2 Merging Vol.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "respiratory-parade",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "sorted-ecuador",
   "metadata": {},
   "source": [
    "# 1-Minute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "champion-alignment",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>t</th>\n",
       "      <th>c</th>\n",
       "      <th>o</th>\n",
       "      <th>h</th>\n",
       "      <th>l</th>\n",
       "      <th>v</th>\n",
       "      <th>vo</th>\n",
       "      <th>s</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1619063100</td>\n",
       "      <td>14161.150391</td>\n",
       "      <td>14218.599609</td>\n",
       "      <td>14218.599609</td>\n",
       "      <td>14155.299805</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>ok</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1619063160</td>\n",
       "      <td>14170.500000</td>\n",
       "      <td>14161.650391</td>\n",
       "      <td>14173.299805</td>\n",
       "      <td>14159.750000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>ok</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1619063220</td>\n",
       "      <td>14169.599609</td>\n",
       "      <td>14175.700195</td>\n",
       "      <td>14176.200195</td>\n",
       "      <td>14169.299805</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>ok</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1619063280</td>\n",
       "      <td>14162.099609</td>\n",
       "      <td>14167.700195</td>\n",
       "      <td>14169.150391</td>\n",
       "      <td>14161.849609</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>ok</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1619063340</td>\n",
       "      <td>14163.349609</td>\n",
       "      <td>14159.200195</td>\n",
       "      <td>14168.250000</td>\n",
       "      <td>14159.200195</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>ok</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>372</th>\n",
       "      <td>1619085420</td>\n",
       "      <td>14399.650391</td>\n",
       "      <td>14400.150391</td>\n",
       "      <td>14401.900391</td>\n",
       "      <td>14398.299805</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>ok</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>373</th>\n",
       "      <td>1619085480</td>\n",
       "      <td>14401.250000</td>\n",
       "      <td>14400.950195</td>\n",
       "      <td>14401.250000</td>\n",
       "      <td>14398.450195</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>ok</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>374</th>\n",
       "      <td>1619085540</td>\n",
       "      <td>14402.750000</td>\n",
       "      <td>14401.349609</td>\n",
       "      <td>14402.750000</td>\n",
       "      <td>14398.549805</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>ok</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375</th>\n",
       "      <td>1619085600</td>\n",
       "      <td>14399.099609</td>\n",
       "      <td>14399.099609</td>\n",
       "      <td>14399.099609</td>\n",
       "      <td>14399.099609</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>ok</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>376</th>\n",
       "      <td>1619085660</td>\n",
       "      <td>14406.150391</td>\n",
       "      <td>14406.150391</td>\n",
       "      <td>14406.150391</td>\n",
       "      <td>14406.150391</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>ok</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>377 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              t             c             o             h             l  v  \\\n",
       "0    1619063100  14161.150391  14218.599609  14218.599609  14155.299805  0   \n",
       "1    1619063160  14170.500000  14161.650391  14173.299805  14159.750000  0   \n",
       "2    1619063220  14169.599609  14175.700195  14176.200195  14169.299805  0   \n",
       "3    1619063280  14162.099609  14167.700195  14169.150391  14161.849609  0   \n",
       "4    1619063340  14163.349609  14159.200195  14168.250000  14159.200195  0   \n",
       "..          ...           ...           ...           ...           ... ..   \n",
       "372  1619085420  14399.650391  14400.150391  14401.900391  14398.299805  0   \n",
       "373  1619085480  14401.250000  14400.950195  14401.250000  14398.450195  0   \n",
       "374  1619085540  14402.750000  14401.349609  14402.750000  14398.549805  0   \n",
       "375  1619085600  14399.099609  14399.099609  14399.099609  14399.099609  0   \n",
       "376  1619085660  14406.150391  14406.150391  14406.150391  14406.150391  0   \n",
       "\n",
       "     vo   s  \n",
       "0     0  ok  \n",
       "1     0  ok  \n",
       "2     0  ok  \n",
       "3     0  ok  \n",
       "4     0  ok  \n",
       "..   ..  ..  \n",
       "372   0  ok  \n",
       "373   0  ok  \n",
       "374   0  ok  \n",
       "375   0  ok  \n",
       "376   0  ok  \n",
       "\n",
       "[377 rows x 8 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv(\"F.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "regulation-thumb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fe7a5d3f9a0>]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.plot(df['c'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "outside-hampton",
   "metadata": {},
   "source": [
    "# 5-Minute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "secure-holder",
   "metadata": {
    "scrolled": true
   },
   "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>t</th>\n",
       "      <th>c</th>\n",
       "      <th>o</th>\n",
       "      <th>h</th>\n",
       "      <th>l</th>\n",
       "      <th>v</th>\n",
       "      <th>vo</th>\n",
       "      <th>s</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1619063100</td>\n",
       "      <td>14163.349609</td>\n",
       "      <td>14218.599609</td>\n",
       "      <td>14218.599609</td>\n",
       "      <td>14155.299805</td>\n",
       "      <td>29105</td>\n",
       "      <td>0</td>\n",
       "      <td>ok</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1619063400</td>\n",
       "      <td>14205.099609</td>\n",
       "      <td>14164.200195</td>\n",
       "      <td>14223.900391</td>\n",
       "      <td>14162.200195</td>\n",
       "      <td>17371</td>\n",
       "      <td>29105</td>\n",
       "      <td>ok</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1619063700</td>\n",
       "      <td>14218.250000</td>\n",
       "      <td>14203.049805</td>\n",
       "      <td>14218.849609</td>\n",
       "      <td>14189.200195</td>\n",
       "      <td>12579</td>\n",
       "      <td>46476</td>\n",
       "      <td>ok</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1619064000</td>\n",
       "      <td>14209.900391</td>\n",
       "      <td>14218.700195</td>\n",
       "      <td>14218.700195</td>\n",
       "      <td>14200.349609</td>\n",
       "      <td>12337</td>\n",
       "      <td>59055</td>\n",
       "      <td>ok</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1619064300</td>\n",
       "      <td>14218.500000</td>\n",
       "      <td>14209.049805</td>\n",
       "      <td>14221.900391</td>\n",
       "      <td>14207.349609</td>\n",
       "      <td>10766</td>\n",
       "      <td>71392</td>\n",
       "      <td>ok</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>1619084400</td>\n",
       "      <td>14406.250000</td>\n",
       "      <td>14411.400391</td>\n",
       "      <td>14415.849609</td>\n",
       "      <td>14406.099609</td>\n",
       "      <td>11526</td>\n",
       "      <td>473389</td>\n",
       "      <td>ok</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>1619084700</td>\n",
       "      <td>14406.400391</td>\n",
       "      <td>14405.400391</td>\n",
       "      <td>14407.150391</td>\n",
       "      <td>14397.750000</td>\n",
       "      <td>15005</td>\n",
       "      <td>484915</td>\n",
       "      <td>ok</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>1619085000</td>\n",
       "      <td>14399.150391</td>\n",
       "      <td>14407.950195</td>\n",
       "      <td>14411.650391</td>\n",
       "      <td>14399.049805</td>\n",
       "      <td>15994</td>\n",
       "      <td>499920</td>\n",
       "      <td>ok</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>1619085300</td>\n",
       "      <td>14402.750000</td>\n",
       "      <td>14399.700195</td>\n",
       "      <td>14402.750000</td>\n",
       "      <td>14396.000000</td>\n",
       "      <td>8502</td>\n",
       "      <td>515914</td>\n",
       "      <td>ok</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>1619085600</td>\n",
       "      <td>14406.150391</td>\n",
       "      <td>14399.099609</td>\n",
       "      <td>14406.150391</td>\n",
       "      <td>14399.099609</td>\n",
       "      <td>55</td>\n",
       "      <td>524416</td>\n",
       "      <td>ok</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>76 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             t             c             o             h             l      v  \\\n",
       "0   1619063100  14163.349609  14218.599609  14218.599609  14155.299805  29105   \n",
       "1   1619063400  14205.099609  14164.200195  14223.900391  14162.200195  17371   \n",
       "2   1619063700  14218.250000  14203.049805  14218.849609  14189.200195  12579   \n",
       "3   1619064000  14209.900391  14218.700195  14218.700195  14200.349609  12337   \n",
       "4   1619064300  14218.500000  14209.049805  14221.900391  14207.349609  10766   \n",
       "..         ...           ...           ...           ...           ...    ...   \n",
       "71  1619084400  14406.250000  14411.400391  14415.849609  14406.099609  11526   \n",
       "72  1619084700  14406.400391  14405.400391  14407.150391  14397.750000  15005   \n",
       "73  1619085000  14399.150391  14407.950195  14411.650391  14399.049805  15994   \n",
       "74  1619085300  14402.750000  14399.700195  14402.750000  14396.000000   8502   \n",
       "75  1619085600  14406.150391  14399.099609  14406.150391  14399.099609     55   \n",
       "\n",
       "        vo   s  \n",
       "0        0  ok  \n",
       "1    29105  ok  \n",
       "2    46476  ok  \n",
       "3    59055  ok  \n",
       "4    71392  ok  \n",
       "..     ...  ..  \n",
       "71  473389  ok  \n",
       "72  484915  ok  \n",
       "73  499920  ok  \n",
       "74  515914  ok  \n",
       "75  524416  ok  \n",
       "\n",
       "[76 rows x 8 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfV=pd.read_csv(\"F_vol.csv\")\n",
    "dfV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "rotary-brain",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fe7a2eb03a0>]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.plot(dfV['c'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "narrative-correction",
   "metadata": {},
   "source": [
    "# Merging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "weighted-queens",
   "metadata": {},
   "outputs": [],
   "source": [
    "npDf=np.array(df)\n",
    "npDfV=np.array(dfV)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "wound-charger",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1619063100, 14163.349609375, 14218.599609375, 14218.599609375,\n",
       "        14155.299804688, 29105, 0, 'ok'],\n",
       "       [1619063400, 14205.099609375, 14164.200195312, 14223.900390625,\n",
       "        14162.200195312, 17371, 29105, 'ok'],\n",
       "       [1619063700, 14218.25, 14203.049804688, 14218.849609375,\n",
       "        14189.200195312, 12579, 46476, 'ok'],\n",
       "       [1619064000, 14209.900390625, 14218.700195312, 14218.700195312,\n",
       "        14200.349609375, 12337, 59055, 'ok'],\n",
       "       [1619064300, 14218.5, 14209.049804688, 14221.900390625,\n",
       "        14207.349609375, 10766, 71392, 'ok'],\n",
       "       [1619064600, 14246.900390625, 14218.950195312, 14255.900390625,\n",
       "        14212.450195312, 10641, 82158, 'ok'],\n",
       "       [1619064900, 14250.25, 14245.049804688, 14253.599609375,\n",
       "        14233.700195312, 8653, 92799, 'ok'],\n",
       "       [1619065200, 14256.700195312, 14252.650390625, 14259.5,\n",
       "        14241.549804688, 9470, 101452, 'ok'],\n",
       "       [1619065500, 14258.349609375, 14257.599609375, 14270.150390625,\n",
       "        14251.599609375, 8706, 110922, 'ok'],\n",
       "       [1619065800, 14238.400390625, 14257.849609375, 14257.849609375,\n",
       "        14237.299804688, 8397, 119628, 'ok'],\n",
       "       [1619066100, 14240.599609375, 14235.0, 14244.950195312,\n",
       "        14224.049804688, 8314, 128025, 'ok'],\n",
       "       [1619066400, 14244.0, 14238.200195312, 14254.650390625,\n",
       "        14236.599609375, 6454, 136339, 'ok'],\n",
       "       [1619066700, 14245.400390625, 14244.400390625, 14250.0,\n",
       "        14234.049804688, 6477, 142793, 'ok'],\n",
       "       [1619067000, 14248.099609375, 14244.650390625, 14251.0,\n",
       "        14240.299804688, 5257, 149270, 'ok'],\n",
       "       [1619067300, 14264.700195312, 14247.950195312, 14270.150390625,\n",
       "        14246.0, 5729, 154527, 'ok'],\n",
       "       [1619067600, 14282.950195312, 14263.650390625, 14285.099609375,\n",
       "        14262.650390625, 7496, 160256, 'ok'],\n",
       "       [1619067900, 14279.450195312, 14282.549804688, 14282.549804688,\n",
       "        14267.849609375, 7434, 167752, 'ok'],\n",
