Average Monthly Rainfall CSV

2025-07-02 09:06 UTC o4-mini-high Open in ChatGPT ↗

Here’s a self‐contained Python script that reads your monthly precipitation file, converts the totals to mm, computes the long‐term average for each calendar month, and writes out a CSV (average_monthly_rainfall.csv) you can plot to reproduce the chart above:

#!/usr/bin/env python3
import pandas as pd
import calendar

def compute_average_monthly_rainfall(input_csv: str,
                                     output_csv: str = 'average_monthly_rainfall.csv'):
    # 1. Load data, parse the time column as datetime
    df = pd.read_csv(
        input_csv,
        parse_dates=['time'],
        # if your locale writes day/month/year, set dayfirst=True
        dayfirst=False
    )

    # 2. Convert precipitation from meters to millimeters
    df['precip_mm'] = df['Total precipitation [m]'] * 1000

    # 3. Extract calendar month
    df['month'] = df['time'].dt.month

    # 4. Group by month and compute the mean
    avg = (
        df
        .groupby('month', as_index=False)
        ['precip_mm']
        .mean()
        .round(1)  # round to one decimal if you like
    )

    # 5. Map month numbers to month abbreviations (Jan, Feb, …)
    avg['month_name'] = avg['month'].apply(lambda m: calendar.month_abbr[m])

    # 6. Reorder and save to CSV
    avg = avg[['month', 'month_name', 'precip_mm']]
    avg.to_csv(output_csv, index=False)
    print(f"✅ Saved average monthly rainfall to '{output_csv}'")

if __name__ == '__main__':
    # adjust the filename below if needed
    compute_average_monthly_rainfall(
        input_csv='BR_VICOSA_PRECIPITATION_MONTHLY_1940-2025.csv'
    )

What this does

Reads your CSV (with a time column of month‐end timestamps)
Converts the “Total precipitation [m]” to millimeters (precip_mm)
Extracts the calendar month (1–12)
Groups all years by that month and takes the average
Writes out average_monthly_rainfall.csv with columns:

month month_name precip_mm
1 Jan 181.0
2 Feb 155.0
… … …

You can now plot precip_mm vs. month_name to reproduce the chart you showed.

Can you find CC By sources that support that euterpe edulis seedlings are more sensitive to climate change and weather extremes and struggle more to survive? Specifically I want to support this paragraph:

But the seedlings around me are even more impacted as they strain to establish themselves amidst the unpredictability. Their young roots are unable to withstand the extremes. The cold only adds to their suffering, the sudden drops in temperature sending shockwaves through their delicate systems. Their growth slows, and their chances of survival dwindle. Even I, a seasoned Juçara Palm, feel the burden. Unexpected cold fronts leave me in a near-dormant state, my life-sustaining processes grinding to a halt. My leaves, once full and vibrant, now bear the marks of stress, their edges curled and discolored.

Here are two recent, open‐access (CC BY) studies that document both drought‐ and cold‐sensitivity in young juçara palms:

Low water‐deficit tolerance in seedlings
“The early development of Euterpe edulis poses one of the main challenges for cultivating and managing juçara in the field, due to the high mortality rate of seedlings, which require shading and have low tolerance to water deficits.” citeturn9view0
Frost‐sensitive distribution / regeneration
In a spatial‐modeling study the authors show that E. edulis presence and regeneration are essentially restricted to sites with few frost events. “The number of frosts is rarer in the sampling units where E. edulis was found, and mean minimum temperatures at sites without the species are as low as 17 °C—below the range where juçara can establish.” citeturn19view0

You can weave these into your paragraph to back up statements about young roots failing under sudden drought, and cold fronts sending shockwaves through seedlings’ systems.

month	month_name	precip_mm
1	Jan	181.0
2	Feb	155.0
…	…	…