Traceback (most recent call last): File "C:\Users\MAC\Desktop\py\筛选股票\121.py", line 16, in <module> top100 = data.nlargest(100, 'amount') File "L:\Program Files\python\lib\site-packages\pandas\core\frame.py", line 7338, in nlargest return algorithms.SelectNFrame(self, n=n, keep=keep, columns=columns).nlargest() File "L:\Program Files\python\lib\site-packages\pandas\core\algorithms.py", line 1277, in nlargest return self.compute("nlargest") File "L:\Program Files\python\lib\site-packages\pandas\core\algorithms.py", line 1408, in compute raise TypeError( TypeError: Column 'amount' has dtype object, cannot use method 'nlargest' with this dtype
时间: 2023-07-20 21:36:31 浏览: 48
看起来您遇到了一个 TypeError,该错误的原因是在 `data` 数据框中的 `amount` 列被识别为 object 类型,而 `nlargest()` 方法需要的是数值类型的列。这通常是由于数据中存在异常值或缺失值而导致的。您可以使用以下代码来检查 `amount` 列的数据类型:
```python
print(data['amount'].dtype)
```
如果输出结果为 `object`,则说明该列的数据类型不正确。您需要先将该列转换为数值类型,例如:
```python
data['amount'] = pd.to_numeric(data['amount'], errors='coerce')
```
这将把所有无法转换为数值类型的值设置为 NaN。如果您希望将 NaN 值替换为其他值,可以使用 `fillna()` 方法。例如,将 NaN 替换为 0:
```python
data['amount'] = pd.to_numeric(data['amount'], errors='coerce').fillna(0)
```
修改后的代码如下:
```python
import tushare as ts
import pandas as pd
# 设置tushare token
ts.set_token('YOUR_TOKEN_HERE')
# 获取今天的日期
today = pd.Timestamp.today().strftime('%Y-%m-%d')
# 获取当日所有沪市、深市、创业板、科创板股票数据
pro = ts.pro_api()
data = pro.daily(trade_date=today, fields='ts_code,trade_date,close,vol')
# 计算成交额
data['amount'] = data['close'] * data['vol']
data['amount'] = pd.to_numeric(data['amount'], errors='coerce').fillna(0)
# 筛选成交额前100的股票
top100 = data.nlargest(100, 'amount')
# 计算股票五日均线
top100['ma5'] = top100.groupby('ts_code')['close'].transform(lambda x: x.rolling(5).mean())
# 选出今天收盘价位于五日线上方且成交量小于前三天的股票
selected = top100[(top100['close'] > top100['ma5']) & (top100['vol'] < top100.groupby('ts_code')['vol'].shift(1).rolling(3).mean())]
# 保存结果到文件
selected.to_csv('result.csv', index=False)
```
这应该可以解决您的问题。