如何读取.CSV文件的特定行/列并将其存储为numpy矩阵？

Question

I have a .CSV file with contents like this:

DATE    OPEN    HIGH    LOW CLOSE   PRICE   YCLOSE  VOL TICKS
13950309    1000000 1000000 1000000 1000000 1000000 1000000 2100000 74
13950326    1050000 1050010 1050000 1050001 1050000 1000000 1648    5
13950329    1030200 1060000 1030200 1044474 1042265 1050001 28469   108
13950330    1040001 1049999 1040001 1042303 1045001 1044474 6518    10
13950331    1049800 1050000 1048600 1048787 1050000 1042303 277 11
13950401    1059973 1059974 1052000 1053807 1055000 1048787 916 17
13950402    1050000 1054498 1043009 1048173 1043009 1053807 2098    29
13950405    1045678 1049989 1040002 1049961 1049979 1048173 28098   14

That for example don't need the DATE column, or the first row(That contains strings). So I like to read from row 2 up to row 25, and column 2 up to end column, then storing the data as a numpy matrix. How can I do this?

EDIT: I tried this code as suggested in one of the answers:

import pandas as pd
import numpy as np

data = pd.read_csv("C:/Users/m/Desktop/python/IRB3MAIZ9936-a.csv", sep="\s")
del data['DATE'] 
np.array(data.values)

But I got this result:

C:\Users\m\Desktop\python\read_csv.py:4: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.
  data = pd.read_csv("C:/Users/m/Desktop/python/IRB3MAIZ9936-a.csv", sep="\s")
Traceback (most recent call last):
  File "C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\indexes\base.py", line 3078, in get_loc
    return self._engine.get_loc(key)
  File "pandas\_libs\index.pyx", line 140, in pandas._libs.index.IndexEngine.get_loc
  File "pandas\_libs\index.pyx", line 162, in pandas._libs.index.IndexEngine.get_loc
  File "pandas\_libs\hashtable_class_helper.pxi", line 1492, in pandas._libs.hashtable.PyObjectHashTable.get_item
  File "pandas\_libs\hashtable_class_helper.pxi", line 1500, in pandas._libs.hashtable.PyObjectHashTable.get_item
KeyError: 'DATE'

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "C:\Users\m\Desktop\python\read_csv.py", line 6, in <module>
    del data['DATE']
  File "C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\generic.py", line 2743, in __delitem__
    self._data.delete(key)
  File "C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\internals.py", line 4174, in delete
    indexer = self.items.get_loc(item)
  File "C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\indexes\base.py", line 3080, in get_loc
    return self._engine.get_loc(self._maybe_cast_indexer(key))
  File "pandas\_libs\index.pyx", line 140, in pandas._libs.index.IndexEngine.get_loc
  File "pandas\_libs\index.pyx", line 162, in pandas._libs.index.IndexEngine.get_loc
  File "pandas\_libs\hashtable_class_helper.pxi", line 1492, in pandas._libs.hashtable.PyObjectHashTable.get_item
  File "pandas\_libs\hashtable_class_helper.pxi", line 1500, in pandas._libs.hashtable.PyObjectHashTable.get_item
KeyError: 'DATE'
[Finished in 1.7s with exit code 1]
[shell_cmd: python -u "C:\Users\m\Desktop\python\read_csv.py"]
[dir: C:\Users\m\Desktop\python]
[path: C:\ProgramData\Anaconda3;C:\ProgramData\Anaconda3\Library\mingw-w64\bin;C:\ProgramData\Anaconda3\Library\usr\bin;C:\ProgramData\Anaconda3\Library\bin;C:\ProgramData\Anaconda3\Scripts;C:\Program Files (x86)\Common Files\Oracle\Java\javapath;C:\Windows\system32;C:\Windows;C:\Windows\System32\Wbem;C:\Windows\System32\WindowsPowerShell\v1.0\;C:\Windows\System32\OpenSSH\;C:\Program Files (x86)\NVIDIA Corporation\PhysX\Common;C:\mingw64\bin;D:\cmake-3.11.3-win64-x64\cmake-3.11.3-win64-x64\bin;C:\opencv\build\install\x64\mingw\bin;C:\Program Files\nodejs\;C:\Program Files\MATLAB\R2018b\runtime\win64;C:\Program Files\MATLAB\R2018b\bin;C:\Program Files\Git\cmd;C:\Program Files\Microsoft SQL Server\130\Tools\Binn\;C:\Program Files\dotnet\;C:\Users\m\AppData\Local\Microsoft\WindowsApps;C:\Users\m\AppData\Roaming\npm;C:\Users\m\AppData\Local\Programs\Microsoft VS Code\bin]

Answer 1

This should give you an Idea about your problem solving.

import pandas as pd
import numpy as np

data = pd.read_csv("/Users/DHarun/Desktop/STD_MASTER/F_Bildverarbeitung/aim2/iaai/stack/xyz.csv", sep="\s")

del data['DATE']

np.array(data.values)

Output:

array([[1000000, 1000000, 1000000, 1000000, 1000000, 1000000, 2100000,
             74],
       [1050000, 1050010, 1050000, 1050001, 1050000, 1000000,    1648,
              5],
       [1030200, 1060000, 1030200, 1044474, 1042265, 1050001,   28469,
            108],
       [1040001, 1049999, 1040001, 1042303, 1045001, 1044474,    6518,
             10],
       [1049800, 1050000, 1048600, 1048787, 1050000, 1042303,     277,
             11],
       [1059973, 1059974, 1052000, 1053807, 1055000, 1048787,     916,
             17],
       [1050000, 1054498, 1043009, 1048173, 1043009, 1053807,    2098,
             29],
       [1045678, 1049989, 1040002, 1049961, 1049979, 1048173,   28098,
             14],
       [1050001, 1053000, 1046700, 1049473, 1046700, 1049961,    5498,
             33]])

Answer 2

Just use the csv module to process the file, skipping first line and first column. Code can be as simple as:

with open('file.csv') as fd:
    next(fd)                                  # skip initial line
    rd = csv.reader(fd, delimiter = ' ', skipinitialspace = True)
    arr =  np.array([[int(i) for i in row[1:]] for row in rd])  # skip initial column

print(repr(arr))

gives as expected:

array([[1000000, 1000000, 1000000, 1000000, 1000000, 1000000, 2100000,
             74],
       [1050000, 1050010, 1050000, 1050001, 1050000, 1000000,    1648,
              5],
       [1030200, 1060000, 1030200, 1044474, 1042265, 1050001,   28469,
            108],
       [1040001, 1049999, 1040001, 1042303, 1045001, 1044474,    6518,
             10],
       [1049800, 1050000, 1048600, 1048787, 1050000, 1042303,     277,
             11],
       [1059973, 1059974, 1052000, 1053807, 1055000, 1048787,     916,
             17],
       [1050000, 1054498, 1043009, 1048173, 1043009, 1053807,    2098,
             29],
       [1045678, 1049989, 1040002, 1049961, 1049979, 1048173,   28098,
             14]])