special type of index for datetimes)\n",
    "x.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.06500799877543463"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# You can do many different calculations on the values\n",
    "x.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2018-01-01    1.113027\n",
       "2018-01-02    1.240883\n",
       "2018-01-03    1.517832\n",
       "2018-01-04    0.056767\n",
       "2018-01-05    2.002319\n",
       "Freq: D, Name: A, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# And other operations...\n",
    "(x**2).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2018-01-01   -1\n",
       "2018-01-02    1\n",
       "2018-01-03   -1\n",
       "2018-01-04    0\n",
       "2018-01-05    1\n",
       "2018-01-06    0\n",
       "2018-01-07    0\n",
       "2018-01-08    0\n",
       "2018-01-09    0\n",
       "2018-01-10    0\n",
       "Freq: D, Name: A, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert to a different data type\n",
    "x.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2018-01-05    1.415033\n",
       "2018-01-02    1.113949\n",
       "2018-01-07    0.702295\n",
       "2018-01-09    0.655728\n",
       "2018-01-04    0.238258\n",
       "Name: A, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.sort_values(ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2018-01-05 00:00:00', freq='D')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.idxmax()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pandas provides an easy way to create plots with pyplot\n",
    "x.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x.hist()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "del x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prepare some random data\n",
    "data = {\n",
    "    'A': np.random.randn(10),\n",
    "    'B': np.random.randn(10),\n",
    "    'C': np.random.randn(10)\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prepare a date range\n",
    "index = pd.date_range('2018-01-01', '2018-01-10')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-01</th>\n",
       "      <td>-0.990605</td>\n",
       "      <td>-0.240924</td>\n",
       "      <td>-0.974926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-02</th>\n",
       "      <td>0.663470</td>\n",
       "      <td>0.424833</td>\n",
       "      <td>0.073048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-03</th>\n",
       "      <td>-1.319911</td>\n",
       "      <td>-0.477794</td>\n",
       "      <td>-0.244228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-04</th>\n",
       "      <td>-0.111922</td>\n",
       "      <td>0.372629</td>\n",
       "      <td>-1.188499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-05</th>\n",
       "      <td>0.712887</td>\n",
       "      <td>0.463138</td>\n",
       "      <td>1.418469</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C\n",
       "2018-01-01 -0.990605 -0.240924 -0.974926\n",
       "2018-01-02  0.663470  0.424833  0.073048\n",
       "2018-01-03 -1.319911 -0.477794 -0.244228\n",
       "2018-01-04 -0.111922  0.372629 -1.188499\n",
       "2018-01-05  0.712887  0.463138  1.418469"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Construct a dataframe (one method)\n",
    "df = pd.DataFrame(data, index=index)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['A', 'B', 'C'], dtype='object')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Column index\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03', '2018-01-04',\n",
       "               '2018-01-05', '2018-01-06', '2018-01-07', '2018-01-08',\n",
       "               '2018-01-09', '2018-01-10'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Row index\n",
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    float64\n",
       "B    float64\n",
       "C    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Data types\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.99060477, -0.24092403, -0.97492639],\n",
       "       [ 0.66347004,  0.42483292,  0.07304827],\n",
       "       [-1.31991147, -0.47779395, -0.24422777],\n",
       "       [-0.11192156,  0.37262894, -1.18849901],\n",
       "       [ 0.71288742,  0.46313834,  1.41846921],\n",
       "       [-0.37120661, -0.39343094,  0.29191246],\n",
       "       [-0.05209442, -0.71301633,  1.21856748],\n",
       "       [-1.31796908,  0.49882261, -1.96457787],\n",
       "       [ 0.24623235,  0.72176298,  1.21273998],\n",
       "       [ 0.33864275,  0.05433997, -0.6915102 ]])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Values (returns a numpy array)\n",
    "df.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A   -2.202475\n",
