0. Introduction.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Beltgel_og.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "widgets": {
      "application/vnd.jupyter.widget-state+json": {
        "efb8db6f15994245b070cfb538da4a15": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "state": {
            "_view_name": "HBoxView",
            "_dom_classes": [],
            "_model_name": "HBoxModel",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "box_style": "",
            "layout": "IPY_MODEL_878eee48e1544371a0c18adb2800a260",
            "_model_module": "@jupyter-widgets/controls",
            "children": [
              "IPY_MODEL_aee6f679137e4459b5fecf369d5b6dd7",
              "IPY_MODEL_ad9a8f4e4f7340cb9d5d216100de9e55"
            ]
          }
        },
        "878eee48e1544371a0c18adb2800a260": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "aee6f679137e4459b5fecf369d5b6dd7": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "state": {
            "_view_name": "ProgressView",
            "style": "IPY_MODEL_14eea11afaef4be18765bf921a8d1284",
            "_dom_classes": [],
            "description": "",
            "_model_name": "FloatProgressModel",
            "bar_style": "success",
            "max": 26421880,
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": 26421880,
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "orientation": "horizontal",
            "min": 0,
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_347c9340fcee4a88a1c4195bee154e84"
          }
        },
        "ad9a8f4e4f7340cb9d5d216100de9e55": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "state": {
            "_view_name": "HTMLView",
            "style": "IPY_MODEL_24322f5e94a948608c81de1b7d545e39",
            "_dom_classes": [],
            "description": "",
            "_model_name": "HTMLModel",
            "placeholder": "​",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": " 26422272/? [3:54:18&lt;00:00, 1879.48it/s]",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_4b041fd3d1054d63b3b43f5eba46192f"
          }
        },
        "14eea11afaef4be18765bf921a8d1284": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "ProgressStyleModel",
            "description_width": "initial",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "bar_color": null,
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "347c9340fcee4a88a1c4195bee154e84": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "24322f5e94a948608c81de1b7d545e39": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "DescriptionStyleModel",
            "description_width": "",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "4b041fd3d1054d63b3b43f5eba46192f": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "30f38dc8db544be3a233c1a6036c8236": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "state": {
            "_view_name": "HBoxView",
            "_dom_classes": [],
            "_model_name": "HBoxModel",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "box_style": "",
            "layout": "IPY_MODEL_b72ee78e33cf43658b05f7828b70f710",
            "_model_module": "@jupyter-widgets/controls",
            "children": [
              "IPY_MODEL_4b84af0b976f49ddaea20feb1fc45044",
              "IPY_MODEL_8ac1ef4857ba4cb2a47aa6edc973dff9"
            ]
          }
        },
        "b72ee78e33cf43658b05f7828b70f710": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "4b84af0b976f49ddaea20feb1fc45044": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "state": {
            "_view_name": "ProgressView",
            "style": "IPY_MODEL_e27662f633c94ea2804bc6d80e2d4ca1",
            "_dom_classes": [],
            "description": "",
            "_model_name": "FloatProgressModel",
            "bar_style": "success",
            "max": 29515,
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": 29515,
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "orientation": "horizontal",
            "min": 0,
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_fd6ba4a85374427792a394d53c714c75"
          }
        },
        "8ac1ef4857ba4cb2a47aa6edc973dff9": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "state": {
            "_view_name": "HTMLView",
            "style": "IPY_MODEL_b35f4e12f7764a08919790eb80856676",
            "_dom_classes": [],
            "description": "",
            "_model_name": "HTMLModel",
            "placeholder": "​",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": " 29696/? [00:00&lt;00:00, 47441.95it/s]",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_3fdde54c0e6747b79d7fecfea23b9274"
          }
        },
        "e27662f633c94ea2804bc6d80e2d4ca1": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "ProgressStyleModel",
            "description_width": "initial",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "bar_color": null,
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "fd6ba4a85374427792a394d53c714c75": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "b35f4e12f7764a08919790eb80856676": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "DescriptionStyleModel",
            "description_width": "",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "3fdde54c0e6747b79d7fecfea23b9274": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "50590d67caa045c39a1059575698c7de": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "state": {
            "_view_name": "HBoxView",
            "_dom_classes": [],
            "_model_name": "HBoxModel",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "box_style": "",
            "layout": "IPY_MODEL_027de05cc634464c95760a07a51cc83c",
            "_model_module": "@jupyter-widgets/controls",
            "children": [
              "IPY_MODEL_5bf80648ca2e4cc5963833d6c7875045",
              "IPY_MODEL_e0bf9d0f47164c0db851180b6d7ea8a5"
            ]
          }
        },
        "027de05cc634464c95760a07a51cc83c": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "5bf80648ca2e4cc5963833d6c7875045": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "state": {
            "_view_name": "ProgressView",
            "style": "IPY_MODEL_96293a24cefb455c89d825cabd138d4f",
            "_dom_classes": [],
            "description": "",
            "_model_name": "FloatProgressModel",
            "bar_style": "success",
            "max": 4422102,
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": 4422102,
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "orientation": "horizontal",
            "min": 0,
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_0631b3efda154c4fbf6a5473a65211c2"
          }
        },
        "e0bf9d0f47164c0db851180b6d7ea8a5": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "state": {
            "_view_name": "HTMLView",
            "style": "IPY_MODEL_7da94c8116394ac498793bcce1a4907c",
            "_dom_classes": [],
            "description": "",
            "_model_name": "HTMLModel",
            "placeholder": "​",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": " 4422656/? [3:54:14&lt;00:00, 314.67it/s]",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_b00a093c4ad04a628312cce2d2a2a042"
          }
        },
        "96293a24cefb455c89d825cabd138d4f": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "ProgressStyleModel",
            "description_width": "initial",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "bar_color": null,
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "0631b3efda154c4fbf6a5473a65211c2": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "7da94c8116394ac498793bcce1a4907c": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "DescriptionStyleModel",
            "description_width": "",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "b00a093c4ad04a628312cce2d2a2a042": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "776a99e40ed347878245190f13ca3cc7": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "state": {
            "_view_name": "HBoxView",
            "_dom_classes": [],
            "_model_name": "HBoxModel",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "box_style": "",
            "layout": "IPY_MODEL_fddedb1da214495192d7b856ad058631",
            "_model_module": "@jupyter-widgets/controls",
            "children": [
              "IPY_MODEL_14e1b2cc00de40ee9ff6316e7eaabc69",
              "IPY_MODEL_a2549d2ed4a748048824f2cfc99f3690"
            ]
          }
        },
        "fddedb1da214495192d7b856ad058631": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "14e1b2cc00de40ee9ff6316e7eaabc69": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "state": {
            "_view_name": "ProgressView",
            "style": "IPY_MODEL_aefb53f9e4884faeab31bb29c1c547b1",
            "_dom_classes": [],
            "description": "",
            "_model_name": "FloatProgressModel",
            "bar_style": "success",
            "max": 5148,
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": 5148,
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "orientation": "horizontal",
            "min": 0,
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_f339b17818fc4a15b7d927902519f68a"
          }
        },
        "a2549d2ed4a748048824f2cfc99f3690": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "state": {
            "_view_name": "HTMLView",
            "style": "IPY_MODEL_c1a165a093964b6fa79e5d400884fc91",
            "_dom_classes": [],
            "description": "",
            "_model_name": "HTMLModel",
            "placeholder": "​",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": " 6144/? [10:04&lt;00:00, 10.16it/s]",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_7cf4ec1f84364d9cb4583b5b3db6c742"
          }
        },
        "aefb53f9e4884faeab31bb29c1c547b1": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "ProgressStyleModel",
            "description_width": "initial",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "bar_color": null,
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "f339b17818fc4a15b7d927902519f68a": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "c1a165a093964b6fa79e5d400884fc91": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "DescriptionStyleModel",
            "description_width": "",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "7cf4ec1f84364d9cb4583b5b3db6c742": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        }
      }
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WljxY9XvLTOH"
      },
      "source": [
        "# Preparation for the training\n",
        "Python, numpy, scipy, pytorch basics, environment\n",
        "\n",
        "Prepared by OG"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r6m9oHJuNqHa"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3_qqG15kNFIB"
      },
      "source": [
        "## Show how to download notebook from Github\n",
        "\n",
        "Open it on Google drive\n",
        "\n",
        "How to run a cell"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LthKlHDZM8DO"
      },
      "source": [
        "## Python basics"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Mp0lo8OpS8mq"
      },
      "source": [
        "Variable assignments"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Go_nwGglM6zq",
        "outputId": "307f2e4e-d342-43bc-9b35-9cf80e2f1c92"
      },
      "source": [
        "x = 5\n",
        "x"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ss2-SONNTBF7"
      },
      "source": [
        "Variable calculations"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SsCc7JR6M63i",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3f969807-0b44-465e-8b10-9f436d34b94a"
      },
      "source": [
        "x+20\n",
        "#x-2\n",
        "#x*5\n",
        "#x**2\n",
        "#x%2\n",
        "#x/float(2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "25"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iBXzqbKATxoq"
      },
      "source": [
        "Types and Type conversion"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "liC7IRB1Q-Aq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "55fabba4-dbc1-4e1f-97d7-dc459116ed2b"
      },
      "source": [
        "str(2)\n",
        "int(\"2\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yXPEJzoiT4gs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1f3f043a-268d-41e6-81ed-eb0e429dbe6b"
      },
      "source": [
        "float(3)\n",
        "bool(1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "00XYAGUdT7aW"
      },
      "source": [
        "Asking for help"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uPyYALPDQ-HM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2035be29-41d5-40ba-9732-7e8737cd4276"
      },
      "source": [
        "help(str)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Help on class str in module builtins:\n",
            "\n",
            "class str(object)\n",
            " |  str(object='') -> str\n",
            " |  str(bytes_or_buffer[, encoding[, errors]]) -> str\n",
            " |  \n",
            " |  Create a new string object from the given object. If encoding or\n",
            " |  errors is specified, then the object must expose a data buffer\n",
            " |  that will be decoded using the given encoding and error handler.\n",
            " |  Otherwise, returns the result of object.__str__() (if defined)\n",
            " |  or repr(object).\n",
            " |  encoding defaults to sys.getdefaultencoding().\n",
            " |  errors defaults to 'strict'.\n",
            " |  \n",
            " |  Methods defined here:\n",
            " |  \n",
            " |  __add__(self, value, /)\n",
            " |      Return self+value.\n",
            " |  \n",
            " |  __contains__(self, key, /)\n",
            " |      Return key in self.\n",
            " |  \n",
