1617329376

“Pandas in Action” makes it easy to dive into Python-based data analysis. You’ll learn to use pandas to automate repetitive spreadsheet functionality and derive insight from data by sorting columns, filtering data subsets, and creating multi-leveled indices. Each chapter is a self-contained tutorial, letting you dip in when you need to troubleshoot tricky problems. Best of all, you won’t be learning from sterile or randomly created data.

📚📚📚

Check out Boris Paskhaver’s book 📖 Pandas in Action | http://mng.bz/5pK1 📖 To save 40% off this book ⭐ DISCOUNT CODE: twitpask40 ⭐Boris is showcasing the high-level features of the Pandas library.

📚📚📚

Pandas in Action | http://mng.bz/5pK1

To save 40% off this book use discount code: twitpask40

#pandas #python

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The techniques for Reshaping, Grouping, and Pivoting the data

Python has turned the world just in a decade with its popularity and efficiency. Python has followed offering a reliable trend of Data Science which comprises of:

· Data Gathering

· Data Cleaning

· Machine Learning models

· Visualization of Data

Pandas is a very fundamental inbuilt library in Python uptakes a lot of the area. It is an open-source library that is easy to use, providing high efficiency and many tools used in the analysis of data for Python programming.

Pandas is an in-memory no SQL type database providing a helping hand for basic SQL constructs, statistical methods, and the capability of graphing. As it was built on top of Cython, it runs quicker along with consuming less time to access some memory within a machine.

→Pandas have a very advanced feature of carrying out some operations on the group of data frames.

→Data Frame: A 2D data that is labeled. It contains different columns and rows.

So, in this article, we’re going to have our quick eyes on some methods of grouping, reshaping, and pivoting the data.

#pandas #data-science #python #artificial-intelligence #playing with pandas library #pandas library

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Pandas is a great library for data preprocessing. Pandas often uses libraries such as NumPy and SciPy for numerical computations and Matplotlib to visualize data. Pandas has methods similar to the methods in NumPy. While NumPy works with the same data types, Pandas can work with different data types.

A data set written in Excel or SQL table data can be easily analyzed with pandas.

Pandas module is an open-source library since 2010. Pandas is constantly updated by developers around the world.

In summary, I will explain the following topics in this post:

- How to install Pandas?
- Series data structure
- Working with Series
- DataFrame data structure

Before starting the topic, our Medium page includes posts on data science, artificial intelligence, machine learning, and deep learning. Please don’t forget to follow us on ** Medium** 🌱 to see these posts and the latest posts.

Let’s get started.

#data-science #pandas-dataframe #pandas-series #pandas #machine-learning

1617329376

“Pandas in Action” makes it easy to dive into Python-based data analysis. You’ll learn to use pandas to automate repetitive spreadsheet functionality and derive insight from data by sorting columns, filtering data subsets, and creating multi-leveled indices. Each chapter is a self-contained tutorial, letting you dip in when you need to troubleshoot tricky problems. Best of all, you won’t be learning from sterile or randomly created data.

📚📚📚

Check out Boris Paskhaver’s book 📖 Pandas in Action | http://mng.bz/5pK1 📖 To save 40% off this book ⭐ DISCOUNT CODE: twitpask40 ⭐Boris is showcasing the high-level features of the Pandas library.

📚📚📚

Pandas in Action | http://mng.bz/5pK1

To save 40% off this book use discount code: twitpask40

#pandas #python

1586702221

In this post, we will learn about pandas’ data structures/objects. Pandas provide two type of data structures:-

Pandas Series is a one dimensional indexed data, which can hold datatypes like integer, string, boolean, float, python object etc. A Pandas Series can hold only one data type at a time. The axis label of the data is called the index of the series. The labels need not to be unique but must be a hashable type. The index of the series can be integer, string and even time-series data. In general, Pandas Series is nothing but a column of an excel sheet with row index being the index of the series.

Pandas dataframe is a primary data structure of pandas. Pandas dataframe is a two-dimensional size mutable array with both flexible row indices and flexible column names. In general, it is just like an excel sheet or SQL table. It can also be seen as a python’s dict-like container for series objects.

#python #python-pandas #pandas-dataframe #pandas-series #pandas-tutorial

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Pandas is used for data manipulation, analysis and cleaning.

**What are Data Frames and Series?**

**Dataframe** is a two dimensional, size mutable, potentially heterogeneous tabular data.

It contains rows and columns, arithmetic operations can be applied on both rows and columns.

**Series** is a one dimensional label array capable of holding data of any type. It can be integer, float, string, python objects etc. Panda series is nothing but a column in an excel sheet.

s = pd.Series([1,2,3,4,56,np.nan,7,8,90])

print(s)

**How to create a dataframe by passing a numpy array?**

- d= pd.date_range(‘20200809’,periods=15)
- print(d)
- df = pd.DataFrame(np.random.randn(15,4), index= d, columns = [‘A’,’B’,’C’,’D’])
- print(df)

#pandas-series #pandas #pandas-in-python #pandas-dataframe #python