       "B    0.710361\n",
       "C   -0.849004\n",
       "dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# You can do operations on the whole dataframe\n",
    "df.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A     5.737251\n",
       "B     2.256063\n",
       "C    11.818660\n",
       "dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pandas methods are designed for 'chaining'\n",
    "df.pow(2).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2018-01-01   -0.990605\n",
       "2018-01-02    0.663470\n",
       "2018-01-03   -1.319911\n",
       "2018-01-04   -0.111922\n",
       "2018-01-05    0.712887\n",
       "Freq: D, Name: A, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select one column (returns a series)\n",
    "df['A'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-01</th>\n",
       "      <td>-0.990605</td>\n",
       "      <td>-0.974926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-02</th>\n",
       "      <td>0.663470</td>\n",
       "      <td>0.073048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-03</th>\n",
       "      <td>-1.319911</td>\n",
       "      <td>-0.244228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-04</th>\n",
       "      <td>-0.111922</td>\n",
       "      <td>-1.188499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-05</th>\n",
       "      <td>0.712887</td>\n",
       "      <td>1.418469</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         C\n",
       "2018-01-01 -0.990605 -0.974926\n",
       "2018-01-02  0.663470  0.073048\n",
       "2018-01-03 -1.319911 -0.244228\n",
       "2018-01-04 -0.111922 -1.188499\n",
       "2018-01-05  0.712887  1.418469"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select multiple columns (returns a dataframe)\n",
    "df[['A', 'C']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A   -1.319911\n",
       "B   -0.477794\n",
       "C   -0.244228\n",
       "Name: 2018-01-03 00:00:00, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select one row\n",
    "df.loc['2018-01-03']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.4777939524658272"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select one row and column\n",
    "df.loc['2018-01-03', 'B']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-03</th>\n",
       "      <td>-1.319911</td>\n",
       "      <td>-0.477794</td>\n",
       "      <td>-0.244228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-04</th>\n",
       "      <td>-0.111922</td>\n",
       "      <td>0.372629</td>\n",
       "      <td>-1.188499</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C\n",
       "2018-01-03 -1.319911 -0.477794 -0.244228\n",
       "2018-01-04 -0.111922  0.372629 -1.188499"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Slicing rows\n",
    "df.loc['2018-01-03':'2018-01-04']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1117f99b0>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pandas provides an easy way to create plots with pyplot\n",
    "df.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-01</th>\n",
       "      <td>-0.990605</td>\n",
       "      <td>-0.240924</td>\n",
       "      <td>-0.974926</td>\n",
       "      <td>0.942489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-02</th>\n",
       "      <td>0.663470</td>\n",
       "      <td>0.424833</td>\n",
       "      <td>0.073048</td>\n",
       "      <td>0.581711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-03</th>\n",
       "      <td>-1.319911</td>\n",
       "      <td>-0.477794</td>\n",
       "      <td>-0.244228</td>\n",
       "      <td>-0.620239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-04</th>\n",
       "      <td>-0.111922</td>\n",
       "      <td>0.372629</td>\n",
       "      <td>-1.188499</td>\n",
       "      <td>-2.243460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-05</th>\n",
       "      <td>0.712887</td>\n",
       "      <td>0.463138</td>\n",
       "      <td>1.418469</td>\n",
       "      <td>-1.899446</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2018-01-01 -0.990605 -0.240924 -0.974926  0.942489\n",
       "2018-01-02  0.663470  0.424833  0.073048  0.581711\n",
       "2018-01-03 -1.319911 -0.477794 -0.244228 -0.620239\n",
       "2018-01-04 -0.111922  0.372629 -1.188499 -2.243460\n",
       "2018-01-05  0.712887  0.463138  1.418469 -1.899446"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add a new column\n",
    "df['D'] = np.random.randn(10)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .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",
       "      <th>Sum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-01</th>\n",
