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Data Analysis in Jupyter

Expert guidance for data analysis, visualization, and Jupyter Notebook development with pandas, matplotlib, seaborn, and numpy.

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Buy it · $99 Read it before you buy $99 Written by mindrally · unverified publisher
Context cost
1.1k tokensestimated from the bundle, loaded when it triggers
Bundle
1 file · 4.6 kBtext throughout, nothing executable
Licence
Apache-2.0paid listing
Last change
—no release on file
Servers it uses
Noneruns standalone

What it does

Expert guidance for data analysis, visualization, and Jupyter Notebook development with pandas, matplotlib, seaborn, and numpy.

Installed, it changes the agent in these ways.

What this skill changes about the agent is not written down here yet. The listing was collected from its source, and the description is in its own SKILL.md.

Expertise

Domain judgement the base model does not have.

pandasjupytermatplotlibnumpy
Filed under

Data Science & ML

The skill itself

This is the whole product. A skill is instructions the model reads, so there is nothing behind the listing you cannot see first — the front matter loads with every session, and the body below it loads when the skill triggers.

SKILL.md4.6 kB · 141 lines
--- name: data-analysis-jupyter description: Expert guidance for data analysis, visualization, and Jupyter Notebook development with pandas, matplotlib, seaborn, and numpy. ---
6# Data Analysis and Jupyter Notebook Development
7
8You are an expert in data analysis, visualization, and Jupyter Notebook development, with a focus on pandas, matplotlib, seaborn, and numpy.
9
10## Key Principles
11
12- Write concise, technical responses with accurate Python examples
13- Prioritize readability and reproducibility in data analysis workflows
14- Favor functional programming approaches; minimize class-based solutions
15- Prefer vectorized operations over explicit loops for better performance
16- Employ descriptive variable nomenclature reflecting data content
17- Follow PEP 8 style guidelines for Python code
18
19## Data Analysis and Manipulation
20
21- Leverage pandas for data manipulation and analytical tasks
22- Prefer method chaining for data transformations when possible
23- Use loc and iloc for explicit data selection
24- Utilize groupby operations for efficient data aggregation
25- Handle datetime data with proper parsing and timezone awareness
26
27```python
28# Example method chaining pattern
29result = (
30 df
31 .query("column_a > 0")
32 .assign(new_col=lambda x: x["col_b"] * 2)
33 .groupby("category")
34 .agg({"value": ["mean", "sum"]})
35 .reset_index()
36)
37```
38
39## Visualization Standards
40
41- Use matplotlib for low-level plotting control and customization
42- Use seaborn for statistical visualizations and aesthetically pleasing defaults
43- Craft plots with informative labels, titles, and legends
44- Apply accessible color schemes considering color-blindness
45- Set appropriate figure sizes for the output medium
46
47```python
48# Example visualization pattern
49fig, ax = plt.subplots(figsize=(10, 6))
50sns.barplot(data=df, x="category", y="value", ax=ax)
51ax.set_title("Descriptive Title")
52ax.set_xlabel("Category Label")
53ax.set_ylabel("Value Label")
54plt.tight_layout()
55```
56
57## Jupyter Notebook Practices
58
59- Structure notebooks with markdown section headers
60- Maintain meaningful cell execution order ensuring reproducibility
61- Document analysis steps through explanatory markdown cells
62- Keep code cells focused and modular
63- Use magic commands like %matplotlib inline for inline plotting
64- Restart kernel and run all before sharing to verify reproducibility
65
66## NumPy Best Practices
67
68- Use broadcasting for element-wise operations
69- Leverage array slicing and fancy indexing
70- Apply appropriate dtypes for memory efficiency
71- Use np.where for conditional operations
72- Implement proper random state handling for reproducibility
73
74```python
75# Example numpy patterns
76np.random.seed(42) # For reproducibility
77mask = np.where(arr > threshold, 1, 0)
78normalized = (arr - arr.mean()) / arr.std()
79```
80
81## Error Handling and Validation
82
83- Implement data quality checks at analysis start
84- Address missing data via imputation, removal, or flagging
85- Use try-except blocks for error-prone operations
86- Validate data types and value ranges
87- Assert expected shapes and column presence
88
89```python
90# Example validation pattern
91assert df.shape[0] > 0, "DataFrame is empty"
92assert "required_column" in df.columns, "Missing required column"
93df["date"] = pd.to_datetime(df["date"], errors="coerce")
94```
95
96## Performance Optimization
97
98- Employ vectorized pandas and numpy operations
99- Utilize efficient data structures (categorical types for low-cardinality columns)
100- Consider dask for larger-than-memory datasets
101- Profile code to identify bottlenecks using %timeit and %prun
102- Use appropriate chunk sizes for file reading
103
104```python
105# Example categorical optimization
106df["category"] = df["category"].astype("category")
107
108# Chunked reading for large files
109chunks = pd.read_csv("large_file.csv", chunksize=10000)
110result = pd.concat([process(chunk) for chunk in chunks])
111```
112
113## Statistical Analysis
114
115- Use scipy.stats for statistical tests
116- Implement proper hypothesis testing workflows
117- Calculate confidence intervals correctly
118- Apply appropriate statistical tests for data types
119- Visualize distributions before applying parametric tests
120
121## Dependencies
122
123- pandas
124- numpy
125- matplotlib
126- seaborn
127- jupyter
128- scikit-learn
129- scipy
130
131## Key Conventions
132
1331. Begin analysis with exploratory data analysis (EDA)
1342. Document assumptions and data quality issues
1353. Use consistent naming conventions throughout notebooks
1364. Save intermediate results for long-running computations
1375. Include data sources and timestamps in notebooks
1386. Export clean data to appropriate formats (parquet, csv)
139
140Refer to pandas, numpy, and matplotlib documentation for best practices and up-to-date APIs.
141
In the file
SKILL.md616 words
Files1
LicenceApache-2.0
Why you can read it

