TS_II_The_ways_to_change_STRING_(date)_to_DATETIME.ipynb

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      "authorship_tag": "ABX9TyNSUbDFFhuUl61tHn3WHsgu",
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)](https://yigitbravecesur.com/)\n",
        "\n",
        "### 🔗 Links\n",
        "\n",
        "\n",
        "[![linkedin](https://img.shields.io/badge/LinkedIn-0A66C2?style=for-the-badge&logo=linkedin&logoColor=white)](https://www.linkedin.com/in/yigitbcesur)\n",
        "\n",
        "[![Youtube](https://img.shields.io/badge/youtube_channel-1DA1F2?style=for-the-badge&logo=youtube&logoColor=white&color=FF0000)](https://www.youtube.com/channel/UCYLMENl4ruLkZbCqLmIKW7A)\n",
        "\n",
        "[![Twitter URL](https://img.shields.io/twitter/url/https/twitter.com/YigitBraveCesur.svg?style=social&label=Follow%20%40YigitBraveCesur)](https://twitter.com/YigitBraveCesur)"
      ],
      "metadata": {
        "id": "gg7NVNC6C-79"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "55ovyOGlIed3"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import datetime as dt\n",
        "import yfinance as yf"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Let's create a dataset"
      ],
      "metadata": {
        "id": "wP1AmaeyM9S0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a datetime index starting at 2022-01-03 with 100 values\n",
        "index = pd.date_range(start='2022-01-03', periods=100, freq='D')\n",
        "\n",
        "# Generate random values between 1 and 100\n",
        "rand_values = np.random.randint(1, 101, size=100)\n",
        "\n",
        "# Create a pandas DataFrame with the datetime index and random values\n",
        "df = pd.DataFrame({'datetime': index, 'price1': rand_values , 'price2': rand_values})\n",
        "#df = pd.DataFrame({'datetime': index, 'price2': rand_values})\n",
        "\n",
        "# Print the pandas DataFrame\n",
        "print(df)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-CYkJhCrLe8x",
        "outputId": "0ee70559-a2db-476e-e97d-2591613fbefd"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     datetime  price1  price2\n",
            "0  2022-01-03      20      20\n",
            "1  2022-01-04      10      10\n",
            "2  2022-01-05      34      34\n",
            "3  2022-01-06      47      47\n",
            "4  2022-01-07      58      58\n",
            "..        ...     ...     ...\n",
            "95 2022-04-08     100     100\n",
            "96 2022-04-09      47      47\n",
            "97 2022-04-10      14      14\n",
            "98 2022-04-11      50      50\n",
            "99 2022-04-12      27      27\n",
            "\n",
            "[100 rows x 3 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Lets see type of Date column\n",
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KU2OrCYS99Ti",
        "outputId": "ad4c4999-8153-42cf-b7e3-e98a413e6e09"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 100 entries, 0 to 99\n",
            "Data columns (total 3 columns):\n",
            " #   Column    Non-Null Count  Dtype         \n",
            "---  ------    --------------  -----         \n",
            " 0   datetime  100 non-null    datetime64[ns]\n",
            " 1   price1    100 non-null    int64         \n",
            " 2   price2    100 non-null    int64         \n",
            "dtypes: datetime64[ns](1), int64(2)\n",
            "memory usage: 2.5 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.to_csv('data.csv', index=False)"
      ],
      "metadata": {
        "id": "d0clCnYXNiBT"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Method 1-  with READ parameters ----> parse dates , index_col**"
      ],
      "metadata": {
        "id": "lHRz2LfO_Omm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df1=pd.read_csv('data.csv',parse_dates=['datetime'], index_col=['datetime'])\n",
        "df1.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ikh5NLAi_ajo",
        "outputId": "72921a9a-3356-4aa2-d6d9-d6a2dcf6c6b0"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "DatetimeIndex: 100 entries, 2022-01-03 to 2022-04-12\n",
            "Data columns (total 2 columns):\n",
            " #   Column  Non-Null Count  Dtype\n",
            "---  ------  --------------  -----\n",
            " 0   price1  100 non-null    int64\n",
            " 1   price2  100 non-null    int64\n",
            "dtypes: int64(2)\n",
            "memory usage: 2.3 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Method 2 :     to_datetime**"
      ],
      "metadata": {
        "id": "ONOJ3Q_eDKTs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df2=df.copy()\n",
        "df2.datetime"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sgAkGXFyARYX",
        "outputId": "abd8d9ff-4aa9-4520-e451-9746aa23d42c"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    2022-01-03\n",
              "1    2022-01-04\n",
              "2    2022-01-05\n",
              "3    2022-01-06\n",
              "4    2022-01-07\n",
              "        ...    \n",
              "95   2022-04-08\n",
              "96   2022-04-09\n",
              "97   2022-04-10\n",
              "98   2022-04-11\n",
              "99   2022-04-12\n",
              "Name: datetime, Length: 100, dtype: datetime64[ns]"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.to_datetime(df2.datetime)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BYB-eo9kAhBd",
        "outputId": "afa045df-f2b0-40fb-d2a2-7343a8ff0509"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    2022-01-03\n",
              "1    2022-01-04\n",
              "2    2022-01-05\n",
              "3    2022-01-06\n",
              "4    2022-01-07\n",
              "        ...    \n",
              "95   2022-04-08\n",
              "96   2022-04-09\n",
              "97   2022-04-10\n",
              "98   2022-04-11\n",
              "99   2022-04-12\n",
              "Name: datetime, Length: 100, dtype: datetime64[ns]"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2=df2.set_index(pd.to_datetime(df2.datetime)).drop('datetime', axis=1)\n",
        "display(df2)\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "UWckMo85BEUG",
        "outputId": "88b1a1d8-9426-442b-8668-14c6010e6077"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "            price1  price2\n",
              "datetime                  \n",
              "2022-01-03      20      20\n",
              "2022-01-04      10      10\n",
              "2022-01-05      34      34\n",
              "2022-01-06      47      47\n",
              "2022-01-07      58      58\n",
              "...            ...     ...\n",
              "2022-04-08     100     100\n",
              "2022-04-09      47      47\n",
              "2022-04-10      14      14\n",
              "2022-04-11      50      50\n",
              "2022-04-12      27      27\n",
              "\n",
              "[100 rows x 2 columns]"
            ],
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              "      <th></th>\n",
              "      <th>price1</th>\n",
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              "    <tr>\n",
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              "    <tr>\n",
              "      <th>2022-01-03</th>\n",
              "      <td>20</td>\n",
              "      <td>20</td>\n",
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              "    <tr>\n",
              "      <th>2022-01-04</th>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
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              "    <tr>\n",
              "      <th>2022-01-05</th>\n",
              "      <td>34</td>\n",
              "      <td>34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-01-06</th>\n",
              "      <td>47</td>\n",
              "      <td>47</td>\n",
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              "      <td>58</td>\n",
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              "      <th>2022-04-09</th>\n",
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              "      <th>2022-04-10</th>\n",
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              "    <tr>\n",
              "      <th>2022-04-11</th>\n",
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              "      <td>50</td>\n",
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              "    <tr>\n",
              "      <th>2022-04-12</th>\n",
              "      <td>27</td>\n",
              "      <td>27</td>\n",
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              "  </tbody>\n",
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              "<p>100 rows × 2 columns</p>\n",
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              "        document.querySelector('#' + key + ' button');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
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            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C5z1P6oOCAmJ",
        "outputId": "1b100fa3-e0e7-40c3-9dfb-f00db8c56aee"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "DatetimeIndex: 100 entries, 2022-01-03 to 2022-04-12\n",
            "Data columns (total 2 columns):\n",
            " #   Column  Non-Null Count  Dtype\n",
            "---  ------  --------------  -----\n",
            " 0   price1  100 non-null    int64\n",
            " 1   price2  100 non-null    int64\n",
            "dtypes: int64(2)\n",
            "memory usage: 2.3 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**---------------------------------------------------------**\n",
        "\n",
        "**Changing Datetime style**\n",
        "\n",
        "Timestamp can not change ---- Timestamp degismiyor"
      ],
      "metadata": {
        "id": "cd4ZoPuPDdb_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pd.to_datetime('May 1 2001')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bhzs4BuoDhwn",
        "outputId": "dd9f74f5-bda3-419d-97d1-5defdfa3c8a6"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Timestamp('2001-05-01 00:00:00')"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.to_datetime('2001 May  1')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PPzi1aM8EM6K",
        "outputId": "fb43d137-03f4-4259-b812-1fddde4910f8"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Timestamp('2001-05-01 00:00:00')"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.to_datetime('2001 1st May ')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XJaRleeXEXRH",
        "outputId": "b6cef9e9-fde9-418e-8e88-46653943d5c9"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Timestamp('2001-05-01 00:00:00')"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.to_datetime(['2001-05-01','June 15th 2001'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EJr8GQA5E29S",
        "outputId": "797cc47c-de0e-4c38-ce12-45e0a8b8d7c7"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatetimeIndex(['2001-05-01', '2001-06-15'], dtype='datetime64[ns]', freq=None)"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.to_datetime(['2001-05-01','June 15th 2001', 'Elephant'], errors='coerce')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "05GRk9XVFbE9",
        "outputId": "df969a30-fb9b-48da-9bc2-6a42223cc15e"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatetimeIndex(['2001-05-01', '2001-06-15', 'NaT'], dtype='datetime64[ns]', freq=None)"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    }
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}