Paula  Hall

Paula Hall


Introduction to Pandas

Pandas is a widely-used Python library built on top of NumPy. Much of the rest of this course will be dedicated to learning about pandas and how it is used in the world of finance.

What is Pandas?

Pandas is a Python library created by Wes McKinney, who built pandas to help work with datasets in Python for his work in finance at his place of employment.

According to the library’s website, pandas is “a fast, powerful, flexible and easy to use open source data analysis and manipulation tool, built on top of the Python programming language.”

Pandas stands for ‘panel data’. Note that pandas is typically stylized as an all-lowercase word, although it is considered a best practice to capitalize its first letter at the beginning of sentences.

Pandas is an open source library, which means that anyone can view its source code and make suggestions using pull requests. If you are curious about this, visit the pandas source code repository on GitHub

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Introduction to Pandas

Udit Vashisht


Python Pandas Objects - Pandas Series and Pandas Dataframe

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

Pandas Series

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

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

Oleta  Becker

Oleta Becker


Pandas in Python

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.

How to create dataframe and series?

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


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How to create a dataframe by passing a numpy array?

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

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

Macey  Kling

Macey Kling


Introduction to Pandas for Data Science -part 01


ata science is the process of deriving knowledge and insights from a huge and diverse set of data through organizing, processing and analysing the data. It involves many different disciplines like mathematical and statistical modelling, extracting data from it source and applying data visualization techniques. Often it also involves handling big data technologies to gather both structured and unstructured data.

Here are the Scenarios where Data science is used widely,

Recommendation systems

Financial Risk management

Improvement in Health Care services

Computer Vision

Efficient Management of Energy

Getting Started with Pandas

Pandas is an open-source Python Library used for high-performance data manipulation and data analysis using its powerful data structures. Python with pandas is in use in a variety of academic and commercial domains, including Finance, Economics, Statistics, Advertising, Web Analytics, and more. Using Pandas, we can accomplish five typical steps in the processing and analysis of data, regardless of the origin of data — load, organize, manipulate, model, and analyse the data.

Key Features of Pandas

Fast and efficient DataFrame object with default and customized indexing.

Tools for loading data into in-memory data objects from different file formats.

Data alignment and integrated handling of missing data.

Reshaping and pivoting of date sets.

Label-based slicing, indexing and subsetting of large data sets.

Columns from a data structure can be deleted or inserted.

Group by data for aggregation and transformations.

High performance merging and joining of data.

Time Series functionality.

Pandas provide essential data structures like series, dataframes, and panels which help in manipulating data sets and time series.

These data structures are built on top of Numpy array, making them fast and efficient.

Pandas possess the power to perform various tasks. Whether it is computing tasks like finding the mean, median and mode of data, or a task of handling large CSV files and manipulating the contents according to our will, Pandas can do it all. In short, to master data science, you must be skillful in Pandas.

Let’s start our Python Pandas tutorial with the methods for installing Pandas.

Just head over to ,

#pandas-dataframe #python #pandas #python-for-datascience #introduction


In my last post, I mentioned the groupby technique  in Pandas library. After creating a groupby object, it is limited to make calculations on grouped data using groupby’s own functions. For example, in the last lesson, we were able to use a few functions such as mean or sum on the object we created with groupby. But with the aggregate () method, we can use both the functions we have written and the methods used with groupby. I will show how to work with groupby in this post.

#pandas-groupby #python-pandas #pandas #data-preprocessing #pandas-tutorial


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