The retail industry is a sector of the economy that is comprised of individuals and companies engaged in the selling of finished products to end user consumers. The Methods of Retailing in the Industry are:
Brick-and-Mortar Store Retailers – Those engaged in the sale of products from physical locations which warehouse and display merchandise with the intent of attracting customers to make purchases on site.
Non-Store Retailers – Those engaged in the sale of products using marketing methods which do not include a physical location. Examples of non-store retailing include: Mobile-only retailing (m-commerce), Internet-only e-commerce, Catalogue Sales, Vending Machines, Multi-Level Marketing.
These software components cover functions across Department Stores and Specialty, Food and Grocery, Restaurant areas.
Popular New Releases in Retail
vuex
v4.0.2
airflow
Apache Airflow 2.2.5
vuepress
v1.9.2
prophet
v1.0
saleor
3.0.0-b.19
Popular Libraries in Retail
by vuejs 
javascript
27468 MIT
🗃️ Centralized State Management for Vue.js.
by apache python
25547 Apache-2.0
Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
by ccxt javascript
24082 MIT
A JavaScript / Python / PHP cryptocurrency trading API with support for more than 100 bitcoin/altcoin exchanges
by vuejs javascript
20197 MIT
📝 Minimalistic Vue-powered static site generator
by nostra13 java
16815 Apache-2.0
Powerful and flexible library for loading, caching and displaying images on Android.
by facebook python
14280 MIT
Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
by mirumee python
13983 NOASSERTION
A modular, high performance, headless e-commerce platform built with Python, GraphQL, Django, and React.
by reactioncommerce javascript
11662 GPL-3.0
Mailchimp Open Commerce is an API-first, headless commerce platform built using Node.js, React, GraphQL. Deployed via Docker and Kubernetes.
by Shopify ruby
9461 MIT
Liquid markup language. Safe, customer facing template language for flexible web apps.
Trending New libraries in Retail
by LGLTeam c++
332 GPL-3.0
Floating mod menu for Android
by Blair2004 php
261 NOASSERTION
The base version of NexoPOS 4.x, which is a web-Based Point Of Sale (POS) System build with Laravel, TailwindCSS, and Vue.Js.
by hyz-xmaster python
208 Apache-2.0
VarifocalNet: An IoU-aware Dense Object Detector
by anhquan291 javascript
169
E-commerce App UI. React native, Expo managed flow, React navigation v5, Notification.
by jeya-maria-jose python
160 MIT
Official Pytorch Code of KiU-Net for Image Segmentation - MICCAI 2020 (Oral)
by aws-samples python
101 Apache-2.0
Hands on workshop and design scenarios for Amazon DynamoDB
by Fitpassu java
94 MIT
Lightweight, easy to integrate and use React native library for Stripe payments (using Payment Intents) compliant with SCA (strong customer authentication)
by lakasir php
91 MIT
Point Of Sale system, free and open source
by spacious-team java
87 AGPL-3.0
Оценка эффективности инвестиций с учетом комиссий, налогов (удержанных и ожидающихся), дивидендов и купонов.
Top Authors in Retail
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Trending Kits in Retail
Java E-Commerce Libraries help build e-commerce websites. It provides various frameworks, tools, and utilities for developing online shopping websites.
There are many Java E-Commerce libraries available in the market, which enable you to write your own e-commerce website in easy steps. Java E-Commerce Libraries like shopizer, micro-ecommerce, and keyist-ecommerce are also used for developing an eCommerce website. These libraries are used in e-commerce applications since they provide a platform for making payment gateways, shopping carts, and other functionalities of an e-commerce website.
Shopizer is the most popular Java e-commerce library that allows you to create a customized online store without any coding. It comes with integrated payment gateways, shipping plugins, and many other features, which make it one of the best options for small businesses looking to start an online store. Keyist-Ecommerce is an open-source Java library that allows you to build an online store with minimal effort and time. The library supports multiple languages, including English, Chinese, Japanese and Korean. It also offers integrated payment gateways, shipping plugins, and other features that make it easy for developers to build custom websites without having to write any code themselves. Micro-eCommerce is an open-source Java library that allows you to build an online store without any coding knowledge required by developers. It has over 350 features built in along with integrated payment gateways, shipping plugins, and many other features that make it easy for developers to create custom websites. Some of the most popular Java E-Commerce Libraries among developers are given below
shopizer:
- Shopizer is an open-source e-commerce software platform designed for building.
- It is a flexible and customizable solution for businesses.
- Shopizer is released under the Apache 2.0 license, making it open-source and free to use.
eCommerce-order-service:
- eCommerce-order-service is a Java library typically used in websites and e-commerce applications.
- It has a Permissive License, and it has a medium support.
- It handles various aspects related to customer orders.
micro-ecommerce:
- Micro-ecommerce might refer to the use of microservices architecture in the context.
- This architecture aims to improve scalability, maintainability, and agility in the development.
- Micro-ecommerce helps describe smaller or lighter e-commerce businesses.
Keyist-Ecommerce:
- Keyist-Ecommerce is a term used within a specific context or community.
- It may be beneficial to seek information from sources associated with that context.
- This will provide the most accurate and up-to-date information about its features and functionalities.
e-commerce-microservice:
- e-commerce microservice refers to the application of microservices architecture in the development.
- Microservices architecture is an approach where a large and complex application decomposes.
- Microservice in the e-commerce system handles a specific business capability.
ddd-example-ecommerce:
- DDD is an approach to software development. It emphasizes understanding the business domain and modeling it in code.
- DDD often involves defining bounded contexts to encapsulate specific business domains.
- DDD distinguishes between entities and value objects. Entities have an identity and are mutable.
Android-E-Commerce-Shopping-Application:
- Android-E-Commerce-Shopping-Application is a Java library. It is typically used in Retail, Security, Authentication, and Firebase applications.
- Android-E-Commerce-Shopping-Application has no bugs or vulnerabilities.
- It has a Permissive License, and it has low support.
Ecommerce-Morningmist-Android:
- Ecommerce-Morningmist-Android is a Java library that is typically used in retail websites.
- Ecommerce-Morningmist-Android has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
FAQ
1. What are Java E-commerce Libraries?
Java E-commerce Libraries are sets of pre-built, reusable code components and functionalities. These libraries often include modules for product management, cart handling, and payment processing.
2. Why use Java for E-commerce Development?
Java is popular for its portability, scalability, and robustness. It's platform-independent, making it suitable for building enterprise-level applications. Java has a vast ecosystem and community support. It also contributes to its popularity in e-commerce.
3. Which Java E-commerce Libraries are widely used?
Some widely used Java E-commerce Libraries include:
- Broadleaf Commerce: An open-source, customizable framework for building e-commerce solutions.
- Hybris: A robust and scalable e-commerce platform.
- Apache OFBiz (Open for Business): An open-source framework for building enterprise automation applications.
4. How can I integrate Payment Gateways with Java E-commerce Libraries?
Java E-commerce Libraries often provide APIs or modules for integrating with popular payment. The Developers follow the documentation provided by the library in a specific way.
5. Are there Java E-commerce Libraries suitable for small businesses?
Yes, some Java E-commerce Libraries cater to the needs of small businesses. The Smaller libraries or frameworks may also be suitable for lightweight e-commerce requirements.
Data scraping is used to gather valuable insights, conduct market research, monitor competitor prices, or build price comparison websites.
In this digital world, the internet has changed the way we do our shopping. Online shopping is the act of purchasing products or services over the internet. It gathers all the information that will make all your internal and external recommendation processes, sales, and distribution based on the chronology of what you have been buying and bought earlier. Some of the libraries below can help you to achieve this as DIY.
Data Collection
Ecommerce Websites
Ecommerce Services
JavaScript has the accessibility and ideal programming model to build product showcase libraries that can guide users through using an app or viewing a product. Developers can choose elements to focus on and add effects or animation to them on the JS platform as well. These libraries are especially useful for an eCommerce web app and enable you to implement elements like carousels, product gallery, zoomable views, and lots more. You can also add step-by-step functionalities using these libraries to build fully responsive user interfaces and control each element on the app effectively. Adding product showcases, animation, and immersive features makes the website more interactive and user-friendly for viewers and end-users. Below are our 32 best JavaScript products that showcase open source libraries in 2021 that enable data visualization and effective app building like Swiper - modern mobile touch slider with hardware accelerated transitions; slick - the last carousel you'll ever need; react-native-snap-carousel - carousel component for React Native featuring previews.
