pd.read_csv(csv_file_path, chunksize=chunk_size):将 CSV 文件按块读取,chunksize为每块的行数。 可以对每个chunk进行数据处理,如数据清洗、分析等操作,避免一次性加载整个文件。 五、使用numpy分块处理大型二进制文件(适用于二进制文件): importnumpyasnpdefread_large_binary_in_chunks(binary_file_path, chunk...
def read_large_binary_in_chunks(binary_file_path, chunk_size=1024): with open(binary_file_path, 'rb') as file: while True: data = np.fromfile(file, dtype=np.float32, count=chunk_size) if data.size == 0: break # 处理数据块,这里仅打印 print(data) np.fromfile(file, dtype=np.fl...
with open(file_path, 'r') as file:使用with语句打开文件,确保文件在使用完毕后自动关闭。 for line in file:文件对象是可迭代的,逐行读取文件内容,避免一次性将整个文件读入内存,节省内存空间,适用于大型文本文件。 二、分块读取大型文件: def read_large_file_in_chunks(file_path, chunk_size=1024): with...
对于二进制文件,可以使用类似以下的代码: # 读取二进制文件defread_large_binary_file(file_path,block_size=1024):withopen(file_path,'rb')asfile:# 以二进制形式打开文件whileTrue:block=file.read(block_size)# 读取指定大小的块ifnotblock:# 如果没有更多内容breakprocess_binary_block(block)# 处理这块内...
it to the console. When the whole file is read, the data will become empty and thebreak statementwill terminate the while loop. This method is also useful in reading a binary file such as images, PDF, word documents, etc. Here is a simple code snippet to make a copy of the file. ...
When working with large datasets inmachine learningproblems, working with files is a basic necessity. Since Python is a majorly used language for data science, you need to be proficient with the different file operations that Python offers. ...
defdownload_big_file(url, target_file_name):"""使用python核心库下载大文件 ref: https://stackoverflow.com/questions/1517616/stream-large-binary-files-with-urllib2-to-file"""importsysifsys.version_info > (2, 7):#Python 3fromurllib.requestimporturlopenelse:#Python 2fromurllib2importurlopen ...
The readlines() method reads all the rows of the entire file, saved in a list variable, one row at a time, but reading large files takes up more memory.文件的全文本操作 Full-text actions for files 遍历全文本（Iterate through the full text:）：法一：一次读入统一处理 Method 1: One-time...
read_csv( 'large.csv', chunksize=chunksize, dtype=dtype_map ) # # 然后每个chunk进行一些压缩内存的操作,比如全都转成sparse类型 # string类型比如,学历,可以转化成sparse的category变量,可以省很多内存 sdf = pd.concat( chunk.to_sparse(fill_value=0.0) for chunk in chunks ) #很稀疏有可能可以装的...
There were more things that Uncle Barry had to share in the PEP; you can read them here. It works well in an interactive environment, but it will raise a SyntaxError when you run via python file (see this issue). However, you can wrap the statement inside an eval or compile to get ...