            " |  __eq__(self, value, /)\n",
            " |      Return self==value.\n",
            " |  \n",
            " |  __format__(self, format_spec, /)\n",
            " |      Return a formatted version of the string as described by format_spec.\n",
            " |  \n",
            " |  __ge__(self, value, /)\n",
            " |      Return self>=value.\n",
            " |  \n",
            " |  __getattribute__(self, name, /)\n",
            " |      Return getattr(self, name).\n",
            " |  \n",
            " |  __getitem__(self, key, /)\n",
            " |      Return self[key].\n",
            " |  \n",
            " |  __getnewargs__(...)\n",
            " |  \n",
            " |  __gt__(self, value, /)\n",
            " |      Return self>value.\n",
            " |  \n",
            " |  __hash__(self, /)\n",
            " |      Return hash(self).\n",
            " |  \n",
            " |  __iter__(self, /)\n",
            " |      Implement iter(self).\n",
            " |  \n",
            " |  __le__(self, value, /)\n",
            " |      Return self<=value.\n",
            " |  \n",
            " |  __len__(self, /)\n",
            " |      Return len(self).\n",
            " |  \n",
            " |  __lt__(self, value, /)\n",
            " |      Return self<value.\n",
            " |  \n",
            " |  __mod__(self, value, /)\n",
            " |      Return self%value.\n",
            " |  \n",
            " |  __mul__(self, value, /)\n",
            " |      Return self*value.\n",
            " |  \n",
            " |  __ne__(self, value, /)\n",
            " |      Return self!=value.\n",
            " |  \n",
            " |  __repr__(self, /)\n",
            " |      Return repr(self).\n",
            " |  \n",
            " |  __rmod__(self, value, /)\n",
            " |      Return value%self.\n",
            " |  \n",
            " |  __rmul__(self, value, /)\n",
            " |      Return value*self.\n",
            " |  \n",
            " |  __sizeof__(self, /)\n",
            " |      Return the size of the string in memory, in bytes.\n",
            " |  \n",
            " |  __str__(self, /)\n",
            " |      Return str(self).\n",
            " |  \n",
            " |  capitalize(self, /)\n",
            " |      Return a capitalized version of the string.\n",
            " |      \n",
            " |      More specifically, make the first character have upper case and the rest lower\n",
            " |      case.\n",
            " |  \n",
            " |  casefold(self, /)\n",
            " |      Return a version of the string suitable for caseless comparisons.\n",
            " |  \n",
            " |  center(self, width, fillchar=' ', /)\n",
            " |      Return a centered string of length width.\n",
            " |      \n",
            " |      Padding is done using the specified fill character (default is a space).\n",
            " |  \n",
            " |  count(...)\n",
            " |      S.count(sub[, start[, end]]) -> int\n",
            " |      \n",
            " |      Return the number of non-overlapping occurrences of substring sub in\n",
            " |      string S[start:end].  Optional arguments start and end are\n",
            " |      interpreted as in slice notation.\n",
            " |  \n",
            " |  encode(self, /, encoding='utf-8', errors='strict')\n",
            " |      Encode the string using the codec registered for encoding.\n",
            " |      \n",
            " |      encoding\n",
            " |        The encoding in which to encode the string.\n",
            " |      errors\n",
            " |        The error handling scheme to use for encoding errors.\n",
            " |        The default is 'strict' meaning that encoding errors raise a\n",
            " |        UnicodeEncodeError.  Other possible values are 'ignore', 'replace' and\n",
            " |        'xmlcharrefreplace' as well as any other name registered with\n",
            " |        codecs.register_error that can handle UnicodeEncodeErrors.\n",
            " |  \n",
            " |  endswith(...)\n",
            " |      S.endswith(suffix[, start[, end]]) -> bool\n",
            " |      \n",
            " |      Return True if S ends with the specified suffix, False otherwise.\n",
            " |      With optional start, test S beginning at that position.\n",
            " |      With optional end, stop comparing S at that position.\n",
            " |      suffix can also be a tuple of strings to try.\n",
            " |  \n",
            " |  expandtabs(self, /, tabsize=8)\n",
            " |      Return a copy where all tab characters are expanded using spaces.\n",
            " |      \n",
            " |      If tabsize is not given, a tab size of 8 characters is assumed.\n",
            " |  \n",
            " |  find(...)\n",
            " |      S.find(sub[, start[, end]]) -> int\n",
            " |      \n",
            " |      Return the lowest index in S where substring sub is found,\n",
            " |      such that sub is contained within S[start:end].  Optional\n",
            " |      arguments start and end are interpreted as in slice notation.\n",
            " |      \n",
            " |      Return -1 on failure.\n",
            " |  \n",
            " |  format(...)\n",
            " |      S.format(*args, **kwargs) -> str\n",
            " |      \n",
            " |      Return a formatted version of S, using substitutions from args and kwargs.\n",
            " |      The substitutions are identified by braces ('{' and '}').\n",
            " |  \n",
            " |  format_map(...)\n",
            " |      S.format_map(mapping) -> str\n",
            " |      \n",
            " |      Return a formatted version of S, using substitutions from mapping.\n",
            " |      The substitutions are identified by braces ('{' and '}').\n",
            " |  \n",
            " |  index(...)\n",
            " |      S.index(sub[, start[, end]]) -> int\n",
            " |      \n",
            " |      Return the lowest index in S where substring sub is found, \n",
            " |      such that sub is contained within S[start:end].  Optional\n",
            " |      arguments start and end are interpreted as in slice notation.\n",
            " |      \n",
            " |      Raises ValueError when the substring is not found.\n",
            " |  \n",
            " |  isalnum(self, /)\n",
            " |      Return True if the string is an alpha-numeric string, False otherwise.\n",
            " |      \n",
            " |      A string is alpha-numeric if all characters in the string are alpha-numeric and\n",
            " |      there is at least one character in the string.\n",
            " |  \n",
            " |  isalpha(self, /)\n",
            " |      Return True if the string is an alphabetic string, False otherwise.\n",
            " |      \n",
            " |      A string is alphabetic if all characters in the string are alphabetic and there\n",
            " |      is at least one character in the string.\n",
            " |  \n",
            " |  isascii(self, /)\n",
            " |      Return True if all characters in the string are ASCII, False otherwise.\n",
            " |      \n",
            " |      ASCII characters have code points in the range U+0000-U+007F.\n",
            " |      Empty string is ASCII too.\n",
            " |  \n",
            " |  isdecimal(self, /)\n",
            " |      Return True if the string is a decimal string, False otherwise.\n",
            " |      \n",
            " |      A string is a decimal string if all characters in the string are decimal and\n",
            " |      there is at least one character in the string.\n",
            " |  \n",
            " |  isdigit(self, /)\n",
            " |      Return True if the string is a digit string, False otherwise.\n",
            " |      \n",
            " |      A string is a digit string if all characters in the string are digits and there\n",
            " |      is at least one character in the string.\n",
            " |  \n",
            " |  isidentifier(self, /)\n",
            " |      Return True if the string is a valid Python identifier, False otherwise.\n",
            " |      \n",
            " |      Use keyword.iskeyword() to test for reserved identifiers such as \"def\" and\n",
            " |      \"class\".\n",
            " |  \n",
            " |  islower(self, /)\n",
            " |      Return True if the string is a lowercase string, False otherwise.\n",
            " |      \n",
            " |      A string is lowercase if all cased characters in the string are lowercase and\n",
            " |      there is at least one cased character in the string.\n",
            " |  \n",
            " |  isnumeric(self, /)\n",
            " |      Return True if the string is a numeric string, False otherwise.\n",
            " |      \n",
            " |      A string is numeric if all characters in the string are numeric and there is at\n",
            " |      least one character in the string.\n",
            " |  \n",
            " |  isprintable(self, /)\n",
            " |      Return True if the string is printable, False otherwise.\n",
            " |      \n",
            " |      A string is printable if all of its characters are considered printable in\n",
            " |      repr() or if it is empty.\n",
            " |  \n",
            " |  isspace(self, /)\n",
            " |      Return True if the string is a whitespace string, False otherwise.\n",
            " |      \n",
            " |      A string is whitespace if all characters in the string are whitespace and there\n",
            " |      is at least one character in the string.\n",
            " |  \n",
            " |  istitle(self, /)\n",
            " |      Return True if the string is a title-cased string, False otherwise.\n",
            " |      \n",
            " |      In a title-cased string, upper- and title-case characters may only\n",
            " |      follow uncased characters and lowercase characters only cased ones.\n",
            " |  \n",
            " |  isupper(self, /)\n",
            " |      Return True if the string is an uppercase string, False otherwise.\n",
            " |      \n",
            " |      A string is uppercase if all cased characters in the string are uppercase and\n",
            " |      there is at least one cased character in the string.\n",
            " |  \n",
            " |  join(self, iterable, /)\n",
            " |      Concatenate any number of strings.\n",
            " |      \n",
            " |      The string whose method is called is inserted in between each given string.\n",
            " |      The result is returned as a new string.\n",
            " |      \n",
            " |      Example: '.'.join(['ab', 'pq', 'rs']) -> 'ab.pq.rs'\n",
            " |  \n",
            " |  ljust(self, width, fillchar=' ', /)\n",
            " |      Return a left-justified string of length width.\n",
            " |      \n",
            " |      Padding is done using the specified fill character (default is a space).\n",
            " |  \n",
            " |  lower(self, /)\n",
            " |      Return a copy of the string converted to lowercase.\n",
            " |  \n",
            " |  lstrip(self, chars=None, /)\n",
            " |      Return a copy of the string with leading whitespace removed.\n",
            " |      \n",
            " |      If chars is given and not None, remove characters in chars instead.\n",
            " |  \n",
            " |  partition(self, sep, /)\n",
            " |      Partition the string into three parts using the given separator.\n",
            " |      \n",
            " |      This will search for the separator in the string.  If the separator is found,\n",
            " |      returns a 3-tuple containing the part before the separator, the separator\n",
            " |      itself, and the part after it.\n",
            " |      \n",
            " |      If the separator is not found, returns a 3-tuple containing the original string\n",
            " |      and two empty strings.\n",
            " |  \n",
            " |  replace(self, old, new, count=-1, /)\n",
            " |      Return a copy with all occurrences of substring old replaced by new.\n",
            " |      \n",
            " |        count\n",
            " |          Maximum number of occurrences to replace.\n",
            " |          -1 (the default value) means replace all occurrences.\n",
            " |      \n",
            " |      If the optional argument count is given, only the first count occurrences are\n",
            " |      replaced.\n",
            " |  \n",
            " |  rfind(...)\n",
            " |      S.rfind(sub[, start[, end]]) -> int\n",
            " |      \n",
            " |      Return the highest index in S where substring sub is found,\n",
            " |      such that sub is contained within S[start:end].  Optional\n",
            " |      arguments start and end are interpreted as in slice notation.\n",
            " |      \n",
            " |      Return -1 on failure.\n",
            " |  \n",
            " |  rindex(...)\n",
            " |      S.rindex(sub[, start[, end]]) -> int\n",
            " |      \n",
            " |      Return the highest index in S where substring sub is found,\n",
            " |      such that sub is contained within S[start:end].  Optional\n",
            " |      arguments start and end are interpreted as in slice notation.\n",
            " |      \n",
            " |      Raises ValueError when the substring is not found.\n",
            " |  \n",
            " |  rjust(self, width, fillchar=' ', /)\n",
