[英]Pandas, split dataframe by monotonic increase of column value
我有一個巨大的數據框,其中包含一個名為time的日期時間類型列,另一個名為dist的浮點類型列,數據框基於時間排序,而dist已經排序。 我想基於dist的單調增加將數據幀分成幾個數據幀。
分裂
dt dist
0 20160811 11:10 1.0
1 20160811 11:15 1.4
2 20160811 12:15 1.8
3 20160811 12:32 0.6
4 20160811 12:34 0.8
5 20160811 14:38 0.2
成
dt dist
0 20160811 11:10 1.0
1 20160811 11:15 1.4
2 20160811 12:15 1.8
dt dist
0 20160811 12:32 0.6
1 20160811 12:34 0.8
dt dist
0 20160811 14:38 0.2
您可以計算dist
列的差異向量,然后在條件diff < 0
執行cumsum()
(只要dist
從之前的值開始減少,就會創建一個新的id)
df['id'] = (df.dist.diff() < 0).cumsum()
print(df)
# dt dist id
#0 20160811 11:10 1.0 0
#1 20160811 11:15 1.4 0
#2 20160811 12:15 1.8 0
#3 20160811 12:32 0.6 1
#4 20160811 12:34 0.8 1
#5 20160811 14:38 0.2 2
for _, g in df.groupby((df.dist.diff() < 0).cumsum()):
print(g)
# dt dist
#0 20160811 11:10 1.0
#1 20160811 11:15 1.4
#2 20160811 12:15 1.8
# dt dist
#3 20160811 12:32 0.6
#4 20160811 12:34 0.8
# dt dist
#5 20160811 14:38 0.2
你可以用np.split()方法做到這一點 :
In [92]: df
Out[92]:
dt dist
0 2016-08-11 11:10:00 1.0
1 2016-08-11 11:15:00 1.4
2 2016-08-11 12:15:00 1.8
3 2016-08-11 12:32:00 0.6
4 2016-08-11 12:34:00 0.8
5 2016-08-11 14:38:00 0.2
In [93]: dfs = np.split(df, df[df.dist.diff().fillna(0) < 0].index)
In [94]: [print(x) for x in dfs]
dt dist
0 2016-08-11 11:10:00 1.0
1 2016-08-11 11:15:00 1.4
2 2016-08-11 12:15:00 1.8
dt dist
3 2016-08-11 12:32:00 0.6
4 2016-08-11 12:34:00 0.8
dt dist
5 2016-08-11 14:38:00 0.2
Out[94]: [None, None, None]
說明:
In [97]: df.dist.diff().fillna(0) < 0
Out[97]:
0 False
1 False
2 False
3 True
4 False
5 True
Name: dist, dtype: bool
In [98]: df[df.dist.diff().fillna(0) < 0]
Out[98]:
dt dist
3 2016-08-11 12:32:00 0.6
5 2016-08-11 14:38:00 0.2
In [99]: df[df.dist.diff().fillna(0) < 0].index
Out[99]: Int64Index([3, 5], dtype='int64')
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