Data selection, aggregation and statistics with Bank Churn Modeling data
Pandas is a Python library that is used for wrangling data, generating statistics, aggregating data and much more. In this post we will discuss how to perform data selection, aggregation and statistical analysis using the Pandas library.
Let’s get started!
#python #data-science #programming #pandas #developer
Welcome to my Blog , In this article, you are going to learn the top 10 python tips and tricks.
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The complete guide to Pandas for beginners
When we talk about data science, we usually refer to the data analysis through summarization, visualizations, sophisticated algorithms that learn patterns in data (machine learning), and other fancy tools. When we discuss the term with software developers, we also hear a lot of Python, the popular programming language.
But why is Python so popular and special in the data science world? There are many reasons, and an important one is the Python ecosystem and libraries that make data science seem natural to Python.
One of these libraries is pandas , which every data science in the world uses, used, or at least heard of (if you are a data scientist who never used pandas, scream in comments).
Pandas is an essential part of the ecosystem that many other data science tools build on top or provide specific functionalities for pandas.
This guide introduces pandas for developers and aims to cover the what, why, and how of pandas’ most commonly used features.
Before we get started, if you want to access the full source code for this project to follow along, you can download the project’s source code from GitHub .
#how to work with pandas in python #python #pandas #work #pandas in python
Welcome to my Blog, In this article, we will learn python lambda function, Map function, and filter function.
Lambda function in python: Lambda is a one line anonymous function and lambda takes any number of arguments but can only have one expression and python lambda syntax is
Syntax: x = lambda arguments : expression
Now i will show you some python lambda function examples:
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Use pandas to upload CSV, TXT and Excel files
Learning Python isn’t the easiest thing to do. But consistency is really the key to arriving at a level that boosts your career.
We hear a lot about millennials wanting things to easy. In reality, there are a lot of young professionals who believe that they can do more for their companies but are being held back by the work cultures they are faced with at the onset of their careers.
Having been lucky enough to have found a job after my studies, I remember immediately feeling a wave of disappointment a very short while after starting my new job. I felt like a cog in a massive machine. I wasn’t really anything other than a ‘resource’. An extra 8–15 hours of daily man power depending on my boss’ whim.
The result, was the eventual disenchantment and lack of motivation simply because, for the most part, I was expected to be quiet and do my job in the hope of one day being senior enough to effect significant changes. And while the older generation would generally tell me to suck it up, I couldn’t see myself sucking it up for 5 years or more. I knew I’d get stale and afraid of change, much like those telling me to stay in my place.
For anyone in a similar situation,**_ do your best to improve on your skills _**and find an environment that works for you. That’s the whole purpose of these articles. To get you on your way to freedom.
For this demonstration, I’ll use data from this Kaggle competition. It’s a simple CSV file containing data on individuals in the Titanic and the different profiles i.e. (age, marital status etc.)
I want to import this file to python. I’ll show you how to do this alongside all the possible troubleshoots you may encounter.
#python #programming #pandas #python for beginners #importing files to python with pandas #python for beginners #2 — importing files to python with pandas
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