            " |      Return a right-justified string of length width.\n",
            " |      \n",
            " |      Padding is done using the specified fill character (default is a space).\n",
            " |  \n",
            " |  rpartition(self, sep, /)\n",
            " |      Partition the string into three parts using the given separator.\n",
            " |      \n",
            " |      This will search for the separator in the string, starting at the end. If\n",
            " |      the separator is found, returns a 3-tuple containing the part before the\n",
            " |      separator, the separator itself, and the part after it.\n",
            " |      \n",
            " |      If the separator is not found, returns a 3-tuple containing two empty strings\n",
            " |      and the original string.\n",
            " |  \n",
            " |  rsplit(self, /, sep=None, maxsplit=-1)\n",
            " |      Return a list of the words in the string, using sep as the delimiter string.\n",
            " |      \n",
            " |        sep\n",
            " |          The delimiter according which to split the string.\n",
            " |          None (the default value) means split according to any whitespace,\n",
            " |          and discard empty strings from the result.\n",
            " |        maxsplit\n",
            " |          Maximum number of splits to do.\n",
            " |          -1 (the default value) means no limit.\n",
            " |      \n",
            " |      Splits are done starting at the end of the string and working to the front.\n",
            " |  \n",
            " |  rstrip(self, chars=None, /)\n",
            " |      Return a copy of the string with trailing whitespace removed.\n",
            " |      \n",
            " |      If chars is given and not None, remove characters in chars instead.\n",
            " |  \n",
            " |  split(self, /, sep=None, maxsplit=-1)\n",
            " |      Return a list of the words in the string, using sep as the delimiter string.\n",
            " |      \n",
            " |      sep\n",
            " |        The delimiter according which to split the string.\n",
            " |        None (the default value) means split according to any whitespace,\n",
            " |        and discard empty strings from the result.\n",
            " |      maxsplit\n",
            " |        Maximum number of splits to do.\n",
            " |        -1 (the default value) means no limit.\n",
            " |  \n",
            " |  splitlines(self, /, keepends=False)\n",
            " |      Return a list of the lines in the string, breaking at line boundaries.\n",
            " |      \n",
            " |      Line breaks are not included in the resulting list unless keepends is given and\n",
            " |      true.\n",
            " |  \n",
            " |  startswith(...)\n",
            " |      S.startswith(prefix[, start[, end]]) -> bool\n",
            " |      \n",
            " |      Return True if S starts with the specified prefix, False otherwise.\n",
            " |      With optional start, test S beginning at that position.\n",
            " |      With optional end, stop comparing S at that position.\n",
            " |      prefix can also be a tuple of strings to try.\n",
            " |  \n",
            " |  strip(self, chars=None, /)\n",
            " |      Return a copy of the string with leading and trailing whitespace removed.\n",
            " |      \n",
            " |      If chars is given and not None, remove characters in chars instead.\n",
            " |  \n",
            " |  swapcase(self, /)\n",
            " |      Convert uppercase characters to lowercase and lowercase characters to uppercase.\n",
            " |  \n",
            " |  title(self, /)\n",
            " |      Return a version of the string where each word is titlecased.\n",
            " |      \n",
            " |      More specifically, words start with uppercased characters and all remaining\n",
            " |      cased characters have lower case.\n",
            " |  \n",
            " |  translate(self, table, /)\n",
            " |      Replace each character in the string using the given translation table.\n",
            " |      \n",
            " |        table\n",
            " |          Translation table, which must be a mapping of Unicode ordinals to\n",
            " |          Unicode ordinals, strings, or None.\n",
            " |      \n",
            " |      The table must implement lookup/indexing via __getitem__, for instance a\n",
            " |      dictionary or list.  If this operation raises LookupError, the character is\n",
            " |      left untouched.  Characters mapped to None are deleted.\n",
            " |  \n",
            " |  upper(self, /)\n",
            " |      Return a copy of the string converted to uppercase.\n",
            " |  \n",
            " |  zfill(self, width, /)\n",
            " |      Pad a numeric string with zeros on the left, to fill a field of the given width.\n",
            " |      \n",
            " |      The string is never truncated.\n",
            " |  \n",
            " |  ----------------------------------------------------------------------\n",
            " |  Static methods defined here:\n",
            " |  \n",
            " |  __new__(*args, **kwargs) from builtins.type\n",
            " |      Create and return a new object.  See help(type) for accurate signature.\n",
            " |  \n",
            " |  maketrans(x, y=None, z=None, /)\n",
            " |      Return a translation table usable for str.translate().\n",
            " |      \n",
            " |      If there is only one argument, it must be a dictionary mapping Unicode\n",
            " |      ordinals (integers) or characters to Unicode ordinals, strings or None.\n",
            " |      Character keys will be then converted to ordinals.\n",
            " |      If there are two arguments, they must be strings of equal length, and\n",
            " |      in the resulting dictionary, each character in x will be mapped to the\n",
            " |      character at the same position in y. If there is a third argument, it\n",
            " |      must be a string, whose characters will be mapped to None in the result.\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IkQY34J_T-Go"
      },
      "source": [
        "Strings and String Operations\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E4c39F5vQ-Kh"
      },
      "source": [
        "my_string = \"thisisOnosstring\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 37
        },
        "id": "ANeQDiL7Q-N3",
        "outputId": "cd42ccda-fbe4-47c4-c8da-e0b28ecde07b"
      },
      "source": [
        "my_string + 'isit'"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'thisisOnosstringisit'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iZhwt2FDQ-RY",
        "outputId": "a06bf54e-cf28-45bf-c556-a8cd1fe94121"
      },
      "source": [
        "'i' in my_string"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aQDnaCd7agG1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 37
        },
        "outputId": "83867894-4df9-4997-ad1e-a49988df2da9"
      },
      "source": [
        "my_string[3]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'s'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cK71CvSTagKV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 37
        },
        "outputId": "90049741-05c8-4c91-d46e-e7fd29bf89a9"
      },
      "source": [
        "my_string[4:9]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'isOno'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gEMxIUzDa0kL"
      },
      "source": [
        "String methods"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mGcOAKYKagN1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 37
        },
        "outputId": "3502ac09-5791-4d11-f09f-53afee1e25b2"
      },
      "source": [
        "my_string.upper()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'THISISONOSSTRING'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gO7rKV29agRi",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 37
        },
        "outputId": "890da470-cf65-4f7e-c1ed-bef773ae022c"
      },
      "source": [
        "my_string.lower()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'thisisonosstring'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J6fpfts6agWH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ab5ecc94-8cfe-455d-83a9-79d5813be4af"
      },
      "source": [
        "my_string.count('w')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GoHnxRDgatOI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 37
        },
        "outputId": "8a41c6dc-e2f5-41b8-dec5-e9a647eadeb6"
      },
      "source": [
        "my_string.replace('e', 'i')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'thisisOnosstring'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "88eNEbOaatTW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 37
        },
        "outputId": "f14685e5-6a0b-46aa-d870-fb69415b2355"
      },
      "source": [
        "my_string.strip()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'thisisOnosstring'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UOqFMY2AUEcc"
      },
      "source": [
        "Lists and Operations"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "knq84CMuSD96"
      },
      "source": [
        "a = 'is'\n",
        "b = 'nice'\n",
        "my_list = ['my', 'list', a, b]\n",
        "my_list2 = [[4, 5, 6, 7], [3, 4, 5, 6]]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JQYEgK2ZS36O"
      },
      "source": [
        "Selecting list elements"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3SRvVCjmRfA2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 37
        },
        "outputId": "165060b1-ec76-411c-acf8-be08f6a80b62"
      },
      "source": [
        "my_list[1]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'list'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JGifNjILRfEQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 37
        },
        "outputId": "7db89c64-4c26-4ab0-dd5b-97e66ca1bfbd"
      },
      "source": [
        "my_list[-3]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'list'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b3MDIOBTRfHn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ce83e6f2-ced0-4a1f-e37f-a4f0618b1e9f"
      },
      "source": [
        "my_list[1:3]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['list', 'is']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "54c0sBjcRfLH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ee51ca0b-32f4-4d7d-e143-5609d604cd7d"
      },
      "source": [
        "my_list[1:]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['list', 'is', 'nice']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jh71yF3LRfOP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c6bc3b01-302a-4401-dac5-60876e328ffc"
      },
      "source": [
        "my_list[:3]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['my', 'list', 'is']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5DnH1eoMRfSp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2c100fe9-7e85-4bdb-a295-0300b23a9aed"
      },
      "source": [
        "my_list[:]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['my', 'list', 'is', 'nice']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rm_lrKSJVY4O",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0a6abef3-c78b-4383-d556-94da803cf821"
      },
      "source": [
        "my_list2[1][0]\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WYWLvrLUVY7t",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ee467715-8341-4a50-bf47-fd0fb961619b"
      },
      "source": [
        "my_list2[1][:2]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[3, 4]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qqVJn-CeYgYF"
      },
      "source": [
        "List Operations"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1Efw5PrQVY_X",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d1622485-0b40-4ed9-8d17-e813bcf8717f"
      },
      "source": [
        "my_list + my_list"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['my', 'list', 'is', 'nice', 'my', 'list', 'is', 'nice']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pMy833Y6VZDP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "166a88f1-88f4-4f79-9cb8-9f8180b50054"
      },
      "source": [
        "my_list * 2"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['my', 'list', 'is', 'nice', 'my', 'list', 'is', 'nice']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "axPFio1lVZHX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "115294f9-50c3-43bd-fba9-c24a0d7d8f74"
      },
      "source": [
        "my_list2"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[[4, 5, 6, 7], [3, 4, 5, 6]]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XDanhDuBYjP9"
      },
      "source": [
        "List Methods"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_8jI4ifXYbB3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0de01bb2-8732-4a67-d0ac-0cec9d38e891"
      },
      "source": [
        "my_list.index(a)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pvzv26xwYbE9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "403cbd2c-94cc-4f2f-f90d-f244bcc01ff5"
      },
      "source": [
        "my_list.count(a)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qeITrduWYbIG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d7ab5177-366e-48ac-f022-3d095b980688"
      },
      "source": [
        "my_list.append('!')\n",
        "my_list"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['my', 'list', 'is', 'nice', '!']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nEqvJTYbYbLs"