Health care industries around the globe are growing day by day. Equally pharmacy industry is rising to the same extent. The pharmacy system stores data, systemizes, and controls the use of the medication process with the pharmacies. The pharmacy management system helps the pharmacist maintain their stock and choose the right medicine by using the computer program. The pharmacist makes use of this system to control the pharmacy reliably. Patients sometimes prefer to visit the pharmacy instead of a doctor for minor illnesses, and the system enables the pharmacist to prescribe over-the-counter drugs for these ailments.
Did you know that the value of Online Returns is more than the US Defense Budget? CNBC reported that in 2021 the US online returns were estimated to be at $761 Billion, while the US Defense budget was $741 Billion. Now that you know the scale of this problem or opportunity, it would be surprising to note that few platforms serve this industry. The returns and the used market has been able to soften the supply chain constraints in the recent past in categories like electronics. Today about two-thirds of the returns are processed through reselling, liquidation and donations. About a third ends up in landfills, or its better-sounding alternative of “Energy Recovery,” which sadly means incineration! Given the focus on sustainability, it becomes more critical to ensure that returns are processed through the circular economy more efficiently. This is the idea that we should be reusing and recycling existing materials rather than creating new ones, so that we don't consume our natural resources faster than they can replenish themselves. One important place to implement this concept is in online returns management. The e-commerce industry has grown steadily over the past two decades, and it's projected to keep growing by leaps and bounds over the next few years. As these companies become more efficient at scaling their processes, online shopping will increasingly become the norm—and that means we will have a lot more waste from packaging and discarded returns. In addition, as consumers become more sophisticated in their understanding of sustainable practices, they are becoming less tolerant of wasteful packaging and discarded returns. In fact, many return customers have said that they would not shop with a brand again if it used too much packaging for their order. It's important for companies with big e-commerce operations to find ways to reduce this waste and optimize returns. There are opportunities across the value chain such as better product discovery to minimize incorrect purchases and returns, better demand planning to optimize overstock, fraud management in returns, liquidation marketplaces, and becoming a YouTube influencer unboxing mystery return pallets! For more information on the CNBC report, visit: https://www.cnbc.com/video/2022/02/19/inside-liquidity-services-and-the-644-billion-liquidation-market.html For ideas and libraries to get you jumpstarted on the circular economy use cases use the below libraries.
Supply Chain Optimization Libraries
These libraries help you avoid waste through planning, tracking and optimization solutions.
Simple Retail Storefronts and Auctions
These libraries help you build storefronts and auctions for return, used products returning them to the circular economy.
Trending Discussions on Retail
Stepper functionality using React
Merging data from a separate .csv file using Pandas
How do I show my closest 3 competitors on a map?
Multiple TypeScript discriminations based on different properties
Grabbing certain data from one object to another object
localizing rows from a dataframe
Fetching data in loop next js
how to order json data header while downloading it into xls format using JavaScript
json_normalize a nested database
joining two dataframes on matching values of two common columns R
QUESTION
Stepper functionality using React
Asked 2022-Mar-09 at 16:24I'm trying to replicate this stepper like functionality using react.
https://www.commbank.com.au/retail/complaints-compliments-form?ei=CTA-MakeComplaint
Below is my stackblitz, How can I achieve this functionality without using any 3rd Party plugins.
ANSWER
Answered 2022-Mar-09 at 16:24I set up the basics of the UI on codesandbox.
The main part of how this works is via scrollIntoView using a reference to the div element on each Step. It's important to note that this will work on every modern browser but safari for the smooth scrolling.
Obviously for the actual form parts and moving data around, all of that will still need to be implemented, but this demonstrates nearly all of the navigation/scrolling behaviors as your example.
For reference, here's the main code:
1import { useEffect, useRef, useState } from "react";
2import A from "./A";
3import B from "./B";
4import C from "./C";
5import D from "./D";
6import "./styles.css";
7
8const steps = [A, B, C, D];
9export default function App() {
10 const [step, setStep] = useState(0);
11 /** Set up a ref that refers to an array, this will be used to hold
12 * a reference to each step
13 */
14 const refs = useRef<(HTMLDivElement | null)[]>([]);
15 /** Whenever the step changes, scroll it into view! useEffect needed to wait
16 * until the new component is rendered so that the ref will properly exist
17 */
18 useEffect(() => {
19 refs.current[step]?.scrollIntoView({ behavior: "smooth" });
20 }, [step]);
21
22 return (
23 <div className="App">
24 {steps
25 .filter((_, index) => index <= step)
26 .map((Step, index) => (
27 <Step
28 key={index}
29 /** using `domRef` here to avoid having to set up forwardRef.
30 * Same behavior regardless, but with less hassle as it's an
31 * ordianry prop.
32 */
33 domRef={(ref) => (refs.current[index] = ref)}
34 /** both prev/next handlers for scrolling into view */
35 toPrev={() => {
36 refs.current[index - 1]?.scrollIntoView({ behavior: "smooth" });
37 }}
38 toNext={() => {
39 if (step === index + 1) {
40 refs.current[index + 1]?.scrollIntoView({ behavior: "smooth" });
41 }
42 /** This mimics behavior in the reference. Clicking next sets the next step
43 */
44 setStep(index + 1);
45 }}
46 /** an override to enable reseting the steps as needed in other ways.
47 * I.e. changing the initial radio resets to the 0th step
48 */
49 setStep={setStep}
50 step={index}
51 />
52 ))}
53 </div>
54 );
55}
56
57
58And component A
1import { useEffect, useRef, useState } from "react";
2import A from "./A";
3import B from "./B";
4import C from "./C";
5import D from "./D";
6import "./styles.css";
7
8const steps = [A, B, C, D];
9export default function App() {
10 const [step, setStep] = useState(0);
11 /** Set up a ref that refers to an array, this will be used to hold
12 * a reference to each step
13 */
14 const refs = useRef<(HTMLDivElement | null)[]>([]);
15 /** Whenever the step changes, scroll it into view! useEffect needed to wait
16 * until the new component is rendered so that the ref will properly exist
17 */
18 useEffect(() => {
19 refs.current[step]?.scrollIntoView({ behavior: "smooth" });
20 }, [step]);
21
22 return (
23 <div className="App">
24 {steps
25 .filter((_, index) => index <= step)
26 .map((Step, index) => (
27 <Step
28 key={index}
29 /** using `domRef` here to avoid having to set up forwardRef.
30 * Same behavior regardless, but with less hassle as it's an
31 * ordianry prop.
32 */
33 domRef={(ref) => (refs.current[index] = ref)}
34 /** both prev/next handlers for scrolling into view */
35 toPrev={() => {
36 refs.current[index - 1]?.scrollIntoView({ behavior: "smooth" });
37 }}
38 toNext={() => {
39 if (step === index + 1) {
40 refs.current[index + 1]?.scrollIntoView({ behavior: "smooth" });
41 }
42 /** This mimics behavior in the reference. Clicking next sets the next step
43 */
44 setStep(index + 1);
45 }}
46 /** an override to enable reseting the steps as needed in other ways.
