Charles Kelly explains how Pandas DataFrame and Series objects manage missing data.
- [Instructor] The missing data file from your exercise…file folder is pre-populated with import statements…for pandas and numpy, a numpy data set,…a date time index, and two sample data frames.…It also initializes a variable name starting date…with the value the first of July, 2016.…The purpose of this video is to explain…how to manage missing data in series and in data frames.…Missing data is a frequent occurrence…in real world data science.…
For example, people may not answer all the questions…within a survey or a data set may be constructed…from multiple sources, not all of which contain…identical time and decease or identical data types.…Begin by placing your cursor in the import cell…and press shift enter.…This creates name spaces for pandas and numpy,…initializes a variable called starting date,…and instantiates these variables and data sets.…According to the documentation,…a data frames re-index function…conforms a data frame to a new index…with optional filling logic, placing not available…or not a number in locations having no value…
Watch this course to gain an overview of Pandas. Charles Kelly helps you get started with time series, data frames, panels, plotting, and visualization. All you need is a copy of the free and interactive Jupyter Notebook app to practice and follow along.
- Using the Markdown language and Jupyter Notebook
- Creating objects
- Selecting objects
- Using operations
- Merging data
- Creating series
- Creating data frames
- Creating panels
- Annotating plots and data frame plots
Skill Level Intermediate
Introduction to Data Analysis with Pythonwith Michele Vallisneri2h 16m Intermediate
NumPy Data Science Essential Trainingwith Charles Kelly3h 54m Intermediate
SQL for Data Reporting and Analysiswith Emma Saunders2h 16m Intermediate
1. Introduction to Notebooks
2. Pandas Overview
4. Data Frames and Panels
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