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Free Certificate
Learn Python Libraries: NumPy, Matplotlib & Pandas
Master core Python tools with this Python libraries free course. Learn how to manipulate data using NumPy, visualize insights with Matplotlib, and analyze datasets with Pandas.
15 hours of learning
NumPy
Vectors
Pandas
Key Highlights Of This Python Libraries Course
What You Will Learn
NumPy
This session introduces you to NumPy, the fundamental package for numerical computing in Python. You’ll learn how to efficiently manage and manipulate large datasets using multidimensional arrays and vectorized operations.
Topics Covered
- Create NumPy arrays – Understand how to define and initialize arrays, the foundational data structure in NumPy, to perform mathematical operations with high efficiency.
- Convert lists & tuples to NumPy arrays – Learn how to convert standard Python sequences such as lists and tuples into NumPy arrays to leverage advanced computational capabilities.
- Inspect the structure of arrays – Analyze the shape, dimensions, data types, and memory layout of arrays to fully understand how your data is structured and stored.
Matplotlib
After learning how to structure data using NumPy, this section equips you with the ability to visualize your data using Matplotlib, Python’s most widely used 2D plotting library.
Topics Covered
- Creating and plotting graphs – Learn how to generate basic graphs such as line plots, bar charts, and scatter plots to represent trends and patterns in data.
- Different chart types – Explore various chart formats—including histograms, pie charts, and stacked graphs—to communicate insights from diverse data structures.
- Modification of charts – Gain control over chart aesthetics by adjusting titles, axes, legends, colors, and line styles to improve readability and presentation quality.
Pandas
Built on top of NumPy, Pandas is the go-to library for data manipulation and analysis in Python. This module introduces key techniques to clean, transform, and analyze structured data efficiently.
Topics Covered
- Creating dataframes – Learn how to build dataframes—Pandas’ core data structure—from dictionaries, lists, or external data sources to structure your datasets effectively.
- Import CSV files as Pandas dataframes – Import and manage data from CSV files, enabling you to handle real-world datasets in a structured and accessible format.
- Reading and summarising dataframes – Use built-in methods to inspect, describe, and extract statistical insights from your datasets at a glance.
- Sorting dataframes – Organize data based on one or more columns to uncover ordered relationships or prepare for deeper analysis.
- Labelling, indexing and slicing data – Select specific data points or segments using intuitive labels or index positions to streamline data processing.
- Merging dataframes using joins – Combine multiple datasets into one cohesive dataframe using inner, outer, left, or right joins—mirroring SQL operations.
- Pivoting and grouping – Reshape datasets with pivot tables and perform aggregations using group-by functions to uncover trends and segment-level insights.
What Are the Benefits of This Course?
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