      },
      "source": [
        "my_list.remove('!')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p1z6iIHcYbPX"
      },
      "source": [
        "del(my_list[0:1])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yA5xjS5YYbTj"
      },
      "source": [
        "my_list.reverse()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZrnoZXb5ZKMh"
      },
      "source": [
        "my_list.extend('!')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yC5CRKrSZKP5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 37
        },
        "outputId": "f4ca2656-76c2-4b9f-b463-91094256e03d"
      },
      "source": [
        "my_list.pop(-1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'!'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RsXH-al7ZKTK"
      },
      "source": [
        "my_list.insert(0, '!')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aT1jBigtZKWX"
      },
      "source": [
        "my_list.sort()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wD6dUZsEbGDM"
      },
      "source": [
        "Import libraries\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RztYSwo4bImU"
      },
      "source": [
        "import numpy"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1MKDUciDbIpv"
      },
      "source": [
        "import numpy as np"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1Ou8DlclbMsM"
      },
      "source": [
        "from math import pi"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_mdxm8zEbQya"
      },
      "source": [
        "## Numpy arrays\n",
        "\n",
        "NumPy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays.\n",
        "\n",
        "[Source](https://en.wikipedia.org/wiki/NumPy)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "biCh9e_kZKZq"
      },
      "source": [
        "my_list =[1, 2, 3, 4]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oOzrGDarZKdM"
      },
      "source": [
        "my_array = np.array(my_list)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cl9i9SE6ZKg6"
      },
      "source": [
        "my_2ndarray = np.array([[1, 2, 3], [4, 5, 6]])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IEI1L_S_ZKj2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "490ff1c1-3f92-4f12-9099-58811ce8d278"
      },
      "source": [
        "my_array[1]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C9bbgfeZdjI-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e5cd7291-61bd-4842-dbe3-b59163629ec7"
      },
      "source": [
        "my_array[0:2]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 2])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Emj6lObydjMe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c46633e7-c0b9-4dfa-e41c-4dce793509f6"
      },
      "source": [
        "my_2ndarray[:,0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 4])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w518BLYQdjP2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "eeb25bac-83b4-48ef-d861-38513d337309"
      },
      "source": [
        "my_array >3"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([False, False, False,  True])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FD6TKa2mdjUI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ce95d92b-a914-4ac8-ef8b-3cfd6474ac56"
      },
      "source": [
        "my_array *2"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2, 4, 6, 8])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aiXl9XIydw0C",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1eb56b03-2cfc-44e3-f8a2-477cc5873dc0"
      },
      "source": [
        "my_array + np.array([5, 6, 7, 8])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 6,  8, 10, 12])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XdrrKyrmeDlD"
      },
      "source": [
        "Numpy array functions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d49f-KFmdw3d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f5e546e9-f50d-4ea0-ec05-289be05174f7"
      },
      "source": [
        "my_array.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZmSGQk1geMWy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "93bcbd20-8ddf-4a31-fa33-40da5a1f4e5f"
      },
      "source": [
        "other_array = [5, 6, 7, 8]\n",
        "other_array = np.array(other_array)\n",
        "other_array"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([5, 6, 7, 8])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8Weyd4OVdw7R",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "35312f82-2211-43f9-e77b-81b84e59fd3e"
      },
      "source": [
        "np.append(my_array, other_array)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 2, 3, 4, 5, 6, 7, 8])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A5iY0HZ2dw--",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a43aad7d-4947-4e0f-8e03-d7d44011c1ed"
      },
      "source": [
        "np.insert(my_array, 1, 5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 5, 2, 3, 4])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ev2jISPPeM9k",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9358a345-de32-4ed8-f160-f63e7e482a1a"
      },
      "source": [
        "np.delete(my_array, [1])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 3, 4])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iXiPu8iieNAy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "55afd75d-a838-45af-d090-7d68b542d006"
      },
      "source": [
        "np.mean(my_array)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2.5"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4nuZTdHZeNEB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "21a5b85f-227a-4bae-aabf-903a368a95a1"
      },
      "source": [
        "np.median(my_array)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2.5"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-nclVeceej22",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "037493e3-b032-4869-856b-054a21bfb38c"
      },
      "source": [
        "np.std(my_array)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.118033988749895"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0oBsMdsrT-Rj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bea9d2ce-8d37-44c1-de98-3311b1e289fc"
      },
      "source": [
        "np.dot(my_array, other_array)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "70"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P0CIAe-AfMQk"
      },
      "source": [
        "Numpy Broadcasting"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fwEUQc89fOol",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a9e277df-7fc5-4bc9-eaa9-c499dd82c5b0"
      },
      "source": [
        "import numpy as np\n",
        "salary = np.array([2000, 4000, 6000])\n",
        "salary_bump = 1.1\n",
        "\n",
        "print(salary * salary_bump)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[2200. 4400. 6600.]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "arstEkgYfdGN"
      },
      "source": [
        "Boolean Indexing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HxcU47U1fOsJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9ba71d80-d98b-4601-814d-97a94c52b11c"
      },
      "source": [
        "import numpy as np\n",
        "a = np.array([[1, 2, 3], \n",
        "              [4, 5, 6], \n",
        "              [7, 8, 9]])\n",
        "\n",
        "indices = np.array([[False, False, True], \n",
        "                    [False, False, False], \n",
        "                    [True, True, False]])\n",
        "\n",
        "print(a[indices])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[3 7 8]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k5SGWq9XgJcz"
      },
      "source": [
        "How to search arrays"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c1QqjtXofOwJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2331a3a5-8a2e-46a6-c2cb-33a1f48577ba"
      },
      "source": [
        "import numpy as np\n",
        "X = np.array([[1, 0, 0], \n",
        "              [0, 2, 2], \n",
        "              [3, 0, 0]])\n",
        "\n",
        "print(np.nonzero(X))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(array([0, 1, 1, 2]), array([0, 1, 2, 0]))\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qv8QnxQnzpq8"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BXG5EuCRhM8N",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "059a45ef-de8b-40dd-d345-08c12972b697"
      },
      "source": [
        "import numpy as np\n",
        "\n",
        "X = np.array(\n",
        "    [[42, 40, 41, 43, 44, 43], \n",
        "     [30, 31, 29, 29, 29, 30], \n",
        "     [8, 13, 31, 11, 11, 9], \n",
        "     [11, 11, 12, 13, 11, 12]]\n",
        ")\n",
        "\n",
        "cities = np.array(['Hong Kong', 'New York', 'Berlin', 'Montreal'])\n",
        "\n",
        "polluted = set(cities[np.nonzero(X>np.average(X))[0]])\n",
        "print(polluted)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'New York', 'Berlin', 'Hong Kong'}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NC0PL5p_0LB3",
        "outputId": "7ee1968e-cd7f-4502-f2ba-d05883740bf4"
      },
      "source": [
        "cities[[0, 1, 2]]\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Hong Kong', 'New York', 'Berlin'], dtype='<U9')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pgh_1YA-PMPq"
      },
      "source": [
        "## Pandas\n",
        "\n",
        "Pandas is a software library written for the Python programming language for data manipulation and analysis. In particular, it offers data structures and operations for manipulating numerical tables and time series. \n",
        "\n",
        "[Source](https://en.wikipedia.org/wiki/Pandas_(software)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A8oQB8J8VDX8"
      },
      "source": [
        "Creating dataframe"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FIdGA1BLPuqB"
      },
      "source": [
        "import pandas as pd\n",
        "\n",
        "df = pd.DataFrame(\n",
        "              {\"a\" : [4 ,5, 6],\n",
        "               \"b\" : [7, 8, 9],\n",
        "               \"c\" : [10, 11, 12]},\n",
        "            index = [1, 2, 3])"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "KL2TZj8mn8G5",
        "outputId": "1f983efe-2c46-4dc2-f988-4aa8a2033f72"
      },
      "source": [
        "df"
      ],
      "execution_count": 5,
      "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>a</th>\n",
              "      <th>b</th>\n",
              "      <th>c</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4</td>\n",
              "      <td>7</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5</td>\n",
              "      <td>8</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>6</td>\n",
              "      <td>9</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   a  b   c\n",
              "1  4  7  10\n",
              "2  5  8  11\n",
              "3  6  9  12"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tl90rUlOUN5K",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "outputId": "c6aa11f4-4634-4893-b413-abf0bb6612ea"
      },
      "source": [
        "df2 = pd.DataFrame( [[4, 7, 10], [5, 8, 11],\n",
        "          [6, 9, 12]],\n",
        "         index=[1, 2, 3],\n",
        "         columns=['a', 'b', 'c'])\n",
        "df2"
      ],
      "execution_count": 8,
      "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>a</th>\n",
              "      <th>b</th>\n",
              "      <th>c</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4</td>\n",
              "      <td>7</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5</td>\n",
              "      <td>8</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>6</td>\n",
              "      <td>9</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   a  b   c\n",
              "1  4  7  10\n",
              "2  5  8  11\n",
              "3  6  9  12"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0lumgedvUUFK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "ae7fe301-afbf-4b03-dcc0-f6f4b6f07f2d"
      },
      "source": [
        "df3 = pd.DataFrame(\n",
        "              {\"a\" : [4 ,5, 6],\n",
        "               \"b\" : [7, 8, 9],\n",
        "               \"c\" : [10, 11, 12]},\n",
        "    index = pd.MultiIndex.from_tuples(\n",
        "              [('d',1),('d',2),('e',2)],\n",
        "                 names=['n','v']))\n",
        "df3"
      ],
      "execution_count": 9,
      "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></th>\n",
              "      <th>a</th>\n",
              "      <th>b</th>\n",
              "      <th>c</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>n</th>\n",
              "      <th>v</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">d</th>\n",
              "      <th>1</th>\n",
              "      <td>4</td>\n",
              "      <td>7</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5</td>\n",
              "      <td>8</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>e</th>\n",
              "      <th>2</th>\n",
              "      <td>6</td>\n",
              "      <td>9</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     a  b   c\n",
              "n v          \n",
              "d 1  4  7  10\n",
              "  2  5  8  11\n",
              "e 2  6  9  12"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MJinp1G4U_NC"
      },
      "source": [
        "Reshaping data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sUtrdfxHUeUk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 328
        },
        "outputId": "d66292a3-e48a-4c6a-be9f-e47f4a958873"
      },
      "source": [
        "pd.melt(df)\n",
        "#Gather columns into rows."
