Python_Stock/Find_Stock_Number_Patterns.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Find Stock Number Patterns"
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "import yfinance as yf\n",
        "yf.pdr_override()"
      ],
      "outputs": [],
      "execution_count": 2,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-05-06T22:30:43.059Z",
          "iopub.execute_input": "2022-05-06T22:30:43.066Z",
          "shell.execute_reply": "2022-05-06T22:30:45.094Z",
          "iopub.status.idle": "2022-05-06T22:30:45.041Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "start = '2021-01-01'\n",
        "end = '2022-01-01'\n",
        "symbol = 'AMD'"
      ],
      "outputs": [],
      "execution_count": 3,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-05-06T22:30:45.052Z",
          "iopub.execute_input": "2022-05-06T22:30:45.058Z",
          "iopub.status.idle": "2022-05-06T22:30:45.070Z",
          "shell.execute_reply": "2022-05-06T22:30:45.098Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = yf.download(symbol, start=start, end=end)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n"
          ]
        }
      ],
      "execution_count": 4,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-05-06T22:30:45.080Z",
          "iopub.execute_input": "2022-05-06T22:30:45.086Z",
          "iopub.status.idle": "2022-05-06T22:30:47.407Z",
          "shell.execute_reply": "2022-05-06T22:30:47.396Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 5,
          "data": {
            "text/plain": "                 Open       High        Low      Close  Adj Close    Volume\nDate                                                                       \n2021-01-04  92.110001  96.059998  90.919998  92.300003  92.300003  51802600\n2021-01-05  92.099998  93.209999  91.410004  92.769997  92.769997  34208000\n2021-01-06  91.620003  92.279999  89.459999  90.330002  90.330002  51911700\n2021-01-07  91.330002  95.510002  91.199997  95.160004  95.160004  42897200\n2021-01-08  95.980003  96.400002  93.269997  94.580002  94.580002  39816400",
            "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>2021-01-04</th>\n      <td>92.110001</td>\n      <td>96.059998</td>\n      <td>90.919998</td>\n      <td>92.300003</td>\n      <td>92.300003</td>\n      <td>51802600</td>\n    </tr>\n    <tr>\n      <th>2021-01-05</th>\n      <td>92.099998</td>\n      <td>93.209999</td>\n      <td>91.410004</td>\n      <td>92.769997</td>\n      <td>92.769997</td>\n      <td>34208000</td>\n    </tr>\n    <tr>\n      <th>2021-01-06</th>\n      <td>91.620003</td>\n      <td>92.279999</td>\n      <td>89.459999</td>\n      <td>90.330002</td>\n      <td>90.330002</td>\n      <td>51911700</td>\n    </tr>\n    <tr>\n      <th>2021-01-07</th>\n      <td>91.330002</td>\n      <td>95.510002</td>\n      <td>91.199997</td>\n      <td>95.160004</td>\n      <td>95.160004</td>\n      <td>42897200</td>\n    </tr>\n    <tr>\n      <th>2021-01-08</th>\n      <td>95.980003</td>\n      <td>96.400002</td>\n      <td>93.269997</td>\n      <td>94.580002</td>\n      <td>94.580002</td>\n      <td>39816400</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 5,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-05-06T22:30:47.416Z",
          "iopub.execute_input": "2022-05-06T22:30:47.422Z",
          "iopub.status.idle": "2022-05-06T22:30:47.438Z",
          "shell.execute_reply": "2022-05-06T22:30:47.949Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.tail()"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 6,
          "data": {
            "text/plain": "                  Open        High         Low       Close   Adj Close  \\\nDate                                                                     \n2021-12-27  147.509995  154.889999  147.250000  154.360001  154.360001   \n2021-12-28  155.880005  156.729996  151.380005  153.149994  153.149994   \n2021-12-29  152.820007  154.339996  147.289993  148.259995  148.259995   \n2021-12-30  147.440002  148.850006  144.850006  145.149994  145.149994   \n2021-12-31  146.160004  148.610001  143.550003  143.899994  143.899994   \n\n              Volume  \nDate                  \n2021-12-27  53296400  \n2021-12-28  58699100  \n2021-12-29  51300200  \n2021-12-30  44358000  \n2021-12-31  49448100  ",
            "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>2021-12-27</th>\n      <td>147.509995</td>\n      <td>154.889999</td>\n      <td>147.250000</td>\n      <td>154.360001</td>\n      <td>154.360001</td>\n      <td>53296400</td>\n    </tr>\n    <tr>\n      <th>2021-12-28</th>\n      <td>155.880005</td>\n      <td>156.729996</td>\n      <td>151.380005</td>\n      <td>153.149994</td>\n      <td>153.149994</td>\n      <td>58699100</td>\n    </tr>\n    <tr>\n      <th>2021-12-29</th>\n      <td>152.820007</td>\n      <td>154.339996</td>\n      <td>147.289993</td>\n      <td>148.259995</td>\n      <td>148.259995</td>\n      <td>51300200</td>\n    </tr>\n    <tr>\n      <th>2021-12-30</th>\n      <td>147.440002</td>\n      <td>148.850006</td>\n      <td>144.850006</td>\n      <td>145.149994</td>\n      <td>145.149994</td>\n      <td>44358000</td>\n    </tr>\n    <tr>\n      <th>2021-12-31</th>\n      <td>146.160004</td>\n      <td>148.610001</td>\n      <td>143.550003</td>\n      <td>143.899994</td>\n      <td>143.899994</td>\n      <td>49448100</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 6,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-05-06T22:30:47.452Z",
          "iopub.execute_input": "2022-05-06T22:30:47.463Z",
          "iopub.status.idle": "2022-05-06T22:30:47.481Z",
          "shell.execute_reply": "2022-05-06T22:30:47.954Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.DataFrame({\"Adj Close\": df['Adj Close'],})"
      ],
      "outputs": [],
      "execution_count": 7,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-05-06T22:30:47.491Z",
          "iopub.execute_input": "2022-05-06T22:30:47.497Z",
          "iopub.status.idle": "2022-05-06T22:30:47.507Z",
          "shell.execute_reply": "2022-05-06T22:30:47.958Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "np.array(data)"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 8,
          "data": {
