07_pandas_df_tutorial_medium.ipynb

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      "metadata": {
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      "source": [
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 1. Creating a Pandas DataFrame"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### DF without index label"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 75,
      "metadata": {},
      "outputs": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>100</td>\n",
              "      <td>101</td>\n",
              "      <td>99</td>\n",
              "      <td>100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>101</td>\n",
              "      <td>102</td>\n",
              "      <td>100</td>\n",
              "      <td>101</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>102</td>\n",
              "      <td>103</td>\n",
              "      <td>101</td>\n",
              "      <td>102</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Open  High  Low  Close\n",
              "0   100   101   99    100\n",
              "1   101   102  100    101\n",
              "2   102   103  101    102"
            ]
          },
          "execution_count": 75,
          "metadata": {},
          "output_type": "execute_result"
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      "source": [
        "data={\n",
        "  'Open': [100, 101, 102],\n",
        "  'High': [101, 102, 103],\n",
        "  'Low': [99, 100, 101],\n",
        "  'Close': [100, 101, 102],\n",
        "  }\n",
        "df = pd.DataFrame(data)\n",
        "df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "\n",
        "### DF with index label"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 76,
      "metadata": {},
      "outputs": [
        {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2021-12-01</th>\n",
              "      <td>100</td>\n",
              "      <td>101</td>\n",
              "      <td>99</td>\n",
              "      <td>100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-12-02</th>\n",
              "      <td>101</td>\n",
              "      <td>102</td>\n",
              "      <td>100</td>\n",
              "      <td>101</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-12-03</th>\n",
              "      <td>102</td>\n",
              "      <td>103</td>\n",
              "      <td>101</td>\n",
              "      <td>102</td>\n",
              "    </tr>\n",
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            "text/plain": [
              "            Open  High  Low  Close\n",
              "2021-12-01   100   101   99    100\n",
              "2021-12-02   101   102  100    101\n",
              "2021-12-03   102   103  101    102"
            ]
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      "source": [
        "data={\n",
        "  'Open': [100, 101, 102],\n",
        "  'High': [101, 102, 103],\n",
        "  'Low': [99, 100, 101],\n",
        "  'Close': [100, 101, 102],\n",
        "  }\n",
        "index_label = ['2021-12-01', '2021-12-02', '2021-12-03']\n",
        "df = pd.DataFrame(data, index=index_label)\n",
        "df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### The Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 77,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7krzWUSksE0W",
        "outputId": "a3e15aaf-ddfc-47c4-bb83-775a985b6252"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n"
          ]
        }
      ],
      "source": [
        "# !pip install yfinance\n",
        "import yfinance as yf\n",
        "df = yf.download('AAPL', start='2019-01-01', end='2021-01-01')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 2. Viewing data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### How to view only the first and last n rows of data?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 78,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        "id": "HCxOMmLGt6tZ",
        "outputId": "a6b5935a-9548-4135-f51a-a4ba3bd1407a"
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      "outputs": [
        {
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-12-29</th>\n",
              "      <td>138.050003</td>\n",
              "      <td>138.789993</td>\n",
              "      <td>134.339996</td>\n",
              "      <td>134.869995</td>\n",
              "      <td>134.043640</td>\n",
              "      <td>121047300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-30</th>\n",
              "      <td>135.580002</td>\n",
              "      <td>135.990005</td>\n",
              "      <td>133.399994</td>\n",
              "      <td>133.720001</td>\n",
              "      <td>132.900696</td>\n",
              "      <td>96452100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-31</th>\n",
              "      <td>134.080002</td>\n",
              "      <td>134.740005</td>\n",
              "      <td>131.720001</td>\n",
              "      <td>132.690002</td>\n",
              "      <td>131.877014</td>\n",
              "      <td>99116600</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            "text/plain": [
              "                  Open        High         Low       Close   Adj Close  \\\n",
              "Date                                                                     \n",
              "2020-12-29  138.050003  138.789993  134.339996  134.869995  134.043640   \n",
              "2020-12-30  135.580002  135.990005  133.399994  133.720001  132.900696   \n",
              "2020-12-31  134.080002  134.740005  131.720001  132.690002  131.877014   \n",
              "\n",
              "               Volume  \n",
              "Date                   \n",
              "2020-12-29  121047300  \n",
              "2020-12-30   96452100  \n",
              "2020-12-31   99116600  "
            ]
          },
          "execution_count": 78,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.tail(3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 79,
      "metadata": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2018-12-31</th>\n",
              "      <td>39.632500</td>\n",
              "      <td>39.840000</td>\n",
              "      <td>39.119999</td>\n",
              "      <td>39.435001</td>\n",
              "      <td>38.282608</td>\n",
              "      <td>140014000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-01-02</th>\n",
              "      <td>38.722500</td>\n",
              "      <td>39.712502</td>\n",
              "      <td>38.557499</td>\n",
              "      <td>39.480000</td>\n",
              "      <td>38.326290</td>\n",
              "      <td>148158800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-01-03</th>\n",
              "      <td>35.994999</td>\n",
              "      <td>36.430000</td>\n",
              "      <td>35.500000</td>\n",
              "      <td>35.547501</td>\n",
              "      <td>34.508709</td>\n",
              "      <td>365248800</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            ],
            "text/plain": [
              "                 Open       High        Low      Close  Adj Close     Volume\n",
              "Date                                                                        \n",
              "2018-12-31  39.632500  39.840000  39.119999  39.435001  38.282608  140014000\n",
              "2019-01-02  38.722500  39.712502  38.557499  39.480000  38.326290  148158800\n",
              "2019-01-03  35.994999  36.430000  35.500000  35.547501  34.508709  365248800"
            ]
          },
          "execution_count": 79,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.head(3)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### How to display all rows?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YAbPrvzkulws"
      },
      "outputs": [],
      "source": [
        "pd.set_option('display.max_rows', None)\n",
        "df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 3. Data selection"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### How to select a specific row?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ayPCTdhGTU5g",
        "outputId": "a993a7ca-d83a-4c94-d470-10cba53580f1"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Open         1.548900e+02\n",
              "High         1.588500e+02\n",
              "Low          1.542300e+02\n",
              "Close        1.579200e+02\n",
              "Adj Close    1.553062e+02\n",
              "Volume       3.703974e+07\n",
              "Name: 2019-01-02 00:00:00, dtype: float64"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.loc['2019-01-02']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VJ10_tFXvH2v",
        "outputId": "647d359e-39cd-4fef-908c-821b268582c8"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Open         1.548900e+02\n",
              "High         1.588500e+02\n",
              "Low          1.542300e+02\n",
              "Close        1.579200e+02\n",
              "Adj Close    1.553063e+02\n",
              "Volume       3.703974e+07\n",
              "Name: 2019-01-02 00:00:00, dtype: float64"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.iloc[0]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### How to select multiple rows?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        "id": "1pYhdttPwemw",
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              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
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              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2019-01-02</th>\n",
