Perform EDA in Python step by step with Pandas, summary statistics, missing-value checks, distributions, relationships and practical questions. For Exploratory Data Analysis in Python, the examples stay small enough for you to predict the result before execution. This page targets the practical intent behind exploratory analysis, not repetition of the phrase itself.
Editorial note: Publication metadata for Exploratory Data Analysis in Python is intentionally unset until this URL is actually released; the lesson already includes its own examples, CSV and notebook.
Choose the question before the chart
For housing data, inspect price and area distributions, compare neighborhoods, plot price against area, check missing bedrooms and look for duplicate listings before training a price model.
- A practical EDA pass checks shape, dtypes, missingness, duplicates, target balance and suspicious ranges before modeling.
- Univariate plots explain distributions, while bivariate plots reveal relationships and potential leakage.
- Segmented summaries can expose Simpson’s paradox or hidden subgroups that disappear in an overall average.
- Keep an EDA log of anomalies and decisions so cleaning steps are traceable rather than ad hoc.
Build and inspect a reproducible visual
For Exploratory Data Analysis in Python, predict one visible result before each snippet runs—a row count, shape, statistic, ranking or error pattern—then compare it with the printed checkpoint.
# Exploratory Data Analysis in Python · example 1
import pandas as pd
import matplotlib.pyplot as plt
df = pd.read_csv('houses.csv')
print(df.info())
print(df.describe(include='all'))
print(df.isna().mean().sort_values(ascending=False).head())
df.plot.scatter(x='area_sqft', y='price', alpha=.4)
plt.show()Exploratory Data Analysis in Python checkpoint 1: Run the cell and inspect the printed values before changing anything.
# Exploratory Data Analysis in Python · example 2
import pandas as pd
import matplotlib.pyplot as plt
df = pd.read_csv('practice.csv')
print(df.dtypes)
print(df.head(3).to_string(index=False))Exploratory Data Analysis in Python checkpoint 2: Check that numeric axes are numeric and categories are not accidentally encoded as measures.
# Exploratory Data Analysis in Python · example 3
num = df.select_dtypes('number').columns[0]
ax = df[num].plot(kind='hist', bins=12, edgecolor='black')
ax.set(title=f'Distribution of {num}', xlabel=num)
plt.tight_layout()Exploratory Data Analysis in Python checkpoint 3: A histogram appears; look for skew, gaps and extreme values before summarizing the center.
# Exploratory Data Analysis in Python · example 4
cat = df.select_dtypes(exclude='number').columns[0]
num = df.select_dtypes('number').columns[0]
print(df.groupby(cat)[num].agg(['count','mean','median']).round(2))Exploratory Data Analysis in Python checkpoint 4: The table shows group sizes beside mean and median so small groups are visible.
# Exploratory Data Analysis in Python · example 5
fig, ax = plt.subplots(figsize=(8,4.5))
df[num].plot(kind='box', ax=ax, vert=False)
ax.set_title(f'{num}: spread and possible outliers')
fig.tight_layout()
fig.savefig('practice-chart.png', dpi=160)Exploratory Data Analysis in Python checkpoint 5: practice-chart.png is saved with fixed dimensions and resolution.
Check scales, groups and misleading encodings
Segmented summaries can expose Simpson’s paradox or hidden subgroups that disappear in an overall average.
Keep an EDA log of anomalies and decisions so cleaning steps are traceable rather than ad hoc.
Self-check with answers
For Exploratory Data Analysis in Python, what result should you be able to explain without the code?
A practical EDA pass checks shape, dtypes, missingness, duplicates, target balance and suspicious ranges before modeling.
Which assumption matters most in this Exploratory Data Analysis in Python exercise?
Segmented summaries can expose Simpson’s paradox or hidden subgroups that disappear in an overall average.
What failure mode would make this Exploratory Data Analysis in Python result untrustworthy?
Keep an EDA log of anomalies and decisions so cleaning steps are traceable rather than ad hoc.
Use the same data to compare alternative views
Try it yourself workspace
Edit this Exploratory Data Analysis in Python starter, copy it, and run it in the downloadable notebook or your local Jupyter environment.
Questions specific to this lesson
How do I choose the right chart for Exploratory Data Analysis (EDA) in Python?
Univariate plots explain distributions, while bivariate plots reveal relationships and potential leakage.
What makes a visualization misleading for Exploratory Data Analysis in Python?
Truncated axes, inconsistent scales, hidden denominators and overloaded color can all change the story a reader takes from the same data.
What should I inspect before plotting for Exploratory Data Analysis in Python?
Check variable type, units, missingness, duplicates and sample size first. Many chart problems are really data-quality problems.
How do I make the chart reproducible for Exploratory Data Analysis in Python?
Keep the transformation and plotting code together, set explicit labels and save the output with fixed dimensions so another person can rebuild it.
Primary references and further reading
For Exploratory Data Analysis in Python, use these primary references when exact syntax, defaults or formal definitions matter beyond the examples on this page.