      ],
      "execution_count": 10,
      "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>variable</th>\n",
              "      <th>value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>a</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>a</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>a</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>b</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>b</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>b</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>c</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>c</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>c</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  variable  value\n",
              "0        a      4\n",
              "1        a      5\n",
              "2        a      6\n",
              "3        b      7\n",
              "4        b      8\n",
              "5        b      9\n",
              "6        c     10\n",
              "7        c     11\n",
              "8        c     12"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E2RbjQyyUeYI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "outputId": "8a46f307-48f9-4c7d-e72d-52f1fd4911fc"
      },
      "source": [
        "df.pivot(columns='a', values='b')\n",
        "#Spread rows into columns."
      ],
      "execution_count": 12,
      "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>a</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>7.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>NaN</td>\n",
              "      <td>8.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>9.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "a    4    5    6\n",
              "1  7.0  NaN  NaN\n",
              "2  NaN  8.0  NaN\n",
              "3  NaN  NaN  9.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RPJq74O6Uebb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "95e2e847-9302-4242-a166-65f3b9c77e41"
      },
      "source": [
        "pd.concat([df,df2])\n",
        "#Append rows of DataFrames"
      ],
      "execution_count": 13,
      "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>a</th>\n",
              "      <th>b</th>\n",
              "      <th>c</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4</td>\n",
              "      <td>7</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5</td>\n",
              "      <td>8</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>6</td>\n",
              "      <td>9</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4</td>\n",
              "      <td>7</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5</td>\n",
              "      <td>8</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>6</td>\n",
              "      <td>9</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   a  b   c\n",
              "1  4  7  10\n",
              "2  5  8  11\n",
              "3  6  9  12\n",
              "1  4  7  10\n",
              "2  5  8  11\n",
              "3  6  9  12"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zAUQbNzyUefT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "outputId": "58b5841e-70f4-42bb-8cdc-b082ebfea91d"
      },
      "source": [
        "pd.concat([df,df2], axis=1)\n",
        "#Append columns of DataFrames"
      ],
      "execution_count": 14,
      "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>a</th>\n",
              "      <th>b</th>\n",
              "      <th>c</th>\n",
              "      <th>a</th>\n",
              "      <th>b</th>\n",
              "      <th>c</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4</td>\n",
              "      <td>7</td>\n",
              "      <td>10</td>\n",
              "      <td>4</td>\n",
              "      <td>7</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5</td>\n",
              "      <td>8</td>\n",
              "      <td>11</td>\n",
              "      <td>5</td>\n",
              "      <td>8</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>6</td>\n",
              "      <td>9</td>\n",
              "      <td>12</td>\n",
              "      <td>6</td>\n",
              "      <td>9</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   a  b   c  a  b   c\n",
              "1  4  7  10  4  7  10\n",
              "2  5  8  11  5  8  11\n",
              "3  6  9  12  6  9  12"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yh-ntYBwsWdE",
        "outputId": "7a441e49-fd53-4f3a-9c17-19304db134b3"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cs2jsoh8u0HK",
        "outputId": "4a8a1d1d-5913-45eb-ca55-8bb329d23dca"
      },
      "source": [
        "df = pd.read_csv('/content/drive/My Drive/cars.csv')\n",
        "df.info()"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 32 entries, 0 to 31\n",
            "Data columns (total 12 columns):\n",
            " #   Column      Non-Null Count  Dtype  \n",
            "---  ------      --------------  -----  \n",
            " 0   Unnamed: 0  32 non-null     object \n",
            " 1   mpg         32 non-null     float64\n",
            " 2   cyl         32 non-null     int64  \n",
            " 3   disp        32 non-null     float64\n",
            " 4   hp          32 non-null     int64  \n",
            " 5   drat        32 non-null     float64\n",
            " 6   wt          32 non-null     float64\n",
            " 7   qsec        32 non-null     float64\n",
            " 8   vs          32 non-null     int64  \n",
            " 9   am          32 non-null     int64  \n",
            " 10  gear        32 non-null     int64  \n",
            " 11  carb        32 non-null     int64  \n",
            "dtypes: float64(5), int64(6), object(1)\n",
            "memory usage: 3.1+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HPrh5k8AUs5I",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "05994ea7-a8d7-425e-ee8e-d0357563a21a"
      },
      "source": [
        "df.sort_values('mpg')\n",
        "#Order rows by values of a column (low to high).\n"
      ],
      "execution_count": 29,
      "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>Unnamed: 0</th>\n",
              "      <th>mpg</th>\n",
              "      <th>cyl</th>\n",
              "      <th>disp</th>\n",
              "      <th>hp</th>\n",
              "      <th>drat</th>\n",
              "      <th>wt</th>\n",
              "      <th>qsec</th>\n",
              "      <th>vs</th>\n",
              "      <th>am</th>\n",
              "      <th>gear</th>\n",
              "      <th>carb</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Lincoln Continental</td>\n",
              "      <td>10.4</td>\n",
              "      <td>8</td>\n",
              "      <td>460.0</td>\n",
              "      <td>215</td>\n",
              "      <td>3.00</td>\n",
              "      <td>5.424</td>\n",
              "      <td>17.82</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Cadillac Fleetwood</td>\n",
              "      <td>10.4</td>\n",
              "      <td>8</td>\n",
              "      <td>472.0</td>\n",
              "      <td>205</td>\n",
              "      <td>2.93</td>\n",
              "      <td>5.250</td>\n",
              "      <td>17.98</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>Camaro Z28</td>\n",
              "      <td>13.3</td>\n",
              "      <td>8</td>\n",
              "      <td>350.0</td>\n",
              "      <td>245</td>\n",
              "      <td>3.73</td>\n",
              "      <td>3.840</td>\n",
              "      <td>15.41</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Duster 360</td>\n",
              "      <td>14.3</td>\n",
              "      <td>8</td>\n",
              "      <td>360.0</td>\n",
              "      <td>245</td>\n",
              "      <td>3.21</td>\n",
              "      <td>3.570</td>\n",
              "      <td>15.84</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Chrysler Imperial</td>\n",
              "      <td>14.7</td>\n",
              "      <td>8</td>\n",
              "      <td>440.0</td>\n",
              "      <td>230</td>\n",
              "      <td>3.23</td>\n",
              "      <td>5.345</td>\n",
              "      <td>17.42</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>Maserati Bora</td>\n",
              "      <td>15.0</td>\n",
              "      <td>8</td>\n",
              "      <td>301.0</td>\n",
              "      <td>335</td>\n",
              "      <td>3.54</td>\n",
              "      <td>3.570</td>\n",
              "      <td>14.60</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Merc 450SLC</td>\n",
              "      <td>15.2</td>\n",
              "      <td>8</td>\n",
              "      <td>275.8</td>\n",
              "      <td>180</td>\n",
              "      <td>3.07</td>\n",
              "      <td>3.780</td>\n",
              "      <td>18.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>AMC Javelin</td>\n",
              "      <td>15.2</td>\n",
              "      <td>8</td>\n",
              "      <td>304.0</td>\n",
              "      <td>150</td>\n",
              "      <td>3.15</td>\n",
              "      <td>3.435</td>\n",
              "      <td>17.30</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>Dodge Challenger</td>\n",
              "      <td>15.5</td>\n",
              "      <td>8</td>\n",
              "      <td>318.0</td>\n",
              "      <td>150</td>\n",
              "      <td>2.76</td>\n",
              "      <td>3.520</td>\n",
              "      <td>16.87</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>Ford Pantera L</td>\n",
              "      <td>15.8</td>\n",
              "      <td>8</td>\n",
              "      <td>351.0</td>\n",
              "      <td>264</td>\n",
              "      <td>4.22</td>\n",
              "      <td>3.170</td>\n",
              "      <td>14.50</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Merc 450SE</td>\n",
              "      <td>16.4</td>\n",
              "      <td>8</td>\n",
              "      <td>275.8</td>\n",
              "      <td>180</td>\n",
              "      <td>3.07</td>\n",
              "      <td>4.070</td>\n",
              "      <td>17.40</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Merc 450SL</td>\n",
              "      <td>17.3</td>\n",
              "      <td>8</td>\n",
              "      <td>275.8</td>\n",
              "      <td>180</td>\n",
              "      <td>3.07</td>\n",
              "      <td>3.730</td>\n",
              "      <td>17.60</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Merc 280C</td>\n",
              "      <td>17.8</td>\n",
              "      <td>6</td>\n",
              "      <td>167.6</td>\n",
              "      <td>123</td>\n",
              "      <td>3.92</td>\n",