              "      <td>154.889999</td>\n",
              "      <td>158.850006</td>\n",
              "      <td>154.229996</td>\n",
              "      <td>157.919998</td>\n",
              "      <td>155.306274</td>\n",
              "      <td>37039737</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-01-03</th>\n",
              "      <td>143.979996</td>\n",
              "      <td>145.720001</td>\n",
              "      <td>142.000000</td>\n",
              "      <td>142.190002</td>\n",
              "      <td>139.836624</td>\n",
              "      <td>91312195</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-01-04</th>\n",
              "      <td>144.529999</td>\n",
              "      <td>148.549896</td>\n",
              "      <td>143.800003</td>\n",
              "      <td>148.259995</td>\n",
              "      <td>145.806137</td>\n",
              "      <td>58607070</td>\n",
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            "text/plain": [
              "                  Open        High  ...   Adj Close    Volume\n",
              "Date                                ...                      \n",
              "2019-01-02  154.889999  158.850006  ...  155.306274  37039737\n",
              "2019-01-03  143.979996  145.720001  ...  139.836624  91312195\n",
              "2019-01-04  144.529999  148.549896  ...  145.806137  58607070\n",
              "\n",
              "[3 rows x 6 columns]"
            ]
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.loc['2019-01-02':'2019-01-04']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
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              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Volume</th>\n",
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              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "    <tr>\n",
              "      <th>2019-01-02</th>\n",
              "      <td>154.889999</td>\n",
              "      <td>158.850006</td>\n",
              "      <td>154.229996</td>\n",
              "      <td>157.919998</td>\n",
              "      <td>155.306274</td>\n",
              "      <td>37039737</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-01-03</th>\n",
              "      <td>143.979996</td>\n",
              "      <td>145.720001</td>\n",
              "      <td>142.000000</td>\n",
              "      <td>142.190002</td>\n",
              "      <td>139.836624</td>\n",
              "      <td>91312195</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-01-04</th>\n",
              "      <td>144.529999</td>\n",
              "      <td>148.549896</td>\n",
              "      <td>143.800003</td>\n",
              "      <td>148.259995</td>\n",
              "      <td>145.806137</td>\n",
              "      <td>58607070</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  Open        High  ...   Adj Close    Volume\n",
              "Date                                ...                      \n",
              "2019-01-02  154.889999  158.850006  ...  155.306274  37039737\n",
              "2019-01-03  143.979996  145.720001  ...  139.836624  91312195\n",
              "2019-01-04  144.529999  148.549896  ...  145.806137  58607070\n",
              "\n",
              "[3 rows x 6 columns]"
            ]
          },
          "execution_count": 28,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.iloc[0:3]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### How to select a specific column?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PDxHtP76ccgg",
        "outputId": "8bbe740f-c395-4943-8a20-d09bd58098bd"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Date\n",
              "2019-01-02    157.919998\n",
              "2019-01-03    142.190002\n",
              "2019-01-04    148.259995\n",
              "2019-01-07    147.929993\n",
              "2019-01-08    150.750000\n",
              "                 ...    \n",
              "2020-12-24    131.970001\n",
              "2020-12-28    136.690002\n",
              "2020-12-29    134.869995\n",
              "2020-12-30    133.720001\n",
              "2020-12-31    132.690002\n",
              "Name: Close, Length: 505, dtype: float64"
            ]
          },
          "execution_count": 37,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df['Close']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### How to select multiple columns?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "isJ0FJZDJ1ZH",
        "outputId": "0c8922f5-9c14-4c97-b525-15c81e326ca9"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "    }\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>Open</th>\n",
              "      <th>Close</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2018-12-31</th>\n",
              "      <td>158.529999</td>\n",
              "      <td>157.740005</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-01-02</th>\n",
              "      <td>154.889999</td>\n",
              "      <td>157.919998</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-01-03</th>\n",
              "      <td>143.979996</td>\n",
              "      <td>142.190002</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-01-04</th>\n",
              "      <td>144.529999</td>\n",
              "      <td>148.259995</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-01-07</th>\n",
              "      <td>148.699997</td>\n",
              "      <td>147.929993</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-24</th>\n",
              "      <td>131.320007</td>\n",
              "      <td>131.970001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-28</th>\n",
              "      <td>133.990005</td>\n",
              "      <td>136.690002</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-29</th>\n",
              "      <td>138.050003</td>\n",
              "      <td>134.869995</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-30</th>\n",
              "      <td>135.580002</td>\n",
              "      <td>133.720001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-31</th>\n",
              "      <td>134.080002</td>\n",
              "      <td>132.690002</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>506 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                  Open       Close\n",
              "Date                              \n",
              "2018-12-31  158.529999  157.740005\n",
              "2019-01-02  154.889999  157.919998\n",
              "2019-01-03  143.979996  142.190002\n",
              "2019-01-04  144.529999  148.259995\n",
              "2019-01-07  148.699997  147.929993\n",
              "...                ...         ...\n",
              "2020-12-24  131.320007  131.970001\n",
              "2020-12-28  133.990005  136.690002\n",
              "2020-12-29  138.050003  134.869995\n",
              "2020-12-30  135.580002  133.720001\n",
              "2020-12-31  134.080002  132.690002\n",
              "\n",
              "[506 rows x 2 columns]"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df[['Open', 'Close']]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\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>Open</th>\n",
              "      <th>Close</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2018-12-31</th>\n",
              "      <td>158.529999</td>\n",
              "      <td>157.740005</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-01-02</th>\n",
              "      <td>154.889999</td>\n",
              "      <td>157.919998</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-01-03</th>\n",
              "      <td>143.979996</td>\n",
              "      <td>142.190002</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-01-04</th>\n",
              "      <td>144.529999</td>\n",
              "      <td>148.259995</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-01-07</th>\n",
              "      <td>148.699997</td>\n",
              "      <td>147.929993</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-24</th>\n",
              "      <td>131.320007</td>\n",
              "      <td>131.970001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-28</th>\n",
              "      <td>133.990005</td>\n",
              "      <td>136.690002</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-29</th>\n",
              "      <td>138.050003</td>\n",
              "      <td>134.869995</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-30</th>\n",
              "      <td>135.580002</td>\n",
              "      <td>133.720001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-31</th>\n",
              "      <td>134.080002</td>\n",
              "      <td>132.690002</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>506 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                  Open       Close\n",
              "Date                              \n",
              "2018-12-31  158.529999  157.740005\n",
              "2019-01-02  154.889999  157.919998\n",
              "2019-01-03  143.979996  142.190002\n",
              "2019-01-04  144.529999  148.259995\n",
              "2019-01-07  148.699997  147.929993\n",
              "...                ...         ...\n",
              "2020-12-24  131.320007  131.970001\n",
              "2020-12-28  133.990005  136.690002\n",
              "2020-12-29  138.050003  134.869995\n",
              "2020-12-30  135.580002  133.720001\n",
              "2020-12-31  134.080002  132.690002\n",
