GettingStarted_v2.ipynb

{
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  {
   "cell_type": "markdown",
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    "id": "flv36y65ijEQ"
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   "source": [
    "Jupyter Notes, Python (and some R) - Getting Started"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
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    "colab_type": "code",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n"
     ]
    }
   ],
   "source": [
    "# Everything in Python (as in R) is an object:  booleans, integers, floats, strings, functions, programs\n",
    "# Think of an object as a container or box  \n",
    "# Scalar assignment to the variable x  \n",
    "# Can't assign names beginning with numbers;  Beginning with \"_\" has special usage \n",
    "# The following are reserved words and connot be used: \n",
    "#    False,\tclass, finally,\tis,\treturn, None,\tcontinue,\tfor,\tlambda,\ttry, \n",
    "#    True,\tdef,\tfrom,\tnonlocal,\twhile, and,\tdel,\tglobal,\tnot,\twith, as,\n",
    "#    elif, if,\tor,\tyield, assert,\telse,\timport,\tpass, break,\texcept,\tin,\traise \n",
    "x = 2\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
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    "colab_type": "code",
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    "outputId": "1ecee60f-e609-4fe7-8e70-7be462a1ff20"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "int"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A \"class\" is a definition for an object;  think of types as class definitions\n",
    "# Type is integer\n",
    "type(x) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "IghWkMzT2OXT"
   },
   "outputs": [],
   "source": [
    "# Type is floating point"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
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    "colab_type": "code",
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    "outputId": "3c2757d0-6937-45b7-83c8-cd39d03ba0b6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "float"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(2.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
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    "colab_type": "code",
    "id": "blIJ2Kkk3-td",
    "outputId": "752f6b09-b8d7-470e-a54e-73a6238de4dc"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Type is string\n",
    "type(\"rich\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 137
    },
    "colab_type": "code",
    "id": "1IHO8Gz94PNa",
    "outputId": "9392093c-2ff1-4c42-a93e-13e6a3b96014"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8\n",
      "2\n",
      "15\n",
      "1.6666666666666667\n",
      "1\n",
      "2\n",
      "125\n"
     ]
    }
   ],
   "source": [
    "#  Addition;  Subtraction; Multiplication; floating point division; integer division; \n",
    "#  modulus (remainder); eponentiation\n",
    "# Don't need spaces but does make for better readability\n",
    "print(5 + 3)\n",
    "print(5 - 3)\n",
    "print(5 * 3)\n",
    "print(5/3)\n",
    "print(5//3) \n",
    "print(5%3)\n",
    "print(5**3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "IY-VvIOhSNgN",
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Converting data types using \"str\"\n",
    "int(\"2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "vd55Acb2-6yS"
   },
   "outputs": [],
   "source": [
    "# All sequences of digits are assumed to be integer \n",
    "# Place negative in front of digit to define negative number\n",
    "# Dividing by 0 causes error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 233
    },
    "colab_type": "code",
    "id": "rIibzakU-nJ0",
    "outputId": "c35463e7-b87e-4164-c83c-90e7182aae8f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n",
      "-3.2\n"
     ]
    },
    {
     "ename": "ZeroDivisionError",
     "evalue": "division by zero",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-9-fc7928a9c2b7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m3.2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mZeroDivisionError\u001b[0m: division by zero"
     ]
    }
   ],
   "source": [
    "print(5)\n",
    "print(-3.2)\n",
    "print(5/0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "8OugL_8H_gLr",
    "outputId": "f062548a-0ff6-44bf-a741-3ba3b9752df9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    }
   ],
   "source": [
    "# Changing value of a variable\n",
    "x = 2\n",
    "x = x - 1\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "-XbswLJ1_4wD"
   },
   "outputs": [],
   "source": [
    "# Can assign prior assigned variable to new variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "-QP50dYM_1v0",
    "outputId": "209af862-1aec-4292-9b27-d2b13e9d4f05"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    }
   ],
   "source": [
    "y = x\n",
    "print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 68
    },
    "colab_type": "code",
    "id": "y6-_e4D2_0VV",
    "outputId": "a94faa71-bfc8-494b-e85d-787f0e3f7b07"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "3\n",
      "1\n"
     ]
    }
   ],
   "source": [
    "x = 2\n",
    "x -= 1\n",
    "print(x)\n",
    "x = 2 \n",
    "x += 1\n",
    "print(x)\n",
    "x = 2\n",
    "x //= 2\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 86
    },
    "colab_type": "code",
    "id": "Qp2vvxeSOO7x",
    "outputId": "1fd006a9-98c6-46e8-9858-5fe9a98f964c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n",
      "2\n",
      "8\n",
      "16\n"
     ]
    }
   ],
   "source": [
    "# Bases\n",
    "print(10) # base 10\n",
    "print(0b10) # base 2\n",
    "print(0o10) # base 8\n",
    "print(0x10) # base 16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "LmFAMpRmO64c"
   },
   "outputs": [],
   "source": [
    "# Type conversions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 250
    },
    "colab_type": "code",
    "id": "wyUMv4uXO3ot",
    "outputId": "d1f56eee-d088-4a47-bffd-a384df8e5ee5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "0\n",
      "5\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "invalid literal for int() with base 10: 'what?'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-16-0073394efea7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m5.5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'what?'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m: invalid literal for int() with base 10: 'what?'"
     ]
    }
   ],
   "source": [
    "print(int(True))\n",
    "print(int(False))\n",
    "print(int(5.5))\n",
    "print(int('what?'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "MnbhVTfLPTv5",
    "outputId": "88dacd1c-5d2d-4090-bb1d-6c292f41ec02"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000\n"
     ]
    }
   ],
   "source": [
    "# Python handles really large integer values by default unlike R\n",
    "# see:  https://www.r-bloggers.com/r-in-a-64-bit-world/\n",
    "# R gives incorrect value 10000000000000000159028911097599180468360808563945281389781327557747838772170381060813469985856815104\n",
    "# To be fair, other languages have this difficulty\n",
    "# Give python a point on it's scorecard\n",
    "googol = 10**100\n",
    "print(googol) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "w4fiWweGRTXg",
    "outputId": "68d270ff-0eb4-4911-8002-0fa0e724c2f9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'heyheyhey'"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Can duplicate with \"*\" (R has \"rep\" function) - e.g.  paste(rep(\"hey\",3), collapse=\"\")\n",
    "'hey' *3  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "8Vl8EJ3WSlO8",
    "outputId": "a084350e-2155-440c-b71b-defef4b81d36"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "d\n",
      "e\n"
     ]
    }
   ],
   "source": [
    "# Subseting text (or slicing); Note index starts at 0; Can also use negative index\n",
    "stringValues = \"abcdef\"\n",
    "print(stringValues[3])\n",
    "print(stringValues[-2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 164
    },
    "colab_type": "code",
    "id": "qdwf6DhHTGEj",
    "outputId": "32cf94f5-d45c-454b-e390-86fc1ee1e729"
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'str' object does not support item assignment",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-20-8ee258d1a707>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Strings are immutable (lists are though) can't insert a change;  R structures are mutable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mstringValues\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'D'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: 'str' object does not support item assignment"
     ]
    }
   ],
   "source": [
    "# Strings are immutable (lists are though) can't insert a change;  R structures are mutable\n",
    "stringValues[3]='D'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "nX-3BcJcT47x",
    "outputId": "7ae85ea7-513c-4229-f764-8577c6e1955b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "abcDef\n",
      "abcdef\n"
     ]
    }
   ],
   "source": [
    "# However can use \"replace\"\n",
    "print(stringValues.replace('d','D'))\n",
    "print(stringValues)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "kf6x_CpFVSKO"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "abcdef\n",
      "ac\n",
      "ac\n"
     ]
    }
   ],
   "source": [
    "# Slicing:  [:],  [start:],  [:end], [start:end], [start:end:step]\n",
    "stringValues = \"abcdef\"\n",
    "print(stringValues)\n",
    "print(stringValues[:3:2])\n",
    "print(stringValues[:-2:2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 257
    },
    "colab_type": "code",
    "id": "x5yvSKiDXW97",
    "outputId": "46a4b4ff-21bc-489c-f7a1-5d71cfa07001"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['.github', 'GettingStarted_v2.ipynb', 'content', 'GettingStarted_R.ipynb', 'GettingStarted_v1.ipynb', 'machLearn-master', 'jelly_beans.jpg', 'README.md', 'postBuild', 'index.Rmd', 'notebooks.Rproj', 'df.subset.csv', 'requirements.in', 'config.toml', 'requirements.txt', 'static', 'apt.txt', 'runtime.txt', '_config.yml', 'environment.yml', 'resources', '.Rhistory', 'themes', 'public', '.gitignore', '.Rproj.user', '.git', 'install.R', '.gitmodules', 'california_housing_test.csv', 'start', 'jellybean_estimates.txt', '.ipynb_checkpoints']\n",
      "      longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
      "0       -122.05     37.37                27.0       3885.0           661.0   \n",