       "      <td>-0.990605</td>\n",
       "      <td>-0.240924</td>\n",
       "      <td>-0.974926</td>\n",
       "      <td>0.942489</td>\n",
       "      <td>-1.263966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-02</th>\n",
       "      <td>0.663470</td>\n",
       "      <td>0.424833</td>\n",
       "      <td>0.073048</td>\n",
       "      <td>0.581711</td>\n",
       "      <td>1.743062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-03</th>\n",
       "      <td>-1.319911</td>\n",
       "      <td>-0.477794</td>\n",
       "      <td>-0.244228</td>\n",
       "      <td>-0.620239</td>\n",
       "      <td>-2.662172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-04</th>\n",
       "      <td>-0.111922</td>\n",
       "      <td>0.372629</td>\n",
       "      <td>-1.188499</td>\n",
       "      <td>-2.243460</td>\n",
       "      <td>-3.171252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-05</th>\n",
       "      <td>0.712887</td>\n",
       "      <td>0.463138</td>\n",
       "      <td>1.418469</td>\n",
       "      <td>-1.899446</td>\n",
       "      <td>0.695049</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D       Sum\n",
       "2018-01-01 -0.990605 -0.240924 -0.974926  0.942489 -1.263966\n",
       "2018-01-02  0.663470  0.424833  0.073048  0.581711  1.743062\n",
       "2018-01-03 -1.319911 -0.477794 -0.244228 -0.620239 -2.662172\n",
       "2018-01-04 -0.111922  0.372629 -1.188499 -2.243460 -3.171252\n",
       "2018-01-05  0.712887  0.463138  1.418469 -1.899446  0.695049"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add a new column from a calculation of other columns\n",
    "df['Sum'] = df.sum(axis=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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",
       "      <th>Sum</th>\n",
       "      <th>SumCheck</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-01</th>\n",
       "      <td>-0.990605</td>\n",
       "      <td>-0.240924</td>\n",
       "      <td>-0.974926</td>\n",
       "      <td>0.942489</td>\n",
       "      <td>-1.263966</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-02</th>\n",
       "      <td>0.663470</td>\n",
       "      <td>0.424833</td>\n",
       "      <td>0.073048</td>\n",
       "      <td>0.581711</td>\n",
       "      <td>1.743062</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-03</th>\n",
       "      <td>-1.319911</td>\n",
       "      <td>-0.477794</td>\n",
       "      <td>-0.244228</td>\n",
       "      <td>-0.620239</td>\n",
       "      <td>-2.662172</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-04</th>\n",
       "      <td>-0.111922</td>\n",
       "      <td>0.372629</td>\n",
       "      <td>-1.188499</td>\n",
       "      <td>-2.243460</td>\n",
       "      <td>-3.171252</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-05</th>\n",
       "      <td>0.712887</td>\n",
       "      <td>0.463138</td>\n",
       "      <td>1.418469</td>\n",
       "      <td>-1.899446</td>\n",
       "      <td>0.695049</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D       Sum  SumCheck\n",
       "2018-01-01 -0.990605 -0.240924 -0.974926  0.942489 -1.263966       NaN\n",
       "2018-01-02  0.663470  0.424833  0.073048  0.581711  1.743062       NaN\n",
       "2018-01-03 -1.319911 -0.477794 -0.244228 -0.620239 -2.662172       NaN\n",
       "2018-01-04 -0.111922  0.372629 -1.188499 -2.243460 -3.171252       NaN\n",
       "2018-01-05  0.712887  0.463138  1.418469 -1.899446  0.695049       NaN"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a new column with one value\n",
    "df['SumCheck'] = np.nan\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>A</th>\n",
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       "      <th>D</th>\n",
       "      <th>Sum</th>\n",
       "      <th>SumCheck</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-01</th>\n",
       "      <td>-0.990605</td>\n",
       "      <td>-0.240924</td>\n",
       "      <td>-0.974926</td>\n",
       "      <td>0.942489</td>\n",
       "      <td>-1.263966</td>\n",
       "      <td>Neg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-02</th>\n",
       "      <td>0.663470</td>\n",
       "      <td>0.424833</td>\n",
       "      <td>0.073048</td>\n",
       "      <td>0.581711</td>\n",
       "      <td>1.743062</td>\n",
       "      <td>Pos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-03</th>\n",
       "      <td>-1.319911</td>\n",
       "      <td>-0.477794</td>\n",
       "      <td>-0.244228</td>\n",
       "      <td>-0.620239</td>\n",
       "      <td>-2.662172</td>\n",
       "      <td>Neg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-04</th>\n",
