Finding the index of a row based on values of the neightbouring rows-CodePudding

I am trying to automate a code for finding the location in which my timeseries has the first y-axis value closest to 0 with an upward trend, that is, the value before my y = 0 is very likely negative and the following one is positive where the exact location that I want to pin point is the closest to 0.

To illustrate what I mean. Let's say I have the given bellow:

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

start_time = 0
end_time = 1
sample_rate = 500
time = np.arange(start_time, end_time, 1/sample_rate)
theta = 32
frequency = 2
amplitude = 1
sinewave = amplitude * np.sin(2 * np.pi * frequency * time   theta)

# DataFrame
df = pd.DataFrame([time, sinewave]).T
df.columns = ['Time','Signal']

plt.plot(df['Time'],df['Signal'])

For my specific example, the solution would be:

Index: 227, Time: 0.454, Signal: 0.00602037947

There are very likely several solutions for that problem. However, I do believe that there is probably a single-line elegant solution with Pandas that could handle the issue.

CodePudding user response：

here is one way do it

check the two consecutive rows where the sign changes for the signal

df[(df['Signal']>0) & 
   (df['Signal'].shift(1)<0)]

         Time   Signal
227     0.454   0.00602
477     0.954   0.00602

OR following to get the first instance

df[(df['Signal']>0) & 
   (df['Signal'].shift(1)<0)].iloc[0]

Time      0.45400
Signal    0.00602
Name: 227, dtype: float64