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.