EnP_training/very_brief_intro_to_python.ipynb

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      "name": "very-brief-intro-to-python.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyOsoMBEC+14tX+9CkEJoJnp",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/yohanesnuwara/python-bootcamp-for-geoengineers/blob/master/EnP_training/very_brief_intro_to_python.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3rNHyqlvt7QR"
      },
      "source": [
        "# Very Brief Intro to Python "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "27zIz-OxyV1T"
      },
      "source": [
        "This is a notebook for very brief intro to Python. There is also a notebook to learn more, you may want to check [here](https://colab.research.google.com/github/yohanesnuwara/python-bootcamp-for-geoengineers/blob/master/petroweek_notebooks/petroweek2020_unit1.ipynb). To open these notebooks in Colab, click \"Copy to Drive\", then you'll have this notebook and freely code. Happy learning! "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9wBPxCMUxqc-"
      },
      "source": [
        "## Using libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lzghh1FH9anX"
      },
      "source": [
        "# import libraries. These one have been pre-installed.\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "\n",
        "# this is style for plotting. There are many, you can try: 'classic', 'seaborn'\n",
        "# but if you don't want any style, you can just omit this code. \n",
        "plt.style.use('ggplot')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wy_3HI6brnpY"
      },
      "source": [
        "## Very Brief Intro to Numpy"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1AP4JcpKrtaM",
        "outputId": "24fc96c0-810b-45ed-af1a-e5ac855ce99b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# generate array from 0 to 10 into 100 numbers\n",
        "a = np.linspace(0, 10, 100)\n",
        "print(a)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[ 0.          0.1010101   0.2020202   0.3030303   0.4040404   0.50505051\n",
            "  0.60606061  0.70707071  0.80808081  0.90909091  1.01010101  1.11111111\n",
            "  1.21212121  1.31313131  1.41414141  1.51515152  1.61616162  1.71717172\n",
            "  1.81818182  1.91919192  2.02020202  2.12121212  2.22222222  2.32323232\n",
            "  2.42424242  2.52525253  2.62626263  2.72727273  2.82828283  2.92929293\n",
            "  3.03030303  3.13131313  3.23232323  3.33333333  3.43434343  3.53535354\n",
            "  3.63636364  3.73737374  3.83838384  3.93939394  4.04040404  4.14141414\n",
            "  4.24242424  4.34343434  4.44444444  4.54545455  4.64646465  4.74747475\n",
            "  4.84848485  4.94949495  5.05050505  5.15151515  5.25252525  5.35353535\n",
            "  5.45454545  5.55555556  5.65656566  5.75757576  5.85858586  5.95959596\n",
            "  6.06060606  6.16161616  6.26262626  6.36363636  6.46464646  6.56565657\n",
            "  6.66666667  6.76767677  6.86868687  6.96969697  7.07070707  7.17171717\n",
            "  7.27272727  7.37373737  7.47474747  7.57575758  7.67676768  7.77777778\n",
            "  7.87878788  7.97979798  8.08080808  8.18181818  8.28282828  8.38383838\n",
            "  8.48484848  8.58585859  8.68686869  8.78787879  8.88888889  8.98989899\n",
            "  9.09090909  9.19191919  9.29292929  9.39393939  9.49494949  9.5959596\n",
            "  9.6969697   9.7979798   9.8989899  10.        ]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jo482MwysGx7",
        "outputId": "2cafcc24-c474-4024-dc72-f67fb1bdcc3c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# generate array from 0 to 10 with increment of 0.25\n",
        "b = np.arange(0, 10, 0.25)\n",
        "print(b)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0.   0.25 0.5  0.75 1.   1.25 1.5  1.75 2.   2.25 2.5  2.75 3.   3.25\n",
            " 3.5  3.75 4.   4.25 4.5  4.75 5.   5.25 5.5  5.75 6.   6.25 6.5  6.75\n",
            " 7.   7.25 7.5  7.75 8.   8.25 8.5  8.75 9.   9.25 9.5  9.75]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "02YeBT6UshSD",
        "outputId": "70a43150-e129-48c2-93ff-709b7fbe3614",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# generate 100 uniform random numbers from 0 to 1\n",
        "c = np.random.random(100)\n",
        "print(c)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0.06271641 0.84059907 0.93631502 0.4105495  0.4366828  0.80674812\n",
            " 0.11499814 0.25113031 0.97106684 0.85181778 0.21059253 0.69901868\n",
            " 0.30253948 0.24178122 0.38980028 0.76120142 0.72298407 0.57884657\n",
            " 0.64937415 0.42691989 0.99936515 0.53249799 0.44871296 0.46200557\n",
            " 0.54352513 0.21811164 0.33975938 0.59161285 0.96058767 0.41063232\n",
            " 0.97949453 0.52216224 0.09316402 0.82459009 0.44937344 0.19771277\n",
            " 0.08246336 0.69515185 0.28866627 0.23361029 0.63478602 0.52177929\n",
            " 0.84935292 0.56927056 0.26741799 0.14553367 0.73545928 0.89551321\n",
