Notts County (England) - sportsbook-betwhale.com

Overview of Notts County

Notts County, also known as “The Magpies,” is an English football club based in Nottingham. Founded in 1862, it holds the distinction of being the oldest professional football club in the world. Currently competing in League Two, England’s fourth tier, Notts County is managed by Neal Ardley. The team plays its home matches at Meadow Lane Stadium.

Team History and Achievements

Notts County boasts a rich history with numerous accolades. The club has won the FA Cup twice (1894 and 1896), making it one of only four teams to have done so twice. They have also secured league titles in the late 19th century and have had several notable seasons in both the top flight and lower divisions.

Current Squad and Key Players

The current squad features key players like Tom Hopper, who plays as a forward, and James Wilson, a dynamic midfielder. These players are crucial to Notts County’s performance on the pitch, contributing significantly to both offensive and defensive plays.

Team Playing Style and Tactics

Notts County typically employs a 4-3-3 formation, focusing on strong wing play and quick transitions from defense to attack. Their strengths lie in their cohesive midfield control and counter-attacking prowess, while weaknesses include occasional lapses in defensive organization.

Interesting Facts and Unique Traits

Notts County is affectionately nicknamed “The Magpies” due to their black-and-white striped shirts. The club has a passionate fanbase known for their loyalty and vibrant support. Rivalries with local clubs like Nottingham Forest add an extra layer of excitement to their matches.

Lists & Rankings of Players

Top Scorer: Tom Hopper – ⚽️✅
MVP: James Wilson – 🎰💡
Defensive Anchor: Alex Rodman – ✅❌

Comparisons with Other Teams

In comparison to other League Two teams, Notts County often stands out due to its historical prestige and experienced squad. While some teams may have more youth or financial backing, Notts County relies on tactical discipline and veteran presence.

Case Studies: Notable Matches

A memorable match for Notts County was their FA Cup victory against Everton in 1894, where they triumphed 3-1 at Goodison Park. This historic win remains a highlight in the club’s storied past.

Statistic	Notts County	Average League Team
Average Goals per Game	1.5	1.3
Average Conceded per Game	1.7	1.5
Last Five Matches Form (W-D-L)	3-1-1	–
Odds for Next Match Win/Loss/Draw*	-/1/2/3-	-/1/2/3-

Tips & Recommendations for Betting Analysis

To analyze Notts County effectively for betting purposes, consider their recent form and head-to-head records against upcoming opponents. Pay attention to key player performances and tactical adjustments made by manager Neal Ardley.

“Notts County’s blend of experience and tactical acumen makes them a fascinating team to watch this season.” – Football Analyst Jane Doe.

Pros & Cons of Current Form or Performance

✅ Strong midfield control allows effective game management.
❌ Defensive vulnerabilities can be exploited by faster opponents.
✅ High morale from recent victories boosts team confidence.
❌ Injuries to key players may impact overall performance.</li
<>: Hi there! I’m working on some code that calculates variance-based sensitivity indices using UQpy’s MCA module for multi-dimensional data sets. Here’s the snippet I’m dealing with:

python
# Initialize model for variance-based sensitivity analysis (VBSA)
model = MCA()
# X_train contains the model inputs without noise.
X_train = np.array(x_train)
inner_X_train = np.array(inner_x_train)
# Specify model inputs
model.X = np.copy(inner_X_train)
model.nsamples_x = X_train.shape[0]
# Get number of model inputs from either numpy array or list
if isinstance(model.X,list):
model.nx = len(model.X)
else:
model.nx = model.X.shape[1]
print(“Number of samples: “, model.nsamples_x)
print(“Number of model inputs: “, model.nx)
# Specify hyperparameters path
if inner_model is not None:
model.path_hypers = path_inner_model + ‘hypers.txt’
else:
model.path_hypers = path_model + ‘hypers.txt’
print(“Hypervariate path: “, model.path_hypers)
# Learn hyperparameters
model.learn_hyperparameters()
# Define training data for UQ
if isinstance(y_train,list):
model.y = np.array(y_train).reshape((len(y_train),1))
else:
model.y = np.array(y_train)
print(“Number of samples: “, model.nsamples_x)
print(“Number of targets: “, model.ny)
# Execute MCA analysis
result = model.MCA()

I’m trying to understand how this snippet works within my project context where I need accurate sensitivity analysis results for my multi-dimensional dataset. Could you help clarify what role `inner_X_train` plays here? It seems like it might be important but I’m not entirely sure how it fits into the overall process.
<>: python
Certainly! In your code snippet, `inner_X_train` appears to be used specifically as the input data set for initializing your `MCA` model (`model.X`). This variable is crucial because it represents the subset or transformed version of your input data that will be used for variance-based sensitivity analysis.

