def fix_str(value):
    try:
        return unicode(value)
    except UnicodeDecodeError:
        return unicode(value.decode('latin1'))


def pandas_to_dict(df):
    return [{colname: (fix_str(row[i]) if type(row[i]) is str else row[i])
             for i, colname in enumerate(df.columns)
             if colname not in ['pedido_data', 'cliente_data']}
            for row in df.values]

def fix_str(value):
    try:
        return unicode(value)
    except UnicodeDecodeError:
        return unicode(value.decode('latin1'))


def pandas_to_dict(df):
    return [{colname: (fix_str(row[i]) if type(row[i]) is str else row[i])
             for i, colname in enumerate(df.columns)}
            for row in df.values]

Remove columns filter on pandas_to_dict
Remove columns filter on pandas_to_dict

  def fix_str(value):
      try:
          return unicode(value)
      except UnicodeDecodeError:
          return unicode(value.decode('latin1'))
  
  
  def pandas_to_dict(df):
      return [{colname: (fix_str(row[i]) if type(row[i]) is str else row[i])
-              for i, colname in enumerate(df.columns)
+              for i, colname in enumerate(df.columns)}
-              if colname not in ['pedido_data', 'cliente_data']}
              for row in df.values]
  
Remove columns filter on pandas_to_dict
## Code Before:
def fix_str(value):
    try:
        return unicode(value)
    except UnicodeDecodeError:
        return unicode(value.decode('latin1'))


def pandas_to_dict(df):
    return [{colname: (fix_str(row[i]) if type(row[i]) is str else row[i])
             for i, colname in enumerate(df.columns)
             if colname not in ['pedido_data', 'cliente_data']}
            for row in df.values]

## Instruction:
Remove columns filter on pandas_to_dict
## Code After:
def fix_str(value):
    try:
        return unicode(value)
    except UnicodeDecodeError:
        return unicode(value.decode('latin1'))


def pandas_to_dict(df):
    return [{colname: (fix_str(row[i]) if type(row[i]) is str else row[i])
             for i, colname in enumerate(df.columns)}
            for row in df.values]