       "      <td>-0.111922</td>\n",
       "      <td>0.372629</td>\n",
       "      <td>-1.188499</td>\n",
       "      <td>-2.243460</td>\n",
       "      <td>-3.171252</td>\n",
       "      <td>Neg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-05</th>\n",
       "      <td>0.712887</td>\n",
       "      <td>0.463138</td>\n",
       "      <td>1.418469</td>\n",
       "      <td>-1.899446</td>\n",
       "      <td>0.695049</td>\n",
       "      <td>Pos</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D       Sum SumCheck\n",
       "2018-01-01 -0.990605 -0.240924 -0.974926  0.942489 -1.263966      Neg\n",
       "2018-01-02  0.663470  0.424833  0.073048  0.581711  1.743062      Pos\n",
       "2018-01-03 -1.319911 -0.477794 -0.244228 -0.620239 -2.662172      Neg\n",
       "2018-01-04 -0.111922  0.372629 -1.188499 -2.243460 -3.171252      Neg\n",
       "2018-01-05  0.712887  0.463138  1.418469 -1.899446  0.695049      Pos"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Assigning values to selected locations using .loc\n",
    "df.loc[df['Sum'] < 0, 'SumCheck'] = \"Neg\"\n",
    "df.loc[df['Sum'] == 0, 'SumCheck'] = \"Zero\"\n",
    "df.loc[df['Sum'] > 0, 'SumCheck'] = \"Pos\"\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>SumCheck</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-02</th>\n",
       "      <td>0.663470</td>\n",
       "      <td>0.424833</td>\n",
       "      <td>0.073048</td>\n",
       "      <td>0.581711</td>\n",
       "      <td>1.743062</td>\n",
       "      <td>Pos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-05</th>\n",
       "      <td>0.712887</td>\n",
       "      <td>0.463138</td>\n",
       "      <td>1.418469</td>\n",
       "      <td>-1.899446</td>\n",
       "      <td>0.695049</td>\n",
       "      <td>Pos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-09</th>\n",
       "      <td>0.246232</td>\n",
       "      <td>0.721763</td>\n",
       "      <td>1.212740</td>\n",
       "      <td>-1.560021</td>\n",
       "      <td>0.620714</td>\n",
       "      <td>Pos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-10</th>\n",
       "      <td>0.338643</td>\n",
       "      <td>0.054340</td>\n",
       "      <td>-0.691510</td>\n",
       "      <td>0.511275</td>\n",
       "      <td>0.212747</td>\n",
       "      <td>Pos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-06</th>\n",
       "      <td>-0.371207</td>\n",
       "      <td>-0.393431</td>\n",
       "      <td>0.291912</td>\n",
       "      <td>0.285683</td>\n",
       "      <td>-0.187042</td>\n",
       "      <td>Neg</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D       Sum SumCheck\n",
       "2018-01-02  0.663470  0.424833  0.073048  0.581711  1.743062      Pos\n",
       "2018-01-05  0.712887  0.463138  1.418469 -1.899446  0.695049      Pos\n",
       "2018-01-09  0.246232  0.721763  1.212740 -1.560021  0.620714      Pos\n",
       "2018-01-10  0.338643  0.054340 -0.691510  0.511275  0.212747      Pos\n",
       "2018-01-06 -0.371207 -0.393431  0.291912  0.285683 -0.187042      Neg"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by='Sum', ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>Sum</th>\n",
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       "    <tr>\n",
       "      <th>SumCheck</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Neg</th>\n",
       "      <td>-0.693951</td>\n",
       "      <td>-0.158952</td>\n",
       "      <td>-0.476959</td>\n",
       "      <td>-0.706371</td>\n",
       "      <td>-2.036233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pos</th>\n",
       "      <td>0.490308</td>\n",
       "      <td>0.416019</td>\n",
       "      <td>0.503187</td>\n",
       "      <td>-0.591620</td>\n",
       "      <td>0.817893</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "                 A         B         C         D       Sum\n",
       "SumCheck                                                  \n",
       "Neg      -0.693951 -0.158952 -0.476959 -0.706371 -2.036233\n",
       "Pos       0.490308  0.416019  0.503187 -0.591620  0.817893"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Many powerful methods like groupby\n",
    "df.groupby('SumCheck').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/billtubbs/anaconda/envs/py36/lib/python3.6/site-packages/ipykernel_launcher.py:4: RuntimeWarning: invalid value encountered in sqrt\n",
      "  after removing the cwd from sys.path.\n"
     ]
    }
   ],
   "source": [
    "# Support for missing (NaN) values\n",
    "\n",
    "# Note the warnings when we do this:\n",