              "\n",
              "[506 rows x 2 columns]"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.iloc[:, [0,3]]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### How to get all column names?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 85,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Index(['Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume'], dtype='object')"
            ]
          },
          "execution_count": 85,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.columns"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### How to select data based on condition?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 80,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-07-31</th>\n",
              "      <td>102.885002</td>\n",
              "      <td>106.415001</td>\n",
              "      <td>100.824997</td>\n",
              "      <td>106.260002</td>\n",
              "      <td>105.237312</td>\n",
              "      <td>374336800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-03</th>\n",
              "      <td>108.199997</td>\n",
              "      <td>111.637497</td>\n",
              "      <td>107.892502</td>\n",
              "      <td>108.937500</td>\n",
              "      <td>107.889046</td>\n",
              "      <td>308151200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-04</th>\n",
              "      <td>109.132500</td>\n",
              "      <td>110.790001</td>\n",
              "      <td>108.387497</td>\n",
              "      <td>109.665001</td>\n",
              "      <td>108.609535</td>\n",
              "      <td>173071600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-05</th>\n",
              "      <td>109.377502</td>\n",
              "      <td>110.392502</td>\n",
              "      <td>108.897499</td>\n",
              "      <td>110.062500</td>\n",
              "      <td>109.003212</td>\n",
              "      <td>121776800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-06</th>\n",
              "      <td>110.404999</td>\n",
              "      <td>114.412498</td>\n",
              "      <td>109.797501</td>\n",
              "      <td>113.902496</td>\n",
              "      <td>112.806259</td>\n",
              "      <td>202428800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-24</th>\n",
              "      <td>131.320007</td>\n",
              "      <td>133.460007</td>\n",
              "      <td>131.100006</td>\n",
              "      <td>131.970001</td>\n",
              "      <td>131.161423</td>\n",
              "      <td>54930100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-28</th>\n",
              "      <td>133.990005</td>\n",
              "      <td>137.339996</td>\n",
              "      <td>133.509995</td>\n",
              "      <td>136.690002</td>\n",
              "      <td>135.852509</td>\n",
              "      <td>124486200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-29</th>\n",
              "      <td>138.050003</td>\n",
              "      <td>138.789993</td>\n",
              "      <td>134.339996</td>\n",
              "      <td>134.869995</td>\n",
              "      <td>134.043640</td>\n",
              "      <td>121047300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-30</th>\n",
              "      <td>135.580002</td>\n",
              "      <td>135.990005</td>\n",
              "      <td>133.399994</td>\n",
              "      <td>133.720001</td>\n",
              "      <td>132.900696</td>\n",
              "      <td>96452100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-31</th>\n",
              "      <td>134.080002</td>\n",
              "      <td>134.740005</td>\n",
              "      <td>131.720001</td>\n",
              "      <td>132.690002</td>\n",
              "      <td>131.877014</td>\n",
              "      <td>99116600</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>107 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                  Open        High         Low       Close   Adj Close  \\\n",
              "Date                                                                     \n",
              "2020-07-31  102.885002  106.415001  100.824997  106.260002  105.237312   \n",
              "2020-08-03  108.199997  111.637497  107.892502  108.937500  107.889046   \n",
              "2020-08-04  109.132500  110.790001  108.387497  109.665001  108.609535   \n",
              "2020-08-05  109.377502  110.392502  108.897499  110.062500  109.003212   \n",
              "2020-08-06  110.404999  114.412498  109.797501  113.902496  112.806259   \n",
              "...                ...         ...         ...         ...         ...   \n",
              "2020-12-24  131.320007  133.460007  131.100006  131.970001  131.161423   \n",
              "2020-12-28  133.990005  137.339996  133.509995  136.690002  135.852509   \n",
              "2020-12-29  138.050003  138.789993  134.339996  134.869995  134.043640   \n",
              "2020-12-30  135.580002  135.990005  133.399994  133.720001  132.900696   \n",
              "2020-12-31  134.080002  134.740005  131.720001  132.690002  131.877014   \n",
              "\n",
              "               Volume  \n",
              "Date                   \n",
              "2020-07-31  374336800  \n",
              "2020-08-03  308151200  \n",
              "2020-08-04  173071600  \n",
              "2020-08-05  121776800  \n",
              "2020-08-06  202428800  \n",
              "...               ...  \n",
              "2020-12-24   54930100  \n",
              "2020-12-28  124486200  \n",
              "2020-12-29  121047300  \n",
              "2020-12-30   96452100  \n",
              "2020-12-31   99116600  \n",
              "\n",
              "[107 rows x 6 columns]"
            ]
          },
          "execution_count": 80,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df[df['Close'] > 100]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 81,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-02-28</th>\n",
              "      <td>64.315002</td>\n",
              "      <td>69.602501</td>\n",
              "      <td>64.092499</td>\n",
              "      <td>68.339996</td>\n",
              "      <td>67.499542</td>\n",
              "      <td>426510000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-02</th>\n",
              "      <td>70.570000</td>\n",
              "      <td>75.360001</td>\n",
              "      <td>69.430000</td>\n",
              "      <td>74.702499</td>\n",
              "      <td>73.783798</td>\n",
              "      <td>341397200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-20</th>\n",
              "      <td>61.794998</td>\n",
              "      <td>62.957500</td>\n",
              "      <td>57.000000</td>\n",
              "      <td>57.310001</td>\n",
              "      <td>56.605198</td>\n",
              "      <td>401693200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-10</th>\n",
              "      <td>120.360001</td>\n",
              "      <td>120.500000</td>\n",
              "      <td>112.500000</td>\n",
              "      <td>113.489998</td>\n",
              "      <td>112.600388</td>\n",
              "      <td>182274400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-21</th>\n",
              "      <td>104.540001</td>\n",
              "      <td>110.190002</td>\n",
              "      <td>103.099998</td>\n",
              "      <td>110.080002</td>\n",
              "      <td>109.217117</td>\n",
              "      <td>195713800</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  Open        High         Low       Close   Adj Close  \\\n",
              "Date                                                                     \n",
              "2020-02-28   64.315002   69.602501   64.092499   68.339996   67.499542   \n",
              "2020-03-02   70.570000   75.360001   69.430000   74.702499   73.783798   \n",
              "2020-03-20   61.794998   62.957500   57.000000   57.310001   56.605198   \n",
              "2020-09-10  120.360001  120.500000  112.500000  113.489998  112.600388   \n",
              "2020-09-21  104.540001  110.190002  103.099998  110.080002  109.217117   \n",
              "\n",
              "               Volume  \n",
              "Date                   \n",
              "2020-02-28  426510000  \n",
              "2020-03-02  341397200  \n",
              "2020-03-20  401693200  \n",
              "2020-09-10  182274400  \n",
              "2020-09-21  195713800  "
            ]
          },
          "execution_count": 81,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "price_change = abs(df['Close'] - df['Open'])/df['Open'] * 100\n",
        "df[price_change > 5]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 4. Handling and manipulating data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### How to delete/remove a specific row?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 82,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\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>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-12-23</th>\n",
              "      <td>132.160004</td>\n",
              "      <td>132.429993</td>\n",
              "      <td>130.779999</td>\n",
              "      <td>130.960007</td>\n",
              "      <td>130.157608</td>\n",
              "      <td>88223700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-24</th>\n",
              "      <td>131.320007</td>\n",
              "      <td>133.460007</td>\n",