47 * I.e. changing the initial radio resets to the 0th step
48 */
49 setStep={setStep}
50 step={index}
51 />
52 ))}
53 </div>
54 );
55}
56
57
58import React, { useEffect, useState } from "react";
59import { Step } from "./utils";
60
61interface AProps extends Step {}
62
63function A(props: AProps) {
64 const [value, setValue] = useState("");
65 const values = [
66 { label: "Complaint", value: "complaint" },
67 { label: "Compliment", value: "compliment" }
68 ];
69 const { step, setStep } = props;
70 useEffect(() => {
71 setStep(step);
72 }, [setStep, step, value]);
73 return (
74 <div className="step" ref={props.domRef}>
75 <h1>Component A</h1>
76 <div>
77 {values.map((option) => (
78 <label key={option.value}>
79 {option.label}
80 <input
81 onChange={(ev) => setValue(ev.target.value)}
82 type="radio"
83 name="type"
84 value={option.value}
85 />
86 </label>
87 ))}
88 </div>
89 <button
90 className="next"
91 onClick={() => {
92 if (value) {
93 props.toNext();
94 }
95 }}
96 >
97 NEXT
98 </button>
99 </div>
100 );
101}
102
103export default A;
104QUESTION
Merging data from a separate .csv file using Pandas
Asked 2022-Mar-02 at 08:26I want to create two new columns in job_transitions_sample.csv and add the wage data from wage_data_sample.csv for both Title 1 and Title 2:
job_transitions_sample.csv:
1 Title 1 Title 2 Count
20 administrative assistant office manager 20
31 accountant cashier 1
42 accountant financial analyst 22
54 accountant senior accountant 23
66 accounting clerk bookkeeper 11
77 accounts payable clerk accounts receivable clerk 8
88 administrative assistant accounting clerk 8
99 administrative assistant administrative clerk 12
10...
11wage_data_sample.csv
1 Title 1 Title 2 Count
20 administrative assistant office manager 20
31 accountant cashier 1
42 accountant financial analyst 22
54 accountant senior accountant 23
66 accounting clerk bookkeeper 11
77 accounts payable clerk accounts receivable clerk 8
88 administrative assistant accounting clerk 8
99 administrative assistant administrative clerk 12
10...
11 title wage
120 cashier 17.00
131 sandwich artist 18.50
142 dishwasher 20.00
153 babysitter 20.00
164 barista 21.50
175 housekeeper 21.50
186 retail sales associate 23.00
197 bartender 23.50
208 cleaner 23.50
219 line cook 23.50
2210 pizza cook 23.50
23...
24I want the end result to look like this:
1 Title 1 Title 2 Count
20 administrative assistant office manager 20
31 accountant cashier 1
42 accountant financial analyst 22
54 accountant senior accountant 23
66 accounting clerk bookkeeper 11
77 accounts payable clerk accounts receivable clerk 8
88 administrative assistant accounting clerk 8
99 administrative assistant administrative clerk 12
10...
11 title wage
120 cashier 17.00
131 sandwich artist 18.50
142 dishwasher 20.00
153 babysitter 20.00
164 barista 21.50
175 housekeeper 21.50
186 retail sales associate 23.00
197 bartender 23.50
208 cleaner 23.50
219 line cook 23.50
2210 pizza cook 23.50
23...
24 Title 1 Title 2 Count Wage of Title 1 Wage of Title 2
250 administrative assistant office manager 20 NaN NaN
261 accountant cashier 1 NaN NaN
272 accountant financial analyst 22 NaN NaN
28...
29I'm thinking of using dictionaries then try to iterate every column but is there a more elegant built in solution? This is my code so far:
1 Title 1 Title 2 Count
20 administrative assistant office manager 20
31 accountant cashier 1
42 accountant financial analyst 22
54 accountant senior accountant 23
66 accounting clerk bookkeeper 11
77 accounts payable clerk accounts receivable clerk 8
88 administrative assistant accounting clerk 8
99 administrative assistant administrative clerk 12
10...
11 title wage
120 cashier 17.00
131 sandwich artist 18.50
142 dishwasher 20.00
153 babysitter 20.00
164 barista 21.50
175 housekeeper 21.50
186 retail sales associate 23.00
197 bartender 23.50
208 cleaner 23.50
219 line cook 23.50
2210 pizza cook 23.50
23...
24 Title 1 Title 2 Count Wage of Title 1 Wage of Title 2
250 administrative assistant office manager 20 NaN NaN
261 accountant cashier 1 NaN NaN
272 accountant financial analyst 22 NaN NaN
28...
29wage_data = pd.read_csv('wage_data_sample.csv')
30dict = dict(zip(wage_data.title, wage_data.wage))
31ANSWER
Answered 2022-Mar-02 at 08:23You can try with 2 merge con the 2 different Titles subsequentely.
For example, let be
df1 : job_transitions_sample.csv
df2 : wage_data_sample.csv
df1.merge(df2, left_on='Title 1', right_on='title',suffixes=('', 'Wage of')).merge(df2, left_on='Title 2', right_on='title',suffixes=('', 'Wage of'))
QUESTION
How do I show my closest 3 competitors on a map?
Asked 2022-Jan-27 at 18:19I work for a retailer and would like to create a map in Power BI for all of our stores and their 3 closest competitors, like the example below (Ideal Output). The blue dot is our store and the red dots are our competitors. Ideally, the map would change automatically when a different store is selected in a drop-down slicer. Please see the example data below (My Data).
Thanks,
Mark
Ideal Output:
My Data
Table 1: All of our stores. Very basic. Every store has its own unique number, along with basic store details including geocodes.
Table 2: All competitors. Every competitor has a unique number along with basic information including geocodes.
Table 3: Our stores and their 3 closest competitors.
ANSWER
Answered 2022-Jan-22 at 21:14Here's the solution that comes to mind for me. Create a new table that combines information from all 3 of the tables provided. It will be formatted like your Table 1 or Table 2, but with two new columns, Type and Slicer Store.
Type will specify whether the row is your own store or a competitor. Slicer Store will specify which rows should be displayed when that store's name is selected in the dropdown slicer.
Each row from your Table 3 will have 4 rows in the new table, one for each of the columns. The Slicer Store column in your new table will contain the Store Number from each row in Table 3. The Type column in your new table is self-explanatory.
You will end up with something like the following.
| Number | Name | Address | Latitude | Longitude | Type | Slicer Store |
|---|---|---|---|---|---|---|
| JL123 | ... | ... | ... | ... | Store | JL123 |
| C1 | ... | ... | ... | ... | Competitor | JL123 |
| C2 | ... | ... | ... | ... | Competitor | JL123 |
| C3 | ... | ... | ... | ... | Competitor | JL123 |
| JL456 | ... | ... | ... | ... | Store | JL456 |
| C2 | ... | ... | ... | ... | Competitor | JL456 |
| C3 | ... | ... | ... | ... | Competitor | JL456 |
| C4 | ... | ... | ... | ... | Competitor | JL456 |
Now for how to use it in Power BI. Create a single-select dropdown slicer using your new Slicer Store column. Then create your map visual using the rows in your new table. Use your new Type column as the Legend/Category. This allows you to color stores vs competitors differently in the visual.
Bing bang boom, you're done. Note that you may have "duplicate" rows for competitors in your new table. For example, if JL123 and JL456 were physically located right next to each other, then rows for C1, C2, and C3 would each appear twice in your new table. The Slicer Store would be different (JL123 or JL456) for these rows, though.
How you create the new table, either manually or with some sort of script, is the hard part.
QUESTION
Multiple TypeScript discriminations based on different properties
Asked 2022-Jan-20 at 09:58I'm trying to build a complex REACT component which supports different use-cases. In order to simplify its use, I want to implement TypeScript discriminations types to better infer the props.
It's not useful to post the full example, but I can show you a simpler one, which is the following one:
1interface ICategoryDiscriminationGame {
2 category: 'game';
3 gameType: 'AAA' | 'AA' | 'A';
4}
5
6interface ICategoryDiscriminationProgram {
7 category: 'program';
8 programType: 'software' | 'freeware';
9}
10
11type TCategoryDiscrimination = (ICategoryDiscriminationGame | ICategoryDiscriminationProgram);
12
13
14interface ISaleDiscriminationPresale {
15 saleType: 'pre-sale',
16 preSaleCost: number;
17}
18
19interface ISaleDiscriminationRetailSale {
20 saleType: 'retail',
21 retailSaleCost: number;
22}
23
24type TSaleDiscrimination = (ISaleDiscriminationPresale | ISaleDiscriminationRetailSale);
25
26
27type TExampleCompProps = TCategoryDiscrimination & TSaleDiscrimination;
28
29export const ExampleComp = (props: TExampleCompProps) => {
30 if (props.category === 'game') { // In here, intellisense infer only use 'props.category' and 'props.saleType' -> NICE
31 console.log(props.gameType); // In here, intellisense infer also 'props.gameType' -> NICE
32 }
33
34 if (props.saleType === 'pre-sale') { // In here, intellisense infer only use 'props.category' and 'props.saleType' -> NICE
35 console.log(props.preSaleCost); // In here, intellisense infer also 'props.preSaleCost' -> NICE
36 }
37
38 if (props.category === 'game' && props.saleType === 'retail') { // In here, intellisense infer only use 'props.category' and 'props.saleType' -> NICE
39 console.log(props.retailSaleCost); // In here, intellisense infer also 'props.retailSaleCost' and 'props.gameType' -> NICE
40 console.log(props.gameType); // In here, intellisense infer also 'props.retailSaleCost' and 'props.gameType' -> NICE
41 }
42
43 return <p>In example comp</p>
44}
45As you can see, inside the ExampleComp, the intellisense is brilliant, and works great. The problem is when I try to use the ExampleComp.