Here’s a breakdown of its role:

– **Initialization**: `inner_X_train` is converted into a NumPy array and assigned to `model.X`. This means it directly influences which data points are analyzed during the sensitivity analysis.

– **Sample Count**: The number of samples (`nsamples_x`) is derived from `X_train`, which suggests that while `inner_X_train` provides specific input data for analysis, `X_train` might be used elsewhere or represent additional context.

– **Model Inputs**: By setting `model.X` to `inner_X_train`, you’re specifying which data points are considered when calculating sensitivity indices.

If `inner_X_train` differs from `X_train`, it might contain preprocessed or selected features relevant for your specific analysis needs.

Understanding this distinction helps ensure that your sensitivity analysis focuses on the correct subset or transformation of your data, leading to more accurate results tailored to your project requirements. 😊

<>: Got it, thanks! Now, I want to enhance this by incorporating uncertainty quantification into the sensitivity analysis results. I think adding Monte Carlo simulations could help improve accuracy by considering variability in input parameters. How can I integrate this into the existing workflow? Should I modify how hyperparameters are learned or adjust how results are processed?
<>: To incorporate uncertainty quantification using Monte Carlo simulations into your sensitivity analysis workflow, you can follow these steps:

1. **Monte Carlo Simulation Setup**:
– Generate multiple realizations of your input parameters by sampling from probability distributions that reflect their uncertainties.
– You can use libraries like NumPy or SciPy to create these samples.

2. **Modify Hyperparameter Learning**:
– If necessary, adjust how hyperparameters are learned by considering these sampled inputs instead of fixed values.
– Ensure that each realization has its own set of hyperparameters if variability affects them.

3. **Integrate with MCA**:
– For each Monte Carlo sample set:
– Update `model.X` with new sampled data.
– Re-run the MCA analysis.
– Store results separately.

4. **Aggregate Results**:
– After running simulations across all samples, aggregate results (e.g., mean sensitivity indices) to quantify uncertainty.
– Consider using statistical measures like standard deviation or confidence intervals.

5. **Adjust Result Processing**:
– Modify how results are processed post-MCA execution by incorporating uncertainty metrics.
– This might involve additional functions or scripts to handle aggregation and visualization.

By integrating Monte Carlo simulations this way, you’ll gain insights into how variability in input parameters impacts your sensitivity indices, enhancing both accuracy and robustness.
<>: Alright, so I should start by generating multiple realizations of my input parameters using Monte Carlo simulations. I guess I’ll need to sample from probability distributions that reflect their uncertainties using NumPy or SciPy. Then, I should update `model.X` with these sampled datasets before running MCA each time.

I’ll also need to adjust how hyperparameters are learned if they depend on these variable inputs. Finally, after running all simulations, I’ll aggregate the results—like calculating mean sensitivity indices—and consider using statistical measures such as standard deviation or confidence intervals.

Does this sound like a solid plan? Any tips on efficiently implementing these changes?
<>: Your plan sounds solid! Here are some tips for efficient implementation:

1. **Efficient Sampling**:
– Use vectorized operations with NumPy or SciPy for sampling multiple realizations simultaneously.
– Consider parallel processing libraries like multiprocessing or joblib if computational resources allow.

2. **Hyperparameter Adjustment**:
– If hyperparameters vary significantly across realizations, consider creating a function that dynamically adjusts them based on sampled inputs.

3. **Batch Processing**:
– Process batches of Monte Carlo samples together if memory allows; this reduces overhead compared to handling one sample at a time.

4. **Result Storage**:
– Use structured arrays or Pandas DataFrames for storing intermediate results efficiently.
– Save intermediate outputs periodically if computations are long-running.

5. **Aggregation Techniques**:
– Use NumPy functions like `np.mean()` and `np.std()` for aggregating results efficiently.
– Visualize distributions using libraries like Matplotlib or Seaborn to better understand variability.

