Getting Monthly Maximums from Hourly Data

In this article, we’ll explore how to extract the monthly maximum values of hourly data using Python and its popular libraries, Pandas, NumPy, and Matplotlib.

Introduction

The problem at hand involves retrieving the highest tide value for each month along with its associated date. The original dataset consists of hourly tide levels recorded over a period of 14 years. To achieve this goal, we’ll first need to convert the timestamp column into datetime format, followed by grouping the data by month and finding the maximum value within that group.

Understanding the Data

The sample dataset is composed of two columns: ‘Date’ and ‘data’. The ‘Date’ column contains timestamps in the format ‘YYYY-MM-DD HH:MM:SS’, while the ‘data’ column holds the corresponding hourly tide level values. We’ll assume this structure applies to our actual data as well.

Converting Timestamps to Datetime Format

Before we can perform any meaningful analysis, it’s crucial that the timestamps are in a format that can be easily manipulated. The Pandas library provides an efficient way to convert these timestamp strings into datetime objects using the pd.to_datetime() function:

df['Date'] = pd.to_datetime(df['Date'], format='%Y-%m-%d %H:%M:%S', errors='ignore')

Here, we specify the desired output format as ‘%Y-%m-%d %H:%M:%S’. The errors='ignore' parameter prevents Pandas from raising an error if it encounters any timestamps that can’t be converted to datetime objects.

Grouping and Finding Monthly Maximums

Once our data is in a suitable format, we’ll utilize the groupby() function provided by Pandas. This method allows us to split our data into smaller groups based on the ‘Date’ column, grouped by month (denoted as ‘M’). We can then apply the max() function to each group within these months:

ddf = df.groupby(pd.Grouper(key='Date', freq='M')).max()

This approach results in a new DataFrame (ddf) containing the maximum tide value for each month, along with its associated date.

Understanding Why Last Day of Each Month is Shown

The original question hints at an issue where only the last day of each month is shown. This could be due to how the max() function treats missing values in the ‘data’ column. If there are any gaps or missing values, Pandas will treat them as NaN (Not a Number). When finding the maximum value for each group, these NaN values won’t affect the result since the max() function ignores them.

However, when displaying the results in the desired format, if you append .set_index('Date') to your code, then these NaN values will be shown as missing dates. In order to achieve what you want (i.e., only showing the date and value for each month), we must first filter out any rows containing NaN.

Here’s how we can modify our code to exclude rows with NaN in the ‘data’ column:

# Remove rows with NaN values in the data column
df_filtered = df.dropna(subset=['data'])

# Group the filtered DataFrame and find monthly maximums
monthly_max_vals = df_filtered.groupby(pd.Grouper(key='Date', freq='M'))['data'].idxmax()

Creating Sample Data

To illustrate this concept better, let’s create some sample data that demonstrates how to achieve our goal:

import pandas as pd
import numpy as np

# Create timestamps ranging from January 1st, 2020 to October 31st, 2020
np.random.seed(0)
timestamps = pd.DatetimeIndex(start='2020-01-01', end='2020-10-31', freq='1h')

# Generate random values between -5 and 5 (simulating different tide levels)
data = np.random.uniform(-5, 5, len(timestamps))

# Create a DataFrame with the timestamps as 'Date' column
df = pd.DataFrame({'Date': timestamps, 'data': data})

# Convert the Date to datetime format for easier manipulation
df['Date'] = pd.to_datetime(df['Date'], format='%Y-%m-%d %H:%M:%S', errors='ignore')

# Filter out rows with missing values in the data column
df_filtered = df.dropna(subset=['data'])

# Find monthly maximums
monthly_max_vals = df_filtered.groupby(pd.Grouper(key='Date', freq='M'))['data'].idxmax()

# Get the desired output (date and max value for each month)
output_df = df_filtered.loc[monthly_max_vals].reset_index(drop=True)

print(output_df)

The result of this code will display a DataFrame with two columns: ‘Date’ and ‘data’. Each row represents the date along with its maximum tide level recorded during that month.

Conclusion

Retrieving monthly maximums from hourly data requires some basic knowledge of Pandas, NumPy, and Matplotlib. This guide walked through each step involved in solving this problem efficiently, using meaningful section headings to improve readability and understanding.

Last modified on 2023-08-31