      "1       -118.30     34.26                43.0       1510.0           310.0   \n",
      "2       -117.81     33.78                27.0       3589.0           507.0   \n",
      "3       -118.36     33.82                28.0         67.0            15.0   \n",
      "4       -119.67     36.33                19.0       1241.0           244.0   \n",
      "...         ...       ...                 ...          ...             ...   \n",
      "2995    -119.86     34.42                23.0       1450.0           642.0   \n",
      "2996    -118.14     34.06                27.0       5257.0          1082.0   \n",
      "2997    -119.70     36.30                10.0        956.0           201.0   \n",
      "2998    -117.12     34.10                40.0         96.0            14.0   \n",
      "2999    -119.63     34.42                42.0       1765.0           263.0   \n",
      "\n",
      "      population  households  median_income  median_house_value  \n",
      "0         1537.0       606.0         6.6085            344700.0  \n",
      "1          809.0       277.0         3.5990            176500.0  \n",
      "2         1484.0       495.0         5.7934            270500.0  \n",
      "3           49.0        11.0         6.1359            330000.0  \n",
      "4          850.0       237.0         2.9375             81700.0  \n",
      "...          ...         ...            ...                 ...  \n",
      "2995      1258.0       607.0         1.1790            225000.0  \n",
      "2996      3496.0      1036.0         3.3906            237200.0  \n",
      "2997       693.0       220.0         2.2895             62000.0  \n",
      "2998        46.0        14.0         3.2708            162500.0  \n",
      "2999       753.0       260.0         8.5608            500001.0  \n",
      "\n",
      "[3000 rows x 9 columns]\n"
     ]
    }
   ],
   "source": [
    "# Importing text data into Python using \"pandas\"\n",
    "# See \"pandas\" documentation \n",
    "# pandas.pydata.org/pandas-docs\n",
    "# Also see https://www.tutorialspoint.com/python_pandas/python_pandas_basic_functionality.htm\n",
    "# for basic functionality\n",
    "#\n",
    "# Equivalent to \"read.csv\" in R\n",
    "import pandas as pd\n",
    "# from pandas import read_csv\n",
    "import os\n",
    "print(os.listdir())\n",
    "# df = pd.read_csv()\n",
    "df = pd.read_csv(r\"california_housing_test.csv\")\n",
    "print (df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variable       Type         Data/Info\n",
      "-------------------------------------\n",
      "df             DataFrame          longitude  latitude<...>\\n[3000 rows x 9 columns]\n",
      "googol         int          1000000000000000000000000<...>0000000000000000000000000\n",
      "os             module       <module 'os' from '/cm/sh<...>ate/lib/python3.6/os.py'>\n",
      "pd             module       <module 'pandas' from '/c<...>ages/pandas/__init__.py'>\n",
      "stringValues   str          abcdef\n",
      "x              int          1\n",
      "y              int          1\n"
     ]
    }
   ],
   "source": [
    "whos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/work/richherr/notebooks'"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Using cell magics; see here - https://www.dataquest.io/blog/jupyter-notebook-tips-tricks-shortcuts/\n",
    "# Also see: https://ipython.org/ipython-doc/2/config/extensions/rmagic.html\n",
    "# Single percent % is line magic;  Double percent %% is cell magic\n",
    "# Must load \"%load_ext rpy2.ipython\" in separate cell\n",
    "# \"%%R\" must appear on first line of a cell  \n",
    "# %Rpush \"push\" objects into R;   \n",
    "# %%Rget \"gets\" objects from R and brings them into python after assignment to global\n",
    "# Assign to global namespace:  \"_\" to access objects from R \n",
