Series_ma'lumotlar_tuzulmasi.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyMlNRmOsGO3YqplqWWMXMqU",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/AbdullahTheAnalyst/Data-Science-and-AI-mohir_dev-/blob/main/Series_ma'lumotlar_tuzulmasi.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "K4NXHQC488av"
      },
      "outputs": [],
      "source": [
        "import pandas as pd  # maxsus jadvallar bilan ishlash uchun kerak. Data Science uchun juda muhim. Katta-katta datalarga ishlov berish uchun juda muhim."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Pandas kutubxonasi asosan Dataframe va Series bilan ishlaydi."
      ],
      "metadata": {
        "id": "SHzoB3dl-DIW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np"
      ],
      "metadata": {
        "id": "UMNnew4W9ALc"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from pandas import Series # pandas Librarydan Seriesni import qilib oldik"
      ],
      "metadata": {
        "id": "DtXlIQy49Csl"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "obj = Series ([1, 2, 5, 6, 7.8])\n",
        "type(obj)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "vCP3njKw-R22",
        "outputId": "30941e09-bebc-42ca-a071-97eef61a61a0"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.series.Series"
            ],
            "text/html": [
              "<div style=\"max-width:800px; border: 1px solid var(--colab-border-color);\"><style>\n",
              "      pre.function-repr-contents {\n",
              "        overflow-x: auto;\n",
              "        padding: 8px 12px;\n",
              "        max-height: 500px;\n",
              "      }\n",
              "\n",
              "      pre.function-repr-contents.function-repr-contents-collapsed {\n",
              "        cursor: pointer;\n",
              "        max-height: 100px;\n",
              "      }\n",
              "    </style>\n",
              "    <pre style=\"white-space: initial; background:\n",
              "         var(--colab-secondary-surface-color); padding: 8px 12px;\n",
              "         border-bottom: 1px solid var(--colab-border-color);\"><b>pandas.core.series.Series</b><br/>def __init__(data=None, index=None, dtype: Dtype | None=None, name=None, copy: bool | None=None, fastpath: bool | lib.NoDefault=lib.no_default) -&gt; None</pre><pre class=\"function-repr-contents function-repr-contents-collapsed\" style=\"\"><a class=\"filepath\" style=\"display:none\" href=\"#\">/usr/local/lib/python3.10/dist-packages/pandas/core/series.py</a>One-dimensional ndarray with axis labels (including time series).\n",
              "\n",
              "Labels need not be unique but must be a hashable type. The object\n",
              "supports both integer- and label-based indexing and provides a host of\n",
              "methods for performing operations involving the index. Statistical\n",
              "methods from ndarray have been overridden to automatically exclude\n",
              "missing data (currently represented as NaN).\n",
              "\n",
              "Operations between Series (+, -, /, \\*, \\*\\*) align values based on their\n",
              "associated index values-- they need not be the same length. The result\n",
              "index will be the sorted union of the two indexes.\n",
              "\n",
              "Parameters\n",
              "----------\n",
              "data : array-like, Iterable, dict, or scalar value\n",
              "    Contains data stored in Series. If data is a dict, argument order is\n",
              "    maintained.\n",
              "index : array-like or Index (1d)\n",
              "    Values must be hashable and have the same length as `data`.\n",
              "    Non-unique index values are allowed. Will default to\n",
              "    RangeIndex (0, 1, 2, ..., n) if not provided. If data is dict-like\n",
              "    and index is None, then the keys in the data are used as the index. If the\n",
              "    index is not None, the resulting Series is reindexed with the index values.\n",
              "dtype : str, numpy.dtype, or ExtensionDtype, optional\n",
              "    Data type for the output Series. If not specified, this will be\n",
              "    inferred from `data`.\n",
              "    See the :ref:`user guide &lt;basics.dtypes&gt;` for more usages.\n",
              "name : Hashable, default None\n",
              "    The name to give to the Series.\n",
              "copy : bool, default False\n",
              "    Copy input data. Only affects Series or 1d ndarray input. See examples.\n",
              "\n",
              "Notes\n",
              "-----\n",
              "Please reference the :ref:`User Guide &lt;basics.series&gt;` for more information.\n",
              "\n",
              "Examples\n",
              "--------\n",
              "Constructing Series from a dictionary with an Index specified\n",
              "\n",
              "&gt;&gt;&gt; d = {&#x27;a&#x27;: 1, &#x27;b&#x27;: 2, &#x27;c&#x27;: 3}\n",
              "&gt;&gt;&gt; ser = pd.Series(data=d, index=[&#x27;a&#x27;, &#x27;b&#x27;, &#x27;c&#x27;])\n",
              "&gt;&gt;&gt; ser\n",
              "a   1\n",
              "b   2\n",
              "c   3\n",
              "dtype: int64\n",
              "\n",
              "The keys of the dictionary match with the Index values, hence the Index\n",
              "values have no effect.\n",
              "\n",
              "&gt;&gt;&gt; d = {&#x27;a&#x27;: 1, &#x27;b&#x27;: 2, &#x27;c&#x27;: 3}\n",
              "&gt;&gt;&gt; ser = pd.Series(data=d, index=[&#x27;x&#x27;, &#x27;y&#x27;, &#x27;z&#x27;])\n",
              "&gt;&gt;&gt; ser\n",
              "x   NaN\n",
              "y   NaN\n",
              "z   NaN\n",
              "dtype: float64\n",
              "\n",
              "Note that the Index is first build with the keys from the dictionary.\n",
              "After this the Series is reindexed with the given Index values, hence we\n",
              "get all NaN as a result.\n",
              "\n",
              "Constructing Series from a list with `copy=False`.\n",
              "\n",
              "&gt;&gt;&gt; r = [1, 2]\n",
              "&gt;&gt;&gt; ser = pd.Series(r, copy=False)\n",
              "&gt;&gt;&gt; ser.iloc[0] = 999\n",
              "&gt;&gt;&gt; r\n",
              "[1, 2]\n",
              "&gt;&gt;&gt; ser\n",
              "0    999\n",
              "1      2\n",
              "dtype: int64\n",
              "\n",
              "Due to input data type the Series has a `copy` of\n",
              "the original data even though `copy=False`, so\n",
              "the data is unchanged.\n",
