Pandas for Data Analysis: From Beginner to Real Projects

In this lecture, you'll learn what Pandas is and why it is one of the most important Python libraries for data analysis. We'll look at how DataFrames help analysts work with structured datasets and how Pandas fits into a typical data-analysis workflow.

Learn how to import Pandas, create DataFrames, and understand the basic structure of tabular data in Python. You'll see how Pandas represents rows and columns and why DataFrames are central to practical data analysis.

Learn how to load real-world CSV and Excel files into Pandas DataFrames using read_csv() and read_excel(). You'll also learn why loading external datasets is the starting point for most practical data-analysis workflows.

Before analyzing data, analysts need to understand what they are working with. In this lecture, you'll learn how to inspect a dataset using techniques such as head(), tail(), shape, columns, dtypes, and info() to understand its structure and data types.

Learn how to select individual columns, multiple columns, and specific rows from a Pandas DataFrame. You'll practice accessing the exact information you need, an essential skill for working efficiently with analytical datasets.

Learn how to filter DataFrames using conditions so you can answer specific analytical questions. You'll work with comparisons such as greater than, less than, and equal to, and use filtering to isolate the records that matter for your analysis.

Learn how to filter DataFrames using conditions so you can answer specific analytical questions. You'll work with comparisons such as greater than, less than, and equal to, and use filtering to isolate the records that matter for your analysis.

Learn how to sort analytical data using Pandas sort_values(). You'll practice sorting by numerical and categorical columns and learn how sorting can help identify high-value transactions and prioritize important records.

Learn how to discover the categories and frequencies within a dataset using unique() and value_counts(). These simple techniques are extremely useful during exploratory analysis because they quickly reveal what values exist and how frequently they occur.

In this lecture, you’ll learn how to use Pandas aggregation functions to summarize and analyze data. You’ll work with functions such as sum(), mean(), min(), max(), and count() to answer practical business questions about sales, prices, quantities, and revenue.

By the end of the lecture, you’ll be able to quickly calculate important statistics from a DataFrame and turn raw data into useful information.

In this lecture, you’ll learn how to group data in Pandas using groupby(). You’ll see how groupby() is similar to SQL’s GROUP BY and how it allows you to calculate statistics for individual categories, products, or other groups.

You’ll work with real-world sales data and learn how to combine grouping with aggregation functions to answer questions such as total sales by category, average price by category, and total quantity sold by product.

In this lecture, you’ll learn how to filter grouped and aggregated results in Pandas, similar to SQL’s HAVING clause.

You’ll first use groupby() to calculate values for each group and then apply conditions to keep only the groups that meet a specific requirement. Using practical sales data, you’ll learn how to answer questions such as which products generated more than a certain amount of revenue or which categories sold more than a specific number of units.

In this lecture, you’ll learn how to create new columns based on conditions, similar to SQL’s CASE WHEN.

You’ll use conditional logic to classify and transform data. Through practical examples, you’ll learn how to categorize sales performance, assign labels based on numerical values, and create useful business classifications directly inside a Pandas DataFrame.

In this lecture, you’ll learn how to combine related DataFrames using Pandas merge(). You’ll see how merge() corresponds to SQL JOIN operations and learn when to use different join types.

Using realistic customer and order data, you’ll practice combining information from separate DataFrames and working with inner, left, right, and outer joins.

In this lecture, you’ll learn how to combine DataFrames vertically using Pandas concat(), similar to SQL’s UNION.

You’ll work with sales data from different months and combine multiple DataFrames into one dataset. You’ll also learn how to control the index and understand the importance of having compatible columns when combining datasets.

By the end of the lecture, you’ll be able to combine datasets from different sources into a single DataFrame for analysis.

In this lecture, you’ll learn how to identify, handle, and clean missing values in Pandas DataFrames.

You’ll work with realistic customer and sales data containing missing prices, quantities, customer information, and other fields. You’ll learn how to use functions such as isna(), notna(), fillna(), and dropna() to prepare incomplete data for analysis.

By the end of the lecture, you’ll understand how missing values affect calculations and how to choose an appropriate strategy for handling them.

In this lecture, you’ll learn how to work with dates and times in Pandas.

You’ll learn how to convert text into real datetime values using pd.to_datetime() and extract useful information such as year, month, day, and weekday. You’ll also learn how to filter data by dates and use dates to analyze business activity over time.

By the end of the lecture, you’ll be able to clean and analyze date-based data in real-world datasets.

In this lecture, you’ll learn how to clean and transform text data using Pandas string methods.

You’ll work with customer names, product names, categories, and other text fields. You’ll learn how to change capitalization, remove unnecessary spaces, search for text, and replace values using the .str accessor.

By the end of the lecture, you’ll be able to perform common text-cleaning operations on real-world DataFrames.

In this lecture, you’ll continue working with text data using more advanced Pandas string operations.

You’ll learn how to extract parts of strings, replace text, check whether values start or end with specific characters, and use string patterns to filter rows. These techniques are especially useful when cleaning customer, product, address, and transaction data.

By the end of the lecture, you’ll be able to use string operations to extract useful information and perform more advanced text-based filtering.

In this lecture, you’ll learn how to reshape data and create pivot tables in Pandas.

You’ll use pivot_table() to summarize sales across multiple dimensions, such as products, categories, and months. You’ll also learn how reshaping can turn detailed transaction data into a format that is easier to analyze and understand.

By the end of the lecture, you’ll be able to create business-style summary tables from detailed datasets.