Nothing in a skill executes. The client loads the text and the model follows it, so a skill can be audited the way a runbook is — by reading it.

What it costs in context

Skills are not billed by the call. They are paid for in context: every token the instructions occupy is a token your code, your diff and your conversation cannot use. Here is what this one takes and when it takes it.

≈40
always loaded
The name and description, so the model knows the skill exists and when to reach for it.
1,110
on trigger
The instruction body, read only when the skill fires.
0.57%
of a 200k window
Ten skills this size would take about 6% of the window before you open a file.
050k100k150k200k context window

1.1k tokens, estimated from the bundle at four bytes to the token, held for the rest of the session once it triggers. Small enough to keep loaded permanently without thinking about it.

Servers bill, skills cost

A server charges by the month. A skill charges once per session, in context, and then keeps charging it for as long as the session lives.

Before and after

The same question, put to the same model twice: once as it comes, and once with these instructions loaded.

No worked example has been published for this skill yet.

Adoption
Installsnone yet
Ratingno reviews yet

The procedure it runs

The procedure has not been published here. It is in the skill’s own SKILL.md, which its author has not sent to the marketplace yet.

Prose, not code

These steps are written for a model to follow, not executed by a runtime. It can still be told to skip one, and it will say so when it does.

Servers it uses

None. This skill calls no MCP servers at all.

Everything it needs is in the instructions, so it works in a project with nothing connected — the model reads the file and changes how it works with what it can already reach.

It writes no files and reaches no network. All it changes is how the model reasons and writes.

What it asks for
Writes filesno
Network accessno

Read from the allowed-tools line of this skill’s own SKILL.md. A skill grants no permissions of its own — it can only ask for tools your client already has.

What it will not do

Every skill is narrow, and the useful ones say where they stop. These are the jobs this one is the wrong tool for.

What this skill is not for has not been published here. Nothing is implied by that: it is a section the author has not filled in.

What is in the bundle

1 file, 4.6 kB on disk. A bundle is text throughout: the instructions the model reads, plus the templates it fills in.

  • SKILL.md4.6 kB
What is not in it

No dependencies and nothing executable: a skill is text the agent reads, so the bundle is 1 file you can review in full before installing. The Apache-2.0 licence covers the templates and examples as well as the instructions.

Install

Installing copies the bundle into your project. Nothing runs at install time — the files sit on disk until the model reads them.

$99 once
Data Analysis in Jupyter · Apache-2.0 · mindrally
one-time
Price$99 once
LicenceApache-2.0 — the author’s, unchanged by this purchase
Paid throughStripe, once, on the card you add at the checkout
Keeps workingfor good — the files are yours once they are on disk
Updatesevery update its author ships, delivered through this account

You can read the whole bundle before paying — the SKILL.md above is the product, not a preview of it. What the money buys is the delivery: the folder packaged and handed to your machine by key, every update its author ships, and our support if it does not do what this listing says. The terms of use are Apache-2.0, set by the author and unchanged by buying it here.

Sign in to buyCreate an account

Payment runs through Stripe, on a page like this one rather than a redirect. Once there is an account it joins the same mcprush invoice as everything else you run, so there is never a second card to enter.

Which clients pick it up on their own

A skill is a folder of text. A client with a skills folder reads it without being told; everywhere else the same text works, it is just handed to the model rather than found.

Claude Code.claude/skills/
Claude Desktop
ChatGPT
Cursor.cursor/skills/
VS Code.github/skills/
Codex CLI.agents/skills/
Gemini CLI.gemini/skills/
Grok.grok/skills/
Zed.agents/skills/
Windsurf.windsurf/skills/
Agent SDK.claude/skills/
HTTP / API
This release
Versionnot versioned
Publishedno release date on file
Price$99
Referencemindrally/data-analysis-in-jupyter

Versions

Its author publishes no version number, so there is nothing here to pin to: what you install is the folder as it stands today. Instructions change more often than APIs do — a skill can be rewritten entirely without anything it depends on moving.

v
  • No earlier releases have been published to the marketplace.
Pinning

Nothing to pin to: this skill carries no version number of its own. What you install is what the folder holds on the day you install it.

Reviews

—no reviews yet · no installs yet

Nobody has reviewed this skill yet. The rating is the mean of the reviews written here, so there is none until somebody writes the first.

Who can post

Only accounts that have had the skill installed for fourteen days, so a review is written after living with it rather than after reading it. Publishers may reply once.

Who wrote it

MI
mindrally

Publishes on mcprush.

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