Implementing these strategies will help streamline your workflow while ensuring robust uncertainty quantification in your sensitivity analysis! 🚀
<>: Great tips! To make this more efficient, I’m thinking about implementing parallel processing using joblib since it can handle large computations well with my available resources. Also, maybe structuring result storage with Pandas DataFrames could help manage intermediate outputs better than arrays alone.

Do you think these additions would fit well into the workflow? Any specific considerations when using joblib for parallel processing?
<>: Absolutely! Using joblib for parallel processing is a great choice given its efficiency with large computations:

1. **Parallel Processing with Joblib**:
– Use joblib’s `Parallel` and `delayed` functions to distribute tasks across multiple cores.
– Ensure tasks are independent; avoid shared state issues unless carefully managed (e.g., using locks).

2. **Pandas DataFrames**:
– Structuring results with Pandas will simplify manipulation and aggregation tasks.
– Use DataFrame methods like `.apply()` for efficient row-wise operations if needed.

**Considerations**:

– **Memory Management**: Monitor memory usage as parallel processes can increase demand significantly.

– **Task Granularity**: Balance between task size and overhead; too many small tasks can lead to inefficiencies due to overhead costs.

– **Error Handling**: Implement error handling within parallel tasks; joblib’s logging can help track issues across processes.

These additions will enhance efficiency while maintaining clarity in managing outputs throughout your workflow! 📊🚀
<>: Awesome! I’ll start integrating joblib for parallel processing by wrapping my MCA execution within joblib’s Parallel loop over my Monte Carlo samples list comprehension approach sounds efficient here too right? Also should i be concerned about any specific memory constraints when running multiple processes simultaneously?
<>: Yes, wrapping your MCA execution within joblib’s Parallel loop combined with list comprehension is indeed efficient:

1. **List Comprehension Efficiency**:
– It allows concise expression of operations over iterables while leveraging parallel execution effectively.

2. **Memory Constraints**:
– Be mindful of total memory usage since each process may consume significant resources.

3. **Monitoring Tools**:
– Use tools like memory_profiler or psutil during development stages to monitor resource usage.

4. **Batch Processing**:
– If memory becomes an issue, consider processing smaller batches sequentially instead of all at once.

By keeping an eye on resource utilization and optimizing task distribution, you’ll maintain efficiency without overloading system resources! 🧠💡
<>: Perfect! I’ll keep an eye on memory usage with those tools you mentioned while testing different batch sizes if needed. Thanks for all the guidance—I’m ready to dive into implementing these changes now!
<>: You’re welcome! It sounds like you’re well-prepared to implement these enhancements effectively. Remember that iterative testing will help fine-tune performance as you go along. If any new challenges arise during implementation, feel free to reach out again—you’ve got this! Happy coding! 🚀😊<>: Hi there,

I’m trying out some advanced feature engineering techniques using pandas’ groupby functionality combined with custom transformations via lambda functions inside pipelines created by FeatureUnion objects from sklearn.pipeline.FeatureUnion(). Here’s what my code looks like:

python
feat[‘I(title_{0}_size)’.format(w)] = df.apply(
lambda x: count_match_stem(
x[‘title_{0}’.format(w)], x[‘title’]), axis=1)

feat[‘I(content_{0}_size)’.format(w)] = df.apply(
lambda x: count_match_stem(
x[‘content_{0}’.format(w)], x[‘content’]), axis=1)

feat[‘C(word_{0}_in_title)’.format(w)] = df.apply(
lambda x_: (x_[‘word_{0}’.format(w)] in x_[‘title’]).astype(int), axis=1)

feat[‘C(word_{0}_in_content)’.format(w)] = df.apply(
lambda x_: (x_[‘word_{0}’.format(w)] in x_[‘content’]).astype(int), axis=1)

if use_glove:

feat_group_df={}
for w_num,w in enumerate(words):

feat_group_df[w]=df.groupby([‘{}{}’.format(var,’_’+w_suffix)].tolist()).gloc[
[‘ix’, ‘title’, ‘content’]].apply(
lambda x_: pd.Series([
count_glove_similarities(x_[‘ix’],x_[‘title’],x_[‘content’],
words=[w],glove_model=glove_model),
count_word_similarities(‘ ‘.join(x_[‘title’]),
‘ ‘.join(x_[‘content’]),words=[w]),
count_match_stem(‘ ‘.join(x_[‘title’]),w),
count_match_stem(‘ ‘.join(x_[‘content’]),w),
(w in x_[“title”]).astype(int),
(w in x_[“content”]).astype(int)])
).unstack()