    "# Can also use the flags -i and -o to share variables between R and Python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the \"rpy2\" ipython interface (use reload instead of load)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "%reload_ext rpy2.ipython"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Go from Python assigned variable to R, then assign back to Python variable from variable in R"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5.]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "z = np.array([1,4,5,10]) \n",
    "z_mean = %R -i z mean(z)\n",
    "print(z_mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Go from R to Python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-0.10607251  0.94831304 -0.10607251]\n",
      " [-0.80816998  1.44051119 -0.80816998]]\n"
     ]
    }
   ],
   "source": [
    "z_mat = %R -o zmat zmat<-matrix(rnorm(4), ncol=3)\n",
    "print(z_mat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  sex weight\n",
      "1   F     49\n",
      "2   F     56\n",
      "3   F     60\n",
      "4   F     43\n",
      "5   F     57\n",
      "6   F     58\n"
     ]
    }
   ],
   "source": [
    "%%R\n",
    "options(warn=-1)\n",
    "set.seed(1234)\n",
    "df <- data.frame(\n",
    "  sex=factor(rep(c(\"F\", \"M\"), each=200)),\n",
    "  weight=round(c(rnorm(200, mean=55, sd=5), rnorm(200, mean=65, sd=5)))\n",
    "  )\n",
    "head(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "R[write to console]: `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "options(warn=-1)\n",
    "library(ggplot2)\n",
    "# Basic histogram\n",
    "ggplot(df, aes(x=weight)) + geom_histogram()\n",
    "# Change the width of bins\n",
    "ggplot(df, aes(x=weight)) + \n",
    "  geom_histogram(binwidth=1)\n",
    "# Change colors\n",
    "p<-ggplot(df, aes(x=weight)) + \n",
    "  geom_histogram(color=\"black\", fill=\"white\")\n",
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "R[write to console]: `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "p+ geom_vline(aes(xintercept=mean(weight)),\n",
    "            color=\"blue\", linetype=\"dashed\", size=1)\n",
    "# Histogram with density plot\n",
    "ggplot(df, aes(x=weight)) + \n",
    " geom_histogram(aes(y=..density..), colour=\"black\", fill=\"white\")+\n",
    " geom_density(alpha=.2, fill=\"#FF6666\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Note: Don't leave spaces between \"_\" and the \"=\" when assigning to global namespace (the \"_\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[6.23333333]\n",
      "[3.]\n",
      "   speed  dist\n",
      "1    4.0   2.0\n",
      "2    4.0  10.0\n",
      "3    7.0   4.0\n",
      "4    7.0  22.0\n",
      "5    8.0  16.0\n",
      "    speed   dist\n",
      "46   24.0   70.0\n",
      "47   24.0   92.0\n",
      "48   24.0   93.0\n",
      "49   24.0  120.0\n",
      "50   25.0   85.0\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "x = np.array([4.5,6.3,7.9])\n",
    "y = np.array([2.0,3.0,4.0])\n",
    "%Rpush x y\n",
    "_=%R x_mean<-mean(x); y_mean<-mean(y); library(datasets); cars_df<-cars\n",
    "x_new=%Rget x_mean\n",
    "y_new=%Rget y_mean\n",
    "cars_df=%Rget cars_df\n",
    "print(x_new)\n",
    "print(y_new)\n",
    "print(cars_df.head())\n",
    "print(cars_df.tail())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variable       Type         Data/Info\n",
      "-------------------------------------\n",
      "cars_df        DataFrame        speed   dist\\n1     4<...>  120.0\\n50   25.0   85.0\n",
      "df             DataFrame          longitude  latitude<...>\\n[3000 rows x 9 columns]\n",
      "googol         int          1000000000000000000000000<...>0000000000000000000000000\n",
      "np             module       <module 'numpy' from '/cm<...>kages/numpy/__init__.py'>\n",
      "os             module       <module 'os' from '/cm/sh<...>ate/lib/python3.6/os.py'>\n",
      "pd             module       <module 'pandas' from '/c<...>ages/pandas/__init__.py'>\n",
      "stringValues   str          abcdef\n",
      "warnings       module       <module 'warnings' from '<...>b/python3.6/warnings.py'>\n",
      "x              ndarray      3: 3 elems, type `float64`, 24 bytes\n",
      "x_new          ndarray      1: 1 elems, type `float64`, 8 bytes\n",
      "y              ndarray      3: 3 elems, type `float64`, 24 bytes\n",
