Master the foundational Python libraries for data manipulation and analysis. This course provides a practical introduction to NumPy arrays and Pandas DataFrames, equipping you with the core skills needed for data handling in AI and data science workflows. Learn to efficiently load, process, clean, and analyze data using these powerful tools.
Prerequisites Basic Python helpful
Level:
NumPy Arrays
Create, manipulate, and perform mathematical operations on NumPy N-dimensional arrays.
Array Indexing
Select and modify subsets of data within NumPy arrays using various indexing techniques.
Pandas Data Structures
Understand and work with Pandas Series and DataFrames, the core structures for data analysis.
Data Loading and Saving
Read data from common file formats (like CSV, Excel) into Pandas DataFrames and save processed data.
Data Cleaning
Handle missing values and perform basic data cleaning operations using Pandas.
Data Selection
Select rows and columns from Pandas DataFrames using labels, positions, and conditions.
Data Aggregation
Group data and calculate summary statistics using Pandas groupby functionality.
Combining Data
Combine data from multiple sources using concatenation and merging techniques in Pandas.