How to Perform a Mann-Kendall Trend Test in Python

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Analyzing trends in time series data is a common task in various scientific disciplines. A well-known method used to detect a trend in a time series dataset is the Mann-Kendall (M-K) trend test. This is a non-parametric test that can identify upward or downward trends.

This tutorial will guide you through the process of performing a Mann-Kendall trend test in Python. We’ll use Python’s pandas for data manipulation, numpy for numerical operations, and pyMannKendall, a Python library specifically designed to carry out the Mann-Kendall trend test.

Before we get into the how-to of the Mann-Kendall test in Python, let’s understand the basic idea behind it.


The Mann-Kendall (M-K) test is a rank-based test that is used to identify trends in data. This non-parametric test does not require the data to be normally distributed and is relatively less affected by abrupt breaks due to inhomogeneous time series.

The null hypothesis (H0) is that the data is independent and identically distributed, meaning there is no trend. The alternate hypothesis (H1) is that there exists a trend. If the p-value is less than a chosen significance level (e.g., 0.05), we reject H0 and say that there is a statistically significant trend.

Step 1: Installing Required Libraries

We will need the following Python libraries: pandas, numpy, and pyMannKendall. If you don’t have them installed, you can do so via pip:

pip install pandas numpy pyMannKendall

Step 2: Import Libraries

Start by importing the necessary Python libraries:

import pandas as pd
import numpy as np
import pymannkendall as mk

Step 3: Load and Preprocess the Data

Let’s assume that you have a time series dataset stored in a CSV file. Here is how you can load it:

# Load the data
df = pd.read_csv('data.csv')

# Convert the index to datetime format
df.index = pd.to_datetime(df.index)

In this example, we assume that the date information is stored in the index. If it’s stored in a different column, you should adjust the code accordingly.

Step 4: Apply Mann-Kendall Trend Test

With the dataset ready, we can now perform the Mann-Kendall trend test. The pymannkendall package provides the mk.original_test function, which can be used to carry out the test.

# Perform Mann-Kendall test
result = mk.original_test(df['value'])


In this code, ‘value’ is the column name containing the time series data. The mk.original_test function will return a named tuple that contains the trend (whether there is an increasing or decreasing trend), h (True if there is a trend, and False otherwise), p (p-value), z (normalized test statistic), Tau (Kendall Tau), s (Mann-Kendall’s score), var_s (Variance S), slope (Sen’s slope (changes per unit time)), and intercept.

Step 5: Interpret the Results

The result of the Mann-Kendall test can be interpreted based on the p-value and the trend.

If p-value < 0.05 (assuming a 5% significance level), then we reject the null hypothesis, meaning we have sufficient evidence to say there is a trend.

The trend can be ‘increasing’ if the Sen’s slope is positive, and ‘decreasing’ if it’s negative.

Here’s how you can interpret the results:

if result.p < 0.05:
    print('There is a significant trend')
    if result.trend == 'increasing':
        print('The trend is increasing with a slope of', result.slope)
        print('The trend is decreasing with a slope of', result.slope)
    print('There is no significant trend')


The Mann-Kendall trend test is a robust non-parametric method used to identify a trend in time series data. It’s an important tool for time series analysis in fields such as environmental science and climate change research, where detecting trends is a crucial task.

This tutorial provided a step-by-step guide on how to perform the Mann-Kendall trend test in Python. Python’s extensive range of statistical libraries, such as pymannkendall, make it a versatile platform for conducting such tests and interpreting their results. The Mann-Kendall test is straightforward to apply and interpret, making it a handy tool in the toolbox of any data analyst or scientist.

While it’s a powerful test, it’s worth noting that the Mann-Kendall test assumes that the data points are independent. In the case where this assumption is not met, an alternative version of the test, the Seasonal Mann-Kendall test, could be used. Remember, understanding your data and the appropriate test conditions is just as important as conducting the test itself.

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