Popular New Releases in Predictive Analytics
pyod
V0.9.9
darts
Release minor 0.19.0
orbit
v1.1.2alpha
alibi-detect
forecast
forecast 8.16
Popular Libraries in Predictive Analytics
by maxbbraun 
python
6055 MIT
A stock trading bot powered by Trump tweets
by yzhao062 python
5422 BSD-2-Clause
A Comprehensive and Scalable Python Library for Outlier Detection (Anomaly Detection)
by yzhao062 python
4777 AGPL-3.0
Anomaly detection related books, papers, videos, and toolboxes
by unit8co python
3904 Apache-2.0
A python library for easy manipulation and forecasting of time series.
by twitter r
3348 GPL-3.0
Anomaly Detection with R
by numenta jupyter notebook
1466 AGPL-3.0
The Numenta Anomaly Benchmark
by uber python
1341 NOASSERTION
A Python package for Bayesian forecasting with object-oriented design and probabilistic models under the hood.
by SeldonIO python
1264 Apache-2.0
Algorithms for outlier, adversarial and drift detection
by shirosaidev python
1134 Apache-2.0
Stock market analyzer and predictor using Elasticsearch, Twitter, News headlines and Python natural language processing and sentiment analysis
Trending New libraries in Predictive Analytics
by uber python
1341 NOASSERTION
A Python package for Bayesian forecasting with object-oriented design and probabilistic models under the hood.
by zillow python
550 Apache-2.0
Luminaire is a python package that provides ML driven solutions for monitoring time series data.
by Nixtla jupyter notebook
530 MIT
Lightning ⚡️ fast forecasting with statistical and econometric models.
by zalandoresearch python
435 NOASSERTION
PyTorch based Probabilistic Time Series forecasting framework based on GluonTS backend
by kaushikjadhav01 python
215
Stock Market Prediction Web App based on Machine Learning and Sentiment Analysis of Tweets (API keys included in code). The front end of the Web App is based on Flask and Wordpress. The App forecasts stock prices of the next seven days for any given stock under NASDAQ or NSE as input by the user. Predictions are made using three algorithms: ARIMA, LSTM, Linear Regression. The Web App combines the predicted prices of the next seven days with the sentiment analysis of tweets to give recommendation whether the price is going to rise or fall
by steve0hh go
181 Apache-2.0
Go implementation of MIDAS: Microcluster-Based Detector of Anomalies in Edge Streams
by cvlab-yonsei python
154
An official implementation of "Learning Memory-guided Normality for Anomaly Detection" (CVPR 2020) in PyTorch.
by adobe python
144 NOASSERTION
One Stop Anomaly Shop: Anomaly detection using two-phase approach: (a) pre-labeling using statistics, Natural Language Processing and static rules; (b) anomaly scoring using supervised and unsupervised machine learning.
by cgpotts jupyter notebook
144 Apache-2.0
DynaSent: Dynamic Sentiment Analysis Dataset
Top Authors in Predictive Analytics
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3
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3 Libraries
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1
6 Libraries
347
2
5 Libraries
10531
3
4 Libraries
114
4
4 Libraries
88
5
4 Libraries
14
6
4 Libraries
908
7
3 Libraries
457
8
3 Libraries
52
9
3 Libraries
414
10
3 Libraries
8
Trending Kits in Predictive Analytics
This Predictive Analytics kit provides an analytical view of students’ performance in mathematics and predicts grades to be scored in the final test.
The key features of this solution are:
- Analysis of grades of students
- Visualisation of patterns
- Prediction of grade in the final test
For a detailed tutorial on installing & executing the solution as well as learning resources including training & certification opportunities, please visit the OpenWeaver Community
Development Environment
VSCode and Jupyter Notebook are used for development and debugging. Jupyter Notebook is a web based interactive environment often used for experiments, whereas VSCode is used to get a typical experience of IDE for developers. Jupyter Notebook is used for our development.
Data Mining
Our solution integrates data from various sources, and we have used below libraries for exploring patterns in these data and understanding correlation between the features.
Data Visualisation
The patterns and relationships are identified by representing data visually and below libraries are used for that.
