Emilie  Okumu

Emilie Okumu

1623183660

Data Visualization Cheat Sheet with Seaborn and Matplotlib (Part 2)

Introduction

In my previous** post**, I introduced some simple visualization tips to quickly build good-looking charts with Seaborn and Matplotlib. Today, I’m gonna show you in detail how to build more complex charts, including combination charts and subplots.

Necessary Package Installation

There are some packages that we should import first.

import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline

Data Description

My dataset is collected from Kaggle public dataset and you can easily download via the following link:

Let’s take a look at our dataset. I will use data related to Germany as an example.

Data Visualization

Combination chart

This type of chart demonstrates multiple variables in different formats on the same chart. Combination chart can be helpful when you want to make comparisons between values in different categories.

As an example, let’s visualize the top 10 product categories in Germany by sales and their corresponding revenues.

In the above chart, the top 10 categories are displayed through bar chart and their revenues are displayed through line chart. By looking at this combination graph, we can observe some interesting insights.

  • The most purchased product is not the one that generates the most revenue. Take product 22326 for instance, its unit sold ranks first but its revenue only ranks third.
  • Although POST is the 3rd most purchased item, it’s the product that generates the most revenue.
  • 22961 is the least purchased product, but its revenue ranks 6th.

Subplots

Subplot helps create multiple plots on a single panel. In this section, I will show you a few ways to make a beautiful graph with multiple plots.

Again, we are using the top 10 product categories in Germany by sales.

Let’s try to combine 4 plots into a figure.

I’m gonna plot:

  • Top 10 consumers by product units purchased
  • Top 10 consumers by amount of money paid
  • Top 10 product categories that Top 10 consumers purchase, by units
  • Top 10 product categories that Top 10 consumers pay most money for

#data-visualization #visualization #data-analysis

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Buddha Community

Data Visualization Cheat Sheet with Seaborn and Matplotlib (Part 2)
최 호민

최 호민

1642390128

파이썬 코딩 무료 강의 - 이미지 처리, 얼굴 인식을 통한 캐릭터 씌우기를 해보아요

파이썬 코딩 무료 강의 (활용편6) - 이미지 처리, 얼굴 인식을 통한 캐릭터 씌우기를 해보아요

파이썬 무료 강의 (활용편6 - 이미지 처리)입니다.
OpenCV 를 이용한 다양한 이미지 처리 기법과 재미있는 프로젝트를 진행합니다.
누구나 볼 수 있도록 쉽고 재미있게 제작하였습니다. ^^

[소개]
(0:00:00) 0.Intro
(0:00:31) 1.소개
(0:02:18) 2.활용편 6 이미지 처리 소개

[OpenCV 전반전]
(0:04:36) 3.환경설정
(0:08:41) 4.이미지 출력
(0:21:51) 5.동영상 출력 #1 파일
(0:29:58) 6.동영상 출력 #2 카메라
(0:34:23) 7.도형 그리기 #1 빈 스케치북
(0:39:49) 8.도형 그리기 #2 영역 색칠
(0:42:26) 9.도형 그리기 #3 직선
(0:51:23) 10.도형 그리기 #4 원
(0:55:09) 11.도형 그리기 #5 사각형
(0:58:32) 12.도형 그리기 #6 다각형
(1:09:23) 13.텍스트 #1 기본
(1:17:45) 14.텍스트 #2 한글 우회
(1:24:14) 15.파일 저장 #1 이미지
(1:29:27) 16.파일 저장 #2 동영상
(1:39:29) 17.크기 조정
(1:50:16) 18.이미지 자르기
(1:57:03) 19.이미지 대칭
(2:01:46) 20.이미지 회전
(2:06:07) 21.이미지 변형 - 흑백
(2:11:25) 22.이미지 변형 - 흐림
(2:18:03) 23.이미지 변형 - 원근 #1
(2:27:45) 24.이미지 변형 - 원근 #2

[반자동 문서 스캐너 프로젝트]
(2:32:50) 25.미니 프로젝트 1 - #1 마우스 이벤트 등록
(2:42:06) 26.미니 프로젝트 1 - #2 기본 코드 완성
(2:49:54) 27.미니 프로젝트 1 - #3 지점 선 긋기
(2:55:24) 28.미니 프로젝트 1 - #4 실시간 선 긋기

[OpenCV 후반전]
(3:01:52) 29.이미지 변형 - 이진화 #1 Trackbar
(3:14:37) 30.이미지 변형 - 이진화 #2 임계값
(3:20:26) 31.이미지 변형 - 이진화 #3 Adaptive Threshold
(3:28:34) 32.이미지 변형 - 이진화 #4 오츠 알고리즘
(3:32:22) 33.이미지 변환 - 팽창
(3:41:10) 34.이미지 변환 - 침식
(3:45:56) 35.이미지 변환 - 열림 & 닫힘
(3:54:10) 36.이미지 검출 - 경계선
(4:05:08) 37.이미지 검출 - 윤곽선 #1 기본
(4:15:26) 38.이미지 검출 - 윤곽선 #2 찾기 모드
(4:20:46) 39.이미지 검출 - 윤곽선 #3 면적

[카드 검출 & 분류기 프로젝트]
(4:27:42) 40.미니프로젝트 2

[퀴즈]
(4:31:57) 41.퀴즈

[얼굴인식 프로젝트]
(4:41:25) 42.환경설정 및 기본 코드 정리
(4:54:48) 43.눈과 코 인식하여 도형 그리기
(5:10:42) 44.그림판 이미지 씌우기
(5:20:52) 45.캐릭터 이미지 씌우기
(5:33:10) 46.보충설명
(5:40:53) 47.마치며 (학습 참고 자료)
(5:42:18) 48.Outro


[학습자료]
수업에 필요한 이미지, 동영상 자료 링크입니다.

