Pandas Data Analysis Tutorial: DataFrames, Aggregations & Missing Data
Hands-on Pandas tutorial for data analysts: read_csv, Boolean filtering, handling NaN values, groupby aggregations, and export operations.
import pandas as pd
# Creating a DataFrame from raw dictionary records
data = {
'Course': ['Java', 'Python', 'Data Science', 'MERN', 'SEO'],
'Enrollments': [120, 145, 110, 95, 80],
'Fee': [25000, 22000, 35000, 28000, 18000]
}
df = pd.DataFrame(data)
# Filter courses with enrollments over 100
popular_courses = df[df['Enrollments'] > 100]
print("Popular Courses:\n", popular_courses)
# Compute descriptive revenue metric
df['TotalRevenue'] = df['Enrollments'] * df['Fee']
print("Summary Statistics:\n", df[['Enrollments', 'TotalRevenue']].describe())1. Series and DataFrames
Pandas is built on two core data structures: Series (1-dimensional labeled array) and DataFrame (2-dimensional tabular structure with labeled axes). It utilizes NumPy under the hood for fast vectorized C computations.
2. Data Cleaning & Aggregation Workflows
Real-world datasets contain missing values and incorrect data types. Pandas provides robust utilities like isna(), fillna(), dropna(), and type conversion with astype(). Aggregation through groupby() enables multi-dimensional group-level summaries.
- Avoid looping with iterrows(); always leverage vectorized Pandas operations for 100x faster execution.
- Use .loc for label-based indexing and .iloc for integer position-based slicing.
- Inspect DataFrame dimensions and memory usage with df.info() and df.shape.
Frequently Asked Questions
Why is Pandas preferred over basic Python lists for data manipulation?
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