What I would expect is that, when I write <ExampleComp, the intellisense allows me only the props category and saleType, since the other ones cannot exist without first defining those two. BUT, instead, it just suggests everything:
So, the question here is: what am I missing that does not make the intellisense works correctly ALSO in the props?
ANSWER
Answered 2022-Jan-20 at 09:58That's just the nature of IntelliSense. Once you start to supply some of the combinations of the required props, the suggested ones will be narrowed to only the ones which are applicable to the current possible combination:
Before adding some props:
After adding some props:
QUESTION
Grabbing certain data from one object to another object
Asked 2022-Jan-08 at 16:38My goal is to grab certain values from database into curated_database, however I am basically stuck at adding multiple items into an object.
1var curated_database = {};
2
3var database = {
4 0: [{name: 'Micheal'},
5 {age: 45},
6 {education: 'BA'},
7 {income: 245000},
8 {occupation: 'director'}],
9 1: [{name: 'John'},
10 {age: 23},
11 {education: 'BA'},
12 {income: 60000},
13 {occupation: 'manager'}],
14 2: [{name: 'Judith'},
15 {age: 45},
16 {education: 'PhD'},
17 {income: 140000},
18 {occupation: 'professor'}],
19 3: [{name: 'Gill'},
20 {age: 28},
21 {education: 'MS'},
22 {income: 98000},
23 {occupation: 'scientist'}],
24 4: [{name: 'Dave'},
25 {age: 17},
26 {education: 'HS'},
27 {income: 30000},
28 {occupation: 'retail associate'}]
29};
30Goal is to grab similar certain information from the larger object
1var curated_database = {};
2
3var database = {
4 0: [{name: 'Micheal'},
5 {age: 45},
6 {education: 'BA'},
7 {income: 245000},
8 {occupation: 'director'}],
9 1: [{name: 'John'},
10 {age: 23},
11 {education: 'BA'},
12 {income: 60000},
13 {occupation: 'manager'}],
14 2: [{name: 'Judith'},
15 {age: 45},
16 {education: 'PhD'},
17 {income: 140000},
18 {occupation: 'professor'}],
19 3: [{name: 'Gill'},
20 {age: 28},
21 {education: 'MS'},
22 {income: 98000},
23 {occupation: 'scientist'}],
24 4: [{name: 'Dave'},
25 {age: 17},
26 {education: 'HS'},
27 {income: 30000},
28 {occupation: 'retail associate'}]
29};
30
31curated_database = {
320 : ['Micheal',245000,'director'],
331: ['John',245000,'manager'],
342: ['Judith',140000,'professor'],
353: ['Gill',98000,'scientist'],
364: ['Dave',30000,'retail associate']
37};
38My attempt
1var curated_database = {};
2
3var database = {
4 0: [{name: 'Micheal'},
5 {age: 45},
6 {education: 'BA'},
7 {income: 245000},
8 {occupation: 'director'}],
9 1: [{name: 'John'},
10 {age: 23},
11 {education: 'BA'},
12 {income: 60000},
13 {occupation: 'manager'}],
14 2: [{name: 'Judith'},
15 {age: 45},
16 {education: 'PhD'},
17 {income: 140000},
18 {occupation: 'professor'}],
19 3: [{name: 'Gill'},
20 {age: 28},
21 {education: 'MS'},
22 {income: 98000},
23 {occupation: 'scientist'}],
24 4: [{name: 'Dave'},
25 {age: 17},
26 {education: 'HS'},
27 {income: 30000},
28 {occupation: 'retail associate'}]
29};
30
31curated_database = {
320 : ['Micheal',245000,'director'],
331: ['John',245000,'manager'],
342: ['Judith',140000,'professor'],
353: ['Gill',98000,'scientist'],
364: ['Dave',30000,'retail associate']
37};
38for(data in database){
39 desired_contents = [0,3,4]
40 for(contents in desired_contents){
41 console.log(database[data][desired_contents[contents]]);
42 }
43 var k = database[data][0];
44 if (!currated_database[k.key]) {
45 currated_database[k.key] = [];
46 }
47 currated_database[k.key].push(k.val);
48}
49ANSWER
Answered 2022-Jan-08 at 16:35you can achieve it this way:
1var curated_database = {};
2
3var database = {
4 0: [{name: 'Micheal'},
5 {age: 45},
6 {education: 'BA'},
7 {income: 245000},
8 {occupation: 'director'}],
9 1: [{name: 'John'},
10 {age: 23},
11 {education: 'BA'},
12 {income: 60000},
13 {occupation: 'manager'}],
14 2: [{name: 'Judith'},
15 {age: 45},
16 {education: 'PhD'},
17 {income: 140000},
18 {occupation: 'professor'}],
19 3: [{name: 'Gill'},
20 {age: 28},
21 {education: 'MS'},
22 {income: 98000},
23 {occupation: 'scientist'}],
24 4: [{name: 'Dave'},
25 {age: 17},
26 {education: 'HS'},
27 {income: 30000},
28 {occupation: 'retail associate'}]
29};
30
31curated_database = {
320 : ['Micheal',245000,'director'],
331: ['John',245000,'manager'],
342: ['Judith',140000,'professor'],
353: ['Gill',98000,'scientist'],
364: ['Dave',30000,'retail associate']
37};
38for(data in database){
39 desired_contents = [0,3,4]
40 for(contents in desired_contents){
41 console.log(database[data][desired_contents[contents]]);
42 }
43 var k = database[data][0];
44 if (!currated_database[k.key]) {
45 currated_database[k.key] = [];
46 }
47 currated_database[k.key].push(k.val);
48}
49var curated_database = {};
50
51var database = {
52 0: [{name: 'Micheal'},
53 {age: 45},
54 {education: 'BA'},
55 {income: 245000},
56 {occupation: 'director'}],
57 1: [{name: 'John'},
58 {age: 23},
59 {education: 'BA'},
60 {income: 60000},
61 {occupation: 'manager'}],
62 2: [{name: 'Judith'},
63 {age: 45},
64 {education: 'PhD'},
65 {income: 140000},
66 {occupation: 'professor'}],
67 3: [{name: 'Gill'},
68 {age: 28},
69 {education: 'MS'},
70 {income: 98000},
71 {occupation: 'scientist'}],
72 4: [{name: 'Dave'},
73 {age: 17},
74 {education: 'HS'},
75 {income: 30000},
76 {occupation: 'retail associate'}]
77};
78
79
80
81var indexToExtract = new Set([0,3,4]);
82
83 var wantedLst = Object.values(database)
84 .map(lst => lst.filter((ob, idx) => indexToExtract.has(idx)))
85 .map((lst, idx) => curated_database[idx] = lst.map(ob => Object.values(ob)[0]))
86
87 console.log(curated_database);QUESTION
localizing rows from a dataframe
Asked 2021-Dec-21 at 12:43I working with a dataframe which has 20 columns but I'm only going to use three of them in my task, which are named "Price","Retail" and "Profit" and are like this:
1cols = ['Price', 'Retail','Profit']
2df3.loc[:,cols]
31cols = ['Price', 'Retail','Profit']
2df3.loc[:,cols]
3 Price Retail Profit
40 861.5 1315.233051 453.733051
51 901.5 1315.233051 413.733051
62 911.0 1315.233051 404.233051
73 901.5 1315.233051 413.733051
84 901.5 1315.233051 413.733051
9... ... ... ...
