How to make good reproducible pandas examples

Shaping the perfect dataset

A flawless example should reflect the complexity of your bugbear while sidestepping additional fluff. Tools like numpy enable us to create systematic groups and control randomness:

import numpy as np
import pandas as pd

np.random.seed(0)  # Ensuring the odds aren't that random!
df = pd.DataFrame({
    'Group': np.repeat(['A', 'B', 'C'], repeats=10),
    'Data': np.random.rand(30)  # Sprinkling some random magic
})
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Voila! We've laid down a sturdy foundation for testing code changes while ensuring consistent output.

Tailoring data for edge cases

Occasionally, edge cases step into the limelight. Custom functions along with numpy's np.tile or np.random.choice can help generate well-structured datasets:

def custom_distribution(size):
    # Distributing gifts (not really, just data)
    values = np.random.choice([0, 1, 5, 10], size=size, p=[0.5, 0.2, 0.2, 0.1])
    return values

df['EdgeCaseData'] = custom_distribution(size=len(df))  # Sending customized invitations!
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While replicating errors, don't forget to share your full adventure (oops! stack trace).

Fine-tuning DataFrame creation

Sometimes, your issue opens the door to advance structures like MultiIndex DataFrames. In such cases, it's important to reset and recreate indices to mirror your actual situation:

df.set_index(['Group', 'Data'], inplace=True)
# We've just gone up a level in DataFrame mastery
df.reset_index(inplace=True)
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Ensure to provide a complete introduction of your DataFrame, including data types.

Outlining with detail

Stating the expected results

Tell us what your magic spell (code) is supposed to achieve. Indicate the expected results and unfold the reasoning:

# Expected party-goers (oops! results) on the stage
# A simple aggregation to find the average party-ers per group:
expected_result = df.groupby('Group')['Data'].mean()
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Your road map guides readers and ensures solutions that fit hand in glove with your expectations.

Generating pseudo-realistic data

Real-world data is like cooking, messy but fun. Creating random dates and values under a realistic range simulates this fascinating chaos:

date_rng = pd.date_range(start='2020-01-01', end='2020-01-10', freq='D')
random_dates = np.random.choice(date_rng, size=30)

df['Date'] = random_dates  # Adding some date spice to our data meal!
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These realistic values validate your example and throw light on potential solution robustness.

Focussing on subset data

Presenting your case through subset data excises redundancy and zooms in on the problem. Use head(), tail(), or sample() to amuse us with meaningful glimpses.