            "text/plain": "array([[ 92.30000305],\n       [ 92.76999664],\n       [ 90.33000183],\n       [ 95.16000366],\n       [ 94.58000183],\n       [ 97.25      ],\n       [ 95.36000061],\n       [ 91.77999878],\n       [ 90.79000092],\n       [ 88.20999908],\n       [ 89.44999695],\n       [ 88.75      ],\n       [ 91.52999878],\n       [ 92.79000092],\n       [ 94.12999725],\n       [ 94.70999908],\n       [ 88.83999634],\n       [ 87.51999664],\n       [ 85.63999939],\n       [ 87.66000366],\n       [ 88.86000061],\n       [ 87.88999939],\n       [ 87.83999634],\n       [ 87.90000153],\n       [ 91.47000122],\n       [ 90.91000366],\n       [ 92.34999847],\n       [ 92.66000366],\n       [ 93.76999664],\n       [ 91.45999908],\n       [ 89.94000244],\n       [ 88.63999939],\n       [ 89.58000183],\n       [ 85.37000275],\n       [ 84.73999786],\n       [ 86.94000244],\n       [ 82.41999817],\n       [ 84.51000214],\n       [ 86.38999939],\n       [ 84.12999725],\n       [ 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          },
          "metadata": {}
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      ],
      "execution_count": 8,
      "metadata": {
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          "shell.execute_reply": "2022-05-06T22:30:47.961Z"
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    },
    {
      "cell_type": "code",
      "source": [
        "data.squeeze()"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 9,
          "data": {
            "text/plain": "Date\n2021-01-04     92.300003\n2021-01-05     92.769997\n2021-01-06     90.330002\n2021-01-07     95.160004\n2021-01-08     94.580002\n                 ...    \n2021-12-27    154.360001\n2021-12-28    153.149994\n2021-12-29    148.259995\n2021-12-30    145.149994\n2021-12-31    143.899994\nName: Adj Close, Length: 252, dtype: float64"
          },
          "metadata": {}
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      ],
      "execution_count": 9,
      "metadata": {
        "collapsed": true,
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        "execution": {
          "iopub.status.busy": "2022-05-06T22:30:47.550Z",
          "iopub.execute_input": "2022-05-06T22:30:47.557Z",
          "iopub.status.idle": "2022-05-06T22:30:47.571Z",
          "shell.execute_reply": "2022-05-06T22:30:47.964Z"
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    },
    {
      "cell_type": "code",
      "source": [
        "x = np.array(df['Adj Close']).squeeze()"
      ],
      "outputs": [],
      "execution_count": 10,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
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        "execution": {
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          "iopub.execute_input": "2022-05-06T22:30:47.587Z",
          "iopub.status.idle": "2022-05-06T22:30:47.597Z",
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    },
    {
      "cell_type": "code",
      "source": [
        "x"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 11,
          "data": {
            "text/plain": "array([ 92.30000305,  92.76999664,  90.33000183,  95.16000366,\n        94.58000183,  97.25      ,  95.36000061,  91.77999878,\n        90.79000092,  88.20999908,  89.44999695,  88.75      ,\n        91.52999878,  92.79000092,  94.12999725,  94.70999908,\n        88.83999634,  87.51999664,  85.63999939,  87.66000366,\n        88.86000061,  87.88999939,  87.83999634,  87.90000153,\n        91.47000122,  90.91000366,  92.34999847,  92.66000366,\n        93.76999664,  91.45999908,  89.94000244,  88.63999939,\n        89.58000183,  85.37000275,  84.73999786,  86.94000244,\n        82.41999817,  84.51000214,  86.38999939,  84.12999725,\n        80.86000061,  77.75      ,  78.51999664,  73.95999908,\n        78.52999878,  77.51999664,  81.23000336,  81.05000305,\n        82.5       ,  82.75      ,  82.62999725,  78.12000275,\n        79.05999756,  80.30000305,  78.37999725,  76.48000336,\n        76.22000122,  77.41000366,  77.13999939,  76.        ,\n        78.5       ,  81.08999634,  81.43000031,  81.44000244,\n        82.19999695,  83.34999847,  82.76000214,  78.58000183,\n        80.19000244,  78.55000305,  83.01000214,  82.15000153,\n        81.11000061,  79.26999664,  81.61000061,  79.05999756,\n        82.76000214,  85.41000366,  85.20999908,  84.01999664,\n        83.91000366,  81.62000275,  78.55000305,  78.61000061,\n        77.83000183,  77.88999939,  78.80999756,  75.98999786,\n        76.83000183,  74.63999939,  73.08999634,  74.58999634,\n        74.65000153,  74.44000244,  76.23000336,  78.05999756,\n        77.16999817,  77.44000244,  77.86000061,  78.33999634,\n        78.41999817,  80.08000183,  80.80999756,  81.97000122,\n        80.27999878,  81.58000183,  81.34999847,  80.88999939,\n        79.95999908,  81.55999756,  81.30999756,  81.55000305,\n        80.47000122,  80.11000061,  84.55999756,  84.65000153,\n        82.58999634,  83.58000183,  83.81999969,  86.09999847,\n        85.62000275,  87.08000183,  89.51999664,  93.93000031,\n        93.30999756,  94.69999695,  94.47000122,  90.54000092,\n        89.73999786,  90.90000153,  90.80999756,  90.26000214,\n        89.05000305,  86.93000031,  85.88999939,  86.58000183,\n        87.11000061,  89.41000366,  91.20999908,  92.15000153,\n        91.81999969,  91.02999878,  97.93000031, 102.94999695,\n       106.19000244, 108.62999725, 112.55999756, 118.76999664,\n       112.34999847, 110.11000061, 107.58000183, 106.48000336,\n       107.68000031, 106.5       , 110.55000305, 107.48000336,\n       107.55999756, 103.44000244, 103.69999695, 104.65000153,\n       108.76999664, 107.65000153, 108.30000305, 107.26999664,\n       111.40000153, 111.31999969, 110.72000122, 109.98999786,\n       109.19999695, 109.91999817, 109.15000153, 106.16999817,\n       106.15000153, 105.19999695, 104.80000305, 105.73000336,\n       105.59999847, 106.22000122, 103.87999725, 101.55000305,\n       102.81999969, 104.37999725, 106.15000153, 105.80000305,\n       108.16000366, 101.51999664, 100.34999847, 102.90000153,\n       102.44999695, 100.33999634, 101.80999756, 103.63999939,\n       106.44999695, 105.05999756, 104.68000031, 105.04000092,\n       109.16000366, 111.98999786, 112.12000275, 116.43000031,\n       116.33000183, 116.38999939, 119.33000183, 119.81999969,\n       122.36000061, 122.93000031, 122.27999878, 121.16000366,\n       120.23000336, 125.23000336, 127.62999725, 130.52999878,\n       137.5       , 136.33999634, 150.16000366, 148.91999817,\n       139.86999512, 146.00999451, 147.88999939, 146.49000549,\n       152.44999695, 151.33999634, 155.02000427, 155.41000366,\n       152.52000427, 149.91999817, 157.80000305, 154.80999756,\n       161.91000366, 158.36999512, 149.11000061, 150.67999268,\n       144.00999451, 139.05999756, 144.8500061 , 145.24000549,\n       138.1000061 , 138.55000305, 133.80000305, 135.6000061 ,\n       146.5       , 138.63999939, 137.75      , 135.80000305,\n       144.25      , 143.88000488, 146.13999939, 154.36000061,\n       153.1499939 , 148.25999451, 145.1499939 , 143.8999939 ])"
