Questions to LLMs

Learning ML with ISLP using Polars, scikit-learn, NumPy, pandas, seaborn, and matplotlib is a great combo. Below is a lightweight workflow you can adapt. Tell me what chapter/topic you are on and I’ll tailor examples.

• Which ISLP chapter/topic are you studying now?
• Do you prefer code-first walkthroughs or short conceptual summaries?
• Any dataset you want to use (ISLP datasets, your CSVs, or seaborn demos)?

1. Load and clean data with Polars (fast, expressive).
2. Convert to pandas/NumPy at the sklearn/seaborn boundary.
3. Build sklearn Pipelines with preprocessing + model + CV.
4. Plot diagnostics (convert to pandas for seaborn/matplotlib).
5. Wrap steps into small reusable functions.

Assume columns: TV, Radio, Newspaper, Sales.

import polars as pl
from sklearn.model_selection import train_test_split
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.pipeline import Pipeline
from sklearn.linear_model import LinearRegression
from sklearn.metrics import r2_score, mean_squared_error
import numpy as np

# Load
df_pl = pl.read_csv("Advertising.csv")

# Select features/target
num_cols = ["TV", "Radio", "Newspaper"]
cat_cols = []  # add any categorical cols here
target = "Sales"

# Convert to pandas for sklearn
df_pd = df_pl.to_pandas()

X = df_pd[num_cols + cat_cols]
y = df_pd[target]

# Train/test split
Xtr, Xte, ytr, yte = train_test_split(
    X, y, test_size=0.2, random_state=42
)

# Preprocess: scale numerics, one-hot categoricals
pre = ColumnTransformer(
    transformers=[
        ("num", StandardScaler(), num_cols),
        ("cat", OneHotEncoder(handle_unknown="ignore"), cat_cols),
    ],
    remainder="drop",
)

# Model pipeline
pipe = Pipeline(steps=[
    ("pre", pre),
    ("est", LinearRegression())
])

pipe.fit(Xtr, ytr)
pred = pipe.predict(Xte)

rmse = mean_squared_error(yte, pred, squared=False)
r2 = r2_score(yte, pred)

print(f"RMSE: {rmse:.3f}  R2: {r2:.3f}")

• Tell me the exact ISLP section or exercise. I can provide a matching solution.
• If you share a small sample of your dataset (cols/types), I’ll tailor code.
• We can add diagnostic plots (residuals, leverage, ROC, calibration) next.

Yes. You can download ISLP datasets over HTTP and load them directly into a Polars DataFrame. You can also handle zip files without writing to disk.

• This approach avoids writing to disk by using BytesIO and zipfile.
• You can pass Polars read_csv kwargs (e.g., has_header, dtypes, null_values).
• Some ISLP zips contain non-CSV text (e.g., space separated). If needed:
  • Try pl.read_csv(..., separator=" ") when the separator is a single space.

  • If the file uses irregular whitespace, fall back to pandas, then convert:

    df = pd.read_csv(f, delim_whitespace=True); pl.from_pandas(df)
    

• For Excel files, use pl.read_excel(...) Install extras:

pip install "polars[excel]"

• For lazy mode, save locally and use pl.scan_csv("file.csv") for out-of-core work.

Edit and run code on your Ubuntu (WSL) files with Spyder on Windows. Below are safe and practical options, from most recommended to least.

• Best if you want Ubuntu packages/env and to work directly in Linux paths.
• Windows 11 (WSLg):
  • Ensure WSLg is enabled (wsl –version shows a WSLg version).
  • In Ubuntu:
    • Create an env and install Spyder + libs, e.g. with conda:
      • conda create -n islp -y python=3.11 spyder numpy pandas polars \ scikit-learn seaborn matplotlib spyder-kernels
      • conda activate islp
      • spyder
    • Or with venv + pip:
      • python3 -m venv ~/.venvs/islp
      • source ~/.venvs/islp/bin/activate
      • pip install -U pip
      • pip install spyder spyder-kernels numpy pandas polars scikit-learn \ seaborn matplotlib
      • spyder
  • Spyder should open as a normal Windows window via WSLg.
• Windows 10 (no WSLg):
  • Install an X server (e.g., VcXsrv) and start it (disable access control).
  • In Ubuntu:

    • export DISPLAY=$(grep -m1 nameserver /etc/resolv.conf \

      awk '{print $2}'):0

    • Launch Spyder as above.

Pros:

• No path quirks, full Linux toolchain, correct file permissions.

Cons:

• Needs WSLg or an X server.

• Put your repo under Windows, e.g.:
  • C:\Users\you\projects\islp
• Open and edit in Windows Spyder normally.
• From Ubuntu, access the same files at:
  • /mnt/c/Users/you/projects/islp

Pros:

• Safe and fast; no cross-OS file corruption risk.
• Simple if you prefer Windows Spyder.