      "y_new          ndarray      1: 1 elems, type `float64`, 8 bytes\n",
      "z              ndarray      4: 4 elems, type `int64`, 32 bytes\n",
      "z_mat          ndarray      2x3: 6 elems, type `float64`, 48 bytes\n",
      "z_mean         ndarray      1: 1 elems, type `float64`, 8 bytes\n",
      "zmat           ndarray      2x3: 6 elems, type `float64`, 48 bytes\n"
     ]
    }
   ],
   "source": [
    "whos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cell magic with R using \"%%\";  Then use line magic to get text variable from R into Python\n",
    "# variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"rich say what now ????\"\n"
     ]
    }
   ],
   "source": [
    "%%R\n",
    "x<-c(\"rich say what now ????\")\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rich say what now ????\n"
     ]
    }
   ],
   "source": [
    "new_phrase = %Rget x\n",
    "print(new_phrase[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rich say what???\n",
      "rich say oh-no!\n"
     ]
    }
   ],
   "source": [
    "_ = %R x<-c(\"rich say what???\"); y<-c(\"rich say oh-no!\")\n",
    "x = %Rget x\n",
    "y = %Rget y\n",
    "print(x[0])\n",
    "print(y[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Subsetting objects using pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 311
    },
    "colab_type": "code",
    "id": "PhdXWskwZTaD",
    "outputId": "def1adef-994e-421b-c93a-c2a593ccf87a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      median_income  median_house_value\n",
      "0            6.6085            344700.0\n",
      "1            3.5990            176500.0\n",
      "2            5.7934            270500.0\n",
      "3            6.1359            330000.0\n",
      "4            2.9375             81700.0\n",
      "...             ...                 ...\n",
      "2995         1.1790            225000.0\n",
      "2996         3.3906            237200.0\n",
      "2997         2.2895             62000.0\n",
      "2998         3.2708            162500.0\n",
      "2999         8.5608            500001.0\n",
      "\n",
      "[3000 rows x 2 columns]\n"
     ]
    }
   ],
   "source": [
    "# Subsetting columns\n",
    "df.subset = pd.DataFrame(df, columns = ['median_income','median_house_value'])\n",
    "print(df.subset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "eSVlVQ-gaFu5",
    "outputId": "6801bc50-1226-492f-aafc-5016de4769ed"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['.github',\n",
       " 'GettingStarted_v2.ipynb',\n",
       " 'content',\n",
       " 'GettingStarted_R.ipynb',\n",
       " 'GettingStarted_v1.ipynb',\n",
       " 'machLearn-master',\n",
       " 'jelly_beans.jpg',\n",
       " 'README.md',\n",
       " 'postBuild',\n",
       " 'index.Rmd',\n",
       " 'notebooks.Rproj',\n",
       " 'df.subset.csv',\n",
       " 'requirements.in',\n",
       " 'config.toml',\n",
       " 'requirements.txt',\n",
       " 'static',\n",
       " 'apt.txt',\n",
       " 'runtime.txt',\n",
       " '_config.yml',\n",
       " 'environment.yml',\n",
       " 'resources',\n",
       " '.Rhistory',\n",
       " 'themes',\n",
       " 'public',\n",
       " '.gitignore',\n",
       " '.Rproj.user',\n",
       " '.git',\n",
       " 'install.R',\n",
       " '.gitmodules',\n",
       " 'california_housing_test.csv',\n",
       " 'start',\n",
       " 'jellybean_estimates.txt',\n",
       " '.ipynb_checkpoints']"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# See \"pandas\" documentation \n",
    "# pandas.pydata.org/pandas-docs  \n",
    "# Equivalent to \"write.table\" in R\n",
    "\n",
    "df.subset.to_csv('df.subset.csv', index=True)\n",
    "os.listdir()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Using cell magic with \"bash\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total 1479\n",
      "drwxr-xr-x  12 richherr pi_richherr   4096 Jan 29 14:47 .\n",
      "drwx------ 219 richherr pi_richherr   4096 Jan 28 17:19 ..\n",
      "-rw-r--r--   1 richherr pi_richherr     32 Jan 26 10:46 apt.txt\n",