          },
          "metadata": {}
        }
      ],
      "execution_count": 11,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-05-06T22:30:47.609Z",
          "iopub.execute_input": "2022-05-06T22:30:47.615Z",
          "iopub.status.idle": "2022-05-06T22:30:47.631Z",
          "shell.execute_reply": "2022-05-06T22:30:47.970Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x = df['Adj Close'].values.tolist()"
      ],
      "outputs": [],
      "execution_count": 12,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-05-06T22:30:47.643Z",
          "iopub.execute_input": "2022-05-06T22:30:47.649Z",
          "iopub.status.idle": "2022-05-06T22:30:47.660Z",
          "shell.execute_reply": "2022-05-06T22:30:47.973Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ls = [] \n",
        "for i in data.values:  \n",
        "  for j in i:        \n",
        "    ls.append(j)\n",
        "\n",
        "for i in range(len(ls)-1):    \n",
        "  print(ls[i+1] - ls[i])"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.46999359130859375\n",
            "-2.4399948120117188\n",
            "4.8300018310546875\n",
            "-0.5800018310546875\n",
            "2.6699981689453125\n",
            "-1.8899993896484375\n",
            "-3.5800018310546875\n",
            "-0.9899978637695312\n",
            "-2.5800018310546875\n",
            "1.2399978637695312\n",
            "-0.6999969482421875\n",
            "2.779998779296875\n",
            "1.2600021362304688\n",
            "1.339996337890625\n",
            "0.5800018310546875\n",
            "-5.870002746582031\n",
            "-1.3199996948242188\n",
            "-1.8799972534179688\n",
            "2.0200042724609375\n",
            "1.1999969482421875\n",
            "-0.970001220703125\n",
            "-0.0500030517578125\n",
            "0.06000518798828125\n",
            "3.5699996948242188\n",
            "-0.55999755859375\n",
            "1.4399948120117188\n",
            "0.31000518798828125\n",
            "1.1099929809570312\n",
            "-2.30999755859375\n",
            "-1.5199966430664062\n",
            "-1.3000030517578125\n",
            "0.94000244140625\n",
            "-4.209999084472656\n",
            "-0.6300048828125\n",
            "2.2000045776367188\n",
            "-4.5200042724609375\n",
            "2.0900039672851562\n",
            "1.8799972534179688\n",
            "-2.2600021362304688\n",
            "-3.2699966430664062\n",
            "-3.1100006103515625\n",
            "0.7699966430664062\n",
            "-4.55999755859375\n",
            "4.569999694824219\n",
            "-1.0100021362304688\n",
            "3.7100067138671875\n",
            "-0.18000030517578125\n",
            "1.4499969482421875\n",
            "0.25\n",
            "-0.12000274658203125\n",
            "-4.5099945068359375\n",
            "0.9399948120117188\n",
            "1.2400054931640625\n",
            "-1.9200057983398438\n",
            "-1.899993896484375\n",
            "-0.26000213623046875\n",
            "1.19000244140625\n",
            "-0.2700042724609375\n",
            "-1.1399993896484375\n",
            "2.5\n",
            "2.589996337890625\n",
            "0.34000396728515625\n",
            "0.01000213623046875\n",
            "0.7599945068359375\n",
            "1.1500015258789062\n",
            "-0.589996337890625\n",
            "-4.180000305175781\n",
            "1.6100006103515625\n",
            "-1.6399993896484375\n",
            "4.459999084472656\n",
            "-0.8600006103515625\n",
            "-1.0400009155273438\n",
            "-1.8400039672851562\n",
            "2.3400039672851562\n",
            "-2.5500030517578125\n",
            "3.7000045776367188\n",
            "2.6500015258789062\n",
            "-0.20000457763671875\n",
            "-1.19000244140625\n",
            "-0.10999298095703125\n",
            "-2.2900009155273438\n",
            "-3.0699996948242188\n",
            "0.05999755859375\n",
            "-0.779998779296875\n",
            "0.05999755859375\n",
            "0.9199981689453125\n",
            "-2.8199996948242188\n",
            "0.8400039672851562\n",
            "-2.19000244140625\n",
            "-1.5500030517578125\n",
            "1.5\n",
            "0.06000518798828125\n",
            "-0.20999908447265625\n",
            "1.7900009155273438\n",
            "1.8299942016601562\n",
            "-0.8899993896484375\n",
            "0.2700042724609375\n",
            "0.4199981689453125\n",
            "0.4799957275390625\n",
            "0.0800018310546875\n",
            "1.660003662109375\n",
            "0.7299957275390625\n",
            "1.160003662109375\n",
            "-1.69000244140625\n",
            "1.3000030517578125\n",
            "-0.23000335693359375\n",
            "-0.45999908447265625\n",
            "-0.9300003051757812\n",
            "1.5999984741210938\n",
            "-0.25\n",
            "0.2400054931640625\n",
            "-1.0800018310546875\n",
            "-0.3600006103515625\n",
            "4.4499969482421875\n",
            "0.09000396728515625\n",
            "-2.0600051879882812\n",
            "0.9900054931640625\n",
            "0.23999786376953125\n",
            "2.279998779296875\n",
            "-0.4799957275390625\n",
            "1.4599990844726562\n",
            "2.4399948120117188\n",
            "4.410003662109375\n",
            "-0.6200027465820312\n",
            "1.3899993896484375\n",
            "-0.2299957275390625\n",
            "-3.9300003051757812\n",
            "-0.8000030517578125\n",
            "1.160003662109375\n",
            "-0.09000396728515625\n",
            "-0.5499954223632812\n",
            "-1.2099990844726562\n",
            "-2.1200027465820312\n",
            "-1.0400009155273438\n",
            "0.69000244140625\n",
            "0.529998779296875\n",
            "2.3000030517578125\n",
            "1.7999954223632812\n",
            "0.94000244140625\n",
            "-0.3300018310546875\n",
            "-0.7900009155273438\n",
            "6.900001525878906\n",
            "5.019996643066406\n",
            "3.2400054931640625\n",
            "2.4399948120117188\n",
            "3.9300003051757812\n",
            "6.209999084472656\n",
            "-6.4199981689453125\n",
            "-2.2399978637695312\n",
            "-2.529998779296875\n",
