Day11NumpyPandasReview.ipynb

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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Array(Numpy) \n",
    "List는 데이터 타입에 제한이 없다   \n",
    "Array는 데이터 타입이 통일되어야 한다"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1. 4. 5. 8.]\n",
      "1.0\n",
      "5.0\n"
     ]
    }
   ],
   "source": [
    "test_array = np.array([1,4,5,8],float)\n",
    "print(test_array)\n",
    "print(test_array[0])\n",
    "print(test_array[2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### 차원과 크기를 알 수있는 shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4,)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print(test_array.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### 이차원 array(matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 2 3 4]\n",
      " [5 6 7 8]]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "mat = np.array([[1,2,3,4],[5,6,7,8]])\n",
    "print(mat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### reshape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 3 4 5 6 7 8]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "mattoarray = mat.reshape(8,)\n",
    "print(mattoarray)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4\n",
      "[[3 4]\n",
      " [7 8]]\n"
     ]
    }
   ],
   "source": [
    "print(mat[0,3])\n",
    "print(mat[:,2:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### arange"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.0, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0, 5.5, 6.0, 6.5, 7.0, 7.5, 8.0, 8.5, 9.0, 9.5]\n"
     ]
    }
   ],
   "source": [
    "L = list(np.arange(0,10,0.5))\n",
    "print(L)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Operation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.   0.5  1.   1.5  2.   2.5]\n",
      " [ 3.   3.5  4.   4.5  5.   5.5]\n",
      " [ 6.   6.5  7.   7.5  8.   8.5]\n",
      " [ 9.   9.5 10.  10.5 11.  11.5]\n",
      " [12.  12.5 13.  13.5 14.  14.5]]\n",
      "[[ 0.  1.  2.  3.  4.  5.]\n",
      " [ 6.  7.  8.  9. 10. 11.]\n",
      " [12. 13. 14. 15. 16. 17.]\n",
      " [18. 19. 20. 21. 22. 23.]\n",
      " [24. 25. 26. 27. 28. 29.]]\n"
     ]
    }
   ],
   "source": [
    "#같은 위치의 원소끼리 연산\n",
    "ts =np.arange(0,15,0.5).reshape(5,6)\n",
    "print(ts)\n",
    "print(ts+ts)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Broadcasting    \n",
    "shape이 같아지도록 확장되어 연산"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 3.   3.5  4.   4.5  5.   5.5]\n",
      " [ 6.   6.5  7.   7.5  8.   8.5]\n",
      " [ 9.   9.5 10.  10.5 11.  11.5]\n",
      " [12.  12.5 13.  13.5 14.  14.5]\n",
      " [15.  15.5 16.  16.5 17.  17.5]]\n"
     ]
    }
   ],
   "source": [
    "scalar = 3\n",
    "print(ts+3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### all&any"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[False False False False False False  True  True  True  True]\n",
      "True\n",
      "False\n"
     ]
    }
   ],
   "source": [
    "a = np.arange(10)\n",
    "print(a>5)\n",
    "print(np.any(a>5))\n",
    "print(np.all(a>5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Boolean Indexing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[6 7 8 9]\n"
     ]
    }
   ],
   "source": [
    "print(a[a>5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4 6 8]\n"
     ]
    }
   ],
   "source": [
    "# 조건이 많은 경우 &나 |사용 and/or안됨\n",
    "print(a[(a%2==0)&(a>3)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas\n",
    "### Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    1\n",
      "1    2\n",
      "2    3\n",
      "3    4\n",
      "4    5\n",
      "5    6\n",
      "6    7\n",
      "7    8\n",
      "8    9\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "ts1 = pd.Series([1,2,3,'4',5,6.0,7,8,9])\n",
    "print(ts1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0     1\n",
      "1     2\n",
      "2     3\n",
      "3     4\n",
      "4     5\n",
      "5     6\n",
      "6     7\n",
      "7     8\n",
      "8     9\n",
      "9    10\n",
      "dtype: int64\n",
      "1\n",
      "2    3\n",
      "4    5\n",
      "6    7\n",
      "8    9\n",
      "dtype: int64\n",
      "0    1\n",
      "1    2\n",
      "2    3\n",
      "3    4\n",
      "4    5\n",
      "5    6\n",
      "6    7\n",
      "7    8\n",
      "8    9\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "ts = pd.Series([1,2,3,4,5,6,7,8,9,10])\n",
    "\n",
    "print(ts)\n",
    "print(ts[0])\n",
    "print(ts[2::2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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