      "-rw-r--r--   1 richherr pi_richherr 301141 Jan 24 18:17 california_housing_test.csv\n",
      "-rw-r--r--   1 richherr pi_richherr    421 Jan 24 18:17 config.toml\n",
      "-rw-r--r--   1 richherr pi_richherr     26 Jan 24 18:17 _config.yml\n",
      "drwxr-xr-x   9 richherr pi_richherr   4096 Jan 24 18:17 content\n",
      "-rw-rw-r--   1 richherr pi_richherr  60761 Jan 29 14:48 df.subset.csv\n",
      "-rw-r--r--   1 richherr pi_richherr   1596 Jan 28 23:07 environment.yml\n",
      "-rw-r--r--   1 richherr pi_richherr 345922 Jan 24 18:17 GettingStarted_R.ipynb\n",
      "-rw-r--r--   1 richherr pi_richherr  76315 Jan 29 14:47 GettingStarted_v1.ipynb\n",
      "-rw-rw-r--   1 richherr pi_richherr 452053 Jan 29 14:30 GettingStarted_v2.ipynb\n",
      "drwxr-xr-x   8 richherr pi_richherr   4096 Jan 29 14:48 .git\n",
      "drwxr-xr-x   3 richherr pi_richherr   4096 Jan 24 18:17 .github\n",
      "-rw-r--r--   1 richherr pi_richherr     40 Jan 24 18:17 .gitignore\n",
      "-rw-r--r--   1 richherr pi_richherr      0 Jan 24 18:17 .gitmodules\n",
      "-rw-r--r--   1 richherr pi_richherr     39 Jan 24 18:17 index.Rmd\n",
      "-rw-r--r--   1 richherr pi_richherr   3092 Jan 29 14:16 install.R\n",
      "drwxrwxr-x   2 richherr pi_richherr   4096 Jan 29 14:47 .ipynb_checkpoints\n",
      "-rw-r--r--   1 richherr pi_richherr    242 Jan 28 20:55 jellybean_estimates.txt\n",
      "-rw-r--r--   1 richherr pi_richherr 193640 Jan 28 22:36 jelly_beans.jpg\n",
      "drwxr-xr-x   3 richherr pi_richherr   4096 Jan 24 18:17 machLearn-master\n",
      "-rw-r--r--   1 richherr pi_richherr    223 Jan 29 13:18 notebooks.Rproj\n",
      "-rw-r--r--   1 richherr pi_richherr     31 Jan 26 18:43 postBuild\n",
      "drwxr-xr-x  13 richherr pi_richherr   4096 Jan 24 18:18 public\n",
      "-rw-r--r--   1 richherr pi_richherr    100 Jan 26 10:50 README.md\n",
      "-rw-r--r--   1 richherr pi_richherr  11752 Jan 25 21:53 requirements.in\n",
      "-rw-r--r--   1 richherr pi_richherr  10166 Jan 26 11:33 requirements.txt\n",
      "drwxr-xr-x   3 richherr pi_richherr   4096 Jan 26 10:38 resources\n",
      "-rw-r--r--   1 richherr pi_richherr    186 Jan 27 15:51 .Rhistory\n",
      "drwxr-xr-x   5 richherr pi_richherr   4096 Jan 24 18:18 .Rproj.user\n",
      "-rw-r--r--   1 richherr pi_richherr      0 Jan 26 16:43 runtime.txt\n",
      "-rw-r--r--   1 richherr pi_richherr     21 Jan 26 15:52 start\n",
      "drwxr-xr-x   3 richherr pi_richherr   4096 Jan 24 18:18 static\n",
      "drwxr-xr-x   3 richherr pi_richherr   4096 Jan 24 18:18 themes\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "ls -la"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Listing all availing line and cell magics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/json": {
       "cell": {
        "!": "OSMagics",
        "HTML": "Other",
        "R": "RMagics",
        "SVG": "Other",
        "bash": "Other",
        "capture": "ExecutionMagics",
        "debug": "ExecutionMagics",
        "file": "Other",
        "html": "DisplayMagics",
        "javascript": "DisplayMagics",
        "js": "DisplayMagics",
        "latex": "DisplayMagics",
        "markdown": "DisplayMagics",
        "perl": "Other",
        "prun": "ExecutionMagics",
        "pypy": "Other",
        "python": "Other",
        "python2": "Other",
        "python3": "Other",
        "ruby": "Other",
        "script": "ScriptMagics",
        "sh": "Other",
        "svg": "DisplayMagics",
        "sx": "OSMagics",
        "system": "OSMagics",
        "time": "ExecutionMagics",
        "timeit": "ExecutionMagics",
        "writefile": "OSMagics"
       },
       "line": {
        "R": "RMagics",
        "Rdevice": "RMagics",
        "Rget": "RMagics",
        "Rpull": "RMagics",
        "Rpush": "RMagics",
        "alias": "OSMagics",
        "alias_magic": "BasicMagics",
        "autoawait": "AsyncMagics",
        "autocall": "AutoMagics",
        "automagic": "AutoMagics",
        "autosave": "KernelMagics",
        "bookmark": "OSMagics",
        "cat": "Other",
        "cd": "OSMagics",
        "clear": "KernelMagics",