            " 0.10552551 0.15782554 0.56070725 0.49757164 0.96738045 0.13882024\n",
            " 0.50748263 0.63565063 0.63753857 0.39261808 0.41205253 0.54826092\n",
            " 0.77198699 0.51717877 0.09984708 0.17657962 0.5716617  0.44753134\n",
            " 0.25421025 0.83113165 0.61910675 0.44473912 0.20686361 0.58735699\n",
            " 0.65604469 0.80112058 0.65692192 0.52961714 0.75158991 0.21859303\n",
            " 0.73827733 0.04736442 0.24721551 0.33872318 0.94283929 0.27653502\n",
            " 0.40291684 0.97492395 0.5605706  0.67186271 0.93494347 0.24894583\n",
            " 0.24855647 0.37986066 0.01700445 0.77480828 0.58049646 0.99249471\n",
            " 0.93013956 0.00650611 0.57805366 0.4116233 ]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z9jaUxxbsqQ9",
        "outputId": "c0dd7efb-ddbb-4568-e04e-052bcf62e3e3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# multiplying a and c arrays\n",
        "print(a * c)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0.         0.084909   0.18915455 0.12440894 0.1764375  0.40744854\n",
            " 0.06969584 0.17756689 0.78470048 0.7743798  0.21271973 0.77668742\n",
            " 0.36671453 0.31749049 0.55123272 1.15333549 1.1684591  0.99397896\n",
            " 1.18068027 0.81934121 2.01891949 1.12954119 0.99713991 1.07334627\n",
            " 1.31763667 0.55078697 0.89229736 1.61348959 2.71681361 1.20286236\n",
            " 2.96816524 1.63505348 0.30113623 2.74863363 1.54330271 0.69898455\n",
            " 0.29986678 2.59804225 1.10801194 0.92028295 2.56479199 2.16090412\n",
            " 3.6033154  2.4725893  1.18852439 0.6615167  3.41728555 4.25142635\n",
            " 0.51163881 0.78115669 2.83185477 2.56324782 5.08119025 0.74317908\n",
            " 2.76808705 3.53139241 3.6062788  2.26052833 2.4140451  3.26741356\n",
            " 4.67870905 3.18665708 0.62530493 1.12368851 3.69559078 2.93833711\n",
            " 1.69473503 5.62483038 4.25245041 3.09969692 1.46267201 4.21235821\n",
            " 4.77123413 5.90725274 4.91032549 4.01225107 5.76978113 1.70016802\n",
            " 5.81673049 0.37795853 1.99770107 2.77137149 7.80937592 2.31842495\n",
            " 3.41868837 8.37055914 4.86960315 5.90424807 8.31060865 2.2379979\n",
            " 2.25960423 3.49164852 0.15802118 7.27850202 5.51178455 9.52393914\n",
            " 9.01953514 0.06374676 5.72214735 4.11623304]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pao-uz0YtTkg",
        "outputId": "dd7c3fe8-10f7-4df8-9633-63931721fd67",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# make a function y=f(x), b as input\n",
        "def cubic(x, a, b, c):\n",
        "  y = a* (x**3) + b * (x**2) + c * x\n",
        "  return y\n",
        "\n",
        "y = cubic(b, 0.5, 0.25, 0.75)\n",
        "print(y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0.00000000e+00 2.10937500e-01 5.00000000e-01 9.14062500e-01\n",
            " 1.50000000e+00 2.30468750e+00 3.37500000e+00 4.75781250e+00\n",
            " 6.50000000e+00 8.64843750e+00 1.12500000e+01 1.43515625e+01\n",
            " 1.80000000e+01 2.22421875e+01 2.71250000e+01 3.26953125e+01\n",
            " 3.90000000e+01 4.60859375e+01 5.40000000e+01 6.27890625e+01\n",
            " 7.25000000e+01 8.31796875e+01 9.48750000e+01 1.07632812e+02\n",
            " 1.21500000e+02 1.36523438e+02 1.52750000e+02 1.70226562e+02\n",
            " 1.89000000e+02 2.09117188e+02 2.30625000e+02 2.53570312e+02\n",
            " 2.78000000e+02 3.03960938e+02 3.31500000e+02 3.60664062e+02\n",
            " 3.91500000e+02 4.24054688e+02 4.58375000e+02 4.94507812e+02]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pEE6w7_OsyPS",
        "outputId": "656504cc-19ca-47f2-fdb8-c259eb5ca134",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# make a 2D array and transpose it\n",
        "d = np.array([[1,2,3,4,5],\n",
        "              [6,7,8,9,10],\n",
        "              [11,12,13,14,15]])\n",
        "d_trans = d.T\n",
        "print(d)\n",
        "print(d_trans)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[ 1  2  3  4  5]\n",
            " [ 6  7  8  9 10]\n",
            " [11 12 13 14 15]]\n",
            "[[ 1  6 11]\n",
            " [ 2  7 12]\n",
            " [ 3  8 13]\n",
            " [ 4  9 14]\n",
            " [ 5 10 15]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bUb_CDqGtNhm"
      },
      "source": [
        "## Very Brief Intro to Matplotlib"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "te0aSEtS9ten",
        "outputId": "51b87a27-726d-49f5-de0c-1f0c695d38b0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        }
      },
      "source": [
        "# make a trigonometric array\n",
        "x = np.linspace(0,360,100)\n",
        "xrad = np.deg2rad(x)\n",
        "\n",
        "y1 = np.sin(xrad)\n",
        "y2 = np.cos(xrad)\n",
        "\n",
        "# plot this function\n",
        "plt.plot(xrad, y1)\n",
        "plt.plot(xrad, y2)\n",
        "plt.title('Trigonometric Function', size=20, pad=10)\n",
        "plt.xlabel('X')\n",
        "plt.ylabel('Y')\n",