102678 14574.0 21546.730769 6972.730769
112679 35708.5 52026.764706 16318.264706
122680 35708.5 52026.764706 16318.264706
132681 163276.5 250882.500000 87606.000000
142682 7369.5 11785.729730 4416.229730
152683 rows × 3 columns
16My goal is to find the lines where the prices are lower than 5000 and sort by the biggest values of profit. How can I make it?
ANSWER
Answered 2021-Dec-21 at 12:41You can do this:
1cols = ['Price', 'Retail','Profit']
2df3.loc[:,cols]
3 Price Retail Profit
40 861.5 1315.233051 453.733051
51 901.5 1315.233051 413.733051
62 911.0 1315.233051 404.233051
73 901.5 1315.233051 413.733051
84 901.5 1315.233051 413.733051
9... ... ... ...
102678 14574.0 21546.730769 6972.730769
112679 35708.5 52026.764706 16318.264706
122680 35708.5 52026.764706 16318.264706
132681 163276.5 250882.500000 87606.000000
142682 7369.5 11785.729730 4416.229730
152683 rows × 3 columns
16df_pofit_less_5000 = df[df['Price']<5000]
17QUESTION
Fetching data in loop next js
Asked 2021-Dec-08 at 02:35I am attempting to use the data from 1 endpoint to call another endpoint that is filtered by id. I am planning on fetching both calls using getServerSideProps and passing the data to another component.
The first call will return an array of categories which then I am attempting to loop and fetch articles that is filtered by id.
I am able to successfully get back the array of categories but when I am attempting to loop and fetch articles I am getting a value of undefined How can I achieve this?
Here is an example of my index.js
1import ArticleList from "../../components/ArticleList";
2
3
4const Index = ({ categories, articles }) => {
5
6
7 return (
8 <>
9 <ArticleList categories={categories} articles={articles} />
10 </>
11 )
12}
13
14export async function getServerSideProps (context) {
15 // console.log('index - getserversideprops() is called')
16 try {
17 let articles = []
18 let response = await fetch('https://example.api/categories')
19 const categories = await response.json()
20
21 for (let i = 0; i < categories.results.length; i++) {
22 response = await fetch (`https://example.api/articleid/` + categories.results[i].id)
23 articles = await response.json()
24 }
25
26 console.log(articles,'33')
27
28
29 if (!categories ) {
30 return {
31 notFound: true,
32 }
33 }
34
35 return {
36 props: {
37 categories: categories,
38 articles: artices
39 }
40 }
41 } catch (error) {
42 console.error('runtime error: ', error)
43 }
44}
45
46export default Index
47Here is an example of my console.log(categories.results) array:
1import ArticleList from "../../components/ArticleList";
2
3
4const Index = ({ categories, articles }) => {
5
6
7 return (
8 <>
9 <ArticleList categories={categories} articles={articles} />
10 </>
11 )
12}
13
14export async function getServerSideProps (context) {
15 // console.log('index - getserversideprops() is called')
16 try {
17 let articles = []
18 let response = await fetch('https://example.api/categories')
19 const categories = await response.json()
20
21 for (let i = 0; i < categories.results.length; i++) {
22 response = await fetch (`https://example.api/articleid/` + categories.results[i].id)
23 articles = await response.json()
24 }
25
26 console.log(articles,'33')
27
28
29 if (!categories ) {
30 return {
31 notFound: true,
32 }
33 }
34
35 return {
36 props: {
37 categories: categories,
38 articles: artices
39 }
40 }
41 } catch (error) {
42 console.error('runtime error: ', error)
43 }
44}
45
46export default Index
47[ {
48"id": 2,
49"name": "Online"
50},
51{
52"id": 11,
53"name": "Retail"
54},
55{
56"id": 14,
57"name": "E-Commerce"
58}]
59I am expecting articles to be 3 separate arrays of data. Is this something that is possible if I am passing the data to another component? If not what will be a better way of handling this?
ANSWER
Answered 2021-Dec-08 at 02:35Try Promise.all
1import ArticleList from "../../components/ArticleList";
2
3
4const Index = ({ categories, articles }) => {
5
6
7 return (
8 <>
9 <ArticleList categories={categories} articles={articles} />
10 </>
11 )
12}
13
14export async function getServerSideProps (context) {
15 // console.log('index - getserversideprops() is called')
16 try {
17 let articles = []
18 let response = await fetch('https://example.api/categories')
19 const categories = await response.json()
20
21 for (let i = 0; i < categories.results.length; i++) {
22 response = await fetch (`https://example.api/articleid/` + categories.results[i].id)
23 articles = await response.json()
24 }
25
26 console.log(articles,'33')
27
28
29 if (!categories ) {
30 return {
31 notFound: true,
32 }
33 }
34
35 return {
36 props: {
37 categories: categories,
38 articles: artices
39 }
40 }
41 } catch (error) {
42 console.error('runtime error: ', error)
43 }
44}
45
46export default Index
47[ {
48"id": 2,
49"name": "Online"
50},
51{
52"id": 11,
53"name": "Retail"
54},
55{
56"id": 14,
57"name": "E-Commerce"
58}]
59export async function getServerSideProps(context) {
60 try {
61 const categories = await fetch('https://example.api/categories').then((response) => response.json());
62
63 if (!categories) {
64 return { notFound: true };
65 }
66
67 const articles = await Promise.all(
68 categories.results.map((result) =>
69 fetch(`https://example.api/articleid/` + result.id).then((response) => response.json())
70 )
71 );
72
73 const props = { categories, articles };
74
75 return { props };
76 } catch (error) {
77 console.error('runtime error: ', error);
78 }
79}
80The code will be clean.
QUESTION
how to order json data header while downloading it into xls format using JavaScript
Asked 2021-Dec-05 at 20:41I have a Json data in which I am generating dynamic keys which is having fiscal year quarter and respective values,I need to download the data into xls format which I am successfully able to do, but the problem is when I download the data the order of the xls header is not same as my json keys.Below is my sample data.