              "      <td>131.100006</td>\n",
              "      <td>131.970001</td>\n",
              "      <td>131.161423</td>\n",
              "      <td>54930100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-28</th>\n",
              "      <td>133.990005</td>\n",
              "      <td>137.339996</td>\n",
              "      <td>133.509995</td>\n",
              "      <td>136.690002</td>\n",
              "      <td>135.852509</td>\n",
              "      <td>124486200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-29</th>\n",
              "      <td>138.050003</td>\n",
              "      <td>138.789993</td>\n",
              "      <td>134.339996</td>\n",
              "      <td>134.869995</td>\n",
              "      <td>134.043640</td>\n",
              "      <td>121047300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-30</th>\n",
              "      <td>135.580002</td>\n",
              "      <td>135.990005</td>\n",
              "      <td>133.399994</td>\n",
              "      <td>133.720001</td>\n",
              "      <td>132.900696</td>\n",
              "      <td>96452100</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  Open        High         Low       Close   Adj Close  \\\n",
              "Date                                                                     \n",
              "2020-12-23  132.160004  132.429993  130.779999  130.960007  130.157608   \n",
              "2020-12-24  131.320007  133.460007  131.100006  131.970001  131.161423   \n",
              "2020-12-28  133.990005  137.339996  133.509995  136.690002  135.852509   \n",
              "2020-12-29  138.050003  138.789993  134.339996  134.869995  134.043640   \n",
              "2020-12-30  135.580002  135.990005  133.399994  133.720001  132.900696   \n",
              "\n",
              "               Volume  \n",
              "Date                   \n",
              "2020-12-23   88223700  \n",
              "2020-12-24   54930100  \n",
              "2020-12-28  124486200  \n",
              "2020-12-29  121047300  \n",
              "2020-12-30   96452100  "
            ]
          },
          "execution_count": 82,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.drop('2020-12-31').tail()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 83,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
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              "      <th></th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-12-23</th>\n",
              "      <td>132.160004</td>\n",
              "      <td>132.429993</td>\n",
              "      <td>130.779999</td>\n",
              "      <td>130.960007</td>\n",
              "      <td>130.157608</td>\n",
              "      <td>88223700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-24</th>\n",
              "      <td>131.320007</td>\n",
              "      <td>133.460007</td>\n",
              "      <td>131.100006</td>\n",
              "      <td>131.970001</td>\n",
              "      <td>131.161423</td>\n",
              "      <td>54930100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-28</th>\n",
              "      <td>133.990005</td>\n",
              "      <td>137.339996</td>\n",
              "      <td>133.509995</td>\n",
              "      <td>136.690002</td>\n",
              "      <td>135.852509</td>\n",
              "      <td>124486200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-29</th>\n",
              "      <td>138.050003</td>\n",
              "      <td>138.789993</td>\n",
              "      <td>134.339996</td>\n",
              "      <td>134.869995</td>\n",
              "      <td>134.043640</td>\n",
              "      <td>121047300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-30</th>\n",
              "      <td>135.580002</td>\n",
              "      <td>135.990005</td>\n",
              "      <td>133.399994</td>\n",
              "      <td>133.720001</td>\n",
              "      <td>132.900696</td>\n",
              "      <td>96452100</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  Open        High         Low       Close   Adj Close  \\\n",
              "Date                                                                     \n",
              "2020-12-23  132.160004  132.429993  130.779999  130.960007  130.157608   \n",
              "2020-12-24  131.320007  133.460007  131.100006  131.970001  131.161423   \n",
              "2020-12-28  133.990005  137.339996  133.509995  136.690002  135.852509   \n",
              "2020-12-29  138.050003  138.789993  134.339996  134.869995  134.043640   \n",
              "2020-12-30  135.580002  135.990005  133.399994  133.720001  132.900696   \n",
              "\n",
              "               Volume  \n",
              "Date                   \n",
              "2020-12-23   88223700  \n",
              "2020-12-24   54930100  \n",
              "2020-12-28  124486200  \n",
              "2020-12-29  121047300  \n",
              "2020-12-30   96452100  "
            ]
          },
          "execution_count": 83,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.drop(df.index[-1]).tail()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### How to delete/remove multiple rows?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
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              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-12-21</th>\n",
              "      <td>125.019997</td>\n",
              "      <td>128.309998</td>\n",
              "      <td>123.448997</td>\n",
              "      <td>128.229996</td>\n",
              "      <td>127.444328</td>\n",
              "      <td>121251553</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-22</th>\n",
              "      <td>131.610001</td>\n",
              "      <td>134.404999</td>\n",
              "      <td>129.649994</td>\n",
              "      <td>131.880005</td>\n",
              "      <td>131.071976</td>\n",
              "      <td>169351825</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-23</th>\n",
              "      <td>132.160004</td>\n",
              "      <td>132.429993</td>\n",
              "      <td>130.779999</td>\n",
              "      <td>130.960007</td>\n",
              "      <td>130.157608</td>\n",
              "      <td>88223692</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-24</th>\n",
              "      <td>131.320007</td>\n",
              "      <td>133.460007</td>\n",
              "      <td>131.100006</td>\n",
              "      <td>131.970001</td>\n",
              "      <td>131.161423</td>\n",
              "      <td>54930064</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-28</th>\n",
              "      <td>133.990005</td>\n",
              "      <td>137.339996</td>\n",
              "      <td>133.509995</td>\n",
              "      <td>136.690002</td>\n",
              "      <td>135.852509</td>\n",
              "      <td>124486237</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  Open        High         Low       Close   Adj Close  \\\n",
              "Date                                                                     \n",
              "2020-12-21  125.019997  128.309998  123.448997  128.229996  127.444328   \n",
              "2020-12-22  131.610001  134.404999  129.649994  131.880005  131.071976   \n",
              "2020-12-23  132.160004  132.429993  130.779999  130.960007  130.157608   \n",
              "2020-12-24  131.320007  133.460007  131.100006  131.970001  131.161423   \n",
              "2020-12-28  133.990005  137.339996  133.509995  136.690002  135.852509   \n",
              "\n",
              "               Volume  \n",
              "Date                   \n",
              "2020-12-21  121251553  \n",
              "2020-12-22  169351825  \n",
              "2020-12-23   88223692  \n",
              "2020-12-24   54930064  \n",
              "2020-12-28  124486237  "
            ]
          },
          "execution_count": 45,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.drop(['2020-12-29', '2020-12-30', '2020-12-31']).tail()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {},
      "outputs": [
        {
          "data": {
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              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Volume</th>\n",
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              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
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              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-12-21</th>\n",
              "      <td>125.019997</td>\n",
              "      <td>128.309998</td>\n",
              "      <td>123.448997</td>\n",
              "      <td>128.229996</td>\n",
              "      <td>127.444328</td>\n",
              "      <td>121251553</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-22</th>\n",
              "      <td>131.610001</td>\n",
              "      <td>134.404999</td>\n",
              "      <td>129.649994</td>\n",
              "      <td>131.880005</td>\n",
              "      <td>131.071976</td>\n",
              "      <td>169351825</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-23</th>\n",
              "      <td>132.160004</td>\n",
              "      <td>132.429993</td>\n",
              "      <td>130.779999</td>\n",
              "      <td>130.960007</td>\n",
              "      <td>130.157608</td>\n",
              "      <td>88223692</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-24</th>\n",
