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  | #pip install pandas | |
| #pip install lxml | |
| #pip install openpyxl | |
| # Importing pandas | |
| import pandas as pd | |
| # The webpage URL whose table we want to extract | |
| url = "https://www.moneycontrol.com/mutual-funds/canara-robeco-blue-chip-equity-fund-direct-plan/portfolio-holdings/" | 
  
    
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  | { | |
| "code": 0, | |
| "reason": "OK", | |
| "result": [ | |
| { | |
| "ctype": "news", | |
| "dtype": 2, | |
| "image": "https://img-staging.yana.asideas.de/image/fetch/w_1280,h_720,c_lfill,f_webp,g_auto:faces,q_auto:eco/https%3A%2F%2Fcdn.images.express.co.uk%2Fimg%2Fdynamic%2F78%2F750x445%2F1187631.jpg", | |
| "imageUrl": [], | |
| "docid": "xOUODFlNVun1vVnycPgLtg", | 
  
    
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  | import json | |
| import requests | |
| with open("saijson.json", "r") as file: | |
| json_data = json.loads(file) | |
| json_s_data = json_data['result'] | |
| output_json = [] | |
| tmp_json = [] | 
  
    
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  | using namespace std; | |
| using namespace boost; | |
| enum class Color {red,green,blue}; | |
| enum class Size {large,medium, small}; | |
| struct Product{ | 
  
    
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  | #include <iostream> | |
| #include <cstdio> | |
| #include <string> | |
| #include <vector> | |
| #include <fstream> | |
| #include <boost/lexical_cast.hpp> | |
| using namespace std; | |
| using namespace boost; | 
  
    
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  | # Compiled source # | |
| ################### | |
| *.com | |
| *.class | |
| *.dll | |
| *.exe | |
| *.o | |
| *.so | |
| # Packages # | 
  
    
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  | Summary | |
| Overall risk level: | |
| High | |
| Risk ratings: | |
| High: | |
| 1 | |
| Medium: | |
| 2 | |
| Low: | |
| 2 | 
  
    
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  | from pyelasticsearch import ElasticSearch | |
| import pandas as pd | |
| from time import time | |
| root_path="<Root Path>" | |
| raw_data_path=root_path+"data/" | |
| csv_filename="predicted_data.csv" | |
| t0=time() | 
  
    
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  | /** | |
| * The first commented line is your dabblet’s title | |
| */ | |
| background: #960; | |
| min-height: 10%; | 
  
    
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  | import cv2 | |
| import numpy as np | |
| from matplotlib import pyplot as plt | |
| img = cv2.imread('images.jpg',cv2.IMREAD_GRAYSCALE) | |
| plt.imshow(img, cmap = 'gray', interpolation = 'bicubic') | |
| plt.xticks([]), plt.yticks([]) # to hide tick values on X and Y axis | |
| plt.plot([200,300,400],[100,200,300],'c', linewidth=5) | |
| plt.show() | 
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