加速数据帧循环

Question

我写了下面给出的代码。有两个Pandas数据框：df包含列timestamp_milli和pressure，df2包含列timestamp_milli和acceleration_z。两个数据帧都有大约100行和3900行。在下面显示的代码中，我搜索df每行的df2行的每个时间戳，其中时间差在一个范围内并且是最小的。

不幸的是，代码非常慢。此外，我收到了来自df_temp["timestamp_milli"] = df_temp["timestamp_milli"] - row["timestamp_milli"]行的以下消息：

  

SettingWithCopyWarning：尝试在a的副本上设置值   从DataFrame切片。尝试使用.loc [row_indexer，col_indexer] =   代替值

如何加速代码并解决警告？

acceleration = []
pressure = []

for index, row in df.iterrows():
    mask = (df2["timestamp_milli"] >= (row["timestamp_milli"] - 5)) & (df2["timestamp_milli"] <= (row["timestamp_milli"] + 5))
    df_temp = df2[mask]

    # Select closest point
    if len(df_temp) > 0:
        df_temp["timestamp_milli"] = df_temp["timestamp_milli"] - row["timestamp_milli"]
        df_temp["timestamp_milli"] = df_temp["timestamp_milli"].abs()

        df_temp = df_temp.loc[df_temp["timestamp_milli"] == df_temp["timestamp_milli"].min()]

        for index2, row2 in df_temp.iterrows():
            pressure.append(row["pressure"])
            acc = row2["acceleration_z"]
            acceleration.append(acc)

Answer 1

我遇到了类似的问题，使用itertuples代替iterrows显示时间显着缩短。 why iterrows have issues. 希望这会有所帮助。