📊 Pandas Descriptive Statistics – Summarize and Explore Your Data Easily

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🧲 Introduction – Why Use Descriptive Statistics in Pandas?

Descriptive statistics help you quickly understand the distribution, central tendency, and spread of your dataset. Pandas offers a wide set of functions to summarize numeric, categorical, and mixed-type data, making it ideal for exploratory data analysis (EDA).

🎯 In this guide, you’ll learn:

• How to compute key statistics (mean, median, mode, std, etc.)
• Use .describe() and .info() for automatic summaries
• Explore data column-wise or group-wise
• Handle missing values and non-numeric columns

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📥 1. Sample DataFrame

import pandas as pd

df = pd.DataFrame({
    'Age': [25, 30, 22, 35, 28],
    'Score': [88, 92, 85, 90, 87],
    'Passed': [True, True, True, True, False]
})

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📦 2. Get Quick Summary with .describe()

df.describe()

👉 Output (numerical columns only):

             Age      Score
count   5.000000   5.000000
mean   28.000000  88.400000
std     4.183300   2.701851
min    22.000000  85.000000
25%    25.000000  87.000000
50%    28.000000  88.000000
75%    30.000000  90.000000
max    35.000000  92.000000

✔️ Use df.describe(include='all') to show all columns, including non-numeric ones.

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🧠 3. Calculate Individual Statistics

df['Score'].mean()       # Mean
df['Score'].median()     # Median
df['Score'].mode()       # Mode (returns Series)
df['Score'].std()        # Standard deviation
df['Score'].var()        # Variance
df['Score'].min()        # Minimum
df['Score'].max()        # Maximum
df['Score'].sum()        # Total sum

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🧮 4. Count Non-Null Entries

df.count()

✔️ Returns the number of non-null values for each column.

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🧾 5. Value Counts for Categorical Data

df['Passed'].value_counts()

👉 Output:

True     4
False    1
Name: Passed, dtype: int64

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📊 6. Correlation and Covariance

df.corr()     # Pearson correlation
df.cov()      # Covariance matrix

✔️ Shows relationships between numeric columns.

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📈 7. Get Quantiles and Ranges

df['Age'].quantile(0.75)  # 75th percentile
df['Age'].max() - df['Age'].min()  # Range

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🧪 8. Summary with .info()

df.info()

✔️ Shows:

• Number of non-null values
• Data types
• Memory usage

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📉 9. Apply Stats Across Rows

df.mean(axis=1)

✔️ Calculates mean per row (only numeric columns).

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📌 Summary – Key Takeaways

Descriptive statistics are the foundation of data exploration. Pandas provides flexible, readable functions to quickly assess the shape, spread, and health of your dataset.

🔍 Key Takeaways:

• Use .describe() and .info() for quick summaries
• Compute individual stats with .mean(), .median(), .std(), etc.
• Analyze relationships using .corr() and .cov()
• Use value_counts() for frequency analysis
• Control axis: axis=0 (columns), axis=1 (rows)

⚙️ Real-world relevance: Used in reporting, analytics, anomaly detection, business intelligence, and machine learning feature understanding.

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❓ FAQs – Descriptive Statistics in Pandas

❓ How do I get stats for non-numeric columns?
Use:

df.describe(include='object')

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❓ What’s the difference between mean() and median()?

• mean() → average (sensitive to outliers)
• median() → middle value (robust to outliers)

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❓ How do I find missing values?
Use:

df.isnull().sum()

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❓ Can I summarize only selected columns?
Yes:

df[['Age', 'Score']].describe()

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❓ Does describe() include Boolean columns?
Only if you use:

df.describe(include='all')

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