LoopyLuci Python Data Science Patterns
Core data science patterns with pandas, numpy, matplotlib, and scikit-learn.
Pandas Workflow
import pandas as pd
import numpy as np
# Load with type optimization
df = pd.read_csv("data.csv",
dtype={"category_col": "category"},
parse_dates=["date_col"],
usecols=lambda c: c != "unnecessary_column")
# Quick profile
df.info() # dtypes, non-null, memory
df.describe() # stats
df.isnull().sum() # missing values
df.nunique() # unique counts
# Filtering (use .query for readability)
df_filtered = df.query("age > 25 and city == 'New York'")
# Group-aggregate
result = (df.groupby("category", as_index=False)
.agg(
total=("price", "sum"),
avg=("price", "mean"),
count=("id", "nunique"),
std=("price", "std"),
))
NumPy Techniques
# Vectorized operations (always prefer over loops)
arr = np.random.randn(1000000)
result = np.where(arr > 0, arr * 2, arr / 2) # fast C-level
# Broadcasting
matrix = np.random.randn(100, 50)
row_mean = matrix.mean(axis=1, keepdims=True)
centered = matrix - row_mean # broadcasts automatically
# Efficient reductions
np.add.reduceat(arr, indices) # segmented sum
np.cumsum(arr) # cumulative sum
Visualization Patterns
import matplotlib.pyplot as plt
import seaborn as sns
# Distribution analysis
fig, axes = plt.subplots(2, 2, figsize=(12, 8))
sns.histplot(df["price"], kde=True, ax=axes[0,0])
sns.boxplot(x="category", y="price", data=df, ax=axes[0,1])
sns.scatterplot(x="feature1", y="feature2", hue="target", data=df, ax=axes[1,0])
sns.heatmap(df.corr(), annot=True, cmap="RdBu", ax=axes[1,1])
plt.tight_layout()
Pitfalls
- Pandas chained indexing (df[df.a > 0]["b"]) is unpredictable — use .loc
- GroupBy without as_index=False leaves a MultiIndex
- CSV loading without dtype optimization uses excessive memory
- Matplotlib state machine causes cross-plot contamination — use figures explicitly
- Seaborn default styles override matplotlib globals