Machine learning
Below libraries and model collections helps to create the machine learning models for the core prediction of use case in our solution.
Support
If you need help using this kit, you may reach us at the OpenWeaver Community.
1-Click Install 
The Isolation Forest algorithm is also known as Isolation-Based Anomaly Detection. It is a powerful method for detecting anomalies in each dataset. Isolation Forest model leverages the concept of isolation trees to isolate individual observations.
The algorithm partitions the data by constructing decision trees using random splits. I can also be done using selected features to create a tree structure. This tree-based model is known as the Isolation Forest. It separates data points from anomalous ones by assigning partitions of the latter. It is particularly effective in detecting anomalies in high-dimensional datasets. Also, in time series data and even for credit card fraud detection.
To implement Isolation Forest, you can use the IsolationForest function provided by scikit-learn. It allows you to isolate and score observations based on their anomaly status. The algorithm assigns an anomaly score to each data point. It indicates its abnormality level. You can identify and flag anomalous observations by comparing scores to the threshold. This threshold value can be adjusted based on the specific requirements.
The Isolation Forest algorithm is an unsupervised outlier detection method. It makes it suitable for scenarios where labeled data is limited. It stands out due to its ability to handle large datasets. It also helps with its capability to handle both numeric and categorical features. The Isolation Forest complements Local Outlier Factor. It offers various tools for anomaly detection tasks.
The Isolation Forest algorithm is a prominent approach for anomaly detection. It uses the isolation trees concept to identify anomalous behavior within a dataset. The isolation tree is built by data partitioning through random splits. This process isolates individual observations as "isolates" in the form of binary trees. Unlike normal points, anomalous ones need fewer random partitions before being isolated.
Implementing the Isolation Forest allows applying the trained model to new data points. It helps determine their anomaly status. This can be particularly useful for real-time anomaly detection in various domains. The Isolation Forest algorithm helps identify anomalous behavior and uncover insights. It may need to be evident through traditional data analysis techniques.
Scatter and box plots can understand the distribution of normal and anomalous observations. These visualizations can help interpret the model's output and aid in decision-making.
How to use isolation forest for anomaly detection in scikit-learn Python
- You must import the appropriate libraries to use Isolation Forest for anomaly identification. It includes the IsolationForest class and numpy.
- Isolation Forest only works with numerical data, so ensure it's in the right format. You can use one-hot or label encoding if you have categorical data. It helps transform them into numerical variables.
- Following that, you create an instance of the IsolationForest class. Define the tree count (n estimators) and the estimated outlier fraction (contamination). We should define a random state for repeatability.
- After constructing the instance, use the fit() method to fit the model to your data. Once the model has been trained, you may use the predict() method to predict abnormalities.
- This method returns an array of -1's and 1's, where -1 represents an anomaly and 1 represents a non-anomaly.
- Finally, you may extract the anomalous data points by using numpy. It filters out the data points with a corresponding -1 value.
Preview of the output obtained
Code
- The fit() method is used to fit the model to the training set. The predict() method is used after the model has been trained to forecast the anomalies in the training, test, and outlier datasets.
- print(y_pred_test) and print(y_pred_outliers) print the projected values for the test and outlier datasets, respectively.
- Because all of the projected values are -1, the Isolation Forest method successfully found all of the outliers in the X outliers dataset. According to the model, the expected values for the test dataset are all 1, indicating that there are no outliers in this dataset.
Follow the steps carefully to get the output easily.
- Install Visual Studio Code in your computer.
- import the required libraries using the commands -
pip install scikit-learn
pip install numpy
- Open the folder in the code editor, copy and paste the above kandi code snippet in the python file.
- Remove the below mentioned parts of the code for better understanding of Isolation forest.
- Run the code using the run command.
I hope you found this useful. I have added version information and depending libraries in the following sections.
I found this code snippet by searching for "isolation forest for anomaly detection in scikit-learn Python" in kandi. You can try any such use case!
Dependent libraries
If you do not have scikit-learn and numpy that is required to run this code, you can install it by clicking on the above link and copying the pip Install command from the page in kandi.
