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# Less known Pandas groupby applications in Python

A few months ago, I published an article about how to master groupby function in Pandas. Then a few days ago, a friend of mine asked a question and it can also be solved with groupby function. So today I list out some less known applications that groupby function can achieve.

## 1. Percentage by each group

In the Excel pivot table, you can select to show the percentage of parent total. This represents that the pivot table shows the percentage of each item out of its total parent subtotal. This can easily be done in Pandas groupby function with the help of lambda.

``````>>> import pandas as pd
>>> df = pd.DataFrame({'A':['a','b','c']*2,
...                    'B':['d','e']*3,
...                    'C':[1,3,6,10,12,16]
...                    })
>>> df
A  B   C
0  a  d   1
1  b  e   3
2  c  d   6
3  a  e  10
4  b  d  12
5  c  e  16
## First Groupby
>>> df['Perc_gpby_A'] = df.groupby('A')['C'].apply(lambda x: x/x.sum())
## Second Groupby
>>> df['Perc_gpby_B'] = df.groupby('B')['C'].apply(lambda x: x/x.sum())
>>> df
A  B   C  Perc_gpby_A  Perc_gpby_B
0  a  d   1     0.090909     0.052632
1  b  e   3     0.200000     0.103448
2  c  d   6     0.272727     0.315789
3  a  e  10     0.909091     0.344828
4  b  d  12     0.800000     0.631579
5  c  e  16     0.727273     0.551724
``````

The main key point from the above example is that you select the appropriate grouping level in the groupby function. In the first groupby statement, the grouping level is column A. There are in total of three groups, a, b, and c. Then column C will be applied with the lambda function. The lambda function is `x/x.sum()` . Therefore each value in column C will be divided by the sum of its respective group. For example, the sum for group a is 11. Therefore in row 0 and 3, the value of c will be divided by 11. Thus the result is 0.0909 and 0.9091. This goes the same for the remaining two groups in column A and the second groupby statement.

#coding #beginner #programming #python #data-science

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## Why use Python for Software Development

No programming language is pretty much as diverse as Python. It enables building cutting edge applications effortlessly. Developers are as yet investigating the full capability of end-to-end Python development services in various areas.

By areas, we mean FinTech, HealthTech, InsureTech, Cybersecurity, and that's just the beginning. These are New Economy areas, and Python has the ability to serve every one of them. The vast majority of them require massive computational abilities. Python's code is dynamic and powerful - equipped for taking care of the heavy traffic and substantial algorithmic capacities.

Programming advancement is multidimensional today. Endeavor programming requires an intelligent application with AI and ML capacities. Shopper based applications require information examination to convey a superior client experience. Netflix, Trello, and Amazon are genuine instances of such applications. Python assists with building them effortlessly.

## 5 Reasons to Utilize Python for Programming Web Apps

Python can do such numerous things that developers can't discover enough reasons to admire it. Python application development isn't restricted to web and enterprise applications. It is exceptionally adaptable and superb for a wide range of uses.

Robust frameworks

Python is known for its tools and frameworks. There's a structure for everything. Django is helpful for building web applications, venture applications, logical applications, and mathematical processing. Flask is another web improvement framework with no conditions.

Web2Py, CherryPy, and Falcon offer incredible capabilities to customize Python development services. A large portion of them are open-source frameworks that allow quick turn of events.

Simple to read and compose

Python has an improved sentence structure - one that is like the English language. New engineers for Python can undoubtedly understand where they stand in the development process. The simplicity of composing allows quick application building.

The motivation behind building Python, as said by its maker Guido Van Rossum, was to empower even beginner engineers to comprehend the programming language. The simple coding likewise permits developers to roll out speedy improvements without getting confused by pointless subtleties.

Utilized by the best

Alright - Python isn't simply one more programming language. It should have something, which is the reason the business giants use it. Furthermore, that too for different purposes. Developers at Google use Python to assemble framework organization systems, parallel information pusher, code audit, testing and QA, and substantially more. Netflix utilizes Python web development services for its recommendation algorithm and media player.

Massive community support

Python has a steadily developing community that offers enormous help. From amateurs to specialists, there's everybody. There are a lot of instructional exercises, documentation, and guides accessible for Python web development solutions.

Today, numerous universities start with Python, adding to the quantity of individuals in the community. Frequently, Python designers team up on various tasks and help each other with algorithmic, utilitarian, and application critical thinking.

Progressive applications

Python is the greatest supporter of data science, Machine Learning, and Artificial Intelligence at any enterprise software development company. Its utilization cases in cutting edge applications are the most compelling motivation for its prosperity. Python is the second most well known tool after R for data analytics.

The simplicity of getting sorted out, overseeing, and visualizing information through unique libraries makes it ideal for data based applications. TensorFlow for neural networks and OpenCV for computer vision are two of Python's most well known use cases for Machine learning applications.

### Summary

Thinking about the advances in programming and innovation, Python is a YES for an assorted scope of utilizations. Game development, web application development services, GUI advancement, ML and AI improvement, Enterprise and customer applications - every one of them uses Python to its full potential.

The disadvantages of Python web improvement arrangements are regularly disregarded by developers and organizations because of the advantages it gives. They focus on quality over speed and performance over blunders. That is the reason it's a good idea to utilize Python for building the applications of the future.

#python development services #python development company #python app development #python development #python in web development #python software development

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## Python for Beginners #2 — Importing files to python with pandas

Use pandas to upload CSV, TXT and Excel files

## Story time before we begin

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.

## Introduction

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

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## 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

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## 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])

print(s)

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

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## How to work with Pandas in Python

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