Pandas, Summary of Movie Rating Notebook

University:
University of California San Diego
Course:
DSC 207R | Python for Data Science
Academic year:

2023
Views:

106

Pages:

2
Author:

anon

Pandas, Summary of Movie Rating Notebook Data Preparation and Exploration Prior to analysis, it is frequently important in data science to prepare and study the data. We discussed a number of Pandas choices in the class to help you accomplish that. To deal with missing data, we looked into procedures like isnull(), any(), and dropna(). We also learned how to use boolean indexing to filter data frames according to predefined criteria. Data Visualization We talked about data exploration and preparation, as well as data visualization. With Pandas' plot() method, we examined examples of inline graphs, box plots, and histograms. To personalize the look of our visualizations, we learnt how to modify the axis formatting and other window settings. Slicing and Aggregating Data It is frequently important in data science to extract particular subsets of data or to aggregate data based on particular criteria. We discussed filtering data frames, removing rows, and aggregating data with the groupby() technique. We also gave examples of how to use these groups with fundamental statistical operations. Merging Data Data from different data frames may need to be combined or joined in specific circumstances. We covered many join kinds, including inner joins, as well as other data frame combining techniques. String Operations Also, we discussed the three primary string operations split(), includes(), and extract (). These operations can be applied to a data frame's strings to extract certain data. Working with Time Stamps Finally, we talked about using Pandas' time stamps. We learnt how to extract specific data from time stamps, like the day of the week or hour of the day, and how to convert strings to timestamps. In Summary This lesson used Pandas to cover a wide range of data science-related topics. We went over the process of ingesting data in a variety of formats, presented two basic Pandas data structures, looked at basic statistical operations, covered data preparation and exploration, and talked about data visualization, slicing and aggregating data, merging data, string operations, and working with time stamps. The movie database notebook, which covers many of the common data science procedures, is a fantastic representative example to keep on hand as a reference for any data science work.