    "df2 = np.sqrt(df[['A', 'B', 'C', 'D']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.970819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-02</th>\n",
       "      <td>0.814537</td>\n",
       "      <td>0.651792</td>\n",
       "      <td>0.270274</td>\n",
       "      <td>0.762700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-03</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-04</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.610433</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-05</th>\n",
       "      <td>0.844327</td>\n",
       "      <td>0.680543</td>\n",
       "      <td>1.190995</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2018-01-01       NaN       NaN       NaN  0.970819\n",
       "2018-01-02  0.814537  0.651792  0.270274  0.762700\n",
       "2018-01-03       NaN       NaN       NaN       NaN\n",
       "2018-01-04       NaN  0.610433       NaN       NaN\n",
       "2018-01-05  0.844327  0.680543  1.190995       NaN"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# But we still get a dataframe of results\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    float64\n",
       "B    float64\n",
       "C    float64\n",
       "D    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>2018-01-01</th>\n",
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       "      <td>False</td>\n",
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       "      <th>2018-01-02</th>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>2018-01-03</th>\n",
       "      <td>True</td>\n",
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       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>2018-01-04</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>2018-01-05</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "                A      B      C      D\n",
       "2018-01-01   True   True   True  False\n",
       "2018-01-02  False  False  False  False\n",
       "2018-01-03   True   True   True   True\n",
       "2018-01-04   True  False   True   True\n",
       "2018-01-05  False  False  False   True"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find missing values\n",
    "df2.isna().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    6\n",
       "B    4\n",
       "C    5\n",
       "D    6\n",
       "dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
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     "data": {
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       "      <th>D</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-02</th>\n",
       "      <td>0.814537</td>\n",
       "      <td>0.651792</td>\n",
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       "                   A         B         C       D\n",
       "2018-01-02  0.814537  0.651792  0.270274  0.7627"
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     "execution_count": 46,
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   ],
   "source": [
    "# Drop all rows with any NaN values\n",
    "df2.dropna()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
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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>2018-01-01</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.970819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-02</th>\n",
       "      <td>0.814537</td>\n",
       "      <td>0.651792</td>\n",
       "      <td>0.270274</td>\n",
       "      <td>0.762700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-03</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-04</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.610433</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-05</th>\n",
       "      <td>0.844327</td>\n",
       "      <td>0.680543</td>\n",
       "      <td>1.190995</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2018-01-01  0.000000  0.000000  0.000000  0.970819\n",
       "2018-01-02  0.814537  0.651792  0.270274  0.762700\n",
       "2018-01-03  0.000000  0.000000  0.000000  0.000000\n",
       "2018-01-04  0.000000  0.610433  0.000000  0.000000\n",
       "2018-01-05  0.844327  0.680543  1.190995  0.000000"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fill NaN values\n",
    "df2.fillna(0.0).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## More advanced topics (Not essential to know)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Views vs. copies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>D</th>\n",
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       "      <th>2018-01-01</th>\n",
       "      <td>-0.990605</td>\n",
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       "      <td>-0.974926</td>\n",
