First #FishyFriday in March

 

It's that time of year when below the waterline gets a seeing-to...

JT hard rock driller in action...

landing time for the visiting inshore trawler Ann Louise...

as Spanish Eyes II waits to come alongside the market...

but has to wait...

for the beam trawler St Georges,,,

to pull away from the quayside...

it's thirsty work looking for breakfast...

change that trawl...

Firiday's market found a good mix of fish from cuttles...

to squid...

to this huge gilt head bream...

and the usual mix of quality flats like these megs and plaice...

large lemons...

and more unusually, grey mullet...

and great weavers...

plenty of big megrims from the beam trawler St Georges...

inshore tails...

and a few reds and black bream to finish...

on a morning of cool blue hues in the harbour.

Fish auction news for the week in Newlyn.

Python

import pandas as pd

files = [
    'Average_Price_Overview_1 2nd.xlsx - Sheet.csv',
    'Average_Price_Overview_1 3rd.xlsx - Sheet.csv',
    'Average_Price_Overview_1 - 4th.xls - Sheet.csv',
    'Average_Price_Overview_1 5th.xlsx - Sheet.csv'
]

dataframes = {}

for f in files:
    # Based on the snippet, it looks like the header is around row 5 or 6 (0-indexed).
    # Let's try reading and seeing what's there.
    df = pd.read_csv(f, skiprows=5)
    dataframes[f] = df
    print(f"File: {f}")
    print(df.head())
    print(df.info())
    print("-" * 30)