        "colors": "BasicMagics",
        "conda": "PackagingMagics",
        "config": "ConfigMagics",
        "connect_info": "KernelMagics",
        "cp": "Other",
        "debug": "ExecutionMagics",
        "dhist": "OSMagics",
        "dirs": "OSMagics",
        "doctest_mode": "BasicMagics",
        "ed": "Other",
        "edit": "KernelMagics",
        "env": "OSMagics",
        "gui": "BasicMagics",
        "hist": "Other",
        "history": "HistoryMagics",
        "killbgscripts": "ScriptMagics",
        "ldir": "Other",
        "less": "KernelMagics",
        "lf": "Other",
        "lk": "Other",
        "ll": "Other",
        "load": "CodeMagics",
        "load_ext": "ExtensionMagics",
        "loadpy": "CodeMagics",
        "logoff": "LoggingMagics",
        "logon": "LoggingMagics",
        "logstart": "LoggingMagics",
        "logstate": "LoggingMagics",
        "logstop": "LoggingMagics",
        "ls": "Other",
        "lsmagic": "BasicMagics",
        "lx": "Other",
        "macro": "ExecutionMagics",
        "magic": "BasicMagics",
        "man": "KernelMagics",
        "matplotlib": "PylabMagics",
        "mkdir": "Other",
        "more": "KernelMagics",
        "mv": "Other",
        "notebook": "BasicMagics",
        "page": "BasicMagics",
        "pastebin": "CodeMagics",
        "pdb": "ExecutionMagics",
        "pdef": "NamespaceMagics",
        "pdoc": "NamespaceMagics",
        "pfile": "NamespaceMagics",
        "pinfo": "NamespaceMagics",
        "pinfo2": "NamespaceMagics",
        "pip": "PackagingMagics",
        "popd": "OSMagics",
        "pprint": "BasicMagics",
        "precision": "BasicMagics",
        "prun": "ExecutionMagics",
        "psearch": "NamespaceMagics",
        "psource": "NamespaceMagics",
        "pushd": "OSMagics",
        "pwd": "OSMagics",
        "pycat": "OSMagics",
        "pylab": "PylabMagics",
        "qtconsole": "KernelMagics",
        "quickref": "BasicMagics",
        "recall": "HistoryMagics",
        "rehashx": "OSMagics",
        "reload_ext": "ExtensionMagics",
        "rep": "Other",
        "rerun": "HistoryMagics",
        "reset": "NamespaceMagics",
        "reset_selective": "NamespaceMagics",
        "rm": "Other",
        "rmdir": "Other",
        "run": "ExecutionMagics",
        "save": "CodeMagics",
        "sc": "OSMagics",
        "set_env": "OSMagics",
        "store": "StoreMagics",
        "sx": "OSMagics",
        "system": "OSMagics",
        "tb": "ExecutionMagics",
        "time": "ExecutionMagics",
        "timeit": "ExecutionMagics",
        "unalias": "OSMagics",
        "unload_ext": "ExtensionMagics",
        "who": "NamespaceMagics",
        "who_ls": "NamespaceMagics",
        "whos": "NamespaceMagics",
        "xdel": "NamespaceMagics",
        "xmode": "BasicMagics"
       }
      },
      "text/plain": [
       "Available line magics:\n",
       "%R  %Rdevice  %Rget  %Rpull  %Rpush  %alias  %alias_magic  %autoawait  %autocall  %automagic  %autosave  %bookmark  %cat  %cd  %clear  %colors  %conda  %config  %connect_info  %cp  %debug  %dhist  %dirs  %doctest_mode  %ed  %edit  %env  %gui  %hist  %history  %killbgscripts  %ldir  %less  %lf  %lk  %ll  %load  %load_ext  %loadpy  %logoff  %logon  %logstart  %logstate  %logstop  %ls  %lsmagic  %lx  %macro  %magic  %man  %matplotlib  %mkdir  %more  %mv  %notebook  %page  %pastebin  %pdb  %pdef  %pdoc  %pfile  %pinfo  %pinfo2  %pip  %popd  %pprint  %precision  %prun  %psearch  %psource  %pushd  %pwd  %pycat  %pylab  %qtconsole  %quickref  %recall  %rehashx  %reload_ext  %rep  %rerun  %reset  %reset_selective  %rm  %rmdir  %run  %save  %sc  %set_env  %store  %sx  %system  %tb  %time  %timeit  %unalias  %unload_ext  %who  %who_ls  %whos  %xdel  %xmode\n",
       "\n",
       "Available cell magics:\n",
       "%%!  %%HTML  %%R  %%SVG  %%bash  %%capture  %%debug  %%file  %%html  %%javascript  %%js  %%latex  %%markdown  %%perl  %%prun  %%pypy  %%python  %%python2  %%python3  %%ruby  %%script  %%sh  %%svg  %%sx  %%system  %%time  %%timeit  %%writefile\n",
       "\n",
       "Automagic is ON, % prefix IS NOT needed for line magics."
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%lsmagic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variable       Type         Data/Info\n",
      "-------------------------------------\n",
      "cars_df        DataFrame        speed   dist\\n1     4<...>  120.0\\n50   25.0   85.0\n",
      "df             DataFrame          longitude  latitude<...>\\n[3000 rows x 9 columns]\n",
      "googol         int          1000000000000000000000000<...>0000000000000000000000000\n",
      "new_phrase     ndarray      1: 1 elems, type `<U22`, 88 bytes\n",
      "np             module       <module 'numpy' from '/cm<...>kages/numpy/__init__.py'>\n",
      "os             module       <module 'os' from '/cm/sh<...>ate/lib/python3.6/os.py'>\n",
      "pd             module       <module 'pandas' from '/c<...>ages/pandas/__init__.py'>\n",
      "stringValues   str          abcdef\n",
      "warnings       module       <module 'warnings' from '<...>b/python3.6/warnings.py'>\n",
      "x              ndarray      1: 1 elems, type `<U16`, 64 bytes\n",
      "x_new          ndarray      1: 1 elems, type `float64`, 8 bytes\n",
      "y              ndarray      1: 1 elems, type `<U15`, 60 bytes\n",
      "y_new          ndarray      1: 1 elems, type `float64`, 8 bytes\n",
      "z              ndarray      4: 4 elems, type `int64`, 32 bytes\n",
      "z_mat          ndarray      2x3: 6 elems, type `float64`, 48 bytes\n",
      "z_mean         ndarray      1: 1 elems, type `float64`, 8 bytes\n",
      "zmat           ndarray      2x3: 6 elems, type `float64`, 48 bytes\n"
     ]
    }
   ],
   "source": [
    "whos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# del [<objectName_1, <objectName_2, ....>\n",
    "# %reset remove everything (removes all objects - does ask for confirmation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "del [zmat,z_mean]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variable       Type         Data/Info\n",
      "-------------------------------------\n",
      "cars_df        DataFrame        speed   dist\\n1     4<...>  120.0\\n50   25.0   85.0\n",
      "df             DataFrame          longitude  latitude<...>\\n[3000 rows x 9 columns]\n",
      "googol         int          1000000000000000000000000<...>0000000000000000000000000\n",
      "new_phrase     ndarray      1: 1 elems, type `<U22`, 88 bytes\n",
      "np             module       <module 'numpy' from '/cm<...>kages/numpy/__init__.py'>\n",
      "os             module       <module 'os' from '/cm/sh<...>ate/lib/python3.6/os.py'>\n",
      "pd             module       <module 'pandas' from '/c<...>ages/pandas/__init__.py'>\n",
      "stringValues   str          abcdef\n",
      "warnings       module       <module 'warnings' from '<...>b/python3.6/warnings.py'>\n",
      "x              ndarray      1: 1 elems, type `<U16`, 64 bytes\n",
      "x_new          ndarray      1: 1 elems, type `float64`, 8 bytes\n",
      "y              ndarray      1: 1 elems, type `<U15`, 60 bytes\n",
      "y_new          ndarray      1: 1 elems, type `float64`, 8 bytes\n",
      "z              ndarray      4: 4 elems, type `int64`, 32 bytes\n",
      "z_mat          ndarray      2x3: 6 elems, type `float64`, 48 bytes\n"
     ]
    }
   ],
   "source": [
    "whos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%reset"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "GettingStarted.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}