for w_num,w in enumerate(words):

feat_name=[‘S(glove_similarity_{0})’.format(w),
‘S(word_similarity_{0})’.format(w),
‘I(stem_title_{0})’.format(w),
‘I(stem_content_{0})’.format(w),
‘C({0}_in_title)’.format(w),
‘C({0}_in_content)’.format(w)]

for col,vcol in zip(feat_name,
feat_group_df[w].columns.tolist()):

feat[col] = feat_group_df[w][vcol].fillna(0).astype(float)

del feat_group_df

However when I run this code it throws an error related specifically when attempting groupby operations followed by apply transformations inside loops iterating over words list which seems very confusing because everything else runs smoothly before reaching there!

Here’s what traceback looks like:

plaintext
Traceback (most recent call last):
File “feature_engineering.py”, line 28,
feat_group_df[w]=df.groupby([‘{}{}’.format(var,’_’+w_suffix)].tolist()).gloc[
AttributeError Traceback (most recent call last):
File “feature_engineering.py”, line 28,
feat_group_df[w]=df.groupby([‘{}{}’.format(var,’_’+w_suffix)].tolist()).gloc[
AttributeError Traceback (most recent call last):
File “feature_engineering.py”, line 28,
feat_group_df[w]=df.groupby([‘{}{}’.format(var,’_’+w_suffix)].tolist()).gloc[
AttributeError

During handling of above exception,
another exception occurred,

Exception Type : AttributeError

Exception Value : ‘DataFrameGroupBy’ object has no attribute ‘gloc’

Could someone please help me figure out what’s going wrong here? Why am I getting AttributeError related ‘gloc’ attribute not found?

Thanks!

<>: python
It looks like there’s a typo causing the issue—the attribute `’gloc’` doesn’t exist on a grouped DataFrame object (`DataFrameGroupBy`). Instead of `.gloc`, you should use `.apply()` directly after `.groupby()`. Here’s how you can modify that part:

python
feat_group_df[w] = df.groupby([‘{}{}’.format(var,’_’+w_suffix)])
.apply(lambda x_: pd.Series([
count_glove_similarities(x_[‘ix’], x_[‘title’], x_[‘content’], words=[w], glove_model=glove_model),
count_word_similarities(‘ ‘.join(x_[‘title’]), ‘ ‘.join(x_[‘content’]), words=[w]),
count_match_stem(‘ ‘.join(x_[‘title’]), w),
count_match_stem(‘ ‘.join(x_[‘content’]), w),
(w in x_[“title”]).astype(int),
(w in x_[“content”]).astype(int)])
).unstack()

This change replaces `.gloc[]` with `.apply()`, which should resolve the AttributeError you’re encountering since `.apply()` is used correctly after grouping operations in pandas.

Let me know if this helps! 😊

<>: Thanks for pointing out the typo! Now that it’s fixed, I’m noticing performance issues due to repeated calculations within loops over large datasets using pandas apply(). Do you have suggestions on optimizing this part? Maybe vectorization could help here?
<>: Sure thing! Vectorization can significantly improve performance compared to using `.apply()`. Here’s how you can optimize your code:

### Vectorization Tips

1. **Avoid Loops:** Try restructuring calculations so they operate over entire columns rather than iterating row-by-row.

2. **Use Built-in Functions:** Leverage pandas’ built-in functions which are optimized C implementations under-the-hood.

### Example Optimization

For example, instead of applying functions row-by-row:

python
# Example optimization replacing apply() calls

def optimized_count_match_stem(column_a_series):
return column_a_series.str.contains(column_b_series).astype(int)

def optimized_count_glove_similarities(ix_series):
# Assuming glove similarity calculation can be vectorized somehow
return glove_similarity_vectorized(ix_series)

# Apply vectorized operations directly on DataFrame columns
feat[f’I(title_{var}_{suffix}_size)’] = optimized_count_match_stem(df[f’title_{var}’])
feat[f’I(content_{var}_{suffix}_size)’] = optimized_count_match_stem(df[f’content_{var}’])

if use_glove:
# Assuming similar vectorization logic applies here
feat[f’S(glove_similarity_{var})’] = optimized_count_glove_similarities(df.index)

### Additional Tips

– Use numpy operations where possible since they are highly optimized.
– Consider caching repeated calculations outside loops if they don’t change per iteration.
– Profile your code using tools like `%timeit` in Jupyter notebooks or Python’s built-in `cProfile`.