              "      <td>131.320007</td>\n",
              "      <td>133.460007</td>\n",
              "      <td>131.100006</td>\n",
              "      <td>131.970001</td>\n",
              "      <td>131.161423</td>\n",
              "      <td>54930064</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-28</th>\n",
              "      <td>133.990005</td>\n",
              "      <td>137.339996</td>\n",
              "      <td>133.509995</td>\n",
              "      <td>136.690002</td>\n",
              "      <td>135.852509</td>\n",
              "      <td>124486237</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  Open        High         Low       Close   Adj Close  \\\n",
              "Date                                                                     \n",
              "2020-12-21  125.019997  128.309998  123.448997  128.229996  127.444328   \n",
              "2020-12-22  131.610001  134.404999  129.649994  131.880005  131.071976   \n",
              "2020-12-23  132.160004  132.429993  130.779999  130.960007  130.157608   \n",
              "2020-12-24  131.320007  133.460007  131.100006  131.970001  131.161423   \n",
              "2020-12-28  133.990005  137.339996  133.509995  136.690002  135.852509   \n",
              "\n",
              "               Volume  \n",
              "Date                   \n",
              "2020-12-21  121251553  \n",
              "2020-12-22  169351825  \n",
              "2020-12-23   88223692  \n",
              "2020-12-24   54930064  \n",
              "2020-12-28  124486237  "
            ]
          },
          "execution_count": 46,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.drop(df.index[-3:]).tail()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### How to delete/remove column(s)?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
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              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-12-24</th>\n",
              "      <td>131.320007</td>\n",
              "      <td>133.460007</td>\n",
              "      <td>131.100006</td>\n",
              "      <td>131.970001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-28</th>\n",
              "      <td>133.990005</td>\n",
              "      <td>137.339996</td>\n",
              "      <td>133.509995</td>\n",
              "      <td>136.690002</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-29</th>\n",
              "      <td>138.050003</td>\n",
              "      <td>138.789001</td>\n",
              "      <td>134.340897</td>\n",
              "      <td>134.869995</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-30</th>\n",
              "      <td>135.580002</td>\n",
              "      <td>135.990005</td>\n",
              "      <td>133.399994</td>\n",
              "      <td>133.720001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-31</th>\n",
              "      <td>134.080002</td>\n",
              "      <td>134.740005</td>\n",
              "      <td>131.720001</td>\n",
              "      <td>132.690002</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  Open        High         Low       Close\n",
              "Date                                                      \n",
              "2020-12-24  131.320007  133.460007  131.100006  131.970001\n",
              "2020-12-28  133.990005  137.339996  133.509995  136.690002\n",
              "2020-12-29  138.050003  138.789001  134.340897  134.869995\n",
              "2020-12-30  135.580002  135.990005  133.399994  133.720001\n",
              "2020-12-31  134.080002  134.740005  131.720001  132.690002"
            ]
          },
          "execution_count": 59,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.drop(columns=['Adj Close', 'Volume']).tail()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {},
      "outputs": [
        {
          "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>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-12-24</th>\n",
              "      <td>131.320007</td>\n",
              "      <td>133.460007</td>\n",
              "      <td>131.100006</td>\n",
              "      <td>131.970001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-28</th>\n",
              "      <td>133.990005</td>\n",
              "      <td>137.339996</td>\n",
              "      <td>133.509995</td>\n",
              "      <td>136.690002</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-29</th>\n",
              "      <td>138.050003</td>\n",
              "      <td>138.789001</td>\n",
              "      <td>134.340897</td>\n",
              "      <td>134.869995</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-30</th>\n",
              "      <td>135.580002</td>\n",
              "      <td>135.990005</td>\n",
              "      <td>133.399994</td>\n",
              "      <td>133.720001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-31</th>\n",
              "      <td>134.080002</td>\n",
              "      <td>134.740005</td>\n",
              "      <td>131.720001</td>\n",
              "      <td>132.690002</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  Open        High         Low       Close\n",
              "Date                                                      \n",
              "2020-12-24  131.320007  133.460007  131.100006  131.970001\n",
              "2020-12-28  133.990005  137.339996  133.509995  136.690002\n",
              "2020-12-29  138.050003  138.789001  134.340897  134.869995\n",
              "2020-12-30  135.580002  135.990005  133.399994  133.720001\n",
              "2020-12-31  134.080002  134.740005  131.720001  132.690002"
            ]
          },
          "execution_count": 60,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.drop(columns=df.columns[4:]).tail()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### How to drop rows with NaN or empty value?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {},
      "outputs": [
        {
          "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>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2018-12-31</th>\n",
              "      <td>158.529999</td>\n",
              "      <td>159.360001</td>\n",
              "      <td>156.479996</td>\n",
              "      <td>157.740005</td>\n",
              "      <td>155.129257</td>\n",
              "      <td>35003466</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-01-02</th>\n",
              "      <td>154.889999</td>\n",
              "      <td>158.850006</td>\n",
              "      <td>154.229996</td>\n",
              "      <td>157.919998</td>\n",
              "      <td>155.306259</td>\n",
              "      <td>37039737</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-01-03</th>\n",
              "      <td>143.979996</td>\n",
              "      <td>145.720001</td>\n",
              "      <td>142.000000</td>\n",
              "      <td>142.190002</td>\n",
              "      <td>139.836609</td>\n",
              "      <td>91312195</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-01-04</th>\n",
              "      <td>144.529999</td>\n",
              "      <td>148.549896</td>\n",
              "      <td>143.800003</td>\n",
              "      <td>148.259995</td>\n",
              "      <td>145.806137</td>\n",
              "      <td>58607070</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-01-07</th>\n",
              "      <td>148.699997</td>\n",
              "      <td>148.830002</td>\n",
              "      <td>145.899994</td>\n",
              "      <td>147.929993</td>\n",
              "      <td>145.481613</td>\n",
              "      <td>54777764</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-24</th>\n",
              "      <td>131.320007</td>\n",
              "      <td>133.460007</td>\n",
              "      <td>131.100006</td>\n",
              "      <td>131.970001</td>\n",
              "      <td>131.161407</td>\n",
              "      <td>54930064</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-28</th>\n",
              "      <td>133.990005</td>\n",
              "      <td>137.339996</td>\n",
              "      <td>133.509995</td>\n",
              "      <td>136.690002</td>\n",
              "      <td>135.852509</td>\n",
              "      <td>124486237</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-29</th>\n",
              "      <td>138.050003</td>\n",
              "      <td>138.789001</td>\n",
              "      <td>134.340897</td>\n",
              "      <td>134.869995</td>\n",
              "      <td>134.043640</td>\n",
              "      <td>121047324</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-30</th>\n",
              "      <td>135.580002</td>\n",
              "      <td>135.990005</td>\n",
              "      <td>133.399994</td>\n",
              "      <td>133.720001</td>\n",
              "      <td>132.900696</td>\n",
              "      <td>96452124</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-31</th>\n",
              "      <td>134.080002</td>\n",
              "      <td>134.740005</td>\n",
              "      <td>131.720001</td>\n",
              "      <td>132.690002</td>\n",
              "      <td>131.876999</td>\n",
              "      <td>99116586</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>506 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                  Open        High         Low       Close   Adj Close  \\\n",
              "Date                                                                     \n",
