Pandas Basics for Beginners - course RUB 990. from Stepik, training 46 lessons, date November 5, 2023.

About the course
1. Information
Numpy Basics
1. Why do you need Numpy when learning Pandas?
2. Practice: shape, dtype, ndim, zeros, ones, arange 
3. Data types and their conversion, arithmetic, element access 
4. Practice: arithmetic, type conversion, element access
5. Two-dimensional and three-dimensional arrays. Learning to use indexes 
6. Practice: strengthening work with indexes 
7. Mask and slicing together, fancy indexing, and also reshape 
8. Practice: learning to change the shape of an array 
9. More about reshape, transpose, unary and binary functions 
10. Practice: consolidating knowledge about unary and binary functions 
11. Logical functions and function within a function, as well as where and statistics 

12. Practice: mastering useful functions 
13. Functions any, all, sort, unique, in1d. Linear Algebra Overview 
14. Permutation and shuffle functions. Saving an array to a file 
Introduction to Pandas
1. First acquaintance with Series 
2. Practice: testing your knowledge about the series 
3. Learn more about series: indices, addition, checking for NaN 
4. Practice: subtleties when working with series 
5. First acquaintance with DataFrame 
6. Learn more about DataFrame: indexes, nested dictionaries, del and .T 
7. Practice: indexing features 
8. Functions reindex, drop and indexing in a dataframe 
9. Practice: pulling out what you need, removing what you don’t need 
10. Operators loc, iloc, at, iat. Addition of multiple dataframes 
11. Addition of dataframes, sorting, arithmetic with gaps 
12. Descriptive statistics. Unique values 
13. Test 
14. Test (continued) 
Pandas: working with data sources
1. What is the CSV format and how to tame it?
2. Learn more about pass processing 
3. Learning to read large files in pieces 
4. Who is JSON and how to make friends with it?
5. Getting to know the formats HTML, XML, PICKLE, HDF5 
6. Good old Excel and a little about databases
Pandas: cleaning and preparing data for analysis
1. Learning to handle passes 
2. We check data for duplicates and get rid of them 
3. About replacing values ​​and discretization 
4. We identify emissions and properly eliminate them 
5. Creating matrices of dummy variables 
6. Working with strings is easier than it seems!
Pandas: Data Joining and Shape Transformation
1. Introducing Hierarchical Indexing
2. Actions with multi-indexes in dataframes
3. Learning to connect dataframes using merge
4. Analogue to merge; concatenation of dataframes using concat
5. Combining data and form transformation