You can search for any dependent library on kandi like scikit-learn.
Environment tested
- This code had been tested using python version 3.8.0
- scikit-learn version 1.2.2 has been used.
- numpy version 1.24.2 has been used.
Support
- For any support on kandi solution kits, please use the chat
- For further learning resources, visit the Open Weaver Community learning page.
FAQ
1. What is Isolation Forest for Anomaly Detection?
Isolation Forest is an unsupervised anomaly detection algorithm. It uses isolation trees to isolate anomalous observations. It leverages the concept of fewer random partitions needed to isolate anomalies. It makes it efficient for identifying anomalies in large datasets.
2. How does the Isolation Forest Algorithm work to detect anomalies?
The Isolation Forest Algorithm detects anomalies by isolating observations. We can do so using a series of random splits and feature selections. It is where anomalous data points need random partitions. It can be isolated compared to normal points.
3. What are isolates, and how do they help with credit card fraud detection?
Isolates are individual observations. They are separated and treated as individual trees in the Isolation Forest algorithm. They help with credit card fraud detection. You can isolate and flag fraudulent credit card transactions as anomalous points. We can do it based on the algorithm's partitioning process and anomaly scoring.
4. Are series data effective when using an Isolation Forest for anomaly detection?
Yes, series data is effective when using an Isolation Forest for anomaly detection. It captures temporal patterns and dependencies. It enables better identification of anomalous behavior over time.
5. How are binary decision trees implemented in the isolation forest model?
Binary decision trees are implemented in the construction of an isolation forest model. You can do so by partitioning the data using random splits on selected features. It creates a tree structure where each internal node represents a binary decision. It is based on a feature and split value.
The Python programming language is one of the most popular languages in data science. It allows to quickly build prototypes in a short amount of time, and it has a large community. Predictive Analytics is the branch of data analytics which makes use of historical data and statistical algorithms to predict future events. Predictive analytics is an important element in the fields of business intelligence, marketing, finance and operations. Prediction is a branch of machine learning that deals with estimating the future outcome on the basis of given set of predictor or independent variables. The algorithms and techniques used in predictive modeling are based on pattern recognition and statistical techniques. Some of the most widely used open source libraries for Python Predictive Analytics among developers include: Sentiment-Analysis-in-Event-Driven-Stock-Price-Movement-Prediction - Use NLP to predict stock price movement associated with news;Time-series-prediction - A collection of time series prediction methods: rnn, seq2seq, cnn, wavenet, transformer, unet, nbeats; palladium - Framework for setting up predictive analytics services.
Predictive analytics is crucial in the world of data science. It is an important concept that have the ability to analyze data, apply statistics and machine learning algorithms, and create predictive models that are used to make predictions. Java is a general purpose programming language, it has been used for many kinds of software development. Predictive analytics is an area of data science that deals with extracting information from data and using it to predict trends and behavior patterns. It is often associated with big data, but it can also be applied to small data sets. Predictive analytics uses statistical techniques from data mining, predictive modeling, artificial intelligence, machine learning and other advanced analytics technologies to analyze current data to make predictions about future events. Popular Java Predictive Analytics open source libraries among developers include: redux-java - java version of Redux : a predictable state container; xgboost-predictor-java - Pure Java implementation of XGBoost predictor; KalmanLocationManager - Android LocationManager that delivers location predictions based.
Ruby is a dynamic, general purpose programming language with a focus on simplicity and productivity. It has an elegant syntax that is natural to read and easy to write. It's this combination of simplicity and power. Predictive analytics is the art and science of predicting future events by using existing statistical data. Predictive analytics can help businesses to make smarter decisions, avoid risks, and increase revenues. Predictive analytics is the use of historical data to predict future events. It's based on the assumption that patterns found in historical data can be used for forecasting. To perform predictive analysis, we need to use statistics, machine learning algorithms, and other tools such as neural networks. A few of the most popular Ruby Predictive Analytics open source libraries for developers are: vcr - test suite's HTTP interactions; concurrent-ruby - Modern concurrency tools including agents; predictor - efficient recommendations and predictions using Redis.
