根据列阈值选择熊猫数据框的行

Question

I have a pandas dataframe with a column "value" and a column "timestamp". Now I would like to filter the rows according to thresholds of the timestamp. I have done the following:

idx = df.index[df['timestamp'] >= start and df['timestamp'] <= end]
df = df.loc[idx]

df is the dataframe and start and end are two integers.

Somehow this does not work. I'm getting an error:

ValueError: The truth value of a DataFrame is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().

EDIT: There is a further problem. start is a dataframe with only one value (one row, one column). End is a dataframe with several rows and only one column (but I'm only interested in the last row). When I do the following

    print(end.iloc[-1])
    print(start.iloc[0])

I'm getting the following output

1508504026077
start_timestamp_milli    1508502348946
Name: 0, dtype: int64

When I then try to do print(df[column] >= start.iloc[0]) I'm getting an error:

ValueError: Can only compare identically-labeled Series objects

Consequently, mask=(df['timestamp'] >= start & df['timestamp'] <= end) also failes.

Answer 1

IIUC

mask=(df['timestamp'] >= start & df['timestamp'] <= end)

df=df[mask]