加速数据的循环

Question

我已经编写了下面的代码。有两个Pandas：df包含timestamp_milli和pressure，df2包含列timestamp_milli和acceleration_z。这两个数据格式都有大约100,000行。在下面所示的代码中，我搜索df的每一行的每个时间戳，df2的行，其中时间差在一个范围内并且最小。

不幸的是，代码非常慢。此外，我还从行df_temp["timestamp_milli"] = df_temp["timestamp_milli"] - row["timestamp_milli"]获得了以下消息

SettingWithCopyWarning:一个值试图在DataFrame的一个切片的副本上设置。尝试使用.locrow_indexer，col_indexer = value代替

我如何加快代码和解决警告？

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

我也遇到了一个类似的问题，使用迭代而不是迭代行会显着地缩短时间。why iterrows have issues.希望这能帮上忙。