Code output

File: Average_Price_Overview_1 2nd.xlsx - Sheet.csv
       Species            Size   Pres.  Unnamed: 3 Qual  Unnamed: 5 # boxes  Sum of kgs  Sum Of Amount    Average
0  Black Bream       (1) 1-3Kg   WHOLE         NaN   A+         NaN       1        1.10        17.7210  16.110000
1  Black Bream     (2) 0.7-1kg   WHOLE         NaN   A+         NaN       3       27.84       417.3516  14.991077
2  Black Bream  (3) 0.5-0.75Kg   WHOLE         NaN   A+         NaN       2       20.50       267.3200  13.040000
3   Blonde Ray        (1) 6Kg+  GUTTED         NaN   A+         NaN       5       97.06       363.9750   3.750000
4   Blonde Ray       (2) 4-6Kg  GUTTED         NaN   A+         NaN       2       24.80        76.1360   3.070000
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 195 entries, 0 to 194
Data columns (total 10 columns):
 #   Column         Non-Null Count  Dtype  
---  ------         --------------  -----  
 0   Species        174 non-null    object 
 1   Size           172 non-null    object 
 2   Pres.          172 non-null    object 
 3   Unnamed: 3     0 non-null      float64
 4   Qual           172 non-null    object 
 5   Unnamed: 5     0 non-null      float64
 6   # boxes        174 non-null    object 
 7   Sum of kgs     173 non-null    float64
 8   Sum Of Amount  173 non-null    float64
 9   Average        172 non-null    float64
dtypes: float64(5), object(5)
memory usage: 15.4+ KB
None
------------------------------
File: Average_Price_Overview_1 3rd.xlsx - Sheet.csv
       Species            Size   Pres.  Unnamed: 3 Qual  Unnamed: 5 # boxes  Sum of kgs  Sum Of Amount  Average
0  Black Bream       (1) 1-3Kg   WHOLE         NaN   A+         NaN       1        2.50        41.3250    16.53
1  Black Bream     (2) 0.7-1kg   WHOLE         NaN   A+         NaN       3       19.35       290.8305    15.03
2  Black Bream  (3) 0.5-0.75Kg   WHOLE         NaN   A+         NaN       1        1.50        18.0000    12.00
3  Black Bream   (4) 0.3-0.5Kg   WHOLE         NaN   A+         NaN       1        0.37         1.8759     5.07
4   Blonde Ray       (2) 4-6Kg  GUTTED         NaN   A+         NaN       1        5.59        22.5836     4.04
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 155 entries, 0 to 154
Data columns (total 10 columns):
 #   Column         Non-Null Count  Dtype  
---  ------         --------------  -----  
 0   Species        134 non-null    object 
 1   Size           132 non-null    object 
 2   Pres.          132 non-null    object 
 3   Unnamed: 3     0 non-null      float64
 4   Qual           132 non-null    object 
 5   Unnamed: 5     0 non-null      float64
 6   # boxes        134 non-null    object 
 7   Sum of kgs     133 non-null    float64
 8   Sum Of Amount  133 non-null    float64
 9   Average        132 non-null    float64
dtypes: float64(5), object(5)
memory usage: 12.2+ KB
None
------------------------------
File: Average_Price_Overview_1 - 4th.xls - Sheet.csv
       Species         Size   Pres.  Unnamed: 3 Qual  Unnamed: 5 # boxes  Sum of kgs  Sum Of Amount   Average
0        Brill  (3) 1-1.5Kg  GUTTED         NaN   A+         NaN     2.0        2.38        43.0066  18.07000
1        Brill  (4) 0.5-1Kg  GUTTED         NaN   A+         NaN     2.0       11.85       150.0165  12.65962
2  Brown Claws            M   WHOLE         NaN   A+         NaN     2.0        2.20        16.5000   7.50000
3   Cuttlefish   (1) 0.5Kg+   WHOLE         NaN   A+         NaN     2.0        5.84        28.3824   4.86000
4   Cuttlefish  (2) 0-0.5Kg   WHOLE         NaN   A+         NaN     2.0        1.08         3.2724   3.03000
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 107 entries, 0 to 106
Data columns (total 10 columns):
 #   Column         Non-Null Count  Dtype  
---  ------         --------------  -----  
 0   Species        98 non-null     object 
 1   Size           96 non-null     object 
 2   Pres.          96 non-null     object 
 3   Unnamed: 3     0 non-null      float64
 4   Qual           96 non-null     object 
 5   Unnamed: 5     0 non-null      float64
 6   # boxes        98 non-null     object 
 7   Sum of kgs     97 non-null     float64
 8   Sum Of Amount  97 non-null     float64
 9   Average        96 non-null     float64
dtypes: float64(5), object(5)
memory usage: 8.5+ KB
None
------------------------------
File: Average_Price_Overview_1 5th.xlsx - Sheet.csv
      Species       Size   Pres.  Unnamed: 3 Qual  Unnamed: 5 # boxes  Sum of kgs  Sum Of Amount   Average
0  Blonde Ray   (1) 6Kg+  GUTTED         NaN   A+         NaN       1       12.77        49.1645  3.850000
1  Blonde Ray  (2) 4-6Kg  GUTTED         NaN   A+         NaN       2       27.80        88.9600  3.200000
2  Blonde Ray  (3) 2-4Kg  GUTTED         NaN   A+         NaN       2       31.11       100.4853  3.230000
3  Blonde Ray  (4) 1-2Kg  GUTTED         NaN   A+         NaN       7      127.65       299.9775  2.350000
4  Blonde Ray  (5) 0-1Kg  GUTTED         NaN   A+         NaN       4       75.06       135.0441  1.799148
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 157 entries, 0 to 156
Data columns (total 10 columns):
 #   Column         Non-Null Count  Dtype  
---  ------         --------------  -----  
 0   Species        146 non-null    object 
 1   Size           144 non-null    object 
 2   Pres.          144 non-null    object 
 3   Unnamed: 3     0 non-null      float64
 4   Qual           144 non-null    object 
 5   Unnamed: 5     0 non-null      float64
 6   # boxes        146 non-null    object 
 7   Sum of kgs     145 non-null    float64
 8   Sum Of Amount  145 non-null    float64
 9   Average        144 non-null    float64
dtypes: float64(5), object(5)
memory usage: 12.4+ KB
None
------------------------------