1var input = [
2 {
3 "FPH Level 1": "iphone",
4 "Geo Level 2": "Austria",
5 "Geo Level 7": "DACH",
6 "RTM": "Retail",
7 "Account": "Austria-epos",
8 "FY202004": "20%",
9 "FY202101": "20%",
10 "FY202102": "20%",
11 "FY202103": "20%",
12 "FY202104": "20%",
13 "Y/Y pt Change": "5%",
14 "Commentary Y/Y": "TESTING",
15 "Q/Q pt Change": "4%",
16 "Commentary Q/Q": "TESTING"
17 },
18 {
19 "FPH Level 1": "iphone",
20 "Geo Level 2": "Austria",
21 "Geo Level 7": "DACH",
22 "RTM": "Retail",
23 "Account": "Austria-epos",
24 "FY202004": "20%",
25 "FY202101": "20%",
26 "FY202102": "20%",
27 "FY202103": "20%",
28 "FY202104": "20%",
29 "Y/Y pt Change": "5%",
30 "Commentary Y/Y": "TESTING",
31 "Q/Q pt Change": "4%",
32 "Commentary Q/Q": "TESTING"
33 },
34 {
35 "FPH Level 1": "iphone",
36 "Geo Level 2": "Austria",
37 "Geo Level 7": "DACH",
38 "RTM": "Retail",
39 "Account": "Austria-epos",
40 "FY202004": "20%",
41 "FY202101": "20%",
42 "FY202102": "20%",
43 "FY202103": "20%",
44 "FY202104": "20%",
45 "Y/Y pt Change": "5%",
46 "Commentary Y/Y": "TESTING",
47 "Q/Q pt Change": "4%",
48 "Commentary Q/Q": "TESTING"
49 },
50 {
51 "FPH Level 1": "iphone",
52 "Geo Level 2": "Austria",
53 "Geo Level 7": "DACH",
54 "RTM": "Retail",
55 "Account": "Austria-epos",
56 "FY202004": "20%",
57 "FY202101": "20%",
58 "FY202102": "20%",
59 "FY202103": "20%",
60 "FY202104": "20%",
61 "Y/Y pt Change": "5%",
62 "Commentary Y/Y": "TESTING",
63 "Q/Q pt Change": "4%",
64 "Commentary Q/Q": "TESTING"
65 },
66 {
67 "FPH Level 1": "iphone",
68 "Geo Level 2": "Austria",
69 "Geo Level 7": "DACH",
70 "RTM": "Retail",
71 "Account": "Austria-epos",
72 "FY202004": "20%",
73 "FY202101": "20%",
74 "FY202102": "20%",
75 "FY202103": "20%",
76 "FY202104": "20%",
77 "Y/Y pt Change": "5%",
78 "Commentary Y/Y": "TESTING",
79 "Q/Q pt Change": "4%",
80 "Commentary Q/Q": "TESTING"
81 },
82]
83here to snipped code I am working to download the data
1var input = [
2 {
3 "FPH Level 1": "iphone",
4 "Geo Level 2": "Austria",
5 "Geo Level 7": "DACH",
6 "RTM": "Retail",
7 "Account": "Austria-epos",
8 "FY202004": "20%",
9 "FY202101": "20%",
10 "FY202102": "20%",
11 "FY202103": "20%",
12 "FY202104": "20%",
13 "Y/Y pt Change": "5%",
14 "Commentary Y/Y": "TESTING",
15 "Q/Q pt Change": "4%",
16 "Commentary Q/Q": "TESTING"
17 },
18 {
19 "FPH Level 1": "iphone",
20 "Geo Level 2": "Austria",
21 "Geo Level 7": "DACH",
22 "RTM": "Retail",
23 "Account": "Austria-epos",
24 "FY202004": "20%",
25 "FY202101": "20%",
26 "FY202102": "20%",
27 "FY202103": "20%",
28 "FY202104": "20%",
29 "Y/Y pt Change": "5%",
30 "Commentary Y/Y": "TESTING",
31 "Q/Q pt Change": "4%",
32 "Commentary Q/Q": "TESTING"
33 },
34 {
35 "FPH Level 1": "iphone",
36 "Geo Level 2": "Austria",
37 "Geo Level 7": "DACH",
38 "RTM": "Retail",
39 "Account": "Austria-epos",
40 "FY202004": "20%",
41 "FY202101": "20%",
42 "FY202102": "20%",
43 "FY202103": "20%",
44 "FY202104": "20%",
45 "Y/Y pt Change": "5%",
46 "Commentary Y/Y": "TESTING",
47 "Q/Q pt Change": "4%",
48 "Commentary Q/Q": "TESTING"
49 },
50 {
51 "FPH Level 1": "iphone",
52 "Geo Level 2": "Austria",
53 "Geo Level 7": "DACH",
54 "RTM": "Retail",
55 "Account": "Austria-epos",
56 "FY202004": "20%",
57 "FY202101": "20%",
58 "FY202102": "20%",
59 "FY202103": "20%",
60 "FY202104": "20%",
61 "Y/Y pt Change": "5%",
62 "Commentary Y/Y": "TESTING",
63 "Q/Q pt Change": "4%",
64 "Commentary Q/Q": "TESTING"
65 },
66 {
67 "FPH Level 1": "iphone",
68 "Geo Level 2": "Austria",
69 "Geo Level 7": "DACH",
70 "RTM": "Retail",
71 "Account": "Austria-epos",
72 "FY202004": "20%",
73 "FY202101": "20%",
74 "FY202102": "20%",
75 "FY202103": "20%",
76 "FY202104": "20%",
77 "Y/Y pt Change": "5%",
78 "Commentary Y/Y": "TESTING",
79 "Q/Q pt Change": "4%",
80 "Commentary Q/Q": "TESTING"
81 },
82]
83const xlsData = input
84 const ws = XLSX.utils.json_to_sheet(xlsData);
85 const wb = { Sheets: { 'data': ws }, SheetNames: ['data'] };
86 const excelBuffer = XLSX.write(wb, { bookType: 'xlsx', type: 'array' });
87 const data = new Blob([excelBuffer], { type: fileType });
88 let fileName = `test`
89 FileSaver.saveAs(data, fileName + fileExtension);
90result after the converted it into xls the header are like this
I am excepting the output the be
ANSWER
Answered 2021-Dec-05 at 20:33In your code snippet change the second line to:
1var input = [
2 {
3 "FPH Level 1": "iphone",
4 "Geo Level 2": "Austria",
5 "Geo Level 7": "DACH",
6 "RTM": "Retail",
7 "Account": "Austria-epos",
8 "FY202004": "20%",
9 "FY202101": "20%",
10 "FY202102": "20%",
11 "FY202103": "20%",
12 "FY202104": "20%",
13 "Y/Y pt Change": "5%",
14 "Commentary Y/Y": "TESTING",
15 "Q/Q pt Change": "4%",
16 "Commentary Q/Q": "TESTING"
17 },
18 {
19 "FPH Level 1": "iphone",
20 "Geo Level 2": "Austria",
21 "Geo Level 7": "DACH",
22 "RTM": "Retail",
23 "Account": "Austria-epos",
24 "FY202004": "20%",
25 "FY202101": "20%",
26 "FY202102": "20%",
27 "FY202103": "20%",
28 "FY202104": "20%",
29 "Y/Y pt Change": "5%",
30 "Commentary Y/Y": "TESTING",
31 "Q/Q pt Change": "4%",
32 "Commentary Q/Q": "TESTING"
33 },
34 {
35 "FPH Level 1": "iphone",
36 "Geo Level 2": "Austria",
37 "Geo Level 7": "DACH",
38 "RTM": "Retail",
39 "Account": "Austria-epos",
40 "FY202004": "20%",
41 "FY202101": "20%",
42 "FY202102": "20%",
43 "FY202103": "20%",
44 "FY202104": "20%",
45 "Y/Y pt Change": "5%",
46 "Commentary Y/Y": "TESTING",
47 "Q/Q pt Change": "4%",
48 "Commentary Q/Q": "TESTING"
49 },
50 {
51 "FPH Level 1": "iphone",
52 "Geo Level 2": "Austria",
53 "Geo Level 7": "DACH",
54 "RTM": "Retail",
55 "Account": "Austria-epos",
56 "FY202004": "20%",
57 "FY202101": "20%",
58 "FY202102": "20%",
59 "FY202103": "20%",
60 "FY202104": "20%",
61 "Y/Y pt Change": "5%",
62 "Commentary Y/Y": "TESTING",
63 "Q/Q pt Change": "4%",
64 "Commentary Q/Q": "TESTING"
65 },
66 {
67 "FPH Level 1": "iphone",
68 "Geo Level 2": "Austria",
69 "Geo Level 7": "DACH",
70 "RTM": "Retail",
71 "Account": "Austria-epos",
72 "FY202004": "20%",
73 "FY202101": "20%",
74 "FY202102": "20%",
75 "FY202103": "20%",
76 "FY202104": "20%",
77 "Y/Y pt Change": "5%",
78 "Commentary Y/Y": "TESTING",
79 "Q/Q pt Change": "4%",
80 "Commentary Q/Q": "TESTING"
81 },
82]
83const xlsData = input
84 const ws = XLSX.utils.json_to_sheet(xlsData);
85 const wb = { Sheets: { 'data': ws }, SheetNames: ['data'] };
86 const excelBuffer = XLSX.write(wb, { bookType: 'xlsx', type: 'array' });
87 const data = new Blob([excelBuffer], { type: fileType });
88 let fileName = `test`
89 FileSaver.saveAs(data, fileName + fileExtension);
