Technology sharing

Python cool library tour - tertia-pars library Pandas (011)

2024-07-12

한어Русский языкEnglishFrançaisIndonesianSanskrit日本語DeutschPortuguêsΕλληνικάespañolItalianoSuomalainenLatina

Tabula contentorum

1. altius explicandum est usus

25. pandas.HDFStore.get function

25-1.

25-2.

25-3.

25-4.

25-5

25-6

25-6-1.

25-6-2.

255-6-3.

26. pandas.HDFStore.select function

26-1.

26-2.

26-3.

26-4.

26-5

26-6

26-6-1.

26-6-2.

26-6-3.

27. pandas.HDFStore.info function

27-1.

27-2.

27-3.

27-4.

27-5

27-6

27-6-1.

27-6-2.

27-6-3.

2. Lectio commendatur

1. Python fundamentum aedificium iter

2. Iter Pythonis munera

3. Python Algorithmus Itinerarium

4. Python iter magicae

5. Blog personalis pagina

1. altius explicandum est usus

25、pandas.HDFStore.getofficium
25-1.
  1. # 25、pandas.HDFStore.get函数
  2. HDFStore.get(key)
  3. Retrieve pandas object stored in file.
  4.  
  5. Parameters:
  6. key
  7. str
  8. Returns:
  9. object
  10. Same type as object stored in file.
25-2.

25-2-1、key(debet)：Fila quae situm vel nomen in HDF5 lima notitiarum insanabilis designat. Haec clavis plerumque respondet nomini vel viae, qua usus est, cum notitia lima HDF5 servata est.

25-3.

Usus est ut notitias ex HDF5 files repositas recuperare (vel accipere) reconderet.

25-4.

In universum, hoc munus reddet pandas obiectum clavem adiunctum, puta DataFrame, Series, vel alia continens pandas possibile.

Specie reditus pretii esse potest;

25-4-1、DataFrame : Si notitia cum clavibus in HDF5 reposita est, tabella est mensa vel quasi tabularum notitiarum compages, methodus accipiet objectum DataFrame reddet. DataFrame est principale notitiae structurae in pandas ad notitias condendas et abusivandas exstructa.

25-4-2、Series: In quibusdam casibus, si data reposita sit una dimensiva, ut temporis series notitia vel notitia unius columnae, ratio reddi potest series rei Series. cum indicibus ordinata) data structura.

25-4-3. : Etsi minus commune, HDF5 fasciculi theoretice alia genera rerum pandas condere possunt, ut Panels (nota: incipiens a pandas versio 0.25.0, Panel deprecata est et e bibliotheca pandas remota est). Tamen, ut pandas evolutum est, haec condicio in dies rarior facta est.

25-4-4, nemo vel default valorem : Si clavis praefinitus in file HDF5 non existit et methodum obtinendi valorem defaltam pro secundo modulo non praebet, tunc KeyError attollere potest. Attamen, si valor defaltus praebetur (quamvis haec norma morum non est ad methodos acquirendas, sicut methodi generaliter non directe sustinent valorem default parametri in HDFStore), hoc casu fieri potest pro DataFrame..get methodi obfuscationis), tunc reddet ipsum valorem default. Autem, in contextu HDFStoris communius est uti experimento-nisi scandalum capere KeyError et causam tractare si opus fuerit.

