061_more_about_pandas.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# more about pandas\n",
    "\n",
    "(mostly optional)\n",
    "\n",
    "- apply()\n",
    "- datetime type\n",
    "- object type\n",
    "- filtering\n",
    "- merge()\n",
    "- fillna()\n",
    "- interpolate()\n",
    "- melt()\n",
    "- aggregation: unique count means size first last \n",
    "- count missing values\n",
    "\n",
    "https://towardsdatascience.com/gaining-a-solid-understanding-of-pandas-series-893fb8f785aa\n",
    "\n",
    " - arithmetic operation\n",
    " - sorting\n",
    " - string operation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## creating a series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1\n",
       "b    3\n",
       "c    5\n",
       "d    7\n",
       "dtype: int64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1 = pd.Series([1,3,5,7], index=[\"a\", \"b\", \"c\", \"d\"])\n",
    "s1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1\n",
       "b    3\n",
       "c    5\n",
       "d    7\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s2 = pd.Series(np.array([1,3,5,7]), index=[\"a\", \"b\", \"c\", \"d\"])\n",
    "s2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## indexing a series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "<class 'numpy.int64'>\n"
     ]
    }
   ],
   "source": [
    "n = s1[\"a\"]\n",
    "print(n)\n",
    "print(type(n))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n",
      "<class 'numpy.int64'>\n"
     ]
    }
   ],
   "source": [
    "n = s1[1]\n",
    "print(n)\n",
    "print(type(n))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n",
      "<class 'numpy.int64'>\n"
     ]
    }
   ],
   "source": [
    "n = s1.loc[\"c\"] # Utilize actual index\n",
    "print(n)\n",
    "print(type(n))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b    3\n",
      "c    5\n",
      "d    7\n",
      "dtype: int64\n",
      "<class 'pandas.core.series.Series'>\n"
     ]
    }
   ],
   "source": [
    "s = s1.loc[\"b\":\"d\"]\n",
    "print(s)\n",
    "print(type(s))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b    3\n",
      "c    5\n",
      "d    7\n",
      "dtype: int64\n",
      "<class 'pandas.core.series.Series'>\n"
     ]
    }
   ],
   "source": [
    "s = s1.iloc[1:4] # Utilize absolute position starting from 0\n",
    "print(s)\n",
    "print(type(s))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Operation between 2 series objects with different indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1\n",
       "b    1\n",
       "c    1\n",
       "d    1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1 = pd.Series([1,1,1,1], index=list(\"abcd\"))\n",
    "s1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "c    2\n",
       "d    2\n",
       "e    2\n",
       "f    2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s2 = pd.Series([2,2,2,2], index=list(\"cdef\"))\n",
    "s2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    NaN\n",
       "b    NaN\n",
       "c    3.0\n",
       "d    3.0\n",
       "e    NaN\n",
       "f    NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1 + s2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A bit of trivia about data frames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['a', 'b', 'c', 'd'], dtype='object')\n",
      "Index(['col1', 'col2'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "df = pd.DataFrame({\"col1\": [1,2,3,4], \"col2\": [5,6,7,8]}, index=list(\"abcd\"))\n",
    "print(df.index)\n",
    "print(df.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## indexing a data frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame([[1,2],[3,4],[5,6],[7,8]], columns=[\"First\", \"Second\"], index=[\"a\", \"b\", \"c\", \"d\"])\n",
    "df1 = df.iloc[1:4]\n",
    "df2 = df.loc[\"b\":\"d\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    },
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       "   First  Second\n",
       "b      3       4\n",
       "c      5       6\n",
       "d      7       8"
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     "metadata": {},
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   ],
   "source": [
    "print(type(df1))\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
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       "   First  Second\n",
       "b      3       4\n",
       "c      5       6\n",
       "d      7       8"
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     "metadata": {},
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   ],
   "source": [
    "print(type(df2))\n",
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Operation between a dataframe and a series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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       "   a  b\n",
       "0  1  2\n",
       "1  1  2\n",
       "2  1  2\n",
       "3  1  2"
      ]
     },
     "execution_count": 15,
     "metadata": {},
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   ],
   "source": [
    "df = pd.DataFrame({\"a\": [1,1,1,1], \"b\": [2,2,2,2]})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b    0\n",
       "c    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series([0, 0], index=list(\"bc\"))\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
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       "    a    b   c\n",
       "0 NaN  2.0 NaN\n",
       "1 NaN  2.0 NaN\n",
       "2 NaN  2.0 NaN\n",
       "3 NaN  2.0 NaN"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df + s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
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       "    a    b   c\n",
       "0 NaN  2.0 NaN\n",
       "1 NaN  2.0 NaN\n",
       "2 NaN  2.0 NaN\n",
       "3 NaN  2.0 NaN"
      ]
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     "execution_count": 18,
     "metadata": {},
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   ],
   "source": [
    "df.add(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
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       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
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      "text/plain": [
       "    a    b   c\n",
       "0 NaN  2.0 NaN\n",
       "1 NaN  2.0 NaN\n",
       "2 NaN  2.0 NaN\n",
       "3 NaN  2.0 NaN"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.add(s, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    a   b\n",
       "0 NaN NaN\n",
       "1 NaN NaN\n",
       "2 NaN NaN\n",
       "3 NaN NaN\n",
       "b NaN NaN\n",
       "c NaN NaN"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.add(s, axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## apply()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## merge()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## references\n",
    "- https://towardsdatascience.com/gaining-a-solid-understanding-of-pandas-series-893fb8f785aa"
   ]
  }
 ],
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