Question

Traceback (most recent call last):
  File "/home/Py_Process_Plots/master_lac.py", line 14, in process
    value=tmp-val
  File "/home/software/anaconda/lib/python2.7/site-packages/pandas/core/ops.py", line 524, in wrapper
    arr = na_op(lvalues, rvalues)
  File "/home/software/anaconda/lib/python2.7/site-packages/pandas/core/ops.py", line 475, in na_op
    result[mask] = op(x[mask], _values_from_object(y[mask]))
TypeError: unsupported operand type(s) for -: 'str' and 'float'

我有两个文件first.csv和second.csv，在first.csv我有两个标题LAC和引用计数，second.csv没有标题可以是任意数字，但它将遵循一个格式，即一列LAC所有id，后跟时间日期序列，我需要从second.csv获取LAC（id）并在first.csv的LAC中搜索引用计数并减去second.csv的所有时间序列的值。您可以更好地理解预期的输出。

..., SysUtils;

type
  EFileNotFoundException = class(Exception)
  end;

function FindFileSize(const Filename: string): Int64;
var
  sr : TSearchRec;
  Err: Integer;
begin
  Err := FindFirst(filename, faAnyFile and (not faDirectory), sr);
  if Err = 0 then
  begin
    FindClose(sr);
    if (sr.Attr and faDirectory) = 0 then
    begin
      Result := sr.Size;
      Exit;
    end;
    Err := ERROR_FILE_NOT_FOUND;
  end
  if Err = ERROR_FILE_NOT_FOUND then begin
    raise EFileNotFoundException.Create(filename + ' not found.');
  end else begin
    RaiseLastOSError(Err);
  end;
end;

procedure TMyForm.Button1Click(Sender: TObject);
begin
  Label1.Caption := IntToStr(FindFileSize('C:\path to\some file.ext'));
end;

这是我的代码

javax.mail:javax.mail-api:1.5.3

我收到此错误

SharedPreferences settings = getPreferences(Context.MODE_PRIVATE);
        credential = GoogleAccountCredential.usingOAuth2(
                getApplicationContext(), Arrays.asList(SCOPES))
                .setBackOff(new ExponentialBackOff())
                .setSelectedAccountName(settings.getString(PREF_ACCOUNT_NAME, null));

    mService = new com.google.api.services.gmail.Gmail.Builder(
            transport, jsonFactory, credential)
            .setApplicationName("Gmail API Application")
            .build();

    String accName = this.getIntent().getStringExtra("ACCOUNT");
    Log.d("TienDH", "ViewActivity - acc : " + accName);
    if (accName != null) {

        credential.setSelectedAccountName(accName);
        settings = getPreferences(Context.MODE_PRIVATE);
        SharedPreferences.Editor editor = settings.edit();
        editor.putString(PREF_ACCOUNT_NAME, accName);
        editor.commit();

        try {
            MimeMessage mm = SendMailCtrl.createEmail(accName, "yaraht2011@gmail.com", "Test", "test send mail API");
            SendMailCtrl.sendMessage(mService, accName, mm);
            Log.d("TienDH", "sent mail");
        } catch (MessagingException e) {
            e.printStackTrace();
        } catch (IOException e) {
            e.printStackTrace();
        }

    }

我的熊猫版本是0.16.2，numpy是1.9.2

请帮我解决问题。

Answer 1

这有效：

df1 = pd.read_csv('First.csv', sep='\t', header=0, index_col=0)
df2 = pd.read_csv('Second.csv', sep='\t', header=0, index_col=0)
dfans = df2.subtract(df1.iloc[:,0], axis=0).dropna()

print(df1)
      Reference_Count
LAC                  
1000              500
2222             1000
3333              500
5555             1000
9999             1500

print(df2)
      10/08/15 00:00  10/08/15 01:00
LAC                                 
1000            2000            2500
2222            3000            4000

print(dfans)
      10/08/15 00:00  10/08/15 01:00
LAC                                 
1000            1500            2000
2222            2000            3000

您需要将LAC作为索引。以下不起作用......

index_default = pd.read_csv('test.csv', sep='\t', header=0)

print(index_default)
    LAC  10/08/15 00:00  10/08/15 01:00
0  2222            3000            4000
1  1000            2000            2500

dfans = index_default.subtract(df1.iloc[:,0], axis=0).dropna()

print(dfans)
Empty DataFrame
Columns: [LAC, 10/08/15 00:00, 10/08/15 01:00]
Index: []

但是以LAC为索引，它有效......

index_lac = pd.read_csv('test.csv', sep='\t', header=0, index_col=0)

print(index_lac)
      10/08/15 00:00  10/08/15 01:00
LAC                                 
2222            3000            4000
1000            2000            2500

dfans = index_lac.subtract(df1.iloc[:,0], axis=0).dropna()

print(dfans)
      10/08/15 00:00  10/08/15 01:00
LAC                                 
1000            1500            2000
2222            2000            3000

在丢弃两个值并再次存储它们时，pandas出错

1 个答案: