ℹ️ Basic DataFrame Info

Before analyzing data, you need to understand what you're working with! Pandas provides simple methods to quickly explore your DataFrame and understand its structure, size, and content.

📊 Getting Basic Information

Let's start with a sample DataFrame and explore it:

import pandas as pd

# Create sample data
students = pd.DataFrame({
    'name': ['Alice', 'Bob', 'Charlie', 'Diana'],
    'age': [20, 22, 21, 23],
    'grade': ['A', 'B', 'A', 'C'],
    'score': [85, 78, 92, 69]
})

print("Our DataFrame:")
print(students)
print()

# Basic info
print(f"Shape: {students.shape}")
print(f"Size: {students.size}")
print(f"Columns: {list(students.columns)}")

👀 Viewing Your Data

The most important methods for looking at your data:

import pandas as pd

# Sample data
data = pd.DataFrame({
    'product': ['Laptop', 'Mouse', 'Keyboard', 'Monitor', 'Tablet'],
    'price': [999, 25, 75, 300, 450],
    'stock': [10, 50, 30, 8, 15]
})

print("First 3 rows:")
print(data.head(3))
print()

print("Last 2 rows:")
print(data.tail(2))
print()

print("Random 2 rows:")
print(data.sample(2))

📋 DataFrame Structure

Understanding what types of data you have:

import pandas as pd

# Mixed data types
employees = pd.DataFrame({
    'name': ['Alice', 'Bob', 'Charlie'],
    'salary': [50000, 60000, 55000],
    'active': [True, False, True]
})

print("DataFrame info:")
print(employees.info())
print()

print("Data types:")
print(employees.dtypes)
print()

print("Column names:")
print(employees.columns.tolist())

📈 Quick Statistics

Get instant statistics for numerical columns:

import pandas as pd

# Numerical data
sales = pd.DataFrame({
    'day': ['Mon', 'Tue', 'Wed', 'Thu', 'Fri'],
    'revenue': [1200, 1500, 900, 1800, 2000],
    'customers': [45, 52, 38, 61, 73]
})

print("Quick statistics:")
print(sales.describe())
print()

print("Just revenue stats:")
print(sales['revenue'].describe())

🔍 Checking for Missing Data

Always check if you have missing values:

import pandas as pd

# Data with missing values
scores = pd.DataFrame({
    'student': ['Alice', 'Bob', 'Charlie', 'Diana'],
    'math': [85, None, 92, 78],
    'english': [90, 87, None, 85]
})

print("DataFrame with missing data:")
print(scores)
print()

print("Missing values count:")
print(scores.isnull().sum())
print()

print("Any missing values?")
print(scores.isnull().any())

📊 Counting Unique Values

See what unique values you have in each column:

import pandas as pd

# Categorical data
survey = pd.DataFrame({
    'age_group': ['18-25', '26-35', '18-25', '36-45', '26-35'],
    'rating': ['Good', 'Excellent', 'Good', 'Poor', 'Good'],
    'city': ['NYC', 'LA', 'NYC', 'Chicago', 'LA']
})

print("Survey data:")
print(survey)
print()

print("Unique values per column:")
print(survey.nunique())
print()

print("Unique ratings:")
print(survey['rating'].unique())
print()

print("Rating counts:")
print(survey['rating'].value_counts())

🎯 Essential DataFrame Methods

Method	What It Shows	Example
`.shape`	(rows, columns)	`(100, 5)`
`.size`	Total number of cells	`500`
`.columns`	Column names	`['name', 'age', 'score']`
`.head(n)`	First n rows	First 5 rows by default
`.tail(n)`	Last n rows	Last 5 rows by default
`.info()`	Complete overview	Data types, memory, missing values
`.describe()`	Statistics	Count, mean, std, min, max, quartiles
`.dtypes`	Data type of each column	`int64`, `object`, `float64`

🔧 Quick Data Exploration Routine

Here's a simple routine to quickly understand any DataFrame:

import pandas as pd

# Sample dataset
data = pd.DataFrame({
    'name': ['Alice', 'Bob', 'Charlie', 'Diana', 'Eve'],
    'department': ['Sales', 'IT', 'Sales', 'HR', 'IT'],
    'salary': [50000, 75000, 52000, 48000, 80000],
    'years': [2, 5, 3, 1, 7]
})

print("=== Quick Data Exploration ===")
print(f"1. Shape: {data.shape}")
print(f"2. Columns: {list(data.columns)}")
print()

print("3. First few rows:")
print(data.head(3))
print()

print("4. Data types:")
print(data.dtypes)
print()

print("5. Missing values:")
print(data.isnull().sum())
print()

print("6. Quick stats:")
print(data.describe())

🎯 Key Takeaways

🚀 What's Next?

Now you know how to explore and understand your DataFrames! Next, let's learn how to load real data from files like CSV and Excel.

Continue to: Reading Files (CSV, Excel, JSON)

You're becoming a data detective! 🕵️‍♂️📊

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ℹ️ Basic DataFrame Info

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