        "# plt.xlim(min(x), max(x))\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fM7KbBAJuj7i"
      },
      "source": [
        "## Very Brief Intro to Pandas"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Md9v557b_Gmt",
        "outputId": "15e8afd8-0cb9-4063-92da-a9b51bedf472",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408
        }
      },
      "source": [
        "# convert the trigonometry result into a dataframe (a.k.a spreadsheet)\n",
        "trig = pd.DataFrame({'X': x, 'sin(x)': y1, 'cos(x)': y2})\n",
        "\n",
        "# print it\n",
        "trig"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>X</th>\n",
              "      <th>sin(x)</th>\n",
              "      <th>cos(x)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3.636364</td>\n",
              "      <td>6.342392e-02</td>\n",
              "      <td>0.997987</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7.272727</td>\n",
              "      <td>1.265925e-01</td>\n",
              "      <td>0.991955</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>10.909091</td>\n",
              "      <td>1.892512e-01</td>\n",
              "      <td>0.981929</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>14.545455</td>\n",
              "      <td>2.511480e-01</td>\n",
              "      <td>0.967949</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>345.454545</td>\n",
              "      <td>-2.511480e-01</td>\n",
              "      <td>0.967949</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96</th>\n",
              "      <td>349.090909</td>\n",
              "      <td>-1.892512e-01</td>\n",
              "      <td>0.981929</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>352.727273</td>\n",
              "      <td>-1.265925e-01</td>\n",
              "      <td>0.991955</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>356.363636</td>\n",
              "      <td>-6.342392e-02</td>\n",
              "      <td>0.997987</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>360.000000</td>\n",
              "      <td>-2.449294e-16</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>100 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             X        sin(x)    cos(x)\n",
              "0     0.000000  0.000000e+00  1.000000\n",
              "1     3.636364  6.342392e-02  0.997987\n",
              "2     7.272727  1.265925e-01  0.991955\n",
              "3    10.909091  1.892512e-01  0.981929\n",
              "4    14.545455  2.511480e-01  0.967949\n",
              "..         ...           ...       ...\n",
              "95  345.454545 -2.511480e-01  0.967949\n",
              "96  349.090909 -1.892512e-01  0.981929\n",
              "97  352.727273 -1.265925e-01  0.991955\n",
              "98  356.363636 -6.342392e-02  0.997987\n",
              "99  360.000000 -2.449294e-16  1.000000\n",
              "\n",
              "[100 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wHCtLwHQvfZu",
        "outputId": "4ea5102b-c413-456f-eff2-5ca0eee8c78e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        }
      },
      "source": [
        "# print the first 10 rows \n",
        "trig.head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>X</th>\n",
              "      <th>sin(x)</th>\n",
              "      <th>cos(x)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3.636364</td>\n",
              "      <td>0.063424</td>\n",
              "      <td>0.997987</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7.272727</td>\n",
              "      <td>0.126592</td>\n",
              "      <td>0.991955</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>10.909091</td>\n",
              "      <td>0.189251</td>\n",
              "      <td>0.981929</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>14.545455</td>\n",
              "      <td>0.251148</td>\n",
              "      <td>0.967949</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>18.181818</td>\n",
              "      <td>0.312033</td>\n",
              "      <td>0.950071</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>21.818182</td>\n",
              "      <td>0.371662</td>\n",
              "      <td>0.928368</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>25.454545</td>\n",
              "      <td>0.429795</td>\n",
              "      <td>0.902927</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>29.090909</td>\n",
              "      <td>0.486197</td>\n",
              "      <td>0.873849</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>32.727273</td>\n",
              "      <td>0.540641</td>\n",
              "      <td>0.841254</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           X    sin(x)    cos(x)\n",
              "0   0.000000  0.000000  1.000000\n",
              "1   3.636364  0.063424  0.997987\n",
              "2   7.272727  0.126592  0.991955\n",
              "3  10.909091  0.189251  0.981929\n",
              "4  14.545455  0.251148  0.967949\n",
