Uploaded November 2024 | Updated September 2026, 1 week ago
The pandas library is massive, and it's common for frequent users to be unaware of many of its more impressive features. The official pandas documentation, while thorough, does not contain many useful examples of how to piece together multiple commands as one would do during an actual analysis. This book guides you, as if you were looking over the shoulder of an expert, through situations that you are highly likely to encounter.
In this session, we will talk about the following things:
1. What are the best practices for handling missing data, outliers, and incorrect data types in large datasets with Pandas?
2. What techniques are available to improve performance on large datasets, especially for tasks like merging and joining?
3. What are the most common challenges in handling time series data, such as time zone management and irregular time intervals, and how can they be managed in Pandas?
4. What are best practices for working with multi-index dataframes, and how can users simplify complex aggregations?
5. Can you provide examples of full workflows, from data ingestion and cleaning to analysis and export, for maintaining clean and reproducible code?
During the live event, you can ask more questions! We will try to get all the questions answered live, during the event.
We will also giveaway Ecopies to 2 lucky winners during the session. Comment with #packt to enter the raffle.
The pandas library is massive, and it's common for frequent users to be unaware of many of its more impressive features. The official pandas documentation, while thorough, does not contain many useful examples of how to piece together multiple commands as one would do during an actual analysis. This book guides you, as if you were looking over the shoulder of an expert, through situations that you are highly likely to encounter.
In this session, we will talk about the following things:
1. What are the best practices for handling missing data, outliers, and incorrect data types in large datasets with Pandas?
2. What techniques are available to improve performance on large datasets, especially for tasks like merging and joining?
3. What are the most common challenges in handling time series data, such as time zone management and irregular time intervals, and how can they be managed in Pandas?
4. What are best practices for working with multi-index dataframes, and how can users simplify complex aggregations?
5. Can you provide examples of full workflows, from data ingestion and cleaning to analysis and export, for maintaining clean and reproducible code?
During the live event, you can ask more questions! We will try to get all the questions answered live, during the event.
We will also giveaway Ecopies to 2 lucky winners during the session. Comment with #packt to enter the raffle.