            "-1.0999984741210938\n",
            "1.1999969482421875\n",
            "-1.1800003051757812\n",
            "4.0500030517578125\n",
            "-3.0699996948242188\n",
            "0.07999420166015625\n",
            "-4.1199951171875\n",
            "0.2599945068359375\n",
            "0.9500045776367188\n",
            "4.1199951171875\n",
            "-1.1199951171875\n",
            "0.6500015258789062\n",
            "-1.0300064086914062\n",
            "4.1300048828125\n",
            "-0.0800018310546875\n",
            "-0.5999984741210938\n",
            "-0.7300033569335938\n",
            "-0.7900009155273438\n",
            "0.720001220703125\n",
            "-0.7699966430664062\n",
            "-2.9800033569335938\n",
            "-0.01999664306640625\n",
            "-0.9500045776367188\n",
            "-0.399993896484375\n",
            "0.9300003051757812\n",
            "-0.1300048828125\n",
            "0.6200027465820312\n",
            "-2.3400039672851562\n",
            "-2.3299942016601562\n",
            "1.2699966430664062\n",
            "1.55999755859375\n",
            "1.7700042724609375\n",
            "-0.34999847412109375\n",
            "2.3600006103515625\n",
            "-6.640007019042969\n",
            "-1.1699981689453125\n",
            "2.5500030517578125\n",
            "-0.45000457763671875\n",
            "-2.1100006103515625\n",
            "1.470001220703125\n",
            "1.8300018310546875\n",
            "2.80999755859375\n",
            "-1.3899993896484375\n",
            "-0.37999725341796875\n",
            "0.3600006103515625\n",
            "4.120002746582031\n",
            "2.8299942016601562\n",
            "0.1300048828125\n",
            "4.30999755859375\n",
            "-0.09999847412109375\n",
            "0.05999755859375\n",
            "2.94000244140625\n",
            "0.48999786376953125\n",
            "2.5400009155273438\n",
            "0.5699996948242188\n",
            "-0.6500015258789062\n",
            "-1.1199951171875\n",
            "-0.9300003051757812\n",
            "5.0\n",
            "2.399993896484375\n",
            "2.9000015258789062\n",
            "6.970001220703125\n",
            "-1.160003662109375\n",
            "13.82000732421875\n",
            "-1.2400054931640625\n",
            "-9.050003051757812\n",
            "6.1399993896484375\n",
            "1.8800048828125\n",
            "-1.399993896484375\n",
            "5.959991455078125\n",
            "-1.1100006103515625\n",
            "3.6800079345703125\n",
            "0.3899993896484375\n",
            "-2.8899993896484375\n",
            "-2.600006103515625\n",
            "7.8800048828125\n",
            "-2.9900054931640625\n",
            "7.100006103515625\n",
            "-3.540008544921875\n",
            "-9.259994506835938\n",
            "1.5699920654296875\n",
            "-6.6699981689453125\n",
            "-4.9499969482421875\n",
            "5.790008544921875\n",
            "0.3899993896484375\n",
            "-7.1399993896484375\n",
            "0.4499969482421875\n",
            "-4.75\n",
            "1.8000030517578125\n",
            "10.899993896484375\n",
            "-7.8600006103515625\n",
            "-0.8899993896484375\n",
            "-1.9499969482421875\n",
            "8.449996948242188\n",
            "-0.3699951171875\n",
            "2.2599945068359375\n",
            "8.220001220703125\n",
            "-1.2100067138671875\n",
            "-4.8899993896484375\n",
            "-3.1100006103515625\n",
            "-1.25\n"
          ]
        }
      ],
      "execution_count": 13,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-05-06T22:30:47.670Z",
          "iopub.execute_input": "2022-05-06T22:30:47.676Z",
          "iopub.status.idle": "2022-05-06T22:30:47.691Z",
          "shell.execute_reply": "2022-05-06T22:30:47.977Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from statistics import mean\n",
        "\n",
        "# x.mean() # average in array\n",
        "mean(x)"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 14,
          "data": {
            "text/plain": "101.14587302435012"
          },
          "metadata": {}
        }
      ],
      "execution_count": 14,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-05-06T22:30:47.699Z",
          "iopub.execute_input": "2022-05-06T22:30:47.705Z",
          "iopub.status.idle": "2022-05-06T22:30:47.717Z",
          "shell.execute_reply": "2022-05-06T22:30:47.980Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# np.repeat(x, x.mean())\n",
        "np.repeat(x, mean(x))"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 15,
          "data": {
            "text/plain": "array([ 92.30000305,  92.30000305,  92.30000305, ..., 143.8999939 ,\n       143.8999939 , 143.8999939 ])"
          },
          "metadata": {}
        }
      ],
      "execution_count": 15,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-05-06T22:30:47.725Z",
          "iopub.execute_input": "2022-05-06T22:30:47.731Z",
          "iopub.status.idle": "2022-05-06T22:30:47.744Z",
          "shell.execute_reply": "2022-05-06T22:30:47.983Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import collections\n",
        "\n",
        "print([item for item, count in collections.Counter(x).items() if count > 1])\n"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[79.05999755859375, 82.76000213623047, 78.55000305175781, 106.1500015258789]\n"
          ]
        }
      ],
      "execution_count": 16,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-05-06T22:30:47.752Z",
          "iopub.execute_input": "2022-05-06T22:30:47.757Z",
          "iopub.status.idle": "2022-05-06T22:30:47.769Z",
          "shell.execute_reply": "2022-05-06T22:30:47.986Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "[i for n, i in enumerate(x) if i in x[:n]]"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 17,
          "data": {
            "text/plain": "[79.05999755859375, 82.76000213623047, 78.55000305175781, 106.1500015258789]"
          },
          "metadata": {}
        }
      ],
      "execution_count": 17,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-05-06T22:30:47.779Z",
          "iopub.execute_input": "2022-05-06T22:30:47.785Z",
          "iopub.status.idle": "2022-05-06T22:30:47.798Z",
          "shell.execute_reply": "2022-05-06T22:30:47.989Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def list_duplicates(seq):\n",
        "  seen = set()\n",
        "  seen_add = seen.add\n",