90const header = ["FPH Level 1", "Geo Level 2", "Geo Level 7", "RTM", "Account"]
91const fy = Object.keys(input[0]).filter(s => s.startsWith("FY")).sort()
92header.push(...fy)
93header.push("Y/Y pt Change", "Commentary Y/Y", "Q/Q pt Change", "Commentary Q/Q")
94const ws = XLSX.utils.json_to_sheet(xlsData, { header })
95QUESTION
json_normalize a nested database
Asked 2021-Nov-10 at 13:56I'm trying to flatten a json database into pandas dataframe and as it's the first time I'm dealing with json format, I can't do what I want. The database is located here https://mtgjson.com/downloads/all-files/#allprices and according to the model, the structure is like this :
1{
2 "0120a941-9cfb-50b5-b5e4-4e0c7bd32410": {
3 "mtgo": {
4 "cardhoarder": {
5 "currency": "USD",
6 "retail": {
7 "foil": {
8 ..., // more rows
9 "2020-04-21": 0.02
10 },
11 "normal": {
12 ..., // more rows
13 "2020-04-21": 0.02
14 }
15 }
16 },
17 },
18 "paper": {
19 "cardkingdom" : {
20 "buylist": {
21 "foil": {
22 ..., // more rows
23 "2020-04-21": 0.6
24 },
25 "normal": {
26 ..., // more rows
27 "2020-04-21": 0.01
28 }
29 },
30 "currency": "USD",
31 "retail": {
32 "foil": {
33 ..., // more rows
34 "2020-04-21": 0.12
35 },
36 "normal": {
37 ..., // more rows
38 "2020-04-21": 0.02
39 }
40 }
41 },
42 "cardmarket": {
43 "currency": "EUR",
44 "retail": {
45 "foil": {
46 ..., // more rows
47 "2020-04-21": 0.12
48 },
49 "normal": {
50 ..., // more rows
51 "2020-04-21": 0.02
52 }
53 }
54 },
55 "tcgplayer": {
56 "currency": "USD",
57 "retail": {
58 "foil": {
59 ..., // more rows
60 "2020-04-21": 0.12
61 },
62 "normal": {
63 ..., // more rows
64 "2020-04-21": 0.02
65 }
66 }
67 }
68 }
69 }
70}
71When I look into the json file I have this :
1{
2 "0120a941-9cfb-50b5-b5e4-4e0c7bd32410": {
3 "mtgo": {
4 "cardhoarder": {
5 "currency": "USD",
6 "retail": {
7 "foil": {
8 ..., // more rows
9 "2020-04-21": 0.02
10 },
11 "normal": {
12 ..., // more rows
13 "2020-04-21": 0.02
14 }
15 }
16 },
17 },
18 "paper": {
19 "cardkingdom" : {
20 "buylist": {
21 "foil": {
22 ..., // more rows
23 "2020-04-21": 0.6
24 },
25 "normal": {
26 ..., // more rows
27 "2020-04-21": 0.01
28 }
29 },
30 "currency": "USD",
31 "retail": {
32 "foil": {
33 ..., // more rows
34 "2020-04-21": 0.12
35 },
36 "normal": {
37 ..., // more rows
38 "2020-04-21": 0.02
39 }
40 }
41 },
42 "cardmarket": {
43 "currency": "EUR",
44 "retail": {
45 "foil": {
46 ..., // more rows
47 "2020-04-21": 0.12
48 },
49 "normal": {
50 ..., // more rows
51 "2020-04-21": 0.02
52 }
53 }
54 },
55 "tcgplayer": {
56 "currency": "USD",
57 "retail": {
58 "foil": {
59 ..., // more rows
60 "2020-04-21": 0.12
61 },
62 "normal": {
63 ..., // more rows
64 "2020-04-21": 0.02
65 }
66 }
67 }
68 }
69 }
70}
71{"meta": {"date": "2021-11-07", "version": "5.1.0+20211107"}, "data": {"00010d56-fe38-5e35-8aed-518019aa36a5": {"paper": {"cardkingdom": {"buylist": {"foil.....
72When I do the basic pd.read_json('AllPrices.json') I got this
| meta | data | |
|---|---|---|
| date | 2021-11-07 | NaN |
| version | 5.1.0+20211107 | NaN |
| 00010d56-fe38-5e35-8aed-518019aa36a5 | NaN | {'paper': {'cardkingdom': {'buylist': {'foil':... |
| 0001e0d0-2dcd-5640-aadc-a84765cf5fc9 | NaN | {'paper': {'cardkingdom': {'buylist': {'normal... |
So I did some research and found the json_normalize and wrote this piece of code :
1{
2 "0120a941-9cfb-50b5-b5e4-4e0c7bd32410": {
3 "mtgo": {
4 "cardhoarder": {
5 "currency": "USD",
6 "retail": {
7 "foil": {
8 ..., // more rows
9 "2020-04-21": 0.02
10 },
11 "normal": {
12 ..., // more rows
13 "2020-04-21": 0.02
14 }
15 }
16 },
17 },
18 "paper": {
19 "cardkingdom" : {
20 "buylist": {
21 "foil": {
22 ..., // more rows
23 "2020-04-21": 0.6
24 },
25 "normal": {
26 ..., // more rows
27 "2020-04-21": 0.01
28 }
29 },
30 "currency": "USD",
31 "retail": {
32 "foil": {
33 ..., // more rows
34 "2020-04-21": 0.12
35 },
36 "normal": {
37 ..., // more rows
38 "2020-04-21": 0.02
39 }
40 }
41 },
42 "cardmarket": {
43 "currency": "EUR",
44 "retail": {
45 "foil": {
46 ..., // more rows
47 "2020-04-21": 0.12
48 },
49 "normal": {
50 ..., // more rows
51 "2020-04-21": 0.02
52 }
53 }
54 },
55 "tcgplayer": {
56 "currency": "USD",
57 "retail": {
58 "foil": {
59 ..., // more rows
60 "2020-04-21": 0.12
61 },
62 "normal": {
63 ..., // more rows
64 "2020-04-21": 0.02
65 }
66 }
67 }
68 }
69 }
70}
71{"meta": {"date": "2021-11-07", "version": "5.1.0+20211107"}, "data": {"00010d56-fe38-5e35-8aed-518019aa36a5": {"paper": {"cardkingdom": {"buylist": {"foil.....
72with open('AllPrices.json','r') as f:
73 data = json.loads(f.read())
74pd.json_normalize(data, errors='ignore')
75This did the job by flattening the json database but I ended with one row and 31 millions columns. What I want is only one information in this database that is the uuid and the cardmarket price of a normal paper card on the date I want like this :
| uuid | paper.cardmarket.retail.normal.2021-11-07 |
|---|---|
| 00010d56-fe38-5e35-8aed-518019aa36a5 | 0.5 |
| 0001e0d0-2dcd-5640-aadc-a84765cf5fc9 | 0.25 |
I played with the record_path = parameter and the meta = parameter but the best I did was not my expected table.
I tried record_path = ['data'] that give me only the uuid in one column.
Thanks for your help
ANSWER
Answered 2021-Nov-10 at 11:03In your case, use json_normalize for each uuid record then extract the desired information 'paper.cardmarket.retail.normal':
1{
2 "0120a941-9cfb-50b5-b5e4-4e0c7bd32410": {
3 "mtgo": {
4 "cardhoarder": {
5 "currency": "USD",
6 "retail": {
7 "foil": {
8 ..., // more rows
9 "2020-04-21": 0.02
10 },
11 "normal": {
12 ..., // more rows
13 "2020-04-21": 0.02
14 }
15 }
16 },
17 },
18 "paper": {
19 "cardkingdom" : {
20 "buylist": {
21 "foil": {
22 ..., // more rows
23 "2020-04-21": 0.6
24 },
25 "normal": {
26 ..., // more rows
27 "2020-04-21": 0.01
28 }
29 },
30 "currency": "USD",
31 "retail": {
32 "foil": {
33 ..., // more rows
34 "2020-04-21": 0.12
35 },
36 "normal": {
37 ..., // more rows
38 "2020-04-21": 0.02
39 }
40 }
41 },
42 "cardmarket": {
43 "currency": "EUR",
44 "retail": {
45 "foil": {
46 ..., // more rows
47 "2020-04-21": 0.12
48 },
49 "normal": {
50 ..., // more rows
51 "2020-04-21": 0.02
52 }
53 }
54 },
55 "tcgplayer": {
56 "currency": "USD",
57 "retail": {
58 "foil": {
59 ..., // more rows
60 "2020-04-21": 0.12
61 },
62 "normal": {
63 ..., // more rows
64 "2020-04-21": 0.02
65 }
66 }
67 }
68 }
69 }
70}
71{"meta": {"date": "2021-11-07", "version": "5.1.0+20211107"}, "data": {"00010d56-fe38-5e35-8aed-518019aa36a5": {"paper": {"cardkingdom": {"buylist": {"foil.....