              "\n",
              "Constructing Series from a 1d ndarray with `copy=False`.\n",
              "\n",
              "&gt;&gt;&gt; r = np.array([1, 2])\n",
              "&gt;&gt;&gt; ser = pd.Series(r, copy=False)\n",
              "&gt;&gt;&gt; ser.iloc[0] = 999\n",
              "&gt;&gt;&gt; r\n",
              "array([999,   2])\n",
              "&gt;&gt;&gt; ser\n",
              "0    999\n",
              "1      2\n",
              "dtype: int64\n",
              "\n",
              "Due to input data type the Series has a `view` on\n",
              "the original data, so\n",
              "the data is changed as well.</pre>\n",
              "      <script>\n",
              "      if (google.colab.kernel.accessAllowed && google.colab.files && google.colab.files.view) {\n",
              "        for (const element of document.querySelectorAll('.filepath')) {\n",
              "          element.style.display = 'block'\n",
              "          element.onclick = (event) => {\n",
              "            event.preventDefault();\n",
              "            event.stopPropagation();\n",
              "            google.colab.files.view(element.textContent, 263);\n",
              "          };\n",
              "        }\n",
              "      }\n",
              "      for (const element of document.querySelectorAll('.function-repr-contents')) {\n",
              "        element.onclick = (event) => {\n",
              "          event.preventDefault();\n",
              "          event.stopPropagation();\n",
              "          element.classList.toggle('function-repr-contents-collapsed');\n",
              "        };\n",
              "      }\n",
              "      </script>\n",
              "      </div>"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print (obj)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WhjzqEH_-pjf",
        "outputId": "cd6a20a3-25c6-47a7-adff-741bb0cda3fa"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0    1.0\n",
            "1    2.0\n",
            "2    5.0\n",
            "3    6.0\n",
            "4    7.8\n",
            "dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "obj.values # elementlarining qiymati"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fzAGYeJX-y9S",
        "outputId": "8a862ea4-b88e-4a9b-d987-242225510266"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1. , 2. , 5. , 6. , 7.8])"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "obj.index # 0~5 gacha 1 pog'ona bo'yicha indekslari ro'yxati"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KiRr6Az7_gfD",
        "outputId": "810c78b8-d833-41e1-9776-530d413277ad"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RangeIndex(start=0, stop=5, step=1)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Series qiymatining listdan asosiy farqi, biz Seriesda index nomini o'zimiz berishimiz mumkin bo'ladi"
      ],
      "metadata": {
        "id": "P9AE0628AdbZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "obj2 = Series ([1,2,3,4,5], index = [\"a\", \"b\", \"c\", \"d\", \"w\"])\n",
        "obj2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "id": "_F49aIX9Aci0",
        "outputId": "5764a4cd-64c5-4bd4-8ba0-5f0a70bec9d1"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "a    1\n",
              "b    2\n",
              "c    3\n",
              "d    4\n",
              "w    5\n",
              "dtype: int64"
            ],
            "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>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>a</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>b</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>c</th>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>d</th>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>w</th>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "obj2.index"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OvtSgF5t_k-R",
        "outputId": "1d74e6b6-11ee-4d76-b5aa-e425ac344e33"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['a', 'b', 'c', 'd', 'w'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "obj2[[\"c\", \"d\"]]  # indexlarning qiymatini aniqlash"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 147
        },
        "id": "u2MjcB4ABH7G",
        "outputId": "9502ccb9-9d56-432b-e3c7-b0f2e38ab02a"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "c    3\n",
              "d    4\n",
              "dtype: int64"
            ],
            "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>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>c</th>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>d</th>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "obj[2]**5 # elementni aniqlab, arifmetik amal bajarish"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3oPjnKUcBTVU",
        "outputId": "0c678df5-f2bb-4538-87f1-4efacb86f950"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3125.0"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "obj2[obj2 > 3] #elementlari 3 dan katta sonlarni aniqladik"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 147
        },
        "id": "DzCLgSzhBVet",
        "outputId": "73125c5f-f78c-4394-bf02-3d5d79a6c666"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "d    4\n",
              "w    5\n",
              "dtype: int64"
            ],
            "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>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>d</th>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>w</th>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.exp(obj2) # numpy kutubxonasidan exponent sonini aniqladik"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "id": "T4JjjO83BpAB",
        "outputId": "fd82278f-d454-4707-c9b1-c1916ad6ee25"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "a      2.718282\n",
              "b      7.389056\n",
              "c     20.085537\n",
              "d     54.598150\n",
              "w    148.413159\n",
              "dtype: float64"
            ],
            "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>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>a</th>\n",
              "      <td>2.718282</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>b</th>\n",
              "      <td>7.389056</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>c</th>\n",
              "      <td>20.085537</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>d</th>\n",
              "      <td>54.598150</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>w</th>\n",
              "      <td>148.413159</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    }
  ]
}