Pandas finding local max and min

Question

I have a pandas data frame with two columns one is temperature the other is time.

I would like to make third and fourth columns called min and max. Each of these columns would be filled with nan's except where there is a local min or max, then it would have the value of that extrema.

Here is a sample of what the data looks like, essentially I am trying to identify all the peaks and low points in the figure.

Are there any built in tools with pandas that can accomplish this?

Answer 1

The solution offered by fuglede is great but if your data is very noisy (like the one in the picture) you will end up with lots of misleading local extremes. I suggest that you use scipy.signal.argrelextrema() method. The .argrelextrema() method has its own limitations but it has a useful feature where you can specify the number of points to be compared, kind of like a noise filtering algorithm. for example:

import numpy as np import matplotlib.pyplot as plt import pandas as pd from scipy.signal import argrelextrema  # Generate a noisy AR(1) sample  np.random.seed(0) rs = np.random.randn(200) xs = [0] for r in rs:     xs.append(xs[-1] * 0.9 + r) df = pd.DataFrame(xs, columns=['data'])  n = 5  # number of points to be checked before and after  # Find local peaks  df['min'] = df.iloc[argrelextrema(df.data.values, np.less_equal,                     order=n)[0]]['data'] df['max'] = df.iloc[argrelextrema(df.data.values, np.greater_equal,                     order=n)[0]]['data']  # Plot results  plt.scatter(df.index, df['min'], c='r') plt.scatter(df.index, df['max'], c='g') plt.plot(df.index, df['data']) plt.show() 

Some points:

• you might need to check the points afterward to ensure there are no twine points very close to each other.
• you can play with n to filter the noisy points
• argrelextrema returns a tuple and the [0] at the end extracts a numpy array