25-5

nullus

25-6
25-6-1.
无
25-6-2.
  1. # 25、pandas.HDFStore.get函数
  2. import pandas as pd
  3. # 创建一个示例的DataFrame
  4. data = pd.DataFrame({
  5.     'A': [1, 2, 3, 4],
  6.     'B': ['foo', 'bar', 'foo', 'bar'],
  7.     'C': [0.1, 0.2, 0.3, 0.4]
  8. })
  9. # 将数据保存到HDF5文件中
  10. filename = 'example.h5'
  11. key = 'data'
  12. data.to_hdf(filename, key=key, format='table', mode='w')
  13. # 从HDF5文件中读取数据
  14. with pd.HDFStore(filename, mode='r') as store:
  15.     df_from_hdf = store.get(key)
  16. # 打印读取的数据
  17. print("Data read from HDF5:")
  18. print(df_from_hdf)
255-6-3.
  1. # 25、pandas.HDFStore.get函数
  2. # Data read from HDF5:
  3. #    A    B    C
  4. # 0  1  foo  0.1
  5. # 1  2  bar  0.2
  6. # 2  3  foo  0.3
  7. # 3  4  bar  0.4
26、pandas.HDFStore.selectofficium
26-1.
  1. # 26、pandas.HDFStore.select函数
  2. HDFStore.select(key, where=None, start=None, stop=None, columns=None, iterator=False, chunksize=None, auto_close=False)
  3. Retrieve pandas object stored in file, optionally based on where criteria.
  4.  
  5. Warning
  6.  
  7. Pandas uses PyTables for reading and writing HDF5 files, which allows serializing object-dtype data with pickle when using the “fixed” format. Loading pickled data received from untrusted sources can be unsafe.
  8.  
  9. See: https://docs.python.org/3/library/pickle.html for more.
  10.  
  11. Parameters:
  12. key
  13. str
  14. Object being retrieved from file.
  15.  
  16. where
  17. list or None
  18. List of Term (or convertible) objects, optional.
  19.  
  20. start
  21. int or None
  22. Row number to start selection.
  23.  
  24. stop
  25. int, default None
  26. Row number to stop selection.
  27.  
  28. columns
  29. list or None
  30. A list of columns that if not None, will limit the return columns.
  31.  
  32. iterator
  33. bool or False
  34. Returns an iterator.
  35.  
  36. chunksize
  37. int or None
  38. Number or rows to include in iteration, return an iterator.
  39.  
  40. auto_close
  41. bool or False
  42. Should automatically close the store when finished.
  43.  
  44. Returns:
  45. object
  46. Retrieved object from file.
26-2.

26-2-1、key(debet)：Clavis (vel semita) in HDF5 lima ut retrieve, hoc nomen vel semita specificata esse solet cum notitia salvifica ad fasciculum HDF5.

26-2-2 ubi(Libitum, valorem default est nemo)： Expressio conditionalis ad colum data. Si chorda sit, validum esse debet Pandas chordae interrogationis, similis illi usus cum .query() methodo in DataFrame utens; sicut input et redit series Boolean indicans quos ordines eligi debent.

26-2-3、satus/statur(Libitum, valorem default est nemo)：Initium/finiens index ordinum insanabilis (0-based).

26-2-4 columnas(Libitum, valorem default est nemo)：Indicem nominum columnae vel unius columnae nomen recuperare.

26-2-5、iterator(Libitum, valorem default falsum est)：Si verum, redit iterator qui notitias FRUSTUM generat per FRUSTUM potius quam integras notitias in memoriam statim onerandas, quod utile est ad magnas datas res expediendas.

26-2-6、chunksize(Libitum, valorem default est nemo)：Cum iterator=Verum est, hic parameter numerum ordinum in unoquoque stipite designat, qui te memoriam consuetudinis moderari sinit et perficiendi cum magnas notitias disponendo emendare potest.

26-2-7、 auto_close(Libitum, valorem default falsum est)： Si verum, tabularium ipso facto clauditur, cum iterator defatigatus est vel exceptio occurrit, quae adiuvat ut tabella recte claudatur etiam in errore incidat. Sed nota, quaeso quod si vis HDFStore objectis utendo pergere, postquam iterator defatigatus est, hunc modulum ad Falsa proponas.

26-3.

Res recipe pandas (ut DataFrame vel Series) sub certa clave ab HDF5 lima condita et utentem colum vel moderari notitias receptas in serie parametri permittit.

26-4.

   Reditus pretii pendet ex notitia speciei consociata cum clavibus in file HDF5 conditis et condiciones interrogationis (si). De more pretii reditus est obiectum pandas, ut:

26-4-1、DataFrame: Si notitia recepta in forma tabulari est, objectum DataFrame reddetur.

26-4-2、Series: Si notitia recepta est una dimensiva (exempli gratia data unius columnae), tunc objectum Series reverti potest, quamvis hoc fieri solet cum una columna expresse ut parametri columnae specificatur.

26-4-3.In theoria posset esse alia continentia tam pandas, sed in contextu imaginum HDF5 frequentissimae sunt DataFrame et Series.

26-5

nullus

26-6
26-6-1.
无
26-6-2.
  1. # 26、pandas.HDFStore.select函数
  2. import pandas as pd
  3. import numpy as np
  4. # 创建一个示例DataFrame
  5. np.random.seed(0)  # 设置随机种子以确保结果可重复
  6. data = pd.DataFrame({
  7.     'A': np.random.randn(100),
  8.     'B': np.random.randn(100),
  9.     'C': np.random.randn(100),
  10.     'D': np.random.randint(0, 2, 100)
  11. })
  12. # 将DataFrame保存到HDF5文件中
  13. with pd.HDFStore('example.h5') as store:
  14.     store.put('data', data, format='table')
  15. # 从HDF5文件中检索数据的示例
  16. with pd.HDFStore('example.h5') as store:
  17.     # 选择所有数据
  18.     print("nAll data:")
  19.     all_data = store.select('data')
  20.     print(all_data.head())  # 只打印前几行以节省空间
  21.     # 选择特定的列
  22.     print("nSpecific columns (A, B):")
  23.     specific_columns = store.select('data', columns=['A', 'B'])
  24.     print(specific_columns.head())
  25.     # 选择部分数据行（注意：HDF5的索引可能不是从0开始的，但这里假设它是）
  26.     print("nPartial data (rows 10 to 19):")
  27.     partial_data = store.select('data', start=10, stop=20)
  28.     print(partial_data)
  29.     # 使用chunksize来逐块读取数据
  30.     print("nData read in chunks:")
  31.     chunks = store.select('data', chunksize=10)
  32.     for i, chunk in enumerate(chunks):
  33.         print(f"Chunk {i + 1}:")
  34.         print(chunk.head())  # 只打印每个块的前几行
26-6-3.
  1. # 26、pandas.HDFStore.select函数
  2. # All data:
  3. #           A         B         C  D
  4. # 0  1.764052  1.883151 -0.369182  0
  5. # 1  0.400157 -1.347759 -0.239379  0
  6. # 2  0.978738 -1.270485  1.099660  1
  7. # 3  2.240893  0.969397  0.655264  1
  8. # 4  1.867558 -1.173123  0.640132  0
  9. # 
  10. # Specific columns (A, B):
  11. #           A         B
  12. # 0  1.764052  1.883151
  13. # 1  0.400157 -1.347759
  14. # 2  0.978738 -1.270485
  15. # 3  2.240893  0.969397
  16. # 4  1.867558 -1.173123
  17. # 
  18. # Partial data (rows 10 to 19):
  19. #            A         B         C  D
  20. # 10  0.144044  1.867559  0.910179  0
  21. # 11  1.454274  0.906045  0.317218  0
  22. # 12  0.761038 -0.861226  0.786328  1
  23. # 13  0.121675  1.910065 -0.466419  0
  24. # 14  0.443863 -0.268003 -0.944446  0
  25. # 15  0.333674  0.802456 -0.410050  0
  26. # 16  1.494079  0.947252 -0.017020  1
  27. # 17 -0.205158 -0.155010  0.379152  1
  28. # 18  0.313068  0.614079  2.259309  0
  29. # 19 -0.854096  0.922207 -0.042257  0
  30. # 
  31. # Data read in chunks:
  32. # Chunk 1:
  33. #           A         B         C  D
  34. # 0  1.764052  1.883151 -0.369182  0
  35. # 1  0.400157 -1.347759 -0.239379  0
  36. # 2  0.978738 -1.270485  1.099660  1
  37. # 3  2.240893  0.969397  0.655264  1
  38. # 4  1.867558 -1.173123  0.640132  0
  39. # Chunk 2:
  40. #            A         B         C  D
  41. # 10  0.144044  1.867559  0.910179  0
  42. # 11  1.454274  0.906045  0.317218  0
  43. # 12  0.761038 -0.861226  0.786328  1
  44. # 13  0.121675  1.910065 -0.466419  0
  45. # 14  0.443863 -0.268003 -0.944446  0
  46. # Chunk 3:
  47. #            A         B         C  D
  48. # 20 -2.552990  0.376426 -0.955945  0
  49. # 21  0.653619 -1.099401 -0.345982  1
  50. # 22  0.864436  0.298238 -0.463596  0
  51. # 23 -0.742165  1.326386  0.481481  0
  52. # 24  2.269755 -0.694568 -1.540797  1
  53. # Chunk 4:
  54. #            A         B         C  D
  55. # 30  0.154947 -0.769916 -1.424061  1
  56. # 31  0.378163  0.539249 -0.493320  0
  57. # 32 -0.887786 -0.674333 -0.542861  0
  58. # 33 -1.980796  0.031831  0.416050  1
  59. # 34 -0.347912 -0.635846 -1.156182  1
  60. # Chunk 5:
  61. #            A         B         C  D
  62. # 40 -1.048553 -1.491258 -0.637437  0
  63. # 41 -1.420018  0.439392 -0.397272  1
  64. # 42 -1.706270  0.166673 -0.132881  0
  65. # 43  1.950775  0.635031 -0.297791  0
  66. # 44 -0.509652  2.383145 -0.309013  0
  67. # Chunk 6:
  68. #            A         B         C  D
  69. # 50 -0.895467 -0.068242  0.521065  1
  70. # 51  0.386902  1.713343 -0.575788  1
  71. # 52 -0.510805 -0.744755  0.141953  0
  72. # 53 -1.180632 -0.826439 -0.319328  0
  73. # 54 -0.028182 -0.098453  0.691539  1
  74. # Chunk 7:
  75. #            A         B         C  D
  76. # 60 -0.672460 -0.498032 -1.188859  1
  77. # 61 -0.359553  1.929532 -0.506816  1
  78. # 62 -0.813146  0.949421 -0.596314  0
  79. # 63 -1.726283  0.087551 -0.052567  0
  80. # 64  0.177426 -1.225436 -1.936280  0
  81. # Chunk 8:
  82. #            A         B         C  D
  83. # 70  0.729091  0.920859  0.399046  0
  84. # 71  0.128983  0.318728 -2.772593  1
  85. # 72  1.139401  0.856831  1.955912  0
  86. # 73 -1.234826 -0.651026  0.390093  1
  87. # 74  0.402342 -1.034243 -0.652409  1
  88. # Chunk 9:
  89. #            A         B         C  D
  90. # 80 -1.165150 -0.353994 -0.110541  0
  91. # 81  0.900826 -1.374951  1.020173  0
  92. # 82  0.465662 -0.643618 -0.692050  1
  93. # 83 -1.536244 -2.223403  1.536377  0
  94. # 84  1.488252  0.625231  0.286344  0
  95. # Chunk 10:
  96. #            A         B         C  D
  97. # 90 -0.403177 -1.292857 -0.628088  1
  98. # 91  1.222445  0.267051 -0.481027  1
  99. # 92  0.208275 -0.039283  2.303917  0
  100. # 93  0.976639 -1.168093 -1.060016  1
  101. # 94  0.356366  0.523277 -0.135950  0
27、pandas.HDFStore.infoofficium
27-1.
  1. # 27、pandas.HDFStore.info函数
  2. HDFStore.info()
  3. Print detailed information on the store.
  4.  
  5. Returns:
  6. str
27-2.

nullus

27-3.

Singulos informationes praebet de datasets (etiam clavibus vel nodis) in tabulariis HDF5 repositis.

27-4.

Nulla reditus directus valor (hoc est, nulla notitia variabilis redditur), sed indicium consolatorium impressum est (vel vexillum output).

27-5

nullus

27-6
27-6-1.
无
27-6-2.
  1. # 27、pandas.HDFStore.info函数
  2. import pandas as pd
  3. import numpy as np
  4. # 创建一个包含随机数的数据帧
  5. data = pd.DataFrame({
  6.     'A': np.random.randn(100),
  7.     'B': np.random.randn(100),
  8.     'C': np.random.randn(100),
  9.     'D': np.random.randint(0, 2, 100)
  10. })
  11. # 将数据写入HDF5文件
  12. with pd.HDFStore('example.h5') as store:
  13.     store.put('data', data, format='table')
  14. # 使用HDFStore.info()函数获取HDF5文件的信息
  15. with pd.HDFStore('example.h5') as store:
  16.     # 打印存储的信息
  17.     store.info()
  18.     # 读取数据以确认
  19.     all_data = store.select('data')
  20.     print("nAll data (first 5 rows):")
  21.     print(all_data.head())
27-6-3.
  1. # 27、pandas.HDFStore.info函数
  2. # All data (first 5 rows):
  3. #           A         B         C  D
  4. # 0 -1.186803 -0.983345  0.661022  1
  5. # 1  0.549244 -0.429500 -0.022329  1
  6. # 2  1.408989  0.779268  0.079574  1
  7. # 3 -1.178696  0.918125  0.174332  0
  8. # 4 -0.538677 -0.124535 -1.165208  1

2. Lectio commendatur

1、Python fundamentum aedificationis iter
2、Python munus pretium
3、Python Algorithmus Itinerarium
4、Python iter magicae
5、Blog profile

Personal profile

technologiae technologiae plus quam 30 annos operam dedit et in variis linguis proficit ut java, linux, javascript, php, css, etc. Multas contributiones in aperto fonte campo fecit elit documentorum statione ad communicandas quaestiones technologiarum progressus ad futuram referentiam

mihi contactus notitia

Mail