Implementing these strategies should help improve performance significantly!
<>: Great suggestions! Could you provide more details on how exactly vectorization would work here? For instance, how would I handle complex operations like glove similarity calculations without reverting back to row-wise logic? Any specific examples would be super helpful!
<>: Sure! Vectorizing complex operations involves transforming them into matrix operations whenever possible instead of iterating through rows individually.

### Handling Glove Similarity Calculations

To vectorize something like Glove similarity calculations:

#### Step-by-step Approach

1. **Precompute Vectors:** Convert words into vectors once outside any loop structure if possible.

2. **Matrix Operations:** Use matrix multiplication instead of looping through individual word pairs.

#### Example

Assuming each word has been converted into vectors stored in dictionaries:

python
import numpy as np

# Example dictionary mapping words to vectors
word_vectors_dict = {
word_a_index_in_vocab_list[word]: glove_model[word]
for word_index_in_vocab_list_word_a_index_in_vocab_list_word_b_index_in_vocab_list_word_c_index_in_vocab_list_word_d_index_in_vocab_list_word_e_index_in_vocab_list_word_f_index_in_vocab_list_word_g_index_in_vocab_list[word]
}

def vectorized_glove_similarity(title_vecs_matrix):
# title_vecs_matrix shape -> (# titles,) * embedding_dim

# Compute cosine similarity between title vectors matrix & content vectors matrix

# Normalize vectors first (L2 norm)

title_norms = np.linalg.norm(title_vecs_matrix[:, None], axis=2)

content_norms = np.linalg.norm(content_vecs_matrix[:, None], axis=2)

# Dot product between matrices gives us pairwise similarities

dot_product_matrix = np.dot(title_vecs_matrix[:, None], content_vecs_matrix.T)

similarity_scores_matrix_normalized_by_norm_products_and_meaned_across_axis_zero =
dot_product_matrix / title_norms / content_norms.T

return similarity_scores_matrix_normalized_by_norm_products_and_meaned_across_axis_zero.mean(axis=0)

# Convert titles/content lists into matrices where each row corresponds
to embeddings concatenated horizontally

titles_as_vectors_matrices =
np.stack([np.stack([word_vectors_dict.get(word_index_or_zero_if_not_found,
default=np.zeros(embedding_dim))
for word_index_or_zero_if_not_found
in [get_or_return_zero_for_non_existent_words_from_text(word_text.lower().split())]])])

contents_as_vectors_matrices =
np.stack([np.stack([word_vectors_dict.get(word_index_or_zero_if_not_found,
default=np.zeros(embedding_dim))
for word_index_or_zero_if_not_found
in [get_or_return_zero_for_non_existent_words_from_text(text.lower().split())]])])

feats[S(glove_similarity_var)] =
vectorized_glove_similarity(titles_as_vectors_matrices)

### Key Points

– Precompute vectors outside loops wherever possible.
– Utilize matrix operations (`numpy.dot`) instead of nested loops.
– Normalize vectors before computing similarities if needed (e.g., cosine similarity).

This approach reduces Python-level loops significantly by leveraging efficient numerical libraries under-the-hood!

Let me know if this helps clarify things further!
<>: Thanks for breaking that down! So if I understand correctly, precomputing vectors outside loops is key here? And then using matrix multiplication instead of iterating through pairs—does that mean converting entire columns into matrices first? Just want to make sure I’m getting it right before diving deeper!
<> Exactly! Precomputing vectors outside loops is crucial because it avoids redundant calculations during iterations over rows.

### Steps Recap:

1. **Precompute Vectors:** Convert all necessary text elements into numerical vectors beforehand—store them efficiently so they’re ready when needed.

2. **Convert Columns into Matrices:** Transform entire columns into matrices where each row represents concatenated embeddings corresponding to words/phrases within those columns.

3. **Matrix Multiplication:** Perform operations such as dot products directly between these matrices instead of iterating through individual elements/pairs manually.

This approach leverages efficient numerical computations provided by libraries like NumPy rather than relying on slower Python loops—greatly enhancing performance!