              "      <td>3.440</td>\n",
              "      <td>18.90</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Valiant</td>\n",
              "      <td>18.1</td>\n",
              "      <td>6</td>\n",
              "      <td>225.0</td>\n",
              "      <td>105</td>\n",
              "      <td>2.76</td>\n",
              "      <td>3.460</td>\n",
              "      <td>20.22</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Hornet Sportabout</td>\n",
              "      <td>18.7</td>\n",
              "      <td>8</td>\n",
              "      <td>360.0</td>\n",
              "      <td>175</td>\n",
              "      <td>3.15</td>\n",
              "      <td>3.440</td>\n",
              "      <td>17.02</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Merc 280</td>\n",
              "      <td>19.2</td>\n",
              "      <td>6</td>\n",
              "      <td>167.6</td>\n",
              "      <td>123</td>\n",
              "      <td>3.92</td>\n",
              "      <td>3.440</td>\n",
              "      <td>18.30</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>Pontiac Firebird</td>\n",
              "      <td>19.2</td>\n",
              "      <td>8</td>\n",
              "      <td>400.0</td>\n",
              "      <td>175</td>\n",
              "      <td>3.08</td>\n",
              "      <td>3.845</td>\n",
              "      <td>17.05</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>Ferrari Dino</td>\n",
              "      <td>19.7</td>\n",
              "      <td>6</td>\n",
              "      <td>145.0</td>\n",
              "      <td>175</td>\n",
              "      <td>3.62</td>\n",
              "      <td>2.770</td>\n",
              "      <td>15.50</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Mazda RX4</td>\n",
              "      <td>21.0</td>\n",
              "      <td>6</td>\n",
              "      <td>160.0</td>\n",
              "      <td>110</td>\n",
              "      <td>3.90</td>\n",
              "      <td>2.620</td>\n",
              "      <td>16.46</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Mazda RX4 Wag</td>\n",
              "      <td>21.0</td>\n",
              "      <td>6</td>\n",
              "      <td>160.0</td>\n",
              "      <td>110</td>\n",
              "      <td>3.90</td>\n",
              "      <td>2.875</td>\n",
              "      <td>17.02</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Hornet 4 Drive</td>\n",
              "      <td>21.4</td>\n",
              "      <td>6</td>\n",
              "      <td>258.0</td>\n",
              "      <td>110</td>\n",
              "      <td>3.08</td>\n",
              "      <td>3.215</td>\n",
              "      <td>19.44</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>Volvo 142E</td>\n",
              "      <td>21.4</td>\n",
              "      <td>4</td>\n",
              "      <td>121.0</td>\n",
              "      <td>109</td>\n",
              "      <td>4.11</td>\n",
              "      <td>2.780</td>\n",
              "      <td>18.60</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Toyota Corona</td>\n",
              "      <td>21.5</td>\n",
              "      <td>4</td>\n",
              "      <td>120.1</td>\n",
              "      <td>97</td>\n",
              "      <td>3.70</td>\n",
              "      <td>2.465</td>\n",
              "      <td>20.01</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Merc 230</td>\n",
              "      <td>22.8</td>\n",
              "      <td>4</td>\n",
              "      <td>140.8</td>\n",
              "      <td>95</td>\n",
              "      <td>3.92</td>\n",
              "      <td>3.150</td>\n",
              "      <td>22.90</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Datsun 710</td>\n",
              "      <td>22.8</td>\n",
              "      <td>4</td>\n",
              "      <td>108.0</td>\n",
              "      <td>93</td>\n",
              "      <td>3.85</td>\n",
              "      <td>2.320</td>\n",
              "      <td>18.61</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Merc 240D</td>\n",
              "      <td>24.4</td>\n",
              "      <td>4</td>\n",
              "      <td>146.7</td>\n",
              "      <td>62</td>\n",
              "      <td>3.69</td>\n",
              "      <td>3.190</td>\n",
              "      <td>20.00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>Porsche 914-2</td>\n",
              "      <td>26.0</td>\n",
              "      <td>4</td>\n",
              "      <td>120.3</td>\n",
              "      <td>91</td>\n",
              "      <td>4.43</td>\n",
              "      <td>2.140</td>\n",
              "      <td>16.70</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>Fiat X1-9</td>\n",
              "      <td>27.3</td>\n",
              "      <td>4</td>\n",
              "      <td>79.0</td>\n",
              "      <td>66</td>\n",
              "      <td>4.08</td>\n",
              "      <td>1.935</td>\n",
              "      <td>18.90</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>Lotus Europa</td>\n",
              "      <td>30.4</td>\n",
              "      <td>4</td>\n",
              "      <td>95.1</td>\n",
              "      <td>113</td>\n",
              "      <td>3.77</td>\n",
              "      <td>1.513</td>\n",
              "      <td>16.90</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Honda Civic</td>\n",
              "      <td>30.4</td>\n",
              "      <td>4</td>\n",
              "      <td>75.7</td>\n",
              "      <td>52</td>\n",
              "      <td>4.93</td>\n",
              "      <td>1.615</td>\n",
              "      <td>18.52</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Fiat 128</td>\n",
              "      <td>32.4</td>\n",
              "      <td>4</td>\n",
              "      <td>78.7</td>\n",
              "      <td>66</td>\n",
              "      <td>4.08</td>\n",
              "      <td>2.200</td>\n",
              "      <td>19.47</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Toyota Corolla</td>\n",
              "      <td>33.9</td>\n",
              "      <td>4</td>\n",
              "      <td>71.1</td>\n",
              "      <td>65</td>\n",
              "      <td>4.22</td>\n",
              "      <td>1.835</td>\n",
              "      <td>19.90</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             Unnamed: 0   mpg  cyl   disp   hp  ...   qsec  vs  am  gear  carb\n",
              "15  Lincoln Continental  10.4    8  460.0  215  ...  17.82   0   0     3     4\n",
              "14   Cadillac Fleetwood  10.4    8  472.0  205  ...  17.98   0   0     3     4\n",
              "23           Camaro Z28  13.3    8  350.0  245  ...  15.41   0   0     3     4\n",
              "6            Duster 360  14.3    8  360.0  245  ...  15.84   0   0     3     4\n",
              "16    Chrysler Imperial  14.7    8  440.0  230  ...  17.42   0   0     3     4\n",
              "30        Maserati Bora  15.0    8  301.0  335  ...  14.60   0   1     5     8\n",
              "13          Merc 450SLC  15.2    8  275.8  180  ...  18.00   0   0     3     3\n",
              "22          AMC Javelin  15.2    8  304.0  150  ...  17.30   0   0     3     2\n",
              "21     Dodge Challenger  15.5    8  318.0  150  ...  16.87   0   0     3     2\n",
              "28       Ford Pantera L  15.8    8  351.0  264  ...  14.50   0   1     5     4\n",
              "11           Merc 450SE  16.4    8  275.8  180  ...  17.40   0   0     3     3\n",
              "12           Merc 450SL  17.3    8  275.8  180  ...  17.60   0   0     3     3\n",
              "10            Merc 280C  17.8    6  167.6  123  ...  18.90   1   0     4     4\n",
              "5               Valiant  18.1    6  225.0  105  ...  20.22   1   0     3     1\n",
              "4     Hornet Sportabout  18.7    8  360.0  175  ...  17.02   0   0     3     2\n",
              "9              Merc 280  19.2    6  167.6  123  ...  18.30   1   0     4     4\n",
              "24     Pontiac Firebird  19.2    8  400.0  175  ...  17.05   0   0     3     2\n",
              "29         Ferrari Dino  19.7    6  145.0  175  ...  15.50   0   1     5     6\n",
              "0             Mazda RX4  21.0    6  160.0  110  ...  16.46   0   1     4     4\n",
              "1         Mazda RX4 Wag  21.0    6  160.0  110  ...  17.02   0   1     4     4\n",
              "3        Hornet 4 Drive  21.4    6  258.0  110  ...  19.44   1   0     3     1\n",
              "31           Volvo 142E  21.4    4  121.0  109  ...  18.60   1   1     4     2\n",
              "20        Toyota Corona  21.5    4  120.1   97  ...  20.01   1   0     3     1\n",
              "8              Merc 230  22.8    4  140.8   95  ...  22.90   1   0     4     2\n",
              "2            Datsun 710  22.8    4  108.0   93  ...  18.61   1   1     4     1\n",
              "7             Merc 240D  24.4    4  146.7   62  ...  20.00   1   0     4     2\n",
              "26        Porsche 914-2  26.0    4  120.3   91  ...  16.70   0   1     5     2\n",
              "25            Fiat X1-9  27.3    4   79.0   66  ...  18.90   1   1     4     1\n",
              "27         Lotus Europa  30.4    4   95.1  113  ...  16.90   1   1     5     2\n",
              "18          Honda Civic  30.4    4   75.7   52  ...  18.52   1   1     4     2\n",
              "17             Fiat 128  32.4    4   78.7   66  ...  19.47   1   1     4     1\n",
              "19       Toyota Corolla  33.9    4   71.1   65  ...  19.90   1   1     4     1\n",
              "\n",
              "[32 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "87eB9S1Dymmh"
      },
      "source": [
        "df.sort_values('mpg',ascending=False)\n",
        "#Order rows by values of a column (high to low).\n",
        "df.rename(columns = {'y':'year'})\n",
        "#Rename the columns of a DataFrame\n",
        "df.sort_index()\n",
        "#Sort the index of a DataFrame\n",
        "df.reset_index()\n",
        "#Reset index of DataFrame to row numbers, moving index to columns.\n",
        "df.drop(columns=['Length','Height'])\n",
        "#Drop columns from DataFrame"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GXf2A8w9VGd4"
      },
      "source": [
        "Subset row operations"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N6D7ibOIVGlb"
      },
      "source": [
        "df[df.Length > 7]\n",
        "#Extract rows that meet logical criteria.\n",
        "df.drop_duplicates()\n",
        "#Remove duplicate rows (only considers columns).\n",
        "df.head(n)\n",
        "#Select first n rows.\n",
        "df.tail(n)\n",
        "#Select last n rows.\n",
        "df.sample(frac=0.5)\n",
        "#Randomly select fraction of rows.\n",
        "df.sample(n=10)\n",