        "  seen_twice = set( x for x in seq if x in seen or seen_add(x) )\n",
        "  return list( seen_twice )\n",
        "\n",
        "list_duplicates(x)\n"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 18,
          "data": {
            "text/plain": "[82.76000213623047, 106.1500015258789, 78.55000305175781, 79.05999755859375]"
          },
          "metadata": {}
        }
      ],
      "execution_count": 18,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-05-06T22:30:47.805Z",
          "iopub.execute_input": "2022-05-06T22:30:47.810Z",
          "iopub.status.idle": "2022-05-06T22:30:47.821Z",
          "shell.execute_reply": "2022-05-06T22:30:47.992Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x_size= int(0.8 * len(x))\n",
        "x_sample = x[:x_size]"
      ],
      "outputs": [],
      "execution_count": 19,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-05-06T22:30:47.829Z",
          "iopub.execute_input": "2022-05-06T22:30:47.835Z",
          "iopub.status.idle": "2022-05-06T22:30:47.843Z",
          "shell.execute_reply": "2022-05-06T22:30:47.995Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x_sample "
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 20,
          "data": {
            "text/plain": "[92.30000305175781,\n 92.7699966430664,\n 90.33000183105469,\n 95.16000366210938,\n 94.58000183105469,\n 97.25,\n 95.36000061035156,\n 91.77999877929688,\n 90.79000091552734,\n 88.20999908447266,\n 89.44999694824219,\n 88.75,\n 91.52999877929688,\n 92.79000091552734,\n 94.12999725341797,\n 94.70999908447266,\n 88.83999633789062,\n 87.5199966430664,\n 85.63999938964844,\n 87.66000366210938,\n 88.86000061035156,\n 87.88999938964844,\n 87.83999633789062,\n 87.9000015258789,\n 91.47000122070312,\n 90.91000366210938,\n 92.3499984741211,\n 92.66000366210938,\n 93.7699966430664,\n 91.45999908447266,\n 89.94000244140625,\n 88.63999938964844,\n 89.58000183105469,\n 85.37000274658203,\n 84.73999786376953,\n 86.94000244140625,\n 82.41999816894531,\n 84.51000213623047,\n 86.38999938964844,\n 84.12999725341797,\n 80.86000061035156,\n 77.75,\n 78.5199966430664,\n 73.95999908447266,\n 78.52999877929688,\n 77.5199966430664,\n 81.2300033569336,\n 81.05000305175781,\n 82.5,\n 82.75,\n 82.62999725341797,\n 78.12000274658203,\n 79.05999755859375,\n 80.30000305175781,\n 78.37999725341797,\n 76.4800033569336,\n 76.22000122070312,\n 77.41000366210938,\n 77.13999938964844,\n 76.0,\n 78.5,\n 81.08999633789062,\n 81.43000030517578,\n 81.44000244140625,\n 82.19999694824219,\n 83.3499984741211,\n 82.76000213623047,\n 78.58000183105469,\n 80.19000244140625,\n 78.55000305175781,\n 83.01000213623047,\n 82.1500015258789,\n 81.11000061035156,\n 79.2699966430664,\n 81.61000061035156,\n 79.05999755859375,\n 82.76000213623047,\n 85.41000366210938,\n 85.20999908447266,\n 84.0199966430664,\n 83.91000366210938,\n 81.62000274658203,\n 78.55000305175781,\n 78.61000061035156,\n 77.83000183105469,\n 77.88999938964844,\n 78.80999755859375,\n 75.98999786376953,\n 76.83000183105469,\n 74.63999938964844,\n 73.08999633789062,\n 74.58999633789062,\n 74.6500015258789,\n 74.44000244140625,\n 76.2300033569336,\n 78.05999755859375,\n 77.16999816894531,\n 77.44000244140625,\n 77.86000061035156,\n 78.33999633789062,\n 78.41999816894531,\n 80.08000183105469,\n 80.80999755859375,\n 81.97000122070312,\n 80.27999877929688,\n 81.58000183105469,\n 81.3499984741211,\n 80.88999938964844,\n 79.95999908447266,\n 81.55999755859375,\n 81.30999755859375,\n 81.55000305175781,\n 80.47000122070312,\n 80.11000061035156,\n 84.55999755859375,\n 84.6500015258789,\n 82.58999633789062,\n 83.58000183105469,\n 83.81999969482422,\n 86.0999984741211,\n 85.62000274658203,\n 87.08000183105469,\n 89.5199966430664,\n 93.93000030517578,\n 93.30999755859375,\n 94.69999694824219,\n 94.47000122070312,\n 90.54000091552734,\n 89.73999786376953,\n 90.9000015258789,\n 90.80999755859375,\n 90.26000213623047,\n 89.05000305175781,\n 86.93000030517578,\n 85.88999938964844,\n 86.58000183105469,\n 87.11000061035156,\n 89.41000366210938,\n 91.20999908447266,\n 92.1500015258789,\n 91.81999969482422,\n 91.02999877929688,\n 97.93000030517578,\n 102.94999694824219,\n 106.19000244140625,\n 108.62999725341797,\n 112.55999755859375,\n 118.7699966430664,\n 112.3499984741211,\n 110.11000061035156,\n 107.58000183105469,\n 106.4800033569336,\n 107.68000030517578,\n 106.5,\n 110.55000305175781,\n 107.4800033569336,\n 107.55999755859375,\n 103.44000244140625,\n 103.69999694824219,\n 104.6500015258789,\n 108.7699966430664,\n 107.6500015258789,\n 108.30000305175781,\n 107.2699966430664,\n 111.4000015258789,\n 111.31999969482422,\n 110.72000122070312,\n 109.98999786376953,\n 109.19999694824219,\n 109.91999816894531,\n 109.1500015258789,\n 106.16999816894531,\n 106.1500015258789,\n 105.19999694824219,\n 104.80000305175781,\n 105.7300033569336,\n 105.5999984741211,\n 106.22000122070312,\n 103.87999725341797,\n 101.55000305175781,\n 102.81999969482422,\n 104.37999725341797,\n 106.1500015258789,\n 105.80000305175781,\n 108.16000366210938,\n 101.5199966430664,\n 100.3499984741211,\n 102.9000015258789,\n 102.44999694824219,\n 100.33999633789062,\n 101.80999755859375,\n 103.63999938964844,\n 106.44999694824219,\n 105.05999755859375,\n 104.68000030517578,\n 105.04000091552734,\n 109.16000366210938,\n 111.98999786376953,\n 112.12000274658203,\n 116.43000030517578,\n 116.33000183105469]"
          },
          "metadata": {}
        }
      ],
      "execution_count": 20,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-05-06T22:30:47.851Z",
          "iopub.execute_input": "2022-05-06T22:30:47.856Z",
          "iopub.status.idle": "2022-05-06T22:30:47.868Z",
          "shell.execute_reply": "2022-05-06T22:30:47.998Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x0 = np.arange(len(x_sample))\n",
        "x1 = np.arange(len(x))\n",
        "plt.figure(figsize=(16,12))\n",
        "plt.plot(x0,x_sample, c='DarkBlue',linewidth=4)\n",
        "plt.plot(x1,x, c='red')"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 21,
          "data": {
            "text/plain": "[<matplotlib.lines.Line2D at 0x2122bcf4160>]"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 1152x864 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "execution_count": 21,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-05-06T22:30:47.877Z",
          "iopub.execute_input": "2022-05-06T22:30:47.882Z",
          "iopub.status.idle": "2022-05-06T22:30:47.898Z",
          "shell.execute_reply": "2022-05-06T22:30:48.001Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import distance\n",
        "\n",
        "score = []\n",
        "for i in range(len(x_sample)):\n",
        "    x_target = x_sample[i:i+len(x)] #rolling the Pattern over windows of SampleTarget\n",
        "    score.append(distance.levenshtein(x, x_target))\n",
        "    \n",
        "print(score)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251]\n"
          ]
        }
      ],
      "execution_count": 22,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-05-06T22:30:47.908Z",
          "iopub.execute_input": "2022-05-06T22:30:47.914Z",
          "shell.execute_reply": "2022-05-06T22:30:49.808Z",
          "iopub.status.idle": "2022-05-06T22:30:49.778Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x2 = np.arange(start = np.argmin(score),stop= np.argmin(score)+len(x))\n",
        "plt.figure(figsize=(16,12))\n",
        "plt.plot(x0,x_sample, c='DarkBlue',linewidth=4)\n",
        "plt.plot(x2,x)"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 23,
          "data": {
            "text/plain": "[<matplotlib.lines.Line2D at 0x211ce805a20>]"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 1152x864 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "execution_count": 23,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-05-06T22:30:49.789Z",
          "iopub.execute_input": "2022-05-06T22:30:49.796Z",
          "shell.execute_reply": "2022-05-06T22:30:49.882Z",
          "iopub.status.idle": "2022-05-06T22:30:49.893Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "min_n = min(x)\n",
        "int(min_n)"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 24,
          "data": {
            "text/plain": "73"
          },
          "metadata": {}
        }
      ],
      "execution_count": 24,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-05-06T22:30:49.904Z",
          "iopub.execute_input": "2022-05-06T22:30:49.911Z",
          "iopub.status.idle": "2022-05-06T22:30:49.924Z",
          "shell.execute_reply": "2022-05-06T22:30:50.122Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "max_n = max(x) \n",
        "int(max_n)"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 25,
          "data": {
            "text/plain": "161"
          },
          "metadata": {}
        }
      ],
      "execution_count": 25,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-05-06T22:30:49.934Z",
          "iopub.execute_input": "2022-05-06T22:30:49.939Z",
          "iopub.status.idle": "2022-05-06T22:30:49.955Z",
          "shell.execute_reply": "2022-05-06T22:30:50.128Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "terms = [1/n**4 for n in range(int(min_n),int(max_n))]\n",
        "sum(terms)"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 26,
          "data": {
            "text/plain": "7.940076362764899e-07"
          },
          "metadata": {}
        }
      ],
      "execution_count": 26,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-05-06T22:30:49.964Z",
          "iopub.execute_input": "2022-05-06T22:30:49.970Z",
          "iopub.status.idle": "2022-05-06T22:30:49.983Z",
          "shell.execute_reply": "2022-05-06T22:30:50.131Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def primes_method(n):\n",
        "    out = list()\n",
        "    sieve = [True] * (n+1)\n",
        "    for p in range(2, n+1):\n",
        "        if (sieve[p]):\n",
        "            out.append(p)\n",
        "            for i in range(p, n+1, p):\n",
        "                sieve[i] = False\n",
        "    return out"
      ],
      "outputs": [],
      "execution_count": 27,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-05-06T22:30:49.994Z",
          "iopub.execute_input": "2022-05-06T22:30:50.001Z",
          "iopub.status.idle": "2022-05-06T22:30:50.011Z",
          "shell.execute_reply": "2022-05-06T22:30:50.134Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 28,
          "data": {
            "text/plain": "[92.30000305175781,\n 92.7699966430664,\n 90.33000183105469,\n 95.16000366210938,\n 94.58000183105469,\n 97.25,\n 95.36000061035156,\n 91.77999877929688,\n 90.79000091552734,\n 88.20999908447266,\n 89.44999694824219,\n 88.75,\n 91.52999877929688,\n 92.79000091552734,\n 94.12999725341797,\n 94.70999908447266,\n 88.83999633789062,\n 87.5199966430664,\n 85.63999938964844,\n 87.66000366210938,\n 88.86000061035156,\n 87.88999938964844,\n 87.83999633789062,\n 87.9000015258789,\n 91.47000122070312,\n 90.91000366210938,\n 92.3499984741211,\n 92.66000366210938,\n 93.7699966430664,\n 91.45999908447266,\n 89.94000244140625,\n 88.63999938964844,\n 89.58000183105469,\n 85.37000274658203,\n 84.73999786376953,\n 86.94000244140625,\n 82.41999816894531,\n 84.51000213623047,\n 86.38999938964844,\n 84.12999725341797,\n 80.86000061035156,\n 77.75,\n 78.5199966430664,\n 73.95999908447266,\n 78.52999877929688,\n 77.5199966430664,\n 81.2300033569336,\n 81.05000305175781,\n 82.5,\n 82.75,\n 82.62999725341797,\n 78.12000274658203,\n 79.05999755859375,\n 80.30000305175781,\n 78.37999725341797,\n 76.4800033569336,\n 76.22000122070312,\n 77.41000366210938,\n 77.13999938964844,\n 76.0,\n 78.5,\n 81.08999633789062,\n 81.43000030517578,\n 81.44000244140625,\n 82.19999694824219,\n 83.3499984741211,\n 82.76000213623047,\n 78.58000183105469,\n 80.19000244140625,\n 78.55000305175781,\n 83.01000213623047,\n 82.1500015258789,\n 81.11000061035156,\n 79.2699966430664,\n 81.61000061035156,\n 79.05999755859375,\n 82.76000213623047,\n 85.41000366210938,\n 85.20999908447266,\n 84.0199966430664,\n 83.91000366210938,\n 81.62000274658203,\n 78.55000305175781,\n 78.61000061035156,\n 77.83000183105469,\n 77.88999938964844,\n 78.80999755859375,\n 75.98999786376953,\n 76.83000183105469,\n 74.63999938964844,\n 73.08999633789062,\n 74.58999633789062,\n 74.6500015258789,\n 74.44000244140625,\n 76.2300033569336,\n 78.05999755859375,\n 77.16999816894531,\n 77.44000244140625,\n 77.86000061035156,\n 78.33999633789062,\n 78.41999816894531,\n 80.08000183105469,\n 80.80999755859375,\n 81.97000122070312,\n 80.27999877929688,\n 81.58000183105469,\n 81.3499984741211,\n 80.88999938964844,\n 79.95999908447266,\n 81.55999755859375,\n 81.30999755859375,\n 81.55000305175781,\n 80.47000122070312,\n 80.11000061035156,\n 84.55999755859375,\n 84.6500015258789,\n 82.58999633789062,\n 83.58000183105469,\n 83.81999969482422,\n 86.0999984741211,\n 85.62000274658203,\n 87.08000183105469,\n 89.5199966430664,\n 93.93000030517578,\n 93.30999755859375,\n 94.69999694824219,\n 94.47000122070312,\n 90.54000091552734,\n 89.73999786376953,\n 90.9000015258789,\n 90.80999755859375,\n 90.26000213623047,\n 