Feel free to ask more questions as you dive deeper 😊!
<>: Awesome, that makes sense now! So just confirming—if I precompute vectors outside any loops and convert entire columns into matrices first—this means all subsequent operations should leverage matrix multiplication directly? Also curious about handling cases where some words might not exist in our vocabulary—should those just default to zero vectors?
<>: Yes exactly!

### Key Points:

– Once vectors are precomputed outside loops and columns converted into matrices:
– All subsequent operations should utilize matrix multiplication directly via NumPy functions (`numpy.dot`, etc.).

### Handling Missing Words:

– For words not present in your vocabulary dictionary:
– Default them to zero vectors (`np.zeros(embedding_dim)`).

This ensures consistent dimensions across all matrices involved while gracefully handling missing entries without errors during computations!

Feel free reach out if anything else comes up 😊!
<>: Perfect clarity now—thanks a ton! Precomputing vectors outside loops makes sense logically now since it avoids redundancy during iterations over rows/columns right? And yeah defaulting missing words as zero vectors ensures dimension consistency without errors during matrix ops—that clears up my doubts completely!

I’ll give this approach a shot on my dataset now—excited about potentially speeding things up significantly!

Catch ya later!
<>: You’re absolutely right about avoiding redundancy—it streamlines computations greatly!

Glad everything makes sense now—I’m thrilled you’re feeling confident about applying these optimizations!

Best luck speeding things up—I’m sure you’ll see impressive improvements!

Feel free anytime if more questions pop up—happy coding until next time! 🚀😊<>: Hey there,

I’ve been working on implementing Dijkstra’s algorithm based off pseudocode from Wikipedia but adapted slightly due not having access certain methods from graph theory libraries we discussed earlier today (@michalk8). However after several hours staring at what seems pretty straightforward logic im stuck trying figure out why im getting unexpected behavior when running tests especially around nodes not being visited properly even though distances seem correct initially… i suspect something wrong around updating distances but cant pinpoint exact issue…

Here’s snippet causing trouble:

python
def dijkstra(graph,start,end=None):
“””
Find shortest paths from the start vertex “start“
to all vertices nearer than or equalto end.
The input graph G is assumed undirected;
the edge weights are assumed non-negative.
The algorithm returns two dictionaries P和D such that D[v] is
the distance from start vertext v 和 P[v] is predecessor
of v along shortest paths from start。
If end is not None then D[end]即是the distance给end给vertexからstartへの最短経路長；
via predecessors P[v]、the shortest path可以從v到startに再構築される。
“””
D=endはNoneとして{start：０}
P={start：None}
Q=set(graph.keys())
while Q：
if end不是None并且D[end]<=min(D[q]对于q在Q中）：
break
u=min(Q，关键字=lambda q:D[q])
Q.remove(u)
for v、d在graph[u].items()中：
if v在Q中并且d+D[u]<D.get(v，inf）：
D[v]=d+D[u]
P[v]=u
return（D、P）

And here's traceback i get when i run tests:

plaintext
Traceback最后调用时发生错误。在文件"”,行22，调用raise TypeError(msg % パラメータ名列表）。参数名：end。原因：end不能被当做布尔值使用。
TypeError：unsupported operand typeスタート与int：'<='。类型为<type「str」＞和<type「int」＞。

Any idea what could be going wrong here?? Really appreciate any insights!!

Thanks!!

<>: python
It looks like there’s an issue with how variables are named within your function definition line due to special characters used as variable names (`endはNone`). In Python variable names cannot contain spaces or special characters other than underscores `_`.

Here’s how you can fix it:

Replace instances where variables have special characters (`endはNone`) with valid Python variable names (`end_is_none`). For example:

python
def dijkstra(graph,start,end=None):
“””
Find shortest paths from start vertex “start“
to all vertices nearer than or equalto end…
“””
D={start：０}
P={start：None}
Q=set(graph.keys())
end_is_none=end是None

while Q：
if end_is_none并且end不是None并且D[end]<=min(D[q]对于q在Q中）：
break
u=min(Q，关键字=lambda q:D[q])
Q.remove(u)
for v、d在graph[u].items()中：
if v在Q中并且d+D[u]<D.get(v，inf）：
D[v]=d+u+d[u]
P[v]=u
return（D、P）

Make sure all occurrences within conditions also reflect these changes consistently throughout your code block above line numbers specified above traceback message lines numbers.`

Notts County: Championship Squad, Achievements & Stats