Converting Calendar Year to Water Year in Pandas

Introduction

In this article, we’ll explore how to convert calendar year data to water year data using pandas in Python. The concept of water years is crucial for environmental monitoring and hydrology studies, as it helps to standardize the analysis of water flow data.

Water years typically start on October 1st of a given year and end on September 30th of the following year. This convention allows researchers and analysts to compare water flow patterns across different regions and time periods. In this article, we’ll use the pandas library in Python to convert calendar year data to water year data.

Background

The U.S. Geological Survey (USGS) provides a wealth of hydrological data, including river discharge rates, through its National Water Information System (NWIS). One such dataset is the Wabash River Flow data, which contains daily discharge rates for the Wabash River in Indiana, USA. The dataset has dates ranging from 2001-10-01 to 2017-09-30.

To convert this calendar year data to water year data, we’ll use a combination of pandas’ built-in functions and clever manipulation of date columns.

Loading Data

Let’s start by loading the Wabash River Flow dataset using pd.read_csv(). We’ll also import necessary libraries and set the datetime column type.

import pandas as pd

# Load data from CSV file
df = pd.read_csv('WabashRiver_Flow.csv', parse_dates=['datetime'])

# Import necessary libraries
from datetime import timedelta
import numpy as np

# Set datetime column to datetime type
df['datetime'] = pd.to_datetime(df['datetime'])

Dropping Missing Values

Next, let’s drop any missing values in the dataset using dropna().

# Drop rows with missing values
df = df.dropna()

Determining Water Year

To convert calendar year data to water year data, we’ll use pandas’ built-in functions. We’ll first extract the month and year numbers from the datetime column.

# Extract month and year numbers from datetime column
df['month'] = df['datetime'].dt.month
df['year'] = df['datetime'].dt.year

# Use Series.where() to assign water year value based on month
def get_water_year(row):
    if row['month'] >= 10:
        return row['year'] + 1
    else:
        return row['year']

df['water_year'] = df.apply(get_water_year, axis=1)

Note that we’re using the apply() function to apply the get_water_year() function to each row of the dataframe. The axis=1 argument specifies that we want to apply this function to each row.

Creating a Bar Plot

Let’s create a bar plot to visualize the discharge rates for each water year.

# Create bar plot of discharge rates by water year
import matplotlib.pyplot as plt

df['discharge'] = df['datetime'].dt.second / 3600  # Convert seconds to hours
plt.figure(figsize=(8,6))
plt.bar(df['water_year'], df['discharge'])
plt.xlabel('Water Year')
plt.ylabel('Discharge (cubic feet/second)')
plt.title('Wabash River Discharge Rates by Water Year')
plt.show()

Comparing Methods Using timeit

Finally, let’s compare the performance of our Series.where() method with other approaches using the timeit library.

# Define functions for comparison
def get_water_year_other(row):
    return np.where(row['month'] >= 10, row['year'] + 1, row['year'])

from timeit import timeit

# Time Series.where() method
print("Series.where() method:", timeit(lambda: df['datetime'].dt.year.where(df['datetime'].dt.month < 10), number=1000))

# Time apply() method with np.where()
print("Apply() with np.where():", timeit(lambda: df.apply(get_water_year_other, axis=1), number=1000))

# Time apply() method with custom function
print("Apply() with custom function:", timeit(lambda: df.apply(get_water_year, axis=1), number=1000))

This code compares the performance of our Series.where() method with other approaches using the timeit library. It measures the execution time for each approach and prints the results.

Conclusion

In this article, we’ve demonstrated how to convert calendar year data to water year data using pandas in Python. We’ve used a combination of pandas’ built-in functions and clever manipulation of date columns to achieve this conversion. Additionally, we’ve compared the performance of our Series.where() method with other approaches using the timeit library. By following these steps, you can easily convert your own calendar year data to water year data using pandas in Python.

Last modified on 2023-05-24