Ctrl+K

Advanced EDA Techniques

Advanced EDA Techniques#

Once basic exploration is complete, analysts often use more advanced visualization tools to reveal complex patterns quickly.

Multivariate Visualization Tools#

Correlation Heatmap#

import matplotlib.pyplot as plt
import seaborn as sns

plt.figure(figsize=(8,6))
sns.heatmap(df.corr(numeric_only=True), annot=True, cmap="coolwarm")
plt.show()

Pairplot#

sns.pairplot(df.select_dtypes(include="number"))

Missingness Map#

!pip install missingno

import missingno as msno
msno.matrix(df)

Think Like a Data Scientist Always ask WHY a pattern exists.

Common Mistake Correlation ≠ Causation.

Visualization Rule If you cannot explain the plot clearly, you do not understand the data yet.

Quick EDA Template#

df.head()
df.info()
df.describe()
df.isnull().sum()

df.hist(figsize=(10,8))

sns.boxplot(data=df)

sns.pairplot(df.select_dtypes(include="number"))

sns.heatmap(df.corr(numeric_only=True), annot=True)

Special Case: Exploring Event Patterns in Temporal Data#

When datasets contain time information, additional patterns may appear, trends, clustering, and seasonality. Temporal data often records events along a continuous time axis, requiring specialized visualization.

Exploring Event Patterns in Temporal Data#

Temporal data often records events (logins, purchases, symptoms, alarms) along a continuous variable such as time. Beyond basic distributions, three practical visual checks help reveal frequency, clustering, and seasonality.

1) Event Frequency Over Time (Counts)#

This view answers: When do events happen more often?
It highlights peaks, dips, and trends by counting events per date.

import pandas as pd
import matplotlib.pyplot as plt

df["date"] = pd.to_datetime(df["date"])

# Count events per date and plot in time order
df["date"].value_counts().sort_index().plot()
plt.xlabel("Time")
plt.ylabel("Event Count")
plt.title("Events Over Time")
plt.show()

2) Event Timeline (Scatter Plot)#

Sometimes you want to see exact timing rather than aggregated counts. A simple trick is to plot every event on the same horizontal line.

# If you have numeric time (e.g., days since start)
df["zeroes"] = 0
df.plot.scatter("days_since_start", "zeroes", alpha=0.4)
plt.xlabel("Time (days since start)")
plt.title("Event Occurrence Timeline")
plt.show()

Tip: Lower alpha helps when many events overlap.

3) Seasonal Pattern Check (Cyclic Time)#

To check for yearly seasonality, map time into a repeating cycle (0–364) and look for repeating structure.

# Map time into a yearly cycle (0–364)
df["day_of_year"] = df["days_since_start"] % 365

# Scatter: if patterns repeat at similar day-of-year values, seasonality is likely
df.plot.scatter("day_of_year", "x", alpha=0.5)
plt.xlabel("Day of Year")
plt.title("Seasonal Pattern Check")
plt.show()

If you want to color by day-of-year (optional):

df["color"] = df["day_of_year"].apply(lambda d: (d/365, 0, 0))
df.plot.scatter("day_of_year", "x", c=df["color"], alpha=0.6)
plt.xlabel("Day of Year")
plt.title("Seasonal Pattern Check (Colored)")
plt.show()

Remember:

  • Counts over time reveal trends and bursts.

  • Timeline scatter reveals clustering and gaps.

  • Cyclic plots reveal seasonality (weekly/monthly/yearly patterns).

Contents