              "2018-12-31  158.529999  159.360001  156.479996  157.740005  155.129257   \n",
              "2019-01-02  154.889999  158.850006  154.229996  157.919998  155.306259   \n",
              "2019-01-03  143.979996  145.720001  142.000000  142.190002  139.836609   \n",
              "2019-01-04  144.529999  148.549896  143.800003  148.259995  145.806137   \n",
              "2019-01-07  148.699997  148.830002  145.899994  147.929993  145.481613   \n",
              "...                ...         ...         ...         ...         ...   \n",
              "2020-12-24  131.320007  133.460007  131.100006  131.970001  131.161407   \n",
              "2020-12-28  133.990005  137.339996  133.509995  136.690002  135.852509   \n",
              "2020-12-29  138.050003  138.789001  134.340897  134.869995  134.043640   \n",
              "2020-12-30  135.580002  135.990005  133.399994  133.720001  132.900696   \n",
              "2020-12-31  134.080002  134.740005  131.720001  132.690002  131.876999   \n",
              "\n",
              "               Volume  \n",
              "Date                   \n",
              "2018-12-31   35003466  \n",
              "2019-01-02   37039737  \n",
              "2019-01-03   91312195  \n",
              "2019-01-04   58607070  \n",
              "2019-01-07   54777764  \n",
              "...               ...  \n",
              "2020-12-24   54930064  \n",
              "2020-12-28  124486237  \n",
              "2020-12-29  121047324  \n",
              "2020-12-30   96452124  \n",
              "2020-12-31   99116586  \n",
              "\n",
              "[506 rows x 6 columns]"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.dropna()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### How to check whether dataframe contains NaN or empty value?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.isnull().values.sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.isnull().values.any()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### How to round all values in a DF?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "    }\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>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-12-24</th>\n",
              "      <td>131.32</td>\n",
              "      <td>133.46</td>\n",
              "      <td>131.10</td>\n",
              "      <td>131.97</td>\n",
              "      <td>131.16</td>\n",
              "      <td>54930064</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-28</th>\n",
              "      <td>133.99</td>\n",
              "      <td>137.34</td>\n",
              "      <td>133.51</td>\n",
              "      <td>136.69</td>\n",
              "      <td>135.85</td>\n",
              "      <td>124486237</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-29</th>\n",
              "      <td>138.05</td>\n",
              "      <td>138.79</td>\n",
              "      <td>134.34</td>\n",
              "      <td>134.87</td>\n",
              "      <td>134.04</td>\n",
              "      <td>121047324</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-30</th>\n",
              "      <td>135.58</td>\n",
              "      <td>135.99</td>\n",
              "      <td>133.40</td>\n",
              "      <td>133.72</td>\n",
              "      <td>132.90</td>\n",
              "      <td>96452124</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-31</th>\n",
              "      <td>134.08</td>\n",
              "      <td>134.74</td>\n",
              "      <td>131.72</td>\n",
              "      <td>132.69</td>\n",
              "      <td>131.88</td>\n",
              "      <td>99116586</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              Open    High     Low   Close  Adj Close     Volume\n",
              "Date                                                            \n",
              "2020-12-24  131.32  133.46  131.10  131.97     131.16   54930064\n",
              "2020-12-28  133.99  137.34  133.51  136.69     135.85  124486237\n",
              "2020-12-29  138.05  138.79  134.34  134.87     134.04  121047324\n",
              "2020-12-30  135.58  135.99  133.40  133.72     132.90   96452124\n",
              "2020-12-31  134.08  134.74  131.72  132.69     131.88   99116586"
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.round(2).tail()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### How to rename column names?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 118,
      "metadata": {},
      "outputs": [],
      "source": [
        "df.rename(columns={'Adj Close': 'Adj_Close'}, inplace=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### How to change the order of columns?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Open</th>\n",
              "      <th>Close</th>\n",
              "      <th>Low</th>\n",
              "      <th>High</th>\n",
              "      <th>Adj_Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2018-12-31</th>\n",
              "      <td>158.529999</td>\n",
              "      <td>157.740005</td>\n",
              "      <td>156.479996</td>\n",
              "      <td>159.360001</td>\n",
              "      <td>155.129257</td>\n",
              "      <td>35003466</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-01-02</th>\n",
              "      <td>154.889999</td>\n",
              "      <td>157.919998</td>\n",
              "      <td>154.229996</td>\n",
              "      <td>158.850006</td>\n",
              "      <td>155.306259</td>\n",
              "      <td>37039737</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-01-03</th>\n",
              "      <td>143.979996</td>\n",
              "      <td>142.190002</td>\n",
              "      <td>142.000000</td>\n",
              "      <td>145.720001</td>\n",
              "      <td>139.836609</td>\n",
              "      <td>91312195</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-01-04</th>\n",
              "      <td>144.529999</td>\n",
              "      <td>148.259995</td>\n",
              "      <td>143.800003</td>\n",
              "      <td>148.549896</td>\n",
              "      <td>145.806137</td>\n",
              "      <td>58607070</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-01-07</th>\n",
              "      <td>148.699997</td>\n",
              "      <td>147.929993</td>\n",
              "      <td>145.899994</td>\n",
              "      <td>148.830002</td>\n",
              "      <td>145.481613</td>\n",
              "      <td>54777764</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-24</th>\n",
              "      <td>131.320007</td>\n",
              "      <td>131.970001</td>\n",
              "      <td>131.100006</td>\n",
              "      <td>133.460007</td>\n",
              "      <td>131.161407</td>\n",
              "      <td>54930064</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-28</th>\n",
              "      <td>133.990005</td>\n",
              "      <td>136.690002</td>\n",
              "      <td>133.509995</td>\n",
              "      <td>137.339996</td>\n",
              "      <td>135.852509</td>\n",
              "      <td>124486237</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-29</th>\n",
              "      <td>138.050003</td>\n",
              "      <td>134.869995</td>\n",
              "      <td>134.340897</td>\n",
              "      <td>138.789001</td>\n",
              "      <td>134.043640</td>\n",
              "      <td>121047324</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-30</th>\n",
              "      <td>135.580002</td>\n",
              "      <td>133.720001</td>\n",
              "      <td>133.399994</td>\n",
              "      <td>135.990005</td>\n",
              "      <td>132.900696</td>\n",
              "      <td>96452124</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-31</th>\n",
              "      <td>134.080002</td>\n",
              "      <td>132.690002</td>\n",
              "      <td>131.720001</td>\n",
              "      <td>134.740005</td>\n",
              "      <td>131.876999</td>\n",
              "      <td>99116586</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>506 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                  Open       Close         Low        High   Adj_Close  \\\n",
              "Date                                                                     \n",
              "2018-12-31  158.529999  157.740005  156.479996  159.360001  155.129257   \n",
              "2019-01-02  154.889999  157.919998  154.229996  158.850006  155.306259   \n",
              "2019-01-03  143.979996  142.190002  142.000000  145.720001  139.836609   \n",
              "2019-01-04  144.529999  148.259995  143.800003  148.549896  145.806137   \n",
              "2019-01-07  148.699997  147.929993  145.899994  148.830002  145.481613   \n",
              "...                ...         ...         ...         ...         ...   \n",
              "2020-12-24  131.320007  131.970001  131.100006  133.460007  131.161407   \n",
              "2020-12-28  133.990005  136.690002  133.509995  137.339996  135.852509   \n",
              "2020-12-29  138.050003  134.869995  134.340897  138.789001  134.043640   \n",
              "2020-12-30  135.580002  133.720001  133.399994  135.990005  132.900696   \n",
              "2020-12-31  134.080002  132.690002  131.720001  134.740005  131.876999   \n",
              "\n",
              "               Volume  \n",
