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  1. import pandas as pd
  2. import matplotlib.pyplot as plt
  3. import random
  4. import seaborn as sns
  5. import numpy as np
  6. from scipy import stats
  7. from random import randint
  8.  
  9. # import data files
  10. # read the large csv file with specified chunksize
  11. intensity_meta = pd.read_csv(r'utwente intensiteiten groot amsterdam  1 dag met metadata (2)_intensiteit_00001.csv', chunksize=1000000, low_memory=False)
  12. travel_times_1 = pd.read_csv(r'utwente reistijden groot amsterdam  _reistijd_00001.csv', chunksize=1000000, low_memory=False)
  13. travel_times_2 = pd.read_csv(r'utwente reistijden groot amsterdam  _reistijd_00002.csv', chunksize=1000000, low_memory=False)
  14. travel_times_3 = pd.read_csv(r'utwente reistijden groot amsterdam  _reistijd_00003.csv', chunksize=1000000, low_memory=False)
  15. travel_times_4 = pd.read_csv(r'utwente reistijden groot amsterdam  _reistijd_00004.csv', chunksize=1000000, low_memory=False)
  16. travel_times_meta = pd.read_csv(r'utwente reistijden groot amsterdam  1 dag met metadata_reistijd_00001.csv', chunksize=1000000, low_memory=False)
  17. speed_meta = pd.read_csv(r'utwente snelheden groot amsterdam  1 dag met metadata_snelheid_00001.csv', chunksize=1000000, low_memory=False)
  18.  
  19. # append each chunk df here
  20. intensity_meta_list = []  
  21. travel_times_1_list = []
  22. travel_times_2_list = []
  23. travel_times_3_list = []
  24. travel_times_4_list = []
  25. travel_times_meta_list = []
  26. speed_meta_list = []
  27.  
  28. # Each chunk is in df format
  29. for chunk in intensity_meta:
  30.     # Once the data filtering is done, append the chunk to list
  31.     intensity_meta_list.append(chunk)
  32. # concat the list into dataframe
  33. intensity_meta_concat = pd.concat(intensity_meta_list)
  34.  
  35. # Each chunk is in df format
  36. for chunk in travel_times_1:
  37.     # Once the data filtering is done, append the chunk to list
  38.     travel_times_1_list.append(chunk)
  39. # concat the list into dataframe
  40. travel_times_1_concat = pd.concat(travel_times_1_list)
  41.  
  42. # Each chunk is in df format
  43. for chunk in travel_times_2:
  44.     # Once the data filtering is done, append the chunk to list
  45.     travel_times_2_list.append(chunk)
  46. # concat the list into dataframe
  47. travel_times_2_concat = pd.concat(travel_times_2_list)
  48.  
  49. # Each chunk is in df format
  50. for chunk in travel_times_3:
  51.     # Once the data filtering is done, append the chunk to list
  52.     travel_times_3_list.append(chunk)
  53. # concat the list into dataframe
  54. travel_times_3_concat = pd.concat(travel_times_3_list)
  55.  
  56. # Each chunk is in df format
  57. for chunk in travel_times_4:
  58.     # Once the data filtering is done, append the chunk to list
  59.     travel_times_4_list.append(chunk)
  60. # concat the list into dataframe
  61. travel_times_4_concat = pd.concat(travel_times_4_list)
  62.  
  63. # Each chunk is in df format
  64. for chunk in intensity_meta:
  65.     # Once the data filtering is done, append the chunk to list
  66.     intensity_meta_list.append(chunk)
  67. # concat the list into dataframe
  68. intensity_meta_concat = pd.concat(intensity_meta_list)
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