Exploratory Data Analysis Best Practices for Data Scientists

Published: at 12:00 AM

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Start with Data Profiling, Not Visualization

The instinct to immediately reach for Matplotlib is understandable but premature. Before plotting anything, run a systematic profiling step that answers four questions: What are the shapes and dtypes? How much data is missing, and is the missingness random or structured? What are the ranges and distributions of numeric features? Are there duplicate rows or suspicious constant columns? Libraries like ydata-profiling automate this audit, but doing it manually forces you to develop intuition for each dataset rather than skimming a generated report you may not fully read.

Pay special attention to the relationship between missing values and your target variable. If records with missing income data are disproportionately from low-income users who skipped the field, imputing with the column mean introduces systematic bias. Visualize missingness patterns with a heatmap — columns that are frequently missing together often share a causal reason, which itself is useful signal.

Distributions, Outliers, and the Stories They Tell

A histogram tells you the shape of a distribution; a box plot tells you where the tails are; a violin plot tells you both. Use all three before deciding how to handle outliers. An observation three standard deviations from the mean might be a data entry error, a genuine extreme event, or an important edge case your model must handle correctly. Context determines the answer. For financial data, extreme values are often real — a 10-sigma price move during a flash crash is not noise. For sensor data, a negative temperature reading from a broken thermometer is.

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

def plot_numeric_summary(df: pd.DataFrame, col: str) -> None:
    fig, axes = plt.subplots(1, 3, figsize=(14, 4))
    df[col].hist(bins=50, ax=axes[0]); axes[0].set_title("Histogram")
    df.boxplot(column=col, ax=axes[1]); axes[1].set_title("Box Plot")
    sns.violinplot(y=df[col], ax=axes[2]); axes[2].set_title("Violin Plot")
    fig.suptitle(f"Distribution of {col}")
    plt.tight_layout()
    plt.show()

Feature Relationships and Target Correlation

Once you understand individual features, study how they interact with each other and with your target. A correlation matrix is a good start for numeric features, but Pearson correlation only captures linear relationships — supplement it with Spearman rank correlation and mutual information scores, which detect monotonic and nonlinear dependencies respectively. For categorical features, Cramér’s V measures association strength without assuming ordinality. The goal of EDA is not to find the single best feature but to build a mental model of the data-generating process: which variables move together, which are redundant, and which carry unique predictive signal that the model will rely on.

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