              "5  18.181818  0.312033  0.950071\n",
              "6  21.818182  0.371662  0.928368\n",
              "7  25.454545  0.429795  0.902927\n",
              "8  29.090909  0.486197  0.873849\n",
              "9  32.727273  0.540641  0.841254"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RhSmMA7GvkkE",
        "outputId": "fe4be42d-839d-4268-d243-323b8a0d34f6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        }
      },
      "source": [
        "# print the last 10 rows\n",
        "trig.tail(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "        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>X</th>\n",
              "      <th>sin(x)</th>\n",
              "      <th>cos(x)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>90</th>\n",
              "      <td>327.272727</td>\n",
              "      <td>-5.406408e-01</td>\n",
              "      <td>0.841254</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>91</th>\n",
              "      <td>330.909091</td>\n",
              "      <td>-4.861967e-01</td>\n",
              "      <td>0.873849</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>92</th>\n",
              "      <td>334.545455</td>\n",
              "      <td>-4.297949e-01</td>\n",
              "      <td>0.902927</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>93</th>\n",
              "      <td>338.181818</td>\n",
              "      <td>-3.716625e-01</td>\n",
              "      <td>0.928368</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>94</th>\n",
              "      <td>341.818182</td>\n",
              "      <td>-3.120334e-01</td>\n",
              "      <td>0.950071</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>345.454545</td>\n",
              "      <td>-2.511480e-01</td>\n",
              "      <td>0.967949</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96</th>\n",
              "      <td>349.090909</td>\n",
              "      <td>-1.892512e-01</td>\n",
              "      <td>0.981929</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>352.727273</td>\n",
              "      <td>-1.265925e-01</td>\n",
              "      <td>0.991955</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>356.363636</td>\n",
              "      <td>-6.342392e-02</td>\n",
              "      <td>0.997987</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>360.000000</td>\n",
              "      <td>-2.449294e-16</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             X        sin(x)    cos(x)\n",
              "90  327.272727 -5.406408e-01  0.841254\n",
              "91  330.909091 -4.861967e-01  0.873849\n",
              "92  334.545455 -4.297949e-01  0.902927\n",
              "93  338.181818 -3.716625e-01  0.928368\n",
              "94  341.818182 -3.120334e-01  0.950071\n",
              "95  345.454545 -2.511480e-01  0.967949\n",
              "96  349.090909 -1.892512e-01  0.981929\n",
              "97  352.727273 -1.265925e-01  0.991955\n",
              "98  356.363636 -6.342392e-02  0.997987\n",
              "99  360.000000 -2.449294e-16  1.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mccQKOzE_jGi",
        "outputId": "24607b22-3e5b-4225-82ad-bb350fe92c32",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        }
      },
      "source": [
        "# create another dataframe \n",
        "names = ['Robby', 'Alice', 'Rick', 'James', 'Alley']\n",
        "ages = [20, 30, 21, 17, 18]\n",
        "cars = [0, 2, 3, 1, 6]\n",
        "\n",
        "data = pd.DataFrame({'Name': names, 'Age': ages, 'Cars': cars})\n",
        "\n",
        "data"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Name</th>\n",
              "      <th>Age</th>\n",
              "      <th>Cars</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Robby</td>\n",
              "      <td>20</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Alice</td>\n",
              "      <td>30</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Rick</td>\n",
              "      <td>21</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>James</td>\n",
              "      <td>17</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Alley</td>\n",
              "      <td>18</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Name  Age  Cars\n",
              "0  Robby   20     0\n",
              "1  Alice   30     2\n",
              "2   Rick   21     3\n",
              "3  James   17     1\n",
              "4  Alley   18     6"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vpP06kODAjIm",
        "outputId": "5ad297b3-2e84-4f50-e1ca-e1ee8fcfb386",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        }
      },
      "source": [
        "# add new columns \"hometown\" and \"gender\" to our data\n",
        "hometown = ['Berlin', 'Madras', 'London', 'Warsaw', 'Paris']\n",
        "gender = ['Male', 'Female', 'Male', 'Male', 'Female']\n",
        "\n",
        "data['Hometown'] = hometown\n",
        "data['Gender'] = gender\n",
        "\n",
