我有一個從資料記錄器創建的 DataFrame，其中每個資料點都有自己的時間戳，如下所示：
df_orig = pd.DataFrame(
{
"val1": [ 1, np.nan, np.nan, 11, np.nan, np.nan, 21, np.nan, np.nan, ],
"val2": [ np.nan, 2, np.nan, np.nan, 12, np.nan, np.nan, 22, np.nan, ],
"val3": [ np.nan, np.nan, 3, np.nan, np.nan, 13, np.nan, np.nan, 23, ],
},
index=pd.to_datetime( [
"2021-01-01 00:00", "2021-01-01 00:00:01", "2021-01-01 00:00:02",
"2021-01-01 00:01", "2021-01-01 00:01:01", "2021-01-01 00:01:02",
"2021-01-01 00:02", "2021-01-01 00:02:01", "2021-01-01 00:02:02",
] )
)
val1 val2 val3
2021-01-01 00:00:00 1.0 NaN NaN
2021-01-01 00:00:01 NaN 2.0 NaN
2021-01-01 00:00:02 NaN NaN 3.0
2021-01-01 00:01:00 11.0 NaN NaN
2021-01-01 00:01:01 NaN 12.0 NaN
2021-01-01 00:01:02 NaN NaN 13.0
2021-01-01 00:02:00 21.0 NaN NaN
2021-01-01 00:02:01 NaN 22.0 NaN
2021-01-01 00:02:02 NaN NaN 23.0
我實際上不需要記錄每個單個資料點的精度。我想通過消除NaNs 并合并非常靠近的行來壓縮 DataFrame 。結果應該是這樣的：
val1 val2 val3
2021-01-01 00:00:00 1 2 3
2021-01-01 00:01:00 11 12 13
2021-01-01 00:02:00 21 22 23
有沒有辦法做到這一點？
uj5u.com熱心網友回復：
如果可能，請使用max或min或first使用以下方法簡化每分鐘重新采樣的解決方案：
df = df_orig.resample('Min').max()
print (df)
val1 val2 val3
2021-01-01 00:00:00 1.0 2.0 3.0
2021-01-01 00:01:00 11.0 12.0 13.0
2021-01-01 00:02:00 21.0 22.0 23.0
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