Python practiced hand, numpy.genfromtxt

Tag:array   eterm   ted   float    in addition to      Default values     functions    prim    use scene    

# -*- coding: utf-8 -*-' About numpy.genfromtxt, means generate from  txt filehttps://docs.scipy.org/doc/numpy/reference/generated/numpy.genfromtxt.htmlnumpy.genfromtxt ( fname, dtype=<type  ' float ' >, comments= ' # ', delimiter=none, skip_header=0,  skip_footer=0,                         converters=none, missing_values=none, filling_values =none, usecols=none, names=none,                         excludelist=None,  Deletechars=none, replace_space= ' _ ', autostrip=false, case_sensitive=true,                           defAultfmt= ' f%i ',  unpack=none, usemask=false, loose=true, invalid_raise=true, max_rows= none,                          encoding= ' bytes ') see also     Numpy.loadtxt equivalent function when no data is missing. " From cryptography.hazmat.primitives.serialization import encoding# ndarray_fromtxt_lt  = numpy.loadtxt (' data.txt ', delimiter= ', ', dtype=numpy.str) # # ndarray_fromtxt_lt =  numpy.loadtxt (Open (' Data.txt ', ' R ', encoding= ' utf-8 '), delimiter= ', ', dtype=numpy.str) # # ndarray_ Fromtxt_lt = numpy.loadtxt (fname, dtype, comments, delimiter, converters,  skiprows, usecols, unpack, ndmin) # print (Type (NDARRAY_FROMTXT_LT)) # print (Ndarray_ FROMTXT_LT) # ndarray_data = numpy. Genfromtxt (fname= ' data.txt ',  dtype=str,delimiter, skip_header, skip_footer, converters,  missing_values, filling_values, usecols, names, excludelist, deletechars,  Replace_space, autostrip, case_sensitive, defaultfmt, unpack, usemask, loose,  invalid_raise, max_rows) # file = open (' Data.txt ', encoding= ' utf-8 ') '      -Usage Scenarios: Data conversion   (encoding conversion, value conversion)     -key Parameters:converters         the set of functions that convert the data of a  column to a value.         The  Converters can also be used to provide a default value for  missing data: converters = {3: lambda s: float (s or 0)}.         -It is a collection of functions, you can write a function (or use lambda) to convert the value of a column, commonly used scenarios are: encoding conversion, value conversion, etc.          -Special attention!!!          -    1, the input of the conversion function, the default is the bytes type, is related to the encoding parameter, regardless of the Dtype parameter.         -         Dtype Impact Data Final presentation form         -         encoding influence Data processing process         -              official description of the encoding parameters:        -                 override this  value to receive unicode arrays and pass strings as input  to converters.         -                 if set to none the  system default is used. the default value is  ' bytes ' .         -    2, the return type of the conversion function, must be consistent with the set Dtype, or it will cause unpredictable data loss.         -        for example, Genfromtxt set Dtype=str, that is, all columns are of type STR, the return type of the conversion function must also be str        -     3, if the data contains Chinese, may conflict with Windows system default ASCII character set, need to transcode to Utf-8 "' Import osos.remove (' data.txt ') fo  = open (' data.txt ', ' a ', encoding= ' Utf-8 ') fo.write ("001, Zhang San, man,24\n") Fo.write ("002, John Doe, man,24\n") Fo.close () Import numpydef convutf8 (x):     return x.decode (' Utf-8 ') def  Convadd (x):     return str (x,encoding= ' utf-8 ')  +  ' + ' "' Correct demonstration: Single row processing ' ' Ndarry_1  = numpy.genfromtxt (fname= ' data.txt ', deliMiter= ', ',dtype=str,                             converters={1: convutf8}) Print (ndarry_1) # [[' 001 '   ' Zhang San '   ' man '   ']#  [' 002 '   ' John Doe '   ' man '   ' 24 ']] "' Correct demonstration: Multi-column processing ' ' Ndarry_1 = numpy.genfromtxt ' (fname= ' data.txt ', delimiter= ', ',dtype=str,                              converters={1: convUTF8,                                            2: lambda x: x.decode (' utf-8 ')  }) print (ndarry_1) # [[' 001 '   ' Zhang San '   ' man '   ']#  [' 002'   ' John Doe '   ' man '   ' 24 ']] "' ERROR demonstration: Because the return type of the conversion function is not consistent with the input type, it can result in data loss ' ndarry_1 =  Numpy.genfromtxt (fname= ' data.txt ', delimiter= ', ',dtype=str,                            &NBSP;&NBSP;CONVERTERS={1:&NBSP;LAMBDA&NBSP;X:&NBSP;1}) print (ndarry_1) # [(', 1,  ',  ')   (', 1,  ',  ') "Error demonstration: For a column, only one conversion function, and can only be processed once, set multiple times, is invalid" "ndarry_1 =  Numpy.genfromtxt (fname= ' data.txt ', delimiter= ', ',dtype=str,                               converters={1: convUTF8,                                           0: convadd,                                          0: convadd}) print (ndarry_1) # [[' 001+ '   ' Zhang San '   ' man '   ']#  [' 002+ '   ' John Doe '   ' man '   ' []] '    '   -Usage Scenarios: Formatting columns     -key parameters:dtype    -     data type of the resulting array.     -     if none, the dtypes will be determined by the contents  of each column, individually.    -     As an element in Ndarray, Dtype can set the data type ' os.remove (' Data.txt ') fo = open (' Data.txt ', ' a ', encoding= ' utf-8 ') Fo.write ("001,zhangsan,man,24\n") fo.write ("002,liSi,man,24\n ")  fo.close ()" Error demonstration: Dtype=none, automatic format, but passable. For non-numeric columns, the default is bytes ' Ndarry_1 = numpy.genfromtxt (fname= ' data.txt ', delimiter= ', ', Dtype=none) print ( ndarry_1) # [(1, b ' Zhangsan ',  b ' man ',  24)   (2, b ' Lisi ',  b ' man ',  24)] " Correct demonstration: All columns are set to a uniform data type. For the data that does not contain Chinese, dtype=str is possible, if it contains Chinese, in addition to set DTYPE=STR, but also with converters to do transcoding "" Ndarry_1 = numpy.genfromtxt (fname = ' Data.txt ', delimiter= ', ', dtype=str) print (ndarry_1) # [[' 001 '   ' Zhangsan '   ' man '   ']#  '  [' 002 '   ' Lisi '   ' man '   ' 24 '] ' "' Correct demonstration: Set the data type on a per-column basis. Need to know more about the type of dtype. "' Ndarry_1 = numpy.genfromtxt (fname= ' data.txt ', delimiter= ', ', dtype=[(' C0 ', ' <i8 '), (' C1 ', ' <u32 ') ), (' C2 ', ' | S3 '), (' C3 ', ' F4 ')]) print (ndarry_1) # [(1,  ' Zhangsan ',  b ' man ',  24.0)   (2,  ' Lisi '),  b ' man ',  24.0) '     -usage scenario: Data slicing     -key parameters:dtype     -    data type of the resulting array.     -    if none, the  dtypes will be determined by the contents of each column,  individually.    -     as an element in Ndarray, Dtype can set the data type ' ' '      -usage Scenarios: Handling of default values     -key parameters:dtype    -     data type of the resulting array.     -     if none, the dtypes will be determined by the contents  of each column, individually.    -     As an element in Ndarray, Dtype can set the data type "" "    -Use scene: Data slice     -key parameter: Skip_header     -     start line     -key parameters:max_rows     -     Maximum number of rows     -key parameter:usecols    -     reserved column     - Key parameter:comments  (the execution order is final, first do row and column slices, then delete comment lines)     -     comment symbol.     -         If it is a comment at the beginning, the line will be discarded;     -         If it is a comment from another location in the line, it will be an error, but the field behind the comment symbol is missing. "' Os.remove (' Data.txt ') fo = open (' Data.txt ', ' a ', encoding= ' Utf-8 ') fo.write (" 001,zhangsan,man,24\n ") Fo.write ("002,lisi,man,24\n") fo.write ("#003, wangwu,man,24\n") fo.write ("004,chenhua,wom,18\n") Fo.close () " Correct demonstration: Pay attention to perform shun, first do row and column slices, then do delete Comment line ' ' ' Ndarry_1 = numpy.genfromtxt (fname= ' data.txt ', delimiter= ', ', DTYPE=STR,                              skip_header=1,max_rows=3,                             usecols=[0,2],                              comments= ' # ',                              ) print (ndarry_1) # [[' 002 '   ' man ']#  [' 004 '   ' WOM '] [] '      -usage Scenario: Fill the missing value (when Dtype=none, fill the missing value This function better use, when dtype=str, this function does not take effect, want to grope again)     - Key parameter:missing_values    -     marked as missing     -key parameter: Filling _values    -     "Os.remove (' data.txt ') to fill the missing position fo = open ( ' Data.txt ', ' a ', encoding= ' Utf-8 ') fo.write ("10,11,,13\n") fo.write ("10,21,22,23\n") fo.write ("10,31,32,33\n") Fo.close () "Correct demonstration: A column is missing for specific settings (other columnsis the default missing), all columns are unified by default to fill ' ndarry_1 = numpy.genfromtxt ' (fname= ' data.txt ', delimiter= ', ',dtype=none,                              missing_values={0:10},                              filling_values=999                             ) Print (ndarry_1) # [[999  11 999  13]#  [999  21  22 &NBSP;&NBSP;23]#&NBSP;&NBSP;[999&NBSP;&NBSP;31&NBSP;&NBSP;32&NBSP;&NBSP;33]] "' Correct demonstration: The absence of a column, the filling of a column ' ' Ndarry_1  = numpy.genfromtxt (fname= ' data.txt ', delimiter= ', ',dtype=none,            &nBsp;                missing_ values={0:10},                             filling_values={0:777,2:999}                              ) print (ndarry_1) # [[777  11 999   13]#  [777  21  22  23]#  [777  31 &NBSP;&NBSP;32&NBSP;&NBSP;33]] "' Correct demonstration: All columns are missing for uniform setting (but default is missing or in effect) ' Ndarry_1 = numpy.genfromtxt (fname= ' Data.txt ', delimiter= ', ',dtype=none,                             missing_values =10,                             filling_values={0:777,2:999}                              ) print (ndarry_1) # [[777  11 999  13]#  [777  &NBSP;21&NBSP;&NBSP;22&NBSP;&NBSP;23]#&NBSP;&NBSP;[777&NBSP;&NBSP;31&NBSP;&NBSP;32&NBSP;&NBSP;33]]

Python practiced hand, numpy.genfromtxt