        "data"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Name</th>\n",
              "      <th>Age</th>\n",
              "      <th>Cars</th>\n",
              "      <th>Hometown</th>\n",
              "      <th>Gender</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Robby</td>\n",
              "      <td>20</td>\n",
              "      <td>0</td>\n",
              "      <td>Berlin</td>\n",
              "      <td>Male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Alice</td>\n",
              "      <td>30</td>\n",
              "      <td>2</td>\n",
              "      <td>Madras</td>\n",
              "      <td>Female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Rick</td>\n",
              "      <td>21</td>\n",
              "      <td>3</td>\n",
              "      <td>London</td>\n",
              "      <td>Male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>James</td>\n",
              "      <td>17</td>\n",
              "      <td>1</td>\n",
              "      <td>Warsaw</td>\n",
              "      <td>Male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Alley</td>\n",
              "      <td>18</td>\n",
              "      <td>6</td>\n",
              "      <td>Paris</td>\n",
              "      <td>Female</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Name  Age  Cars Hometown  Gender\n",
              "0  Robby   20     0   Berlin    Male\n",
              "1  Alice   30     2   Madras  Female\n",
              "2   Rick   21     3   London    Male\n",
              "3  James   17     1   Warsaw    Male\n",
              "4  Alley   18     6    Paris  Female"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ds_HN0wlL07w",
        "outputId": "7efa6281-59f8-465d-b1bd-138c3c4f7eaf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# print only the data with \"Name\" column\n",
        "data['Name']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    Robby\n",
              "1    Alice\n",
              "2     Rick\n",
              "3    James\n",
              "4    Alley\n",
              "Name: Name, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qCxNMBQRv-mQ",
        "outputId": "f5d2be9b-d657-4e08-d864-a18aebb295d8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# or another alternative\n",
        "data.iloc[:,0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    Robby\n",
              "1    Alice\n",
              "2     Rick\n",
              "3    James\n",
              "4    Alley\n",
              "Name: Name, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uxuDXpMBBa3y",
        "outputId": "08c38c6d-c1c1-4eff-de60-80fc576c0db4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# print only the data in the first row\n",
        "data.iloc[1,:]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Name         Alice\n",
              "Age             30\n",
              "Cars             2\n",
              "Hometown    Madras\n",
              "Gender      Female\n",
              "Name: 1, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DmCdUvvqA1s4",
        "outputId": "5222246c-8ec3-4754-caf8-49d43822d1b6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 108
        }
      },
      "source": [
        "# print only the data with \"Gender\" as \"Female\"\n",
        "mask_male = data['Gender'] == 'Female'\n",
        "\n",
        "data[mask_male]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Name</th>\n",
              "      <th>Age</th>\n",
              "      <th>Cars</th>\n",
              "      <th>Hometown</th>\n",
              "      <th>Gender</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Alice</td>\n",
              "      <td>30</td>\n",
              "      <td>2</td>\n",
              "      <td>Madras</td>\n",
              "      <td>Female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Alley</td>\n",
              "      <td>18</td>\n",
              "      <td>6</td>\n",
              "      <td>Paris</td>\n",
              "      <td>Female</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Name  Age  Cars Hometown  Gender\n",
              "1  Alice   30     2   Madras  Female\n",
              "4  Alley   18     6    Paris  Female"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2RkG9Tb1weqp"
      },
      "source": [
        "## Copyright\n",
        "\n",
        "This notebook is copyright of Yohanes Nuwara (2020). It is contained in [this repository](https://github.com/yohanesnuwara/python-bootcamp-for-geoengineers) You may freely distribute for self-study and tutorials, but you will consider the authorship of all the codes written here. \n",
        "\n",
        "<a rel=\"license\" href=\"http://creativecommons.org/licenses/by/4.0/\"><img alt=\"Creative Commons License\" style=\"border-width:0\" src=\"https://i.creativecommons.org/l/by/4.0/88x31.png\" /></a><br />This work is licensed under a <a rel=\"license\" href=\"http://creativecommons.org/licenses/by/4.0/\">Creative Commons Attribution 4.0 International License</a>."
      ]
    }
  ]
}