import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
df = pd.DataFrame({"events": [1,2,1,1,-3,-4,7,8,9,10,-3,5,6,7,-10]})
df.plot(figsize = (10,8))
plt.axhline(y = 0, c = 'r')
plt.show()
df["sign"]=np.sign(df.events)
df['diff_sign'] = df.sign.diff()
df["change"]=np.cumsum(df.sign.diff() != 0)
df
| events | sign | diff_sign | change | |
|---|---|---|---|---|
| 0 | 1 | 1 | NaN | 1 |
| 1 | 2 | 1 | 0.0 | 1 |
| 2 | 1 | 1 | 0.0 | 1 |
| 3 | 1 | 1 | 0.0 | 1 |
| 4 | -3 | -1 | -2.0 | 2 |
| 5 | -4 | -1 | 0.0 | 2 |
| 6 | 7 | 1 | 2.0 | 3 |
| 7 | 8 | 1 | 0.0 | 3 |
| 8 | 9 | 1 | 0.0 | 3 |
| 9 | 10 | 1 | 0.0 | 3 |
| 10 | -3 | -1 | -2.0 | 4 |
| 11 | 5 | 1 | 2.0 | 5 |
| 12 | 6 | 1 | 0.0 | 5 |
| 13 | 7 | 1 | 0.0 | 5 |
| 14 | -10 | -1 | -2.0 | 6 |
df["change"]=np.cumsum(df.sign.diff() != 0)
valores = df.groupby("change").events.transform(lambda x: x.abs().max())*df.sign
valores
0 2
1 2
2 2
3 2
4 -4
5 -4
6 10
7 10
8 10
9 10
10 -3
11 7
12 7
13 7
14 -10
dtype: int64
mask = (df.events == valores).mask(lambda x: x == 0, np.nan)
df.events*mask
0 NaN
1 2.0
2 NaN
3 NaN
4 NaN
5 -4.0
6 NaN
7 NaN
8 NaN
9 10.0
10 -3.0
11 NaN
12 NaN
13 7.0
14 -10.0
dtype: float64
df.events.plot(figsize = (10,8))
plt.axhline(y = 0, c = 'r')
plt.plot(df.events*mask, 'o', c = 'k')
plt.show()