        "#Randomly select n rows.\n",
        "df.iloc[10:20]\n",
        "#Select rows by position.\n",
        "df.nlargest(n, 'value')\n",
        "#Select and order top n entries.\n",
        "df.nsmallest(n, 'value')\n",
        "#Select and order bottom n entries."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JhEF5y1Ky4B6",
        "outputId": "f9ce8ef3-756c-4773-8666-5244be0c93fe"
      },
      "source": [
        "df.columns"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Unnamed: 0', 'mpg', 'cyl', 'disp', 'hp', 'drat', 'wt', 'qsec', 'vs',\n",
              "       'am', 'gear', 'carb'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uSQg3F4kV1mI"
      },
      "source": [
        "Subset column variables"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oAeavwswVxsC"
      },
      "source": [
        "df[['mpg','cyl','disp']]\n",
        "#Select multiple columns with specific names.\n",
        "df['mpg'] or df.mpg\n",
        "#Select single column with specific name.\n",
        "df.filter(regex='+t')\n",
        "#Select columns whose name matches regular expression regex."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o0TQDeckWUE-"
      },
      "source": [
        "Summarize data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qWw_CaGdWFLO"
      },
      "source": [
        "df['cyl'].value_counts()\n",
        "# Count number of rows with each unique value of variable\n",
        "len(df)\n",
        "# of rows in DataFrame.\n",
        "df['cyl'].nunique()\n",
        "# of distinct values in a column.\n",
        "df.describe()\n",
        "# Basic descriptive statistics for each column (or GroupBy)\n",
        "sum()\n",
        "#Sum values of each object.\n",
        "count()\n",
        "#Count non-NA/null values of each object.\n",
        "median()\n",
        "#Median value of each object.\n",
        "quantile([0.25,0.75])\n",
        "#Quantiles of each object.\n",
        "apply(function)\n",
        "#Apply function to each object.\n",
        "min()\n",
        "#Minimum value in each object.\n",
        "max()\n",
        "#Maximum value in each object.\n",
        "mean()\n",
        "#Mean value of each object.\n",
        "var()\n",
        "#Variance of each object.\n",
        "std()\n",
        "#Standard deviation of each object."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3pC3qCDyWgIx"
      },
      "source": [
        "Handling missing data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CbaVprIYWgSI"
      },
      "source": [
        "df.dropna()\n",
        "#Drop rows with any column having NA/null data.\n",
        "df.fillna(value)\n",
        "#Replace all NA/null data with value."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JAnAtMKzW1tS"
      },
      "source": [
        "Make new columns"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GrLmrDVDW13g"
      },
      "source": [
        "df.assign(Area=lambda df: df.Length*df.Height)\n",
        "#Compute and append one or more new columns.\n",
        "df['Volume'] = df.Length*df.Height*df.Depth #Add single column.\n",
        "pd.qcut(df.col, n, labels=False) #Bin column into n buckets.\n",
        "max(axis=1) #Element-wisemax. \n",
        "min(axis=1) #Element-wisemin.\n",
        "clip(lower=-10,upper=10)  #Trim values at input thresholds \n",
        "abs() # Absolute value.\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ztQGjyY5Xvaz"
      },
      "source": [
        "Group data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nZcLU3fUXvnB"
      },
      "source": [
        "df.groupby(by=\"col\") #Return a GroupBy object, grouped by values in column named \"col\".\n",
        "df.groupby(level=\"ind\") #Return a GroupBy object, grouped by values in index level named \"ind\"."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qD1q2qPdYALM"
      },
      "source": [
        "size() #Size of each group."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bBXZJa7sYATL"
      },
      "source": [
        "agg(function) #Aggregate group using function."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DwKF2sP_YAZN"
      },
      "source": [
        "shift(1) #Copy with values shifted by 1.\n",
        "rank(method='dense') #Ranks with no gaps.\n",
        "rank(method='min') #Ranks. Ties get min rank.\n",
        "rank(pct=True) #Ranks rescaled to interval [0, 1].\n",
        "rank(method='first') #Ranks. Ties go to first value.\n",
        "shift(-1) #Copywithvalueslaggedby1.\n",
        "cumsum() #Cumulative sum.\n",
        "cummax() #Cumulative max.\n",
        "cummin() #Cumulative min.\n",
        "cumprod() #Cumulative product."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MVat8wS5YAdy"
      },
      "source": [
        "pd.merge(adf, bdf, how='left', on='x1') #Join matching rows from bdf to adf."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TK2e1MB7HE6x"
      },
      "source": [
        "## Scikit-Learn"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vb4OAULgPJgX"
      },
      "source": [
        "Loading the data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IkRKw9AZPYgu"
      },
      "source": [
        "import numpy as np\n",
        "X = np.random.random((10, 5))\n",
        "y = np.array(['M', 'M', 'F', 'F', 'M', 'F', 'M', 'M', 'F', 'F', 'F'])\n",
        "X[X<0.7] =0"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZSTfCdsdPMDI"
      },
      "source": [
        "Training and Test data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-B33qUUEGZsV"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state =0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LMYQiOiPPMLt"
      },
      "source": [
        "Preprocessing the data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pa38GK2QPMHh"
      },
      "source": [
        "Standardization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QvbjBJlF5eCU"
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "scaler = StandardScaler().fit(X_train)\n",
        "standardized_X = scaler.transform(X_train)\n",
        "standardized_X_test = scaler.transform(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C0EBgOo_HEso"
      },
      "source": [
        "Normalization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DL7qwoPSHE1O"
      },
      "source": [
        "from sklearn.preprocessing import Normalizer\n",
        "scaler = Normalizer().fit(X_train)\n",
        "normalized_X = scaler.transform(X_train)\n",
        "normalized_X_test = scaler.transform(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YLqk_al7HFS9"
      },
      "source": [
        "Binarization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aSjqhwMBHFad"
      },
      "source": [
        "from sklearn.preprocessing import Binarizer\n",
        "binarizer = Binarizer(threshold=0.0).fit(X)\n",
        "binary_X = binarizer.transform(X)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZI1YnqLtHFsr"
      },
      "source": [
        "Encoding Categorical Features"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cc7U34B2HF1a"
      },
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "enc = LabelEncoder()\n",
        "y = enc.fit_transform(y)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q7oZhT5KISdK"
      },
      "source": [
        "Imputing missing values"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zy94MB7FISkp"
      },
      "source": [
        "from sklearn.preprocessing import Imputer\n",
        "imp = Imputer(missing_values = 0, strategy = 'mean', axis=0)\n",
        "imp.fit_transform(X_train)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sbQxnVRHISvW"
      },
      "source": [
        "Generating Polynomial Features"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qyaY8sbWIS5d"
      },
      "source": [
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "poly = PolynomialFeatures(5)\n",
        "poly.fit_transform(X)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Gq3jWDunafQZ"
      },
      "source": [
        "# PyTorch Basics\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "opRo83HSwmp1"
      },
      "source": [
        "import torch\n",
        "import numpy as np"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d3lhZd_7zUl0"
      },
      "source": [
        "data = [[1, 2],[3, 4]]\n",
        "x_data = torch.tensor(data)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SpYjgzU9zVud"
      },
      "source": [
        "#from numpy array\n",
        "np_array = np.array(data)\n",
        "x_np = torch.from_numpy(np_array)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h4HXqKYWzVyG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "23d1eba9-63fe-4df5-8a0a-09c9f981bf16"
      },
      "source": [
        "#from another tensor\n",
        "x_ones = torch.ones_like(x_data) # retains the properties of x_data\n",
        "print(f\"Ones Tensor: \\n {x_ones} \\n\")\n",
        "\n",
        "x_rand = torch.rand_like(x_data, dtype=torch.float) # overrides the datatype of x_data\n",
        "print(f\"Random Tensor: \\n {x_rand} \\n\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Ones Tensor: \n",
            " tensor([[1, 1],\n",
            "        [1, 1]]) \n",
            "\n",
            "Random Tensor: \n",
            " tensor([[0.8314, 0.3387],\n",
            "        [0.1434, 0.1750]]) \n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ebxxlp8uzV2K",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "de40f1e9-0566-49fc-a9fc-c037d8fa55ae"
      },
      "source": [
        "#with random or constant value\n",
        "shape = (2,3,)\n",
        "rand_tensor = torch.rand(shape)\n",
        "ones_tensor = torch.ones(shape)\n",
        "zeros_tensor = torch.zeros(shape)\n",
        "\n",
        "print(f\"Random Tensor: \\n {rand_tensor} \\n\")\n",
        "print(f\"Ones Tensor: \\n {ones_tensor} \\n\")\n",
        "print(f\"Zeros Tensor: \\n {zeros_tensor}\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Random Tensor: \n",
            " tensor([[0.8885, 0.3996, 0.3628],\n",
            "        [0.6700, 0.1642, 0.3731]]) \n",
            "\n",
            "Ones Tensor: \n",
            " tensor([[1., 1., 1.],\n",
            "        [1., 1., 1.]]) \n",
            "\n",
            "Zeros Tensor: \n",
            " tensor([[0., 0., 0.],\n",
            "        [0., 0., 0.]])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0e6L0HlQzV6W",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7c3f14d1-2fb5-4103-af02-dbfeafcedb65"
      },
      "source": [
        "tensor = torch.rand(3,4)\n",
        "\n",
        "print(f\"Shape of tensor: {tensor.shape}\")\n",
        "print(f\"Datatype of tensor: {tensor.dtype}\")\n",
        "print(f\"Device tensor is stored on: {tensor.device}\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Shape of tensor: torch.Size([3, 4])\n",
            "Datatype of tensor: torch.float32\n",