       "      <td>0.942489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-02</th>\n",
       "      <td>0.663470</td>\n",
       "      <td>0.424833</td>\n",
       "      <td>0.073048</td>\n",
       "      <td>0.581711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-03</th>\n",
       "      <td>-1.319911</td>\n",
       "      <td>-0.477794</td>\n",
       "      <td>-0.244228</td>\n",
       "      <td>-0.620239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-04</th>\n",
       "      <td>-0.111922</td>\n",
       "      <td>0.372629</td>\n",
       "      <td>-1.188499</td>\n",
       "      <td>-2.243460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-05</th>\n",
       "      <td>0.712887</td>\n",
       "      <td>0.463138</td>\n",
       "      <td>1.418469</td>\n",
       "      <td>-1.899446</td>\n",
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      "text/plain": [
       "                   A         B         C         D\n",
       "2018-01-01 -0.990605 -0.240924 -0.974926  0.942489\n",
       "2018-01-02  0.663470  0.424833  0.073048  0.581711\n",
       "2018-01-03 -1.319911 -0.477794 -0.244228 -0.620239\n",
       "2018-01-04 -0.111922  0.372629 -1.188499 -2.243460\n",
       "2018-01-05  0.712887  0.463138  1.418469 -1.899446"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df[['A', 'B', 'C', 'D']]\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/billtubbs/anaconda/envs/py36/lib/python3.6/site-packages/pandas/core/indexing.py:189: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self._setitem_with_indexer(indexer, value)\n",
      "/Users/billtubbs/anaconda/envs/py36/lib/python3.6/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "# See the warning when you try to change a value in x\n",
    "df2.loc['2018-01-01', 'A'] = 0.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.000000</td>\n",
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       "      <td>0.073048</td>\n",
       "      <td>0.581711</td>\n",
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       "    <tr>\n",
       "      <th>2018-01-03</th>\n",
       "      <td>-1.319911</td>\n",
       "      <td>-0.477794</td>\n",
       "      <td>-0.244228</td>\n",
       "      <td>-0.620239</td>\n",
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       "    <tr>\n",
       "      <th>2018-01-04</th>\n",
       "      <td>-0.111922</td>\n",
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       "      <td>-1.188499</td>\n",
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       "    <tr>\n",
       "      <th>2018-01-05</th>\n",
       "      <td>0.712887</td>\n",
       "      <td>0.463138</td>\n",
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       "      <td>-1.899446</td>\n",
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      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2018-01-01  0.000000  0.000000  0.000000  0.000000\n",
       "2018-01-02  0.663470  0.424833  0.073048  0.581711\n",
       "2018-01-03 -1.319911 -0.477794 -0.244228 -0.620239\n",
       "2018-01-04 -0.111922  0.372629 -1.188499 -2.243460\n",
       "2018-01-05  0.712887  0.463138  1.418469 -1.899446"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This is because x is a copy of some data from df\n",
    "# To avoid the warning, make it explicit that you are copying df\n",
    "df2 = df[['A', 'B', 'C', 'D']].copy()\n",
    "df2.loc['2018-01-01', :] = 0.0\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-01</th>\n",
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       "      <td>-0.974926</td>\n",
       "      <td>0.942489</td>\n",
       "      <td>-1.263966</td>\n",
       "      <td>Neg</td>\n",
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       "    <tr>\n",
       "      <th>2018-01-02</th>\n",
       "      <td>0.663470</td>\n",
       "      <td>0.424833</td>\n",
       "      <td>0.073048</td>\n",
       "      <td>0.581711</td>\n",
       "      <td>1.743062</td>\n",
       "      <td>Pos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-03</th>\n",
       "      <td>-1.319911</td>\n",
       "      <td>-0.477794</td>\n",
       "      <td>-0.244228</td>\n",
       "      <td>-0.620239</td>\n",
       "      <td>-2.662172</td>\n",
       "      <td>Neg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-04</th>\n",
       "      <td>-0.111922</td>\n",
       "      <td>0.372629</td>\n",
       "      <td>-1.188499</td>\n",
       "      <td>-2.243460</td>\n",
       "      <td>-3.171252</td>\n",
       "      <td>Neg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-05</th>\n",
       "      <td>0.712887</td>\n",
       "      <td>0.463138</td>\n",
       "      <td>1.418469</td>\n",
