Friday, 6 December 2024
Tuesday, 13 February 2024
Pandas in Action
Python Coding February 13, 2024 Books, Pandas No comments
Take the next steps in your data science career! This friendly and hands-on guide shows you how to start mastering Pandas with skills you already know from spreadsheet software.
In Pandas in Action you will learn how to:
Import datasets, identify issues with their data structures, and optimize them for efficiency
Sort, filter, pivot, and draw conclusions from a dataset and its subsets
Identify trends from text-based and time-based data
Organize, group, merge, and join separate datasets
Use a GroupBy object to store multiple DataFrames
Pandas has rapidly become one of Python's most popular data analysis libraries. In Pandas in Action, a friendly and example-rich introduction, author Boris Paskhaver shows you how to master this versatile tool and take the next steps in your data science career. You'll learn how easy Pandas makes it to efficiently sort, analyze, filter and munge almost any type of data.
Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
About the technology
Data analysis with Python doesn't have to be hard. If you can use a spreadsheet, you can learn pandas! While its grid-style layouts may remind you of Excel, pandas is far more flexible and powerful. This Python library quickly performs operations on millions of rows, and it interfaces easily with other tools in the Python data ecosystem. It's a perfect way to up your data game.
About the book
Pandas in Action introduces Python-based data analysis using the amazing pandas library. You'll learn to automate repetitive operations and gain deeper insights into your data that would be impractical—or impossible—in Excel. Each chapter is a self-contained tutorial. Realistic downloadable datasets help you learn from the kind of messy data you'll find in the real world.
What's inside
Organize, group, merge, split, and join datasets
Find trends in text-based and time-based data
Sort, filter, pivot, optimize, and draw conclusions
Apply aggregate operations
About the reader
For readers experienced with spreadsheets and basic Python programming.
About the author
Boris Paskhaver is a software engineer, Agile consultant, and online educator. His programming courses have been taken by 300,000 students across 190 countries.
Table of Contents
PART 1 CORE PANDAS
1 Introducing pandas
2 The Series object
3 Series methods
4 The DataFrame object
5 Filtering a DataFrame
PART 2 APPLIED PANDAS
6 Working with text data
7 MultiIndex DataFrames
8 Reshaping and pivoting
9 The GroupBy object
10 Merging, joining, and concatenating
11 Working with dates and times
12 Imports and exports
13 Configuring pandas
14 Visualization
Hard Copy: Pandas in Action
Sunday, 18 June 2023
Data Analytics Course Handwritten Notes
Author June 18, 2023 Pandas, Python No comments
Tuesday, 5 October 2021
Operations On Dataframe in Python
Author October 05, 2021 Pandas, Python No comments
- If you want to check the data type of each column, because whenever you have been given a data, you want to really check what is the structure of the data; that means, which variable has which data type?
- In, that case you can use dtypes, because that returns a series with the data type of each column and the syntax would be you use dtypes along with the Data Frame name.
- So, Data Frame.dtypes will give you a series with the data type of each column
- So, now we have an overall idea about what are the data types that we are going to work with using the cars_data.
- There is also an option where you can get the count of unique data types available in your Data Frame.
- So, in that case get_dtype_counts, returns the counts of unique data types in the data frame.
- So, now we also have an overall idea about the count of unique data types that we are going to handle with.
- So, now, we know about how to get the data type of each variables. So, there might be cases where you want to perform the operations only on a numerical data type.
- Similarly, there can be cases where you are going to work with only categorical data type.
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