Python

import pandas as pd

files = [
    ('2nd', 'Average_Price_Overview_1 2nd.xlsx - Sheet.csv'),
    ('3rd', 'Average_Price_Overview_1 3rd.xlsx - Sheet.csv'),
    ('4th', 'Average_Price_Overview_1 - 4th.xls - Sheet.csv'),
    ('5th', 'Average_Price_Overview_1 5th.xlsx - Sheet.csv')
]

daily_summaries = []

for day_label, file_path in files:
    df = pd.read_csv(file_path, skiprows=5)
    
    # Drop rows where 'Species' is null or "Total"
    df = df[df['Species'].notnull()]
    df = df[~df['Species'].str.contains('Total|species', case=False, na=False)]
    
    # Ensure numeric columns are numeric
    df['Sum of kgs'] = pd.to_numeric(df['Sum of kgs'], errors='coerce')
    df['Sum Of Amount'] = pd.to_numeric(df['Sum Of Amount'], errors='coerce')
    
    # Drop rows that failed to convert
    df = df.dropna(subset=['Sum of kgs', 'Sum Of Amount'])
    
    total_kgs = df['Sum of kgs'].sum()
    total_value = df['Sum Of Amount'].sum()
    avg_price = total_value / total_kgs if total_kgs > 0 else 0
    
    # Key species by value
    species_summary = df.groupby('Species').agg({'Sum of kgs': 'sum', 'Sum Of Amount': 'sum'}).reset_index()
    species_summary['Avg Price'] = species_summary['Sum Of Amount'] / species_summary['Sum of kgs']
    
    top_species_value = species_summary.sort_values(by='Sum Of Amount', ascending=False).head(5)
    
    daily_summaries.append({
        'day': day_label,
        'total_kgs': total_kgs,
        'total_value': total_value,
        'avg_price': avg_price,
        'top_species': top_species_value.to_dict('records')
    })

# Print the results to examine
for s in daily_summaries:
    print(f"Day: {s['day']}")
    print(f"Total Weight: {s['total_kgs']:.2f} kg")
    print(f"Total Value: £{s['total_value']:.2f}")
    print(f"Average Price: £{s['avg_price']:.2f}/kg")
    print("Top Species by Value:")
    for sp in s['top_species']:
        print(f" - {sp['Species']}: £{sp['Sum Of Amount']:.2f} ({sp['Sum of kgs']:.2f} kg, £{sp['Avg Price']:.2f}/kg)")
    print("-" * 30)

Code output

Day: 2nd
Total Weight: 39771.04 kg
Total Value: £226988.83
Average Price: £5.71/kg
Top Species by Value:
 - Mediterranean Octopus Pot Caught: £74077.78 (10288.58 kg, £7.20/kg)
 - MSC Hake: £69239.78 (13991.00 kg, £4.95/kg)
 - Pollack, Lythe : £21480.08 (5232.32 kg, £4.11/kg)
 - Monk Or Anglers: £15446.16 (1103.07 kg, £14.00/kg)
 - John Dory: £7916.85 (648.18 kg, £12.21/kg)
------------------------------
Day: 3rd
Total Weight: 17294.34 kg
Total Value: £94492.98
Average Price: £5.46/kg
Top Species by Value:
 - MSC Hake: £38297.46 (8174.90 kg, £4.68/kg)
 - Mediterranean Octopus Pot Caught: £23487.73 (3257.66 kg, £7.21/kg)
 - Monk Or Anglers: £7546.58 (542.85 kg, £13.90/kg)
 - Pollack, Lythe : £7480.94 (1834.10 kg, £4.08/kg)
 - John Dory: £5463.61 (623.41 kg, £8.76/kg)
------------------------------
Day: 4th
Total Weight: 7596.61 kg
Total Value: £40416.46
Average Price: £5.32/kg
Top Species by Value:
 - Mediterranean Octopus Pot Caught: £17240.85 (2385.11 kg, £7.23/kg)
 - MSC Hake: £14591.26 (3105.00 kg, £4.70/kg)
 - Pollack, Lythe : £3503.62 (815.98 kg, £4.29/kg)
 - Wings - Blonde: £861.23 (133.90 kg, £6.43/kg)
 - Gurnard and Latchet: £738.21 (182.28 kg, £4.05/kg)
------------------------------
Day: 5th
Total Weight: 14027.12 kg
Total Value: £111016.75
Average Price: £7.91/kg
Top Species by Value:
 - Dover Sole: £58603.87 (3450.62 kg, £16.98/kg)
 - Mediterranean Octopus Pot Caught: £21531.33 (2973.94 kg, £7.24/kg)
 - Monk Or Anglers: £8342.73 (635.26 kg, £13.13/kg)
 - Lemon Sole: £3520.90 (838.06 kg, £4.20/kg)
 - Wings - Blonde: £2059.59 (294.01 kg, £7.01/kg)
------------------------------