       "      <td>-1.899446</td>\n",
       "      <td>0.695049</td>\n",
       "      <td>Pos</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D       Sum SumCheck\n",
       "2018-01-01 -0.990605 -0.240924 -0.974926  0.942489 -1.263966      Neg\n",
       "2018-01-02  0.663470  0.424833  0.073048  0.581711  1.743062      Pos\n",
       "2018-01-03 -1.319911 -0.477794 -0.244228 -0.620239 -2.662172      Neg\n",
       "2018-01-04 -0.111922  0.372629 -1.188499 -2.243460 -3.171252      Neg\n",
       "2018-01-05  0.712887  0.463138  1.418469 -1.899446  0.695049      Pos"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Note: original df is unchanged\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/billtubbs/anaconda/envs/py36/lib/python3.6/site-packages/pandas/core/indexing.py:543: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self.obj[item] = s\n"
     ]
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
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       "      <th>2018-01-02</th>\n",
       "      <td>0.663470</td>\n",
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       "      <td>0.073048</td>\n",
       "      <td>0.581711</td>\n",
       "      <td>1.743062</td>\n",
       "      <td>Pos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-03</th>\n",
       "      <td>-1.319911</td>\n",
       "      <td>-0.477794</td>\n",
       "      <td>-0.244228</td>\n",
       "      <td>-0.620239</td>\n",
       "      <td>-2.662172</td>\n",
       "      <td>Neg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-04</th>\n",
       "      <td>-0.111922</td>\n",
       "      <td>0.372629</td>\n",
       "      <td>-1.188499</td>\n",
       "      <td>-2.243460</td>\n",
       "      <td>-3.171252</td>\n",
       "      <td>Neg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-05</th>\n",
       "      <td>0.712887</td>\n",
       "      <td>0.463138</td>\n",
       "      <td>1.418469</td>\n",
       "      <td>-1.899446</td>\n",
       "      <td>0.695049</td>\n",
       "      <td>Pos</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D       Sum SumCheck\n",
       "2018-01-01  0.000000  0.000000  0.000000  0.000000  0.000000        0\n",
       "2018-01-02  0.663470  0.424833  0.073048  0.581711  1.743062      Pos\n",
       "2018-01-03 -1.319911 -0.477794 -0.244228 -0.620239 -2.662172      Neg\n",
       "2018-01-04 -0.111922  0.372629 -1.188499 -2.243460 -3.171252      Neg\n",
       "2018-01-05  0.712887  0.463138  1.418469 -1.899446  0.695049      Pos"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Another example...\n",
    "df2 = df.loc['2018-01-01':'2018-01-05', :]\n",
    "df2.loc['2018-01-01', :] = 0.0\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
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       "      <td>Pos</td>\n",
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       "    <tr>\n",
       "      <th>2018-01-03</th>\n",
       "      <td>-1.319911</td>\n",
       "      <td>-0.477794</td>\n",
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       "      <td>-0.620239</td>\n",
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       "      <td>Neg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-04</th>\n",
       "      <td>-0.111922</td>\n",
       "      <td>0.372629</td>\n",
       "      <td>-1.188499</td>\n",
       "      <td>-2.243460</td>\n",
       "      <td>-3.171252</td>\n",
       "      <td>Neg</td>\n",
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       "    <tr>\n",
       "      <th>2018-01-05</th>\n",
       "      <td>0.712887</td>\n",
       "      <td>0.463138</td>\n",
       "      <td>1.418469</td>\n",
       "      <td>-1.899446</td>\n",
       "      <td>0.695049</td>\n",
       "      <td>Pos</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D       Sum SumCheck\n",
       "2018-01-01  0.000000  0.000000  0.000000  0.000000  0.000000        0\n",
       "2018-01-02  0.663470  0.424833  0.073048  0.581711  1.743062      Pos\n",
       "2018-01-03 -1.319911 -0.477794 -0.244228 -0.620239 -2.662172      Neg\n",
       "2018-01-04 -0.111922  0.372629 -1.188499 -2.243460 -3.171252      Neg\n",
       "2018-01-05  0.712887  0.463138  1.418469 -1.899446  0.695049      Pos"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This time it was a view (NOT a copy), so no warning\n",
    "# And df has now changed\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "See [this Stackoverflow answer](https://stackoverflow.com/a/23296545/1609514) for rules about when pandas makes a view instead of a copy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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