고양이 이미지 : https://pixabay.com/images/id-2083492/ 
크기 : 640 x 390  
파일명 : img.jpg

고양이 동영상 : https://www.pexels.com/video/7515833/ 
크기 : SD (360 x 640)  
파일명 : video.mp4

신문 이미지 : https://pixabay.com/images/id-350376/ 
크기 : 1280 x 853  
파일명 : newspaper.jpg

카드 이미지 1 : https://pixabay.com/images/id-682332/ 
크기 : 1280 x 1019  
파일명 : poker.jpg

책 이미지 : https://www.pexels.com/photo/1029807/ 
크기 : Small (640 x 853)  
파일명 : book.jpg

눈사람 이미지 : https://pixabay.com/images/id-1300089/ 
크기 : 1280 x 904  
파일명 : snowman.png

카드 이미지 2 : https://pixabay.com/images/id-161404/ 
크기 : 640 x 408  
파일명 : card.png

퀴즈용 동영상 : https://www.pexels.com/video/3121459/ 
크기 : HD (1280 x 720)  
파일명 : city.mp4

프로젝트용 동영상 : https://www.pexels.com/video/3256542/ 
크기 : Full HD (1920 x 1080)  
파일명 : face_video.mp4

프로젝트용 캐릭터 이미지 : https://www.freepik.com/free-vector/cute-animal-masks-video-chat-application-effect-filters-set_6380101.htm  
파일명 : right_eye.png (100 x 100), left_eye.png (100 x 100), nose.png (300 x 100)

무료 이미지 편집 도구 : https://pixlr.com/kr/
(Pixlr E -Advanced Editor)

#python #opencv 

Siphiwe  Nair

Siphiwe Nair

1620466520

Your Data Architecture: Simple Best Practices for Your Data Strategy

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition

Sid  Schuppe

Sid Schuppe

1617988080

How To Blend Data in Google Data Studio For Better Data Analysis

Using data to inform decisions is essential to product management, or anything really. And thankfully, we aren’t short of it. Any online application generates an abundance of data and it’s up to us to collect it and then make sense of it.

Google Data Studio helps us understand the meaning behind data, enabling us to build beautiful visualizations and dashboards that transform data into stories. If it wasn’t already, data literacy is as much a fundamental skill as learning to read or write. Or it certainly will be.

Nothing is more powerful than data democracy, where anyone in your organization can regularly make decisions informed with data. As part of enabling this, we need to be able to visualize data in a way that brings it to life and makes it more accessible. I’ve recently been learning how to do this and wanted to share some of the cool ways you can do this in Google Data Studio.

#google-data-studio #blending-data #dashboard #data-visualization #creating-visualizations #how-to-visualize-data #data-analysis #data-visualisation

Emilie  Okumu

Emilie Okumu

1623183660

Data Visualization Cheat Sheet with Seaborn and Matplotlib (Part 2)

Introduction

In my previous** post**, I introduced some simple visualization tips to quickly build good-looking charts with Seaborn and Matplotlib. Today, I’m gonna show you in detail how to build more complex charts, including combination charts and subplots.

Necessary Package Installation

There are some packages that we should import first.

import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline

Data Description

My dataset is collected from Kaggle public dataset and you can easily download via the following link:

Let’s take a look at our dataset. I will use data related to Germany as an example.

Data Visualization

Combination chart

This type of chart demonstrates multiple variables in different formats on the same chart. Combination chart can be helpful when you want to make comparisons between values in different categories.

As an example, let’s visualize the top 10 product categories in Germany by sales and their corresponding revenues.

In the above chart, the top 10 categories are displayed through bar chart and their revenues are displayed through line chart. By looking at this combination graph, we can observe some interesting insights.

  • The most purchased product is not the one that generates the most revenue. Take product 22326 for instance, its unit sold ranks first but its revenue only ranks third.
  • Although POST is the 3rd most purchased item, it’s the product that generates the most revenue.
  • 22961 is the least purchased product, but its revenue ranks 6th.

Subplots

Subplot helps create multiple plots on a single panel. In this section, I will show you a few ways to make a beautiful graph with multiple plots.

Again, we are using the top 10 product categories in Germany by sales.

Let’s try to combine 4 plots into a figure.

I’m gonna plot:

  • Top 10 consumers by product units purchased
  • Top 10 consumers by amount of money paid
  • Top 10 product categories that Top 10 consumers purchase, by units
  • Top 10 product categories that Top 10 consumers pay most money for

#data-visualization #visualization #data-analysis

Data Visualization Cheat Sheet with Seaborn and Matplotlib

Introduction

Exploratory Data Analysis — EDA is an indispensable step in data mining. To interpret various aspects of a data set like its distribution, principal or interference, it is necessary to visualize our data in different graphs or images. Fortunately, Python offers a lot of libraries to make visualization more convenient and easier than ever. Some of which are widely used today such as Matplotlib, Seaborn, Plotly or Bokeh.

Since my job concentrates on scrutinizing all angles of data, I have been exposed to many types of graphs. However, because there are way too many functions and the codes are not easy to remember, I sometimes forget the syntax and have to review or search for similar codes on the Internet. Without doubt, it has wasted a lot of my time, hence my motivation for writing this article. Hopefully, it can be a small help to anyone who has a memory of a goldfish like me.

Data Description

My dataset is downloaded from public Kaggle dataset. It is a grocery dataset, and you can easily get the data from the link below:

Groceries dataset

This grocery data consists of 3 columns, which are:

  • Member_number: id numbers of customers
  • Date: date of purchasing
  • itemDescription: Item name

#data-science #seaborn #data-analysis #data-visualization