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DEMO - Pandas in Data Science
DEMO - Pandas in Data ScienceAnalytics Courses |
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| Description | ||||
| Pandas for Data Science is an introduction to one of the hottest new tools available to data science and business analytics specialists. Pandas is an open-source library that provides high-performance, easy-to-use data structures and data analysis tools. While Python has excellent capabilities for data manipulation and data preparation, Pandas adds data analysis and modeling tools so that users can perform entire data science workflows. 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. Topics include: Using the Markdown language and Jupyter Notebook Creating objects Selecting objects Using operations Merging data Grouping Creating series Creating data frames Creating panels Plotting Annotating plots and data frame plots Introduction Welcome 34s What you should know 8s NumPy, data science, and IMQAV 4m 21s Using the exercise files 1m 36s Install software 3m 29s 1. Introduction to Notebooks Introduction to Jupyter Notebook 1m 51s Launch Jupyter Notebook 1m 15s Notebook basics 4m 44s Markdown 4m 40s Markdown tables 1m 51s Beautiful mathematics typesetting 3m 2s 2. Pandas Overview Object creation 7m 39s Selection 7m Assignment statements 4m 8s Missing data 4m 1s Operations 2m 38s Merge: concat, join, append 2m 48s Input and output 2m 40s Remote data access 2m 9s Grouping 1m 31s Categoricals 2m 2s Time series resampling 3m 24s 3. Series Create series 3m 55s Vectorized operations 5m 30s Date arithmetic 4m 33s 4. Data Frames and Panels Create data frames 3m 19s Select, add, and delete 2m 24s Indexing and selection 4m 53s NumPy universal functions 2m 39s Create panels 4m 41s 5. Plotting Inline plotting 3m 42s Figures and subplots 3m 7s Multiple lines in a single plot 6m 8s Tick marks, labels, and grids 2m 19s Plot annotations 2m 28s Data frame plots 4m 53s Conclusion Next steps 1m | ||||
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