            "Device tensor is stored on: cpu\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3Trvov3tzV--"
      },
      "source": [
        "# We move our tensor to the GPU if available\n",
        "if torch.cuda.is_available():\n",
        "  tensor = tensor.to('cuda')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1IpWKJ0H2Nnx"
      },
      "source": [
        "#Standard numpy-like indexing and slicing:\n",
        "tensor = torch.ones(4, 4)\n",
        "print('First row: ',tensor[0])\n",
        "print('First column: ', tensor[:, 0])\n",
        "print('Last column:', tensor[..., -1])\n",
        "tensor[:,1] = 0\n",
        "print(tensor)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TocN4nwb2Nrz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7b3be933-825e-403d-fb1e-cd67d94f642b"
      },
      "source": [
        "#Joining tensor\n",
        "t1 = torch.cat([tensor, tensor, tensor], dim=1)\n",
        "print(t1)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensor([[1., 0., 1., 1., 1., 0., 1., 1., 1., 0., 1., 1.],\n",
            "        [1., 0., 1., 1., 1., 0., 1., 1., 1., 0., 1., 1.],\n",
            "        [1., 0., 1., 1., 1., 0., 1., 1., 1., 0., 1., 1.],\n",
            "        [1., 0., 1., 1., 1., 0., 1., 1., 1., 0., 1., 1.]])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HGsFuJ5f2euV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ef5c465e-e474-4370-9673-52aff179fdcf"
      },
      "source": [
        "#Arithmetic operations\n",
        "# This computes the matrix multiplication between two tensors. y1, y2, y3 will have the same value\n",
        "y1 = tensor @ tensor.T\n",
        "y2 = tensor.matmul(tensor.T)\n",
        "\n",
        "y3 = torch.rand_like(tensor)\n",
        "torch.matmul(tensor, tensor.T, out=y3)\n",
        "\n",
        "\n",
        "# This computes the element-wise product. z1, z2, z3 will have the same value\n",
        "z1 = tensor * tensor\n",
        "z2 = tensor.mul(tensor)\n",
        "\n",
        "z3 = torch.rand_like(tensor)\n",
        "torch.mul(tensor, tensor, out=z3)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[1., 0., 1., 1.],\n",
              "        [1., 0., 1., 1.],\n",
              "        [1., 0., 1., 1.],\n",
              "        [1., 0., 1., 1.]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l553kPYz2e36",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bc43e60d-83bc-4631-f178-1205baaba5db"
      },
      "source": [
        "#Single-element tensors\n",
        "agg = tensor.sum()\n",
        "agg_item = agg.item()\n",
        "print(agg_item, type(agg_item))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "12.0 <class 'float'>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m_Dfcz5s2e9z",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d574f58c-15b0-4d52-fa35-b6b24a2ce89a"
      },
      "source": [
        "#In-place operations\n",
        "print(tensor, \"\\n\")\n",
        "tensor.add_(5)\n",
        "print(tensor)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensor([[1., 0., 1., 1.],\n",
            "        [1., 0., 1., 1.],\n",
            "        [1., 0., 1., 1.],\n",
            "        [1., 0., 1., 1.]]) \n",
            "\n",
            "tensor([[6., 5., 6., 6.],\n",
            "        [6., 5., 6., 6.],\n",
            "        [6., 5., 6., 6.],\n",
            "        [6., 5., 6., 6.]])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RfCNC6dA2fEH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cfcea5f9-5159-453d-c1be-1e52baa9fa56"
      },
      "source": [
        "#Tensor to NumPy array\n",
        "t = torch.ones(5)\n",
        "print(f\"t: {t}\")\n",
        "n = t.numpy()\n",
        "print(f\"n: {n}\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "t: tensor([1., 1., 1., 1., 1.])\n",
            "n: [1. 1. 1. 1. 1.]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ijVecaC52NwK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c35e7c99-be47-4919-93bc-8d16e8315d48"
      },
      "source": [
        "#A change in the tensor reflects in the NumPy array.\n",
        "t.add_(1)\n",
        "print(f\"t: {t}\")\n",
        "print(f\"n: {n}\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "t: tensor([2., 2., 2., 2., 2.])\n",
            "n: [2. 2. 2. 2. 2.]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xoy7e_vp2ezS"
      },
      "source": [
        "#NumPy array to Tensor\n",
        "n = np.ones(5)\n",
        "t = torch.from_numpy(n)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Iye0ClIW2N0C",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d575e348-8969-45ba-ed70-dacaaf53feb0"
      },
      "source": [
        "#Changes in the NumPy array reflects in the tensor.\n",
        "np.add(n, 1, out=n)\n",
        "print(f\"t: {t}\")\n",
        "print(f\"n: {n}\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "t: tensor([2., 2., 2., 2., 2.], dtype=torch.float64)\n",
            "n: [2. 2. 2. 2. 2.]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S8HP_ZDl5Ne6"
      },
      "source": [
        "## Loading a Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q_gRv3g_5JlK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 539,
          "referenced_widgets": [
            "efb8db6f15994245b070cfb538da4a15",
            "878eee48e1544371a0c18adb2800a260",
            "aee6f679137e4459b5fecf369d5b6dd7",
            "ad9a8f4e4f7340cb9d5d216100de9e55",
            "14eea11afaef4be18765bf921a8d1284",
            "347c9340fcee4a88a1c4195bee154e84",
            "24322f5e94a948608c81de1b7d545e39",
            "4b041fd3d1054d63b3b43f5eba46192f",
            "30f38dc8db544be3a233c1a6036c8236",
            "b72ee78e33cf43658b05f7828b70f710",
            "4b84af0b976f49ddaea20feb1fc45044",
            "8ac1ef4857ba4cb2a47aa6edc973dff9",
            "e27662f633c94ea2804bc6d80e2d4ca1",
            "fd6ba4a85374427792a394d53c714c75",
            "b35f4e12f7764a08919790eb80856676",
            "3fdde54c0e6747b79d7fecfea23b9274",
            "50590d67caa045c39a1059575698c7de",
            "027de05cc634464c95760a07a51cc83c",
            "5bf80648ca2e4cc5963833d6c7875045",
            "e0bf9d0f47164c0db851180b6d7ea8a5",
            "96293a24cefb455c89d825cabd138d4f",
            "0631b3efda154c4fbf6a5473a65211c2",
            "7da94c8116394ac498793bcce1a4907c",
            "b00a093c4ad04a628312cce2d2a2a042",
            "776a99e40ed347878245190f13ca3cc7",
            "fddedb1da214495192d7b856ad058631",
            "14e1b2cc00de40ee9ff6316e7eaabc69",
            "a2549d2ed4a748048824f2cfc99f3690",
            "aefb53f9e4884faeab31bb29c1c547b1",
            "f339b17818fc4a15b7d927902519f68a",
            "c1a165a093964b6fa79e5d400884fc91",
            "7cf4ec1f84364d9cb4583b5b3db6c742"
          ]
        },
        "outputId": "a220c825-04e3-4d5d-9da0-26c960c71525"
      },
      "source": [
        "import torch\n",
        "from torch.utils.data import Dataset\n",
        "from torchvision import datasets\n",
        "from torchvision.transforms import ToTensor\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "training_data = datasets.FashionMNIST(\n",
        "    root=\"data\",\n",
        "    train=True,\n",
        "    download=True,\n",
        "    transform=ToTensor()\n",
        ")\n",
        "\n",
        "test_data = datasets.FashionMNIST(\n",
        "    root=\"data\",\n",
        "    train=False,\n",
        "    download=True,\n",
        "    transform=ToTensor()\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz to data/FashionMNIST/raw/train-images-idx3-ubyte.gz\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "efb8db6f15994245b070cfb538da4a15",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=26421880.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Extracting data/FashionMNIST/raw/train-images-idx3-ubyte.gz to data/FashionMNIST/raw\n",
            "\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz to data/FashionMNIST/raw/train-labels-idx1-ubyte.gz\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "30f38dc8db544be3a233c1a6036c8236",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=29515.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Extracting data/FashionMNIST/raw/train-labels-idx1-ubyte.gz to data/FashionMNIST/raw\n",
            "\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz to data/FashionMNIST/raw/t10k-images-idx3-ubyte.gz\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "50590d67caa045c39a1059575698c7de",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=4422102.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Extracting data/FashionMNIST/raw/t10k-images-idx3-ubyte.gz to data/FashionMNIST/raw\n",
            "\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz to data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "776a99e40ed347878245190f13ca3cc7",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=5148.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Extracting data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz to data/FashionMNIST/raw\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torchvision/datasets/mnist.py:498: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at  /pytorch/torch/csrc/utils/tensor_numpy.cpp:180.)\n",
            "  return torch.from_numpy(parsed.astype(m[2], copy=False)).view(*s)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IJ2s9ctDTatX"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "teMXaGFD5W5S"
      },
      "source": [
        "## Iterating and Visualizing the Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e-sl7_2h5dY2"
      },
      "source": [
        "labels_map = {\n",
        "    0: \"T-Shirt\",\n",
        "    1: \"Trouser\",\n",
        "    2: \"Pullover\",\n",
        "    3: \"Dress\",\n",
        "    4: \"Coat\",\n",
        "    5: \"Sandal\",\n",
        "    6: \"Shirt\",\n",
        "    7: \"Sneaker\",\n",
        "    8: \"Bag\",\n",
        "    9: \"Ankle Boot\",\n",
        "}\n",
        "figure = plt.figure(figsize=(8, 8))\n",
        "cols, rows = 3, 3\n",
        "for i in range(1, cols * rows + 1):\n",
        "    sample_idx = torch.randint(len(training_data), size=(1,)).item()\n",
        "    img, label = training_data[sample_idx]\n",
        "    figure.add_subplot(rows, cols, i)\n",
        "    plt.title(labels_map[label])\n",
        "    plt.axis(\"off\")\n",
        "    plt.imshow(img.squeeze(), cmap=\"gray\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8asD99E3zrnw"
      },
      "source": [
        "# References\n",
        "\n",
        "[Numpy documentation] (https://numpy.org/devdocs/reference/)\n",
        "[Pandas documentation](https://pandas.pydata.org/docs/reference/index.html)\n",
        "[Scikit documentation](https://scikit-learn.org/stable/modules/classes.html#)\n",
        "[PyTorch documentation](https://pytorch.org/docs/stable/index.html)"
      ]
    }
  ]
}