English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 2h 30m | 427 MB
Unique solutions to Pandas data cleaning and wrangling issues. Overcome any roadblock in your Pandas projects
Pandas is a powerful and popular scientific computing Python library for analyzing and manipulating data. Pandas is used to tidy messy data, independently analyze groups within your data, make powerful time-series calculations, and create beautiful visualizations during exploratory data analysis.
However, it also comes with a set of problems when used for the aforementioned tasks. So, if you’re facing any issues in your analysis or visualization tasks, then this is your course.
With clear, simple, and unique solutions, this course will help you tackle any issues that you face while working with Pandas.
The course is full of hands-on instructions, interesting and illustrative visualizations, and, clear explanations from a data scientist. It is packed full of useful tips and relevant advice. Throughout the course, we maintain a focus on practicality and getting things done, not fancy mathematical theory.
What You Will Learn
- Speed up your data analysis by importing data into Pandas
- Keep relevant data points by selecting subsets of your data
- Create a high-quality dataset by cleaning data and fixing missing values
- Compute actionable analytics with grouping and aggregation
- Crunch IoT and financial data by mastering time-series functionality
- Present your analysis by exporting from Pandas
01 The Course Overview
02 Dealing with Messy Excel Sheets and Misformatted CSV Files
03 Coping with Unstructured HTML and JSON Formats
04 Handling Too Much Data from HDF5 and SQL Sources
05 Dropping Useless Data with Indices
06 Advanced Data Cleaning with Query and Where
07 Untangling Chained Indices with Views and Copies
08 Working with Spelling Mistakes and Typos in Text Data
09 Filling in Missing Data and NAs
10 Parsing Stubborn Date Strings into DatetimeObjects
11 Splitting and Clustering Seemingly Random Data Points
12 Resolving Incorrect Data Collection with Lambdas and Functions
13 Cleaning up Misaggregated Satistics and Bad Pivot Tables
14 Fixing Messy Time Series Data with DateTimeIndex
15 Segmenting and Offsetting Time Series Data to Find the Right Subset
16 Repairing Misaligned Data with Shifting and Filling Operators
17 Keeping the Right Data and Formatting into Excel Sheets
18 Dealing with Incompatible Data for HTML and JSON
19 Purging Big Data to HDF5 and SQL Sources