数据框中的 Pandas 列表理解
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Pandas list comprehension in a dataframe
提问by Michele Reilly
I would like to pull out the price at the next day's open currently stored in (row + 1) and store it in a new column, if some condition is met.
如果满足某些条件，我想提取当前存储在（行 + 1）中的第二天开盘价并将其存储在新列中。
df['b']=''
df['shift']=''
df['shift']=df['open'].shift(-1)
df['b']=df[x for x in df['shift'] if df["MA10"]>df["MA100"]]
回答by DSM
There are a few approaches. Using apply:
有几种方法。使用apply：
>>> df = pd.read_csv("bondstack.csv")
>>> df["shift"] = df["open"].shift(-1)
>>> df["b"] = df.apply(lambda row: row["shift"] if row["MA10"] > row["MA100"] else np.nan, axis=1)
which produces
产生
>>> df[["MA10", "MA100", "shift", "b"]][:10]
MA10 MA100 shift b
0 16.915625 17.405625 16.734375 NaN
1 16.871875 17.358750 17.171875 NaN
2 16.893750 17.317187 17.359375 NaN
3 16.950000 17.279062 17.359375 NaN
4 17.137500 17.254062 18.640625 NaN
5 17.365625 17.229063 18.921875 18.921875
6 17.550000 17.200312 18.296875 18.296875
7 17.681250 17.177500 18.640625 18.640625
8 17.812500 17.159375 18.609375 18.609375
9 17.943750 17.142813 18.234375 18.234375
For a more vectorized approach, you could use
对于更矢量化的方法，您可以使用
>>> df = pd.read_csv("bondstack.csv")
>>> df["b"] = np.nan
>>> df["b"][df["MA10"] > df["MA100"]] = df["open"].shift(-1)
or my preferred approach:
或我的首选方法：
>>> df = pd.read_csv("bondstack.csv")
>>> df["b"] = df["open"].shift(-1).where(df["MA10"] > df["MA100"])
回答by user3226167
Modifying DSM's approach 3, stating True/False values in np.whereexplicitly:
修改 DSM 的方法 3，np.where明确说明 True/False 值：
#numpy.where(condition, x, y)
df["b"] = np.where(df["MA10"] > df["MA100"], df["open"].shift(-1), np.nan)
Using list comprehension explicitly:
明确使用列表理解：
#[xv if c else yv for (c,xv,yv) in zip(condition,x,y)] #np.where documentation
df['b'] = [ xv if c else np.nan for (c,xv) in zip(df["MA10"]> df["MA100"], df["open"].shift(-1))]