              "Date                   \n",
              "2018-12-31   35003466  \n",
              "2019-01-02   37039737  \n",
              "2019-01-03   91312195  \n",
              "2019-01-04   58607070  \n",
              "2019-01-07   54777764  \n",
              "...               ...  \n",
              "2020-12-24   54930064  \n",
              "2020-12-28  124486237  \n",
              "2020-12-29  121047324  \n",
              "2020-12-30   96452124  \n",
              "2020-12-31   99116586  \n",
              "\n",
              "[506 rows x 6 columns]"
            ]
          },
          "execution_count": 30,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.reindex(columns=['Open', 'Close', 'Low', 'High', 'Adj_Close', 'Volume'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### How to add a new column?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "    }\n",
              "\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj_Close</th>\n",
              "      <th>Volume</th>\n",
              "      <th>New_Column</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-12-24</th>\n",
              "      <td>131.320007</td>\n",
              "      <td>133.460007</td>\n",
              "      <td>131.100006</td>\n",
              "      <td>131.970001</td>\n",
              "      <td>131.161407</td>\n",
              "      <td>54930064</td>\n",
              "      <td>2.360001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-28</th>\n",
              "      <td>133.990005</td>\n",
              "      <td>137.339996</td>\n",
              "      <td>133.509995</td>\n",
              "      <td>136.690002</td>\n",
              "      <td>135.852509</td>\n",
              "      <td>124486237</td>\n",
              "      <td>3.830002</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-29</th>\n",
              "      <td>138.050003</td>\n",
              "      <td>138.789001</td>\n",
              "      <td>134.340897</td>\n",
              "      <td>134.869995</td>\n",
              "      <td>134.043640</td>\n",
              "      <td>121047324</td>\n",
              "      <td>4.448105</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-30</th>\n",
              "      <td>135.580002</td>\n",
              "      <td>135.990005</td>\n",
              "      <td>133.399994</td>\n",
              "      <td>133.720001</td>\n",
              "      <td>132.900696</td>\n",
              "      <td>96452124</td>\n",
              "      <td>2.590012</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-31</th>\n",
              "      <td>134.080002</td>\n",
              "      <td>134.740005</td>\n",
              "      <td>131.720001</td>\n",
              "      <td>132.690002</td>\n",
              "      <td>131.876999</td>\n",
              "      <td>99116586</td>\n",
              "      <td>3.020004</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  Open        High         Low       Close   Adj_Close  \\\n",
              "Date                                                                     \n",
              "2020-12-24  131.320007  133.460007  131.100006  131.970001  131.161407   \n",
              "2020-12-28  133.990005  137.339996  133.509995  136.690002  135.852509   \n",
              "2020-12-29  138.050003  138.789001  134.340897  134.869995  134.043640   \n",
              "2020-12-30  135.580002  135.990005  133.399994  133.720001  132.900696   \n",
              "2020-12-31  134.080002  134.740005  131.720001  132.690002  131.876999   \n",
              "\n",
              "               Volume  New_Column  \n",
              "Date                               \n",
              "2020-12-24   54930064    2.360001  \n",
              "2020-12-28  124486237    3.830002  \n",
              "2020-12-29  121047324    4.448105  \n",
              "2020-12-30   96452124    2.590012  \n",
              "2020-12-31   99116586    3.020004  "
            ]
          },
          "execution_count": 40,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df['New_Column'] = df['High'] - df['Low']\n",
        "df.tail()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>New_Column</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj_Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-12-24</th>\n",
              "      <td>131.320007</td>\n",
              "      <td>133.460007</td>\n",
              "      <td>131.100006</td>\n",
              "      <td>2.360001</td>\n",
              "      <td>131.970001</td>\n",
              "      <td>131.161407</td>\n",
              "      <td>54930064</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-28</th>\n",
              "      <td>133.990005</td>\n",
              "      <td>137.339996</td>\n",
              "      <td>133.509995</td>\n",
              "      <td>3.830002</td>\n",
              "      <td>136.690002</td>\n",
              "      <td>135.852509</td>\n",
              "      <td>124486237</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-29</th>\n",
              "      <td>138.050003</td>\n",
              "      <td>138.789001</td>\n",
              "      <td>134.340897</td>\n",
              "      <td>4.448105</td>\n",
              "      <td>134.869995</td>\n",
              "      <td>134.043640</td>\n",
              "      <td>121047324</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-30</th>\n",
              "      <td>135.580002</td>\n",
              "      <td>135.990005</td>\n",
              "      <td>133.399994</td>\n",
              "      <td>2.590012</td>\n",
              "      <td>133.720001</td>\n",
              "      <td>132.900696</td>\n",
              "      <td>96452124</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-31</th>\n",
              "      <td>134.080002</td>\n",
              "      <td>134.740005</td>\n",
              "      <td>131.720001</td>\n",
              "      <td>3.020004</td>\n",
              "      <td>132.690002</td>\n",
              "      <td>131.876999</td>\n",
              "      <td>99116586</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  Open        High         Low  New_Column       Close  \\\n",
              "Date                                                                     \n",
              "2020-12-24  131.320007  133.460007  131.100006    2.360001  131.970001   \n",
              "2020-12-28  133.990005  137.339996  133.509995    3.830002  136.690002   \n",
              "2020-12-29  138.050003  138.789001  134.340897    4.448105  134.869995   \n",
              "2020-12-30  135.580002  135.990005  133.399994    2.590012  133.720001   \n",
              "2020-12-31  134.080002  134.740005  131.720001    3.020004  132.690002   \n",
              "\n",
              "             Adj_Close     Volume  \n",
              "Date                               \n",
              "2020-12-24  131.161407   54930064  \n",
              "2020-12-28  135.852509  124486237  \n",
              "2020-12-29  134.043640  121047324  \n",
              "2020-12-30  132.900696   96452124  \n",
              "2020-12-31  131.876999   99116586  "
            ]
          },
          "execution_count": 36,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "new_column = df['High'] - df['Low']\n",
        "df.insert(3, 'New_Column', new_column)\n",
        "df.tail()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 5. Reading and writing files"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### How to add a new row to an existing dataframe?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 84,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "\n",
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              "    }\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>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-12-28</th>\n",
              "      <td>133.990005</td>\n",
              "      <td>137.339996</td>\n",
              "      <td>133.509995</td>\n",
              "      <td>136.690002</td>\n",
              "      <td>135.852509</td>\n",
              "      <td>124486200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-29</th>\n",
              "      <td>138.050003</td>\n",
              "      <td>138.789993</td>\n",
              "      <td>134.339996</td>\n",
              "      <td>134.869995</td>\n",
              "      <td>134.043640</td>\n",
              "      <td>121047300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-30</th>\n",
              "      <td>135.580002</td>\n",
              "      <td>135.990005</td>\n",
              "      <td>133.399994</td>\n",
              "      <td>133.720001</td>\n",
              "      <td>132.900696</td>\n",
              "      <td>96452100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-31</th>\n",
              "      <td>134.080002</td>\n",
              "      <td>134.740005</td>\n",
              "      <td>131.720001</td>\n",
              "      <td>132.690002</td>\n",
              "      <td>131.877014</td>\n",
              "      <td>99116600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-01-02</th>\n",
              "      <td>135.000000</td>\n",
              "      <td>136.000000</td>\n",
              "      <td>134.000000</td>\n",
              "      <td>135.000000</td>\n",
              "      <td>133.000000</td>\n",
              "      <td>1000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  Open        High         Low       Close   Adj Close  \\\n",
              "Date                                                                     \n",