89.05000305175781,\n 86.93000030517578,\n 85.88999938964844,\n 86.58000183105469,\n 87.11000061035156,\n 89.41000366210938,\n 91.20999908447266,\n 92.1500015258789,\n 91.81999969482422,\n 91.02999877929688,\n 97.93000030517578,\n 102.94999694824219,\n 106.19000244140625,\n 108.62999725341797,\n 112.55999755859375,\n 118.7699966430664,\n 112.3499984741211,\n 110.11000061035156,\n 107.58000183105469,\n 106.4800033569336,\n 107.68000030517578,\n 106.5,\n 110.55000305175781,\n 107.4800033569336,\n 107.55999755859375,\n 103.44000244140625,\n 103.69999694824219,\n 104.6500015258789,\n 108.7699966430664,\n 107.6500015258789,\n 108.30000305175781,\n 107.2699966430664,\n 111.4000015258789,\n 111.31999969482422,\n 110.72000122070312,\n 109.98999786376953,\n 109.19999694824219,\n 109.91999816894531,\n 109.1500015258789,\n 106.16999816894531,\n 106.1500015258789,\n 105.19999694824219,\n 104.80000305175781,\n 105.7300033569336,\n 105.5999984741211,\n 106.22000122070312,\n 103.87999725341797,\n 101.55000305175781,\n 102.81999969482422,\n 104.37999725341797,\n 106.1500015258789,\n 105.80000305175781,\n 108.16000366210938,\n 101.5199966430664,\n 100.3499984741211,\n 102.9000015258789,\n 102.44999694824219,\n 100.33999633789062,\n 101.80999755859375,\n 103.63999938964844,\n 106.44999694824219,\n 105.05999755859375,\n 104.68000030517578,\n 105.04000091552734,\n 109.16000366210938,\n 111.98999786376953,\n 112.12000274658203,\n 116.43000030517578,\n 116.33000183105469,\n 116.38999938964844,\n 119.33000183105469,\n 119.81999969482422,\n 122.36000061035156,\n 122.93000030517578,\n 122.27999877929688,\n 121.16000366210938,\n 120.2300033569336,\n 125.2300033569336,\n 127.62999725341797,\n 130.52999877929688,\n 137.5,\n 136.33999633789062,\n 150.16000366210938,\n 148.9199981689453,\n 139.8699951171875,\n 146.00999450683594,\n 147.88999938964844,\n 146.49000549316406,\n 152.4499969482422,\n 151.33999633789062,\n 155.02000427246094,\n 155.41000366210938,\n 152.52000427246094,\n 149.9199981689453,\n 157.8000030517578,\n 154.80999755859375,\n 161.91000366210938,\n 158.3699951171875,\n 149.11000061035156,\n 150.67999267578125,\n 144.00999450683594,\n 139.05999755859375,\n 144.85000610351562,\n 145.24000549316406,\n 138.10000610351562,\n 138.5500030517578,\n 133.8000030517578,\n 135.60000610351562,\n 146.5,\n 138.63999938964844,\n 137.75,\n 135.8000030517578,\n 144.25,\n 143.8800048828125,\n 146.13999938964844,\n 154.36000061035156,\n 153.14999389648438,\n 148.25999450683594,\n 145.14999389648438,\n 143.89999389648438]"
          },
          "metadata": {}
        }
      ],
      "execution_count": 28,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-05-06T22:30:50.020Z",
          "iopub.execute_input": "2022-05-06T22:30:50.025Z",
          "iopub.status.idle": "2022-05-06T22:30:50.038Z",
          "shell.execute_reply": "2022-05-06T22:30:50.137Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "prime=[]\n",
        "\n",
        "for i in x:\n",
        "  c=0\n",
        "for j in np.arange(1,i):\n",
        "  if i%j==0:\n",
        "    c+=1\n",
        "  if c==1:\n",
        "    prime.append(i)\n",
        "print(prime)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[]\n"
          ]
        }
      ],
      "execution_count": 29,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-05-06T22:30:50.047Z",
          "iopub.execute_input": "2022-05-06T22:30:50.053Z",
          "iopub.status.idle": "2022-05-06T22:30:50.065Z",
          "shell.execute_reply": "2022-05-06T22:30:50.140Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def findPrime(n): \n",
        "        flag = 0  \n",
        "        if n > 1: \n",
        "            for i in np.arange(2, n//2): \n",
        "                if (n % i) == 0: \n",
        "                    flag = 0 \n",
        "                    break \n",
        "                else: \n",
        "                    flag = 1 \n",
        "        else: \n",
        "            flag = 0 \n",
        "        return flag \n",
        "     \n",
        "n = x\n",
        "for i in range(len(n)): \n",
        "  f = findPrime(n[i]) \n",
        "  if f == 0:\n",
        "    print(n[i],\" is not a prime number\") \n",
        "  else: \n",
        "    print(n[i],\" is a prime number\") "
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "92.30000305175781  is a prime number\n",
            "92.7699966430664  is a prime number\n",
            "90.33000183105469  is a prime number\n",
            "95.16000366210938  is a prime number\n",
            "94.58000183105469  is a prime number\n",
            "97.25  is a prime number\n",
            "95.36000061035156  is a prime number\n",
            "91.77999877929688  is a prime number\n",
            "90.79000091552734  is a prime number\n",
            "88.20999908447266  is a prime number\n",
            "89.44999694824219  is a prime number\n",
            "88.75  is a prime number\n",
            "91.52999877929688  is a prime number\n",
            "92.79000091552734  is a prime number\n",
            "94.12999725341797  is a prime number\n",
            "94.70999908447266  is a prime number\n",
            "88.83999633789062  is a prime number\n",
            "87.5199966430664  is a prime number\n",
            "85.63999938964844  is a prime number\n",
            "87.66000366210938  is a prime number\n",
            "88.86000061035156  is a prime number\n",
            "87.88999938964844  is a prime number\n",
            "87.83999633789062  is a prime number\n",
            "87.9000015258789  is a prime number\n",
            "91.47000122070312  is a prime number\n",
            "90.91000366210938  is a prime number\n",
            "92.3499984741211  is a prime number\n",
            "92.66000366210938  is a prime number\n",
            "93.7699966430664  is a prime number\n",
            "91.45999908447266  is a prime number\n",
            "89.94000244140625  is a prime number\n",
            "88.63999938964844  is a prime number\n",
            "89.58000183105469  is a prime number\n",
            "85.37000274658203  is a prime number\n",
            "84.73999786376953  is a prime number\n",
            "86.94000244140625  is a prime number\n",
            "82.41999816894531  is a prime number\n",
            "84.51000213623047  is a prime number\n",
            "86.38999938964844  is a prime number\n",
            "84.12999725341797  is a prime number\n",
            "80.86000061035156  is a prime number\n",
            "77.75  is a prime number\n",
            "78.5199966430664  is a prime number\n",