Cons:

• Your Python runtime is Windows unless you connect to a WSL kernel (below).

• In Spyder on Windows, open files directly under:

  \\wsl$\Ubuntu\home\your_user\your_project
  

• You can also map that path as a network drive in Windows Explorer.

Important cautions:

• Microsoft warns against modifying Linux files with Windows tools, as it can cause data loss or permission issues. Performance can also be worse.
• Prefer Options 1 or 2 unless you accept the risks.

• Edit in Windows Spyder, but run code in your Ubuntu Python environment.
• In Ubuntu (WSL), in your env:
  • pip/conda install spyder-kernels matching your Spyder major version.

  • Example:

    pip install "spyder-kernels>=2.4,<2.5"
    

  • Start a kernel:

    python -m spyder_kernels.console
    

  • Note the connection file path printed, e.g.:

    /home/you/.local/share/jupyter/runtime/kernel-XXXX.json
    

• In Windows Spyder:
  • Consoles > Connect to an existing kernel.

  • Browse to the JSON over:

    \\wsl$\Ubuntu\home\you\.local\share\jupyter\runtime\kernel-XXXX.json
    

  • Set the working directory in Spyder (or in code) to your project, e.g.:
    • Linux path in WSL: /home/you/your_project
    • Or Windows path mounted in WSL: /mnt/c/Users/you/projects/islp

Pros:

• Use Windows Spyder UI; code runs in Ubuntu with its packages.
• Avoids most file-permission issues if you keep sources on Windows (Option 2).

Cons:

• A bit of setup; relies on networking/ports. If connect fails:

  • Start the kernel with:

    python -m spyder_kernels.console --ip=0.0.0.0
    

  • Then reconnect using the updated JSON, or ensure localhost forwarding.

• For pure Ubuntu workflow: use Option 1 (Spyder inside WSL via WSLg).
• For Windows UI with shared files: use Option 2; optionally add Option 4 to run on Ubuntu’s Python.
• Avoid Option 3 unless necessary.

• Keep one canonical project location to avoid path confusion.
• If using git, store the repo on Windows (Option 2) to edit safely from both sides, or work entirely inside WSL (Option 1).
• For plotting in WSL:
  • WSLg: works out of the box.
  • X server: set DISPLAY as shown above.
• Match library versions across environments to avoid subtle differences.

Polars has no implicit index like pandas. You don't "set_index". Keep the column and use it explicitly (joins, filters, sorts). If you need a row number like pandas' RangeIndex, add it yourself.

• Add a monotonically increasing row id (RangeIndex-like):

  import polars as pl
  
  df = pl.DataFrame({"id": [3, 1, 2], "val": [10, 20, 30]})
  df = df.with_row_count(name="idx")  # idx: 0,1,2,...
  

• Treat a column as the key for alignment (instead of index-based alignment):

  dim = pl.DataFrame({"id": [1, 2, 3], "name": ["a", "b", "c"]})
  out = df.join(dim, on="id", how="left")
  

• Select/lookup by key values:

  row_2 = df.filter(pl.col("id") == 2)
  some = df.filter(pl.col("id").is_in([1, 3]))
  

• Keep a stable order by a "logical index" column:

  df_sorted = df.sort("id")
  

• Time-series operations use an explicit index_column parameter:

  ts = pl.datetime_range(
      low=pl.datetime(2024, 1, 1, 0, 0),
      high=pl.datetime(2024, 1, 1, 0, 3),
      interval="1m",
      eager=True,
  )
  tdf = pl.DataFrame({"ts": ts, "val": [1, 2, 3, 4]})
  
  # Window by time using the "ts" column as index
  out = (
      tdf.sort("ts")
         .group_by_dynamic(index_column="ts", every="2m")
         .agg(pl.col("val").sum().alias("sum_val"))
  )
  

• Asof/time-aware joins use a column too (not an index):

  left = pl.DataFrame({"ts": ts, "x": [1, 2, 3, 4]}).sort("ts")
  right = pl.DataFrame({"ts": ts, "y": [10, 20, 30, 40]}).sort("ts")
  joined = left.join_asof(right, on="ts")
  

• Pivot uses an index argument, but it stays a column (not an index):

  df = pl.DataFrame(
      {"id": [1, 1, 2, 2], "col": ["A", "B", "A", "B"], "val": [5, 6, 7, 8]}
  )
  pv = df.pivot(values="val", index="id", columns="col")
  # index="id" is just a column in the result
  

Convert to pandas and set it:

pdf = df.to_pandas()
pdf = pdf.set_index("id")

To install the ISLP package in your Anaconda distribution, you have a few options: