{"id":26216,"date":"2022-01-03T09:12:57","date_gmt":"2022-01-03T03:42:57","guid":{"rendered":"https:\/\/python-programs.com\/?p=26216"},"modified":"2022-01-03T09:13:16","modified_gmt":"2022-01-03T03:43:16","slug":"how-do-i-save-a-file-in-npy-format-in-python","status":"publish","type":"post","link":"https:\/\/python-programs.com\/how-do-i-save-a-file-in-npy-format-in-python\/","title":{"rendered":"How Do I Save a File in.npy Format in Python?"},"content":{"rendered":"
Have you ever come across a .npy<\/strong> file?<\/p>\n Let us see how to save in .npy format in this article. Numpy’s binary data storage format is NPY.<\/p>\n Numpy is a necessary module for performing data science operations efficiently. Importing, saving, and processing data consumes a significant amount of time in the field of Data Science. CSV files are a good choice for importing and exporting data.<\/p>\n However, there are times when you need to save data in order to use it again in Python. Numpy provides the.npy format in such cases.<\/p>\n Importing and exporting data from and to .npy<\/strong> files are faster and efficient than other methods.<\/p>\n Numpy provides the numpy.save()<\/strong> method for saving files in.npy format. It only allows you to save data in array format. Before saving, it converts the array to a binary file. It is ultimately this binary file that is saved.<\/p>\n numpy.save() method :<\/strong> The numpy.save() function saves the input array to a disc file with the npy extension (.npy).<\/p>\n Approach:<\/strong><\/p>\n Below is the implementation:<\/strong><\/p>\n Output:<\/strong><\/p>\n The above code will save your array to a binary file called ‘Python-programs.npy’ as file name.<\/p>\n To load the data back into Python, we’ll use numpy.load()<\/strong> method.<\/p>\n Approach:<\/strong><\/p>\n Below is the implementation:<\/strong><\/p>\n Output:<\/strong><\/p>\n Have you ever come across a .npy file? Let us see how to save in .npy format in this article. Numpy’s binary data storage format is NPY. Numpy is a necessary module for performing data science operations efficiently. Importing, saving, and processing data consumes a significant amount of time in the field of Data Science. …<\/p>\nUsing numpy.save() Method To save a File<\/strong><\/h4>\n
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# Import numpy module as np using the import keyword\r\nimport numpy as np\r\n# Pass some random number to the np.arange() method to get an array from 0 to 7\r\n# Store it in a variable.\r\ngot_arry = np.arange(8)\r\n# Print the above obtained array.\r\nprint(\"The above obtained array is:\")\r\nprint(got_arry)\r\n# Save the above obtained array to 'Python-programs' file using the np.save() method\r\nnp.save('Python-programs', got_arry)\r\nprint(\"File saved succesfully in Python-programs.npy\")\r\n<\/pre>\n
The above obtained array is: \r\n[0 1 2 3 4 5 6 7] \r\nFile saved succesfully in Python-programs.npy<\/pre>\n
How to Import .npy File?<\/h4>\n
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# Import numpy module as np using the import keyword\r\nimport numpy as np\r\n# Pass the file name to the np.load() method to get back the data from the file\r\n# (Python-programs.npy)\r\n# Store it in a variable.\r\nrslt_data = np.load('Python-programs.npy')\r\n# Print the data from the file after loading back the File\r\nprint(\"The result data after loading back the File:\")\r\nprint(rslt_data)\r\n<\/pre>\n
The result data after loading back the File: \r\n[0 1 2 3 4 5 6 7]<\/pre>\n","protected":false},"excerpt":{"rendered":"