            "73.95999908447266  is a prime number\n",
            "78.52999877929688  is a prime number\n",
            "77.5199966430664  is a prime number\n",
            "81.2300033569336  is a prime number\n",
            "81.05000305175781  is a prime number\n",
            "82.5  is a prime number\n",
            "82.75  is a prime number\n",
            "82.62999725341797  is a prime number\n",
            "78.12000274658203  is a prime number\n",
            "79.05999755859375  is a prime number\n",
            "80.30000305175781  is a prime number\n",
            "78.37999725341797  is a prime number\n",
            "76.4800033569336  is a prime number\n",
            "76.22000122070312  is a prime number\n",
            "77.41000366210938  is a prime number\n",
            "77.13999938964844  is a prime number\n",
            "76.0  is not a prime number\n",
            "78.5  is a prime number\n",
            "81.08999633789062  is a prime number\n",
            "81.43000030517578  is a prime number\n",
            "81.44000244140625  is a prime number\n",
            "82.19999694824219  is a prime number\n",
            "83.3499984741211  is a prime number\n",
            "82.76000213623047  is a prime number\n",
            "78.58000183105469  is a prime number\n",
            "80.19000244140625  is a prime number\n",
            "78.55000305175781  is a prime number\n",
            "83.01000213623047  is a prime number\n",
            "82.1500015258789  is a prime number\n",
            "81.11000061035156  is a prime number\n",
            "79.2699966430664  is a prime number\n",
            "81.61000061035156  is a prime number\n",
            "79.05999755859375  is a prime number\n",
            "82.76000213623047  is a prime number\n",
            "85.41000366210938  is a prime number\n",
            "85.20999908447266  is a prime number\n",
            "84.0199966430664  is a prime number\n",
            "83.91000366210938  is a prime number\n",
            "81.62000274658203  is a prime number\n",
            "78.55000305175781  is a prime number\n",
            "78.61000061035156  is a prime number\n",
            "77.83000183105469  is a prime number\n",
            "77.88999938964844  is a prime number\n",
            "78.80999755859375  is a prime number\n",
            "75.98999786376953  is a prime number\n",
            "76.83000183105469  is a prime number\n",
            "74.63999938964844  is a prime number\n",
            "73.08999633789062  is a prime number\n",
            "74.58999633789062  is a prime number\n",
            "74.6500015258789  is a prime number\n",
            "74.44000244140625  is a prime number\n",
            "76.2300033569336  is a prime number\n",
            "78.05999755859375  is a prime number\n",
            "77.16999816894531  is a prime number\n",
            "77.44000244140625  is a prime number\n",
            "77.86000061035156  is a prime number\n",
            "78.33999633789062  is a prime number\n",
            "78.41999816894531  is a prime number\n",
            "80.08000183105469  is a prime number\n",
            "80.80999755859375  is a prime number\n",
            "81.97000122070312  is a prime number\n",
            "80.27999877929688  is a prime number\n",
            "81.58000183105469  is a prime number\n",
            "81.3499984741211  is a prime number\n",
            "80.88999938964844  is a prime number\n",
            "79.95999908447266  is a prime number\n",
            "81.55999755859375  is a prime number\n",
            "81.30999755859375  is a prime number\n",
            "81.55000305175781  is a prime number\n",
            "80.47000122070312  is a prime number\n",
            "80.11000061035156  is a prime number\n",
            "84.55999755859375  is a prime number\n",
            "84.6500015258789  is a prime number\n",
            "82.58999633789062  is a prime number\n",
            "83.58000183105469  is a prime number\n",
            "83.81999969482422  is a prime number\n",
            "86.0999984741211  is a prime number\n",
            "85.62000274658203  is a prime number\n",
            "87.08000183105469  is a prime number\n",
            "89.5199966430664  is a prime number\n",
            "93.93000030517578  is a prime number\n",
            "93.30999755859375  is a prime number\n",
            "94.69999694824219  is a prime number\n",
            "94.47000122070312  is a prime number\n",
            "90.54000091552734  is a prime number\n",
            "89.73999786376953  is a prime number\n",
            "90.9000015258789  is a prime number\n",
            "90.80999755859375  is a prime number\n",
            "90.26000213623047  is a prime number\n",
            "89.05000305175781  is a prime number\n",
            "86.93000030517578  is a prime number\n",
            "85.88999938964844  is a prime number\n",
            "86.58000183105469  is a prime number\n",
            "87.11000061035156  is a prime number\n",
            "89.41000366210938  is a prime number\n",
            "91.20999908447266  is a prime number\n",
            "92.1500015258789  is a prime number\n",
            "91.81999969482422  is a prime number\n",
            "91.02999877929688  is a prime number\n",
            "97.93000030517578  is a prime number\n",
            "102.94999694824219  is a prime number\n",
            "106.19000244140625  is a prime number\n",
            "108.62999725341797  is a prime number\n",
            "112.55999755859375  is a prime number\n",
            "118.7699966430664  is a prime number\n",
            "112.3499984741211  is a prime number\n",
            "110.11000061035156  is a prime number\n",
            "107.58000183105469  is a prime number\n",
            "106.4800033569336  is a prime number\n",
            "107.68000030517578  is a prime number\n",
            "106.5  is a prime number\n",
            "110.55000305175781  is a prime number\n",
            "107.4800033569336  is a prime number\n",
            "107.55999755859375  is a prime number\n",
            "103.44000244140625  is a prime number\n",
            "103.69999694824219  is a prime number\n",
            "104.6500015258789  is a prime number\n",
            "108.7699966430664  is a prime number\n",
            "107.6500015258789  is a prime number\n",
            "108.30000305175781  is a prime number\n",
            "107.2699966430664  is a prime number\n",
            "111.4000015258789  is a prime number\n",
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