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Pandas

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Pandas: Masterful Data Wrangling and Analysis

Transform, clean, and analyze any dataset with Pandas, the most indispensable data analysis library in Python. This course will empower you to turn messy, real-world data into structured, actionable insights.

Who is this course for?

This course is for anyone who needs to work with tabular data (spreadsheets, database tables, etc.) in Python. It's a must-have skill for data analysts, data scientists, financial analysts, and researchers.

What You Will Learn:

  • Master the core Pandas data structures: The Series and the DataFrame.

  • Import data from various sources, including CSV, Excel, and SQL databases.

  • Handle messy data like a pro: Find and treat missing values, remove duplicates, and correct data types.

  • Filter, select, and subset data with precision using loc, iloc, and boolean indexing.

  • Perform powerful group-by operations to aggregate and summarize your data.

  • Merge, join, and concatenate multiple datasets.

  • Analyze time-series data with Pandas' powerful date/time functionality.

Detailed Course Curriculum

Module 1: Introduction to Pandas

  • 1.1 What is Pandas and why is it essential for data analysis?

  • 1.2 The Series Object: A one-dimensional powerhouse.

  • 1.3 The DataFrame Object: Your primary tool for data analysis.

  • 1.4 Importing Data: pd.read_csv, pd.read_excel.

Module 2: Data Selection and Filtering

  • 2.1 Selecting Columns and Rows.

  • 2.2 Label-based selection with .loc.

  • 2.3 Position-based selection with .iloc.

  • 2.4 Conditional Filtering and Multi-conditional logic.

Module 3: Data Cleaning

  • 3.1 Identifying and Handling Missing Data: isnull(), dropna(), fillna().

  • 3.2 Correcting Data Types with astype().

  • 3.3 Finding and Removing Duplicates.

  • 3.4 String Manipulation with the .str accessor.

Module 4: Grouping and Combining Data

  • 4.1 The Split-Apply-Combine pattern with groupby().

  • 4.2 Aggregation functions: sum(), mean(), count(), agg().

  • 4.3 Merging and Joining DataFrames: pd.merge, pd.concat.

  • 4.4 Introduction to Pivot Tables.

Module 5: Time Series Analysis

  • 5.1 Working with Datetime objects.

  • 5.2 Time-based indexing and slicing.

  • 5.3 Resampling: Changing time-series frequency (e.g., daily to monthly).

Hands-on Project:

  • Hands-on Project on real data sets

Prerequisites:

  • Foundational Python knowledge.

  • Basic NumPy knowledge is helpful

Certification

Earn a Certificate of Completion powered by DigData — trusted by professionals, valued by employers, and aligned with real-world industry needs.

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