              "2020-12-28  133.990005  137.339996  133.509995  136.690002  135.852509   \n",
              "2020-12-29  138.050003  138.789993  134.339996  134.869995  134.043640   \n",
              "2020-12-30  135.580002  135.990005  133.399994  133.720001  132.900696   \n",
              "2020-12-31  134.080002  134.740005  131.720001  132.690002  131.877014   \n",
              "2021-01-02  135.000000  136.000000  134.000000  135.000000  133.000000   \n",
              "\n",
              "               Volume  \n",
              "Date                   \n",
              "2020-12-28  124486200  \n",
              "2020-12-29  121047300  \n",
              "2020-12-30   96452100  \n",
              "2020-12-31   99116600  \n",
              "2021-01-02    1000000  "
            ]
          },
          "execution_count": 84,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "new_row = [135, 136, 134, 135, 133, 1000000]\n",
        "new_index = pd.DatetimeIndex(data=['2021-01-02'])[0]\n",
        "df.loc[new_index] = new_row\n",
        "df.tail()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 142,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj_Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-12-28</th>\n",
              "      <td>133.990005</td>\n",
              "      <td>137.339996</td>\n",
              "      <td>133.509995</td>\n",
              "      <td>136.690002</td>\n",
              "      <td>135.852509</td>\n",
              "      <td>124486200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-29</th>\n",
              "      <td>138.050003</td>\n",
              "      <td>138.789993</td>\n",
              "      <td>134.339996</td>\n",
              "      <td>134.869995</td>\n",
              "      <td>134.043640</td>\n",
              "      <td>121047300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-30</th>\n",
              "      <td>135.580002</td>\n",
              "      <td>135.990005</td>\n",
              "      <td>133.399994</td>\n",
              "      <td>133.720001</td>\n",
              "      <td>132.900711</td>\n",
              "      <td>96452100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-31</th>\n",
              "      <td>134.080002</td>\n",
              "      <td>134.740005</td>\n",
              "      <td>131.720001</td>\n",
              "      <td>132.690002</td>\n",
              "      <td>131.877014</td>\n",
              "      <td>99116600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-01-02</th>\n",
              "      <td>135.000000</td>\n",
              "      <td>136.000000</td>\n",
              "      <td>136.000000</td>\n",
              "      <td>136.000000</td>\n",
              "      <td>133.000000</td>\n",
              "      <td>1000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  Open        High         Low       Close   Adj_Close  \\\n",
              "2020-12-28  133.990005  137.339996  133.509995  136.690002  135.852509   \n",
              "2020-12-29  138.050003  138.789993  134.339996  134.869995  134.043640   \n",
              "2020-12-30  135.580002  135.990005  133.399994  133.720001  132.900711   \n",
              "2020-12-31  134.080002  134.740005  131.720001  132.690002  131.877014   \n",
              "2021-01-02  135.000000  136.000000  136.000000  136.000000  133.000000   \n",
              "\n",
              "               Volume  \n",
              "2020-12-28  124486200  \n",
              "2020-12-29  121047300  \n",
              "2020-12-30   96452100  \n",
              "2020-12-31   99116600  \n",
              "2021-01-02    1000000  "
            ]
          },
          "execution_count": 142,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "new_index = pd.DatetimeIndex(data=['2021-01-02'])\n",
        "new_row = pd.DataFrame(data=[{\n",
        "  'Open': 135,\n",
        "  'High': 136,\n",
        "  'Low': 136,\n",
        "  'Close': 136,\n",
        "  'Adj_Close': 133,\n",
        "  'Volume': 1000000}], index=new_index)\n",
        "df = df.append(new_row)\n",
        "df.tail()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### How to read and write to csv/excel file?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 194,
      "metadata": {},
      "outputs": [
        {
          "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>Symbol</th>\n",
              "      <th>Company Name</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>MMM</td>\n",
              "      <td>3M Company</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ABT</td>\n",
              "      <td>Abbott Laboratories</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ABBV</td>\n",
              "      <td>AbbVie Inc.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ABMD</td>\n",
              "      <td>ABIOMED Inc</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ACN</td>\n",
              "      <td>Accenture plc</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>500</th>\n",
              "      <td>YUM</td>\n",
              "      <td>Yum! Brands Inc</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>501</th>\n",
              "      <td>ZBRA</td>\n",
              "      <td>Zebra Technologies</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>502</th>\n",
              "      <td>ZBH</td>\n",
              "      <td>Zimmer Biomet Holdings</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>503</th>\n",
              "      <td>ZION</td>\n",
              "      <td>Zions Bancorp</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>504</th>\n",
              "      <td>ZTS</td>\n",
              "      <td>Zoetis</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>505 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "    Symbol            Company Name\n",
              "0      MMM              3M Company\n",
              "1      ABT     Abbott Laboratories\n",
              "2     ABBV             AbbVie Inc.\n",
              "3     ABMD             ABIOMED Inc\n",
              "4      ACN           Accenture plc\n",
              "..     ...                     ...\n",
              "500    YUM         Yum! Brands Inc\n",
              "501   ZBRA      Zebra Technologies\n",
              "502    ZBH  Zimmer Biomet Holdings\n",
              "503   ZION           Zions Bancorp\n",
              "504    ZTS                  Zoetis\n",
              "\n",
              "[505 rows x 2 columns]"
            ]
          },
          "execution_count": 194,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "symbol_df = pd.read_csv('./dataset/symbols.csv')\n",
        "symbol_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 196,
      "metadata": {},
      "outputs": [],
      "source": [
        "df.to_csv('price_data.csv')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 6. Plotting"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### How to create plots in panda?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 202,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<AxesSubplot:xlabel='Date'>"
            ]
          },
          "execution_count": 202,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "df['Close'].plot()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### How to configure figure size?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<AxesSubplot:xlabel='Date'>"
            ]
          },
          "execution_count": 2,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1152x648 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "df['Close'].plot(figsize=(16,9))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1600x900 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "ax = df['Close'].plot(figsize=(16,9))\n",
        "ax.figure.savefig('test.png')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### How to change the plot resolution?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 600x400 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.rcParams[\"figure.dpi\"] = 800\n",
        "ax = df['Close'].plot(figsize=(16,9))\n",
        "ax.figure.savefig('test_higher_resolution.png')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### How to add axis labels and plot title?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 600x400 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "df['Close'].plot(ylabel = 'Price', xlabel='Date')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### How to change the label and tick font size?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 600x400 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "df['Close'].plot(\n",
        "  ylabel = 'Price',\n",
        "  xlabel='Date',\n",
        "  title='APPL Close Price',\n",
        "  fontsize=18)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 72,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 600x400 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.rcParams[\"font.size\"] = 14\n",
        "df['Close'].plot(\n",
        "  ylabel = 'Price',\n",
        "  xlabel='Date',\n",
        "  title='APPL Close Price')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 73,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 600x400 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "df['Close'].plot(\n",
        "  ylabel = 'Price',\n",
        "  xlabel='Date',\n",
        "  title='APPL Close Price',\n",
        "  rot=90)\n",
        "plt.show()"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "name": "pandas-df-tutorial-medium.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
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