       "       [1619068200, 14269.650390625, 14278.450195312, 14288.049804688,\n",
       "        14261.049804688, 6891, 175186, 'ok'],\n",
       "       [1619068500, 14266.549804688, 14267.049804688, 14270.549804688,\n",
       "        14258.299804688, 4792, 182077, 'ok'],\n",
       "       [1619068800, 14272.549804688, 14266.099609375, 14272.799804688,\n",
       "        14262.200195312, 3719, 186869, 'ok'],\n",
       "       [1619069100, 14257.299804688, 14273.900390625, 14274.299804688,\n",
       "        14256.849609375, 4320, 190588, 'ok'],\n",
       "       [1619069400, 14243.299804688, 14256.599609375, 14256.599609375,\n",
       "        14243.099609375, 6357, 194908, 'ok'],\n",
       "       [1619069700, 14239.849609375, 14244.0, 14244.0, 14233.849609375,\n",
       "        5408, 201265, 'ok'],\n",
       "       [1619070000, 14253.849609375, 14237.950195312, 14254.099609375,\n",
       "        14237.099609375, 4332, 206673, 'ok'],\n",
       "       [1619070300, 14258.700195312, 14252.0, 14258.700195312,\n",
       "        14249.299804688, 3779, 211005, 'ok'],\n",
       "       [1619070600, 14251.5, 14258.099609375, 14261.849609375, 14249.75,\n",
       "        3881, 214784, 'ok'],\n",
       "       [1619070900, 14268.650390625, 14251.400390625, 14270.0,\n",
       "        14250.950195312, 5321, 218665, 'ok'],\n",
       "       [1619071200, 14282.200195312, 14272.049804688, 14284.849609375,\n",
       "        14272.049804688, 5332, 223986, 'ok'],\n",
       "       [1619071500, 14280.700195312, 14281.849609375, 14284.75, 14269.0,\n",
       "        4795, 229318, 'ok'],\n",
       "       [1619071800, 14301.950195312, 14281.349609375, 14302.099609375,\n",
       "        14273.099609375, 5526, 234113, 'ok'],\n",
       "       [1619072100, 14319.849609375, 14303.599609375, 14319.849609375,\n",
       "        14298.75, 6586, 239639, 'ok'],\n",
       "       [1619072400, 14322.400390625, 14320.700195312, 14329.75,\n",
       "        14311.349609375, 5552, 246225, 'ok'],\n",
       "       [1619072700, 14316.75, 14324.599609375, 14324.599609375,\n",
       "        14310.799804688, 3484, 251777, 'ok'],\n",
       "       [1619073000, 14294.849609375, 14317.099609375, 14317.099609375,\n",
       "        14292.650390625, 4699, 255261, 'ok'],\n",
       "       [1619073300, 14301.25, 14293.950195312, 14303.200195312,\n",
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       "       [1619083800, 14407.900390625, 14395.799804688, 14424.150390625,\n",
       "        14394.049804688, 9526, 453039, 'ok'],\n",
       "       [1619084100, 14410.0, 14409.049804688, 14411.549804688,\n",
       "        14399.650390625, 10824, 462565, 'ok'],\n",
       "       [1619084400, 14406.25, 14411.400390625, 14415.849609375,\n",
       "        14406.099609375, 11526, 473389, 'ok'],\n",
       "       [1619084700, 14406.400390625, 14405.400390625, 14407.150390625,\n",
       "        14397.75, 15005, 484915, 'ok'],\n",
       "       [1619085000, 14399.150390625, 14407.950195312, 14411.650390625,\n",
       "        14399.049804688, 15994, 499920, 'ok'],\n",
       "       [1619085300, 14402.75, 14399.700195312, 14402.75, 14396.0, 8502,\n",
       "        515914, 'ok'],\n",
       "       [1619085600, 14406.150390625, 14399.099609375, 14406.150390625,\n",
       "        14399.099609375, 55, 524416, 'ok']], dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "npDfV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "engaged-compound",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "counter=0\n",
    "opti=0\n",
    "for i in npDfV[1:,:]:\n",
    "    buff=i[0]\n",
    "    \n",
    "    for j in range(opti,npDf.shape[0]):\n",
    "        if npDf[j][0]==buff:\n",
    "            opti=j\n",
    "            break\n",
    "    \n",
    "    npDf[j-4:j+1,-3]=i[-3]/5\n",
    "    if counter%5000==0:\n",
    "        print(counter)\n",
    "    counter+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "declared-investment",
   "metadata": {},
   "outputs": [],
   "source": [
    "MergedData=pd.DataFrame(npDf[:,:-2],columns=['time','currentVal','open','high','low','volume'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "distinguished-stopping",
   "metadata": {},
   "outputs": [],
   "source": [
    "MergedData.to_csv(\"Nifty50.csv\",index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "hollywood-appendix",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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