Pepin_Persia_A.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Basic Python"
      ],
      "metadata": {
        "id": "McSxJAwcOdZ1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1. Split this string"
      ],
      "metadata": {
        "id": "CU48hgo4Owz5"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "kijqOTEMKIW6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "s = \"Hi there Sam!\""
      ],
      "metadata": {
        "id": "s07c7JK7Oqt-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x = s.split()\n",
        "print(x)"
      ],
      "metadata": {
        "id": "6mGVa3SQYLkb",
        "outputId": "86edfaf5-42d3-4b9a-b491-dec561476efb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Hi', 'there', 'Sam!']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2. Use .format() to print the following string. \n",
        "\n",
        "### Output should be: The diameter of Earth is 12742 kilometers."
      ],
      "metadata": {
        "id": "GH1QBn8HP375"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "planet = \"Earth\"\n",
        "diameter = 12742"
      ],
      "metadata": {
        "id": "_ZHoml3kPqic"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x = \"The diameter of {planet} is {diameter} kilometers\".format(planet=\"Earth\",diameter=12742)\n",
        "print(x)"
      ],
      "metadata": {
        "id": "HyRyJv6CYPb4",
        "outputId": "2167bd5b-519a-437a-de91-ae2134fcdd11",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The diameter of Earth is 12742 kilometers\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3. In this nest dictionary grab the word \"hello\""
      ],
      "metadata": {
        "id": "KE74ZEwkRExZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "d = {'k1':[1,2,3,{'tricky':['oh','man','inception',{'target':[1,2,3,'hello']}]}]}"
      ],
      "metadata": {
        "id": "fcVwbCc1QrQI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(d['k1'][3]['tricky'][3]['target'][3])"
      ],
      "metadata": {
        "id": "MvbkMZpXYRaw",
        "outputId": "3440b57b-b444-4d22-cc3b-7b512b6614ff",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "hello\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Numpy"
      ],
      "metadata": {
        "id": "bw0vVp-9ddjv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np"
      ],
      "metadata": {
        "id": "LLiE_TYrhA1O"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 4.1 Create an array of 10 zeros? \n",
        "## 4.2 Create an array of 10 fives?"
      ],
      "metadata": {
        "id": "wOg8hinbgx30"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "np.zeros(10)"
      ],
      "metadata": {
        "id": "NHrirmgCYXvU",
        "outputId": "e38e5237-a020-49af-a686-e23843b7866a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.ones(10)*5"
      ],
      "metadata": {
        "id": "e4005lsTYXxx",
        "outputId": "0062e2ff-a16c-4d91-a4e2-4549ca45e672",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([5., 5., 5., 5., 5., 5., 5., 5., 5., 5.])"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 5. Create an array of all the even integers from 20 to 35"
      ],
      "metadata": {
        "id": "gZHHDUBvrMX4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "np.arange(20,36,2)"
      ],
      "metadata": {
        "id": "oAI2tbU2Yag-",
        "outputId": "6898a9e2-7ce4-4bab-a671-2d0d12509ce9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([20, 22, 24, 26, 28, 30, 32, 34])"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 6. Create a 3x3 matrix with values ranging from 0 to 8"
      ],
      "metadata": {
        "id": "NaOM308NsRpZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "np.arange(0,9).reshape(3,3)"
      ],
      "metadata": {
        "id": "tOlEVH7BYceE",
        "outputId": "5e288d7c-0842-49aa-fa54-1e35cf985263",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0, 1, 2],\n",
              "       [3, 4, 5],\n",
              "       [6, 7, 8]])"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 7. Concatenate a and b \n",
        "## a = np.array([1, 2, 3]), b = np.array([4, 5, 6])"
      ],
      "metadata": {
        "id": "hQ0dnhAQuU_p"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "a = np.array([1, 2, 3])\n",
        "b = np.array([4, 5, 6])\n",
        "np.concatenate((a,b))"
      ],
      "metadata": {
        "id": "rAPSw97aYfE0",
        "outputId": "165998b6-bde1-4530-ad8c-13a9b36ad7a0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 2, 3, 4, 5, 6])"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Pandas"
      ],
      "metadata": {
        "id": "dlPEY9DRwZga"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 8. Create a dataframe with 3 rows and 2 columns"
      ],
      "metadata": {
        "id": "ijoYW51zwr87"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n"
      ],
      "metadata": {
        "id": "T5OxJRZ8uvR7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "d = {'col1': [1, 2,3],\n",
        "     'col2': [4, 5,6]} \n",
        "  \n",
        "k = pd.DataFrame(d)\n",
        "print(k)"
      ],
      "metadata": {
        "id": "xNpI_XXoYhs0",
        "outputId": "d04287e6-20fd-48bd-b9d2-d1640b0c2761",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   col1  col2\n",
            "0     1     4\n",
            "1     2     5\n",
            "2     3     6\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 9. Generate the series of dates from 1st Jan, 2023 to 10th Feb, 2023"
      ],
      "metadata": {
        "id": "UXSmdNclyJQD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "date= pd.date_range(start ='1-1-2023', \n",
        "         end ='02-10-2023')\n",
        "for val in date:\n",
        "    print(val)"
      ],
      "metadata": {
        "id": "dgyC0JhVYl4F",
        "outputId": "024db59b-416b-4f6b-8702-681e1560d5af",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2023-01-01 00:00:00\n",
            "2023-01-02 00:00:00\n",
            "2023-01-03 00:00:00\n",
            "2023-01-04 00:00:00\n",
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            "2023-01-19 00:00:00\n",
            "2023-01-20 00:00:00\n",
            "2023-01-21 00:00:00\n",
            "2023-01-22 00:00:00\n",
            "2023-01-23 00:00:00\n",
            "2023-01-24 00:00:00\n",
            "2023-01-25 00:00:00\n",
            "2023-01-26 00:00:00\n",
            "2023-01-27 00:00:00\n",
            "2023-01-28 00:00:00\n",
            "2023-01-29 00:00:00\n",
            "2023-01-30 00:00:00\n",
            "2023-01-31 00:00:00\n",
            "2023-02-01 00:00:00\n",
            "2023-02-02 00:00:00\n",
            "2023-02-03 00:00:00\n",
            "2023-02-04 00:00:00\n",
            "2023-02-05 00:00:00\n",
            "2023-02-06 00:00:00\n",
            "2023-02-07 00:00:00\n",
            "2023-02-08 00:00:00\n",
            "2023-02-09 00:00:00\n",
            "2023-02-10 00:00:00\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 10. Create 2D list to DataFrame\n",
        "\n",
        "lists = [[1, 'aaa', 22],\n",
        "         [2, 'bbb', 25],\n",
        "         [3, 'ccc', 24]]"
      ],
      "metadata": {
        "id": "ZizSetD-y5az"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "lists = [[1, 'aaa', 22], [2, 'bbb', 25], [3, 'ccc', 24]]"
      ],
      "metadata": {
        "id": "_XMC8aEt0llB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "l = pd.DataFrame(lists)\n",
        "print(l)"
      ],
      "metadata": {
        "id": "knH76sDKYsVX",
        "outputId": "33c247ea-b70e-4b56-dedd-495f0e88dbaa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   0    1   2\n",
            "0  1  aaa  22\n",
            "1  2  bbb  25\n",
            "2  3  ccc  24\n"
          ]
        }
      ]
    }
  ]
}