Python

import pandas as pd
from datetime import datetime

# Day of week for March 2nd to 5th 2026
dates = [datetime(2026, 3, 2), datetime(2026, 3, 3), datetime(2026, 3, 4), datetime(2026, 3, 5)]
day_names = [d.strftime("%A") for d in dates]
print(f"Days of the week: {day_names}")

# Species-specific tracking (Dover Sole, MSC Hake, Octopus)
key_tracking = ["Dover Sole", "MSC Hake", "Mediterranean Octopus Pot Caught", "Monk Or Anglers", "Pollack, Lythe "]
trends = {sp: [] for sp in key_tracking}

all_data = []

for day_label, file_path in files:
    df = pd.read_csv(file_path, skiprows=5)
    df = df[df['Species'].notnull()]
    df = df[~df['Species'].str.contains('Total|species', case=False, na=False)]
    df['Sum of kgs'] = pd.to_numeric(df['Sum of kgs'], errors='coerce')
    df['Sum Of Amount'] = pd.to_numeric(df['Sum Of Amount'], errors='coerce')
    df = df.dropna(subset=['Sum of kgs', 'Sum Of Amount'])
    
    species_summary = df.groupby('Species').agg({'Sum of kgs': 'sum', 'Sum Of Amount': 'sum'}).reset_index()
    species_summary['Avg Price'] = species_summary['Sum Of Amount'] / species_summary['Sum of kgs']
    
    for sp in key_tracking:
        row = species_summary[species_summary['Species'] == sp]
        if not row.empty:
            trends[sp].append({
                'day': day_label,
                'avg_price': row.iloc[0]['Avg Price'],
                'kgs': row.iloc[0]['Sum of kgs']
            })
        else:
            trends[sp].append({'day': day_label, 'avg_price': 0, 'kgs': 0})
            
    all_data.append(df)

full_week_df = pd.concat(all_data)
week_kgs = full_week_df['Sum of kgs'].sum()
week_value = full_week_df['Sum Of Amount'].sum()
week_avg = week_value / week_kgs

print(f"\nWeek Summary:")
print(f"Total Weight: {week_kgs:.2f} kg")
print(f"Total Value: £{week_value:.2f}")
print(f"Average Price: £{week_avg:.2f}/kg")

print("\nSpecific Species Trends:")
for sp, data in trends.items():
    print(f"{sp}:")
    for d in data:
        print(f"  {d['day']}: £{d['avg_price']:.2f}/kg ({d['kgs']:.2f} kg)")

Code output

Days of the week: ['Monday', 'Tuesday', 'Wednesday', 'Thursday']

Week Summary:
Total Weight: 78689.11 kg
Total Value: £472915.03
Average Price: £6.01/kg