72with open('AllPrices.json','r') as f:
73 data = json.loads(f.read())
74pd.json_normalize(data, errors='ignore')
75with open('AllPrices.json') as fp:
76 prices = json.load(fp)
77
78 data = []
79 for uuid in prices['data']:
80 df = pd.json_normalize(prices['data'][uuid]) \
81 .filter(like='paper.cardmarket.retail.normal')
82 if df.empty:
83 continue
84 df.columns = df.columns.str.rsplit('.', 1).str[-1]
85 df.index = [uuid]
86 data.append(df)
87 df = pd.concat(data)
88Output: (tested on AllPrices.json file)
1{
2 "0120a941-9cfb-50b5-b5e4-4e0c7bd32410": {
3 "mtgo": {
4 "cardhoarder": {
5 "currency": "USD",
6 "retail": {
7 "foil": {
8 ..., // more rows
9 "2020-04-21": 0.02
10 },
11 "normal": {
12 ..., // more rows
13 "2020-04-21": 0.02
14 }
15 }
16 },
17 },
18 "paper": {
19 "cardkingdom" : {
20 "buylist": {
21 "foil": {
22 ..., // more rows
23 "2020-04-21": 0.6
24 },
25 "normal": {
26 ..., // more rows
27 "2020-04-21": 0.01
28 }
29 },
30 "currency": "USD",
31 "retail": {
32 "foil": {
33 ..., // more rows
34 "2020-04-21": 0.12
35 },
36 "normal": {
37 ..., // more rows
38 "2020-04-21": 0.02
39 }
40 }
41 },
42 "cardmarket": {
43 "currency": "EUR",
44 "retail": {
45 "foil": {
46 ..., // more rows
47 "2020-04-21": 0.12
48 },
49 "normal": {
50 ..., // more rows
51 "2020-04-21": 0.02
52 }
53 }
54 },
55 "tcgplayer": {
56 "currency": "USD",
57 "retail": {
58 "foil": {
59 ..., // more rows
60 "2020-04-21": 0.12
61 },
62 "normal": {
63 ..., // more rows
64 "2020-04-21": 0.02
65 }
66 }
67 }
68 }
69 }
70}
71{"meta": {"date": "2021-11-07", "version": "5.1.0+20211107"}, "data": {"00010d56-fe38-5e35-8aed-518019aa36a5": {"paper": {"cardkingdom": {"buylist": {"foil.....
72with open('AllPrices.json','r') as f:
73 data = json.loads(f.read())
74pd.json_normalize(data, errors='ignore')
75with open('AllPrices.json') as fp:
76 prices = json.load(fp)
77
78 data = []
79 for uuid in prices['data']:
80 df = pd.json_normalize(prices['data'][uuid]) \
81 .filter(like='paper.cardmarket.retail.normal')
82 if df.empty:
83 continue
84 df.columns = df.columns.str.rsplit('.', 1).str[-1]
85 df.index = [uuid]
86 data.append(df)
87 df = pd.concat(data)
88 2021-08-09 2021-08-11 2021-08-12 2021-08-13 ... 2021-10-27 2021-10-28 2021-11-02 2021-11-09
8900010d56-fe38-5e35-8aed-518019aa36a5 4.35 4.35 4.35 4.35 ... 4.35 4.35 4.35 4.35
900001e0d0-2dcd-5640-aadc-a84765cf5fc9 4.95 4.95 3.45 3.45 ... 7.99 6.81 5.42 6.77
910003caab-9ff5-5d1a-bc06-976dd0457f19 0.24 0.27 0.07 0.38 ... 0.25 0.04 0.13 0.36
920003d249-25d9-5223-af1e-1130f09622a7 0.30 0.30 0.75 0.75 ... 0.15 0.20 0.04 0.25
930004a4fb-92c6-59b2-bdbe-ceb584a9e401 0.27 0.14 0.19 0.10 ... 0.10 0.05 0.19 0.13
94... ... ... ... ... ... ... ... ... ...
95fffa4ccf-733e-513a-98f9-181b9549de62 0.23 0.15 0.21 0.21 ... 0.10 0.21 0.15 0.20
96fffb659e-b3fa-5cd8-9423-fe5ac74248b5 0.49 0.49 0.49 0.49 ... 0.35 0.35 0.35 0.20
97fffbc95a-c4d1-56aa-8653-8a7c71fe19ce 6.95 6.95 6.95 6.95 ... 10.26 10.26 10.26 6.43
98fffc1305-a118-559b-9504-3d7b56ca0bde 0.18 0.18 0.18 0.18 ... 0.04 0.04 0.04 0.04
99fffdd333-3789-5104-a8be-37be199a2cb1 0.87 0.73 0.99 0.99 ... 0.49 0.45 0.45 0.15
100
101[50568 rows x 75 columns]
102QUESTION
joining two dataframes on matching values of two common columns R
Asked 2021-Oct-07 at 16:35I have a two dataframes A and B that both have multiple columns. They share the common columns "week" and "store". I would like to join these two dataframes on the matching values of the common columns.
For example this is a small subset of the data that I have:
1A = data.frame(retailer = c(2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2),
2store = c(5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6),
3week = c(2021100301, 2021092601, 2021091901, 2021091201, 2021082901, 2021082201, 2021081501, 2021080801,
4 2021080101, 2021072501, 2021071801, 2021071101, 2021070401, 2021062701, 2021062001, 2021061301),
5dollars = c(121817.9, 367566.7, 507674.5, 421257.8, 453330.3, 607551.4, 462674.8,
6 464329.1, 339342.3, 549271.5, 496720.1, 554858.7, 382675.5,
7 373210.9, 422534.2, 381668.6))
8and
1A = data.frame(retailer = c(2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2),
2store = c(5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6),
3week = c(2021100301, 2021092601, 2021091901, 2021091201, 2021082901, 2021082201, 2021081501, 2021080801,
4 2021080101, 2021072501, 2021071801, 2021071101, 2021070401, 2021062701, 2021062001, 2021061301),
5dollars = c(121817.9, 367566.7, 507674.5, 421257.8, 453330.3, 607551.4, 462674.8,
6 464329.1, 339342.3, 549271.5, 496720.1, 554858.7, 382675.5,
7 373210.9, 422534.2, 381668.6))
8B = data.frame(
9 week = c("2020080901", "2017111101", "2017061801", "2020090701", "2020090701", "2020090701",
10 "2020091201","2020082301", "2019122201", "2017102901"),
11 store = c(14071, 11468, 2428, 17777, 14821, 10935, 5127, 14772, 14772, 14772),
12 fill = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1)
13)
14I would like to join these two tables on the matching week AND store values in order to incorporate the "fill" column from B into A. Where the values don't match, I would like to have a label "0" in the fill column, instead of a 1. Is there a way I can do this? I am not sure which join to use as well, or if "merge" would be better for this? Essentially I am NOT trying to get rid of any rows that do not have the matching values for the two common columns. Thanks for any help!
ANSWER
Answered 2021-Oct-07 at 16:35We may do a left_join
1A = data.frame(retailer = c(2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2),
2store = c(5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6),
3week = c(2021100301, 2021092601, 2021091901, 2021091201, 2021082901, 2021082201, 2021081501, 2021080801,
4 2021080101, 2021072501, 2021071801, 2021071101, 2021070401, 2021062701, 2021062001, 2021061301),
5dollars = c(121817.9, 367566.7, 507674.5, 421257.8, 453330.3, 607551.4, 462674.8,
6 464329.1, 339342.3, 549271.5, 496720.1, 554858.7, 382675.5,
7 373210.9, 422534.2, 381668.6))
8B = data.frame(
9 week = c("2020080901", "2017111101", "2017061801", "2020090701", "2020090701", "2020090701",
10 "2020091201","2020082301", "2019122201", "2017102901"),
11 store = c(14071, 11468, 2428, 17777, 14821, 10935, 5127, 14772, 14772, 14772),
12 fill = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1)
13)
14library(dplyr)
15library(tidyr)
16A %>%
17 mutate(week = as.character(week)) %>%
18 left_join(B) %>%
19 mutate(fill = replace_na(fill, 0))
20Community Discussions contain sources that include Stack Exchange Network
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