Specific Species Trends:
Dover Sole:
  2nd: £18.24/kg (221.67 kg)
  3rd: £17.78/kg (135.14 kg)
  4th: £19.27/kg (26.58 kg)
  5th: £16.98/kg (3450.62 kg)
MSC Hake:
  2nd: £4.95/kg (13991.00 kg)
  3rd: £4.68/kg (8174.90 kg)
  4th: £4.70/kg (3105.00 kg)
  5th: £0.00/kg (0.00 kg)
Mediterranean Octopus Pot Caught:
  2nd: £7.20/kg (10288.58 kg)
  3rd: £7.21/kg (3257.66 kg)
  4th: £7.23/kg (2385.11 kg)
  5th: £7.24/kg (2973.94 kg)
Monk Or Anglers:
  2nd: £14.00/kg (1103.07 kg)
  3rd: £13.90/kg (542.85 kg)
  4th: £11.10/kg (8.90 kg)
  5th: £13.13/kg (635.26 kg)
Pollack, Lythe :
  2nd: £4.11/kg (5232.32 kg)
  3rd: £4.08/kg (1834.10 kg)
  4th: £4.29/kg (815.98 kg)
  5th: £4.15/kg (179.29 kg)

Monday, March 2nd

• Market Overview: A high-volume start to the week with over 39,771 kg of fish traded for a total value of £226,988.83. The average price per kilogram across all species was £5.71.

• Key Species:

  • Mediterranean Octopus (Pot Caught): The clear winner in value, bringing in £74,077.78 (over 10 tonnes at £7.20/kg).

  • MSC Hake: Dominant in volume with nearly 14 tonnes sold at an average of £4.95/kg.

  • Monkfish (Monk or Anglers): Fetched a high price of £14.00/kg.

• Trends: The market opened with strong volume, particularly in "high-bulk" species like Hake and Octopus, which together made up the majority of the day's turnover.

Tuesday, March 3rd

• Market Overview: Volume cooled significantly to 17,294 kg, with a total value of £94,492.98. The average price dipped slightly to £5.46/kg.

• Key Species:

  • MSC Hake: Overtook Octopus as the highest-value species for the day (£38,297.46), though the price dropped slightly to £4.68/kg.

  • John Dory: Saw a noticeable volume but at a lower average price of £8.76/kg compared to the previous day’s high-end premium.

• Trends: Mid-week cooling was evident as both volume and value were less than half of Monday’s peak. Prices for Hake remained under pressure due to consistent supply.

Wednesday, March 4th

• Market Overview: The quietest day of the week, with volume falling to 7,596 kg and a total value of £40,416.46. Average price held steady at £5.32/kg.

• Key Species:

  • Mediterranean Octopus: Remained the primary value driver (£17,240.85), with prices incredibly consistent at £7.23/kg.

  • Wings (Blonde): Emerged in the top five value species, fetching £6.43/kg.

• Trends: Despite the low volume, the "base" species like Octopus and Hake continued to provide the backbone of the auction's value.

Thursday, March 5th

• Market Overview: A "quality over quantity" day. While volume was moderate (14,027 kg), the total value spiked to £111,016.75 because of a surge in premium fish. This pushed the average price up to a weekly high of £7.91/kg.

• Key Species:

  • Dover Sole: A massive day for Sole, accounting for over £58,000 in value (nearly 3.5 tonnes). Prices remained strong at £16.98/kg.

  • Lemon Sole: Also saw a value boost, contributing £3,520.90 to the day's total.

• Trends: The arrival of significant quantities of Dover Sole completely shifted the market dynamics, driving the average price per kilo up by nearly 50% compared to previous days.

---

Weekly Summary

The week was characterised by a strong, high-volume opening followed by a mid-week lull, ending with a high-value flourish on Thursday.

• Total Weekly Volume: 78,689 kg

• Total Weekly Value: £472,915.03

• Average Weekly Price: £6.01/kg

Overall Trends:

1. Consistency in Octopus: The Mediterranean Octopus (Pot Caught) was the most reliable performer of the week, maintaining a price point between £7.20 and £7.24/kg regardless of volume fluctuations.

2. The "Sole" Surge: Thursday’s auction was transformed by the influx of Dover Sole, which proved that even with lower overall volume than Monday, high-value premium species can significantly bolster the market's bottom line.

3. Hake Stability: MSC Hake provided the most significant volume throughout the first three days, with prices remaining remarkably stable between £4.68 and £4.95/kg, proving its status as a staple of the auction.