JavaScript is the High-level, dynamic, untyped, and interpreted programming language. It is one of the most popular programming languages in the world. It has a massive community, and it's growing daily. This means it's easier than ever to work with open source software. JavaScript Predictive Analytics software is used to analyze data and make predictions based on that data. This software can be used in a variety of scenarios, such as predicting the likelihood of an event, assessing the impact of an event, or predicting future outcomes. The best JavaScript Predictive Analytics libraries are open source and built by companies like Google, Facebook, and Apple. They help developers to improve the performance of their sites and apps by providing them with all kinds of functions, from visualizing data to gathering statistics. Some of the most popular JavaScript Predictive Analytics among developers are: lime - Lime: Explaining the predictions of any machine learning classifier; premonish - Predict which DOM element a user will interact with next; trial-js - Mouse position monitoring and user input prediction.
Predictive analytics is the branch of the advanced analytics which is used to make predictions about unknown future events. Predictive analytics uses many techniques from data mining, statistics, modeling, machine learning, and artificial intelligence to analyze current data to make predictions about future. With the rapid growth of data science and technologies, predictive analytics is becoming a much more common solution to problems in many industries. It uses algorithms to analyze current and historical data to help businesses make decisions about their future. The goal is to find patterns in the data and create a model that can use to make predictions. Some of the most widely used C# Predictive Analytics open source libraries among developers include: UnityLockstep - Modern Lockstep with clientside prediction and rollback; Punchkeyboard - opensource keyboard for virtual reality; KSPTrajectories - Kerbal Space Program mod to display trajectory predictions.
C++ is a general-purpose programming language that was developed by Bjarne Stroustrup. It has evolved into a robust and reliable programming language, with speed, efficiency and reliability. C++ is also the language of choice for multiple large scale open source projects in predictive analytics. C++ is one of the most widely used programming languages in data science and analytics. Predictive analytics can be defined as a field of analytic techniques that extract patterns from large volumes of data, and then use those patterns to predict future trends and behaviors. Predictive analytics is also referred to as predictive modeling, which involves an iterative process of building predictive models. Predictive Analytics is the process of using data to make predictions based on past and current data. Popular C++ Predictive Analytics open source libraries for developers include: continuable - C14 asynchronous allocation aware futures; control-toolbox - The Control Toolbox An OpenSource C Library for Robotics, Optimal and Model Predictive Control; CaPTk - Cancer Imaging Phenomics Toolkit is a software platform.
Go is one of the fastest developing programming languages that makes it easy to build simple, reliable, and efficient software. It is a statically typed language, memory safe language and provides garbage collection, type safety, dynamic-typing capability, many advanced built-in types such as variable length arrays and key-value maps. Go has a rich ecosystem of tools and libraries for predictive analytics. Predictive analytics is the branch of the advanced analytics which is used to make predictions about unknown future events. Predictive analytics uses many techniques from data mining, statistics, modeling, machine learning, and artificial intelligence to analyze current data to make predictions about future. Developers tend to use some of the following Go Predictive Analytics open source libraries: terraform - Terraform enables you to safely and predictably create; leaves - pure Go implementation of prediction part; predictive-horizontal-pod-autoscaler - Horizontal Pod Autoscaler built.
Trending Discussions on Predictive Analytics
will TensorFlow utilize GPU for predictive Analysis?
Restructuring Pandas Dataframe for large number of columns
Display data from two json files in react native
QUESTION
will TensorFlow utilize GPU for predictive Analysis?
Asked 2020-Nov-21 at 21:35GPU is good for parallel computing but the problem is some machine learning libraries don't utilize the GPU, unless that machine learning based on image processing or some sort of graphics processing, what if I am using machine learning for predictive Analytics? do libraries like TensorFlow utilize the GPU? or they use only CPU? or can I choose which processing unit to use? whats the deal here?
note: predictive Analysis requires no graphics processing.
ANSWER
Answered 2020-Nov-21 at 21:35The computation that happens in the GPU in any of the machine learning frameworks that support GPUs is not limited to graphical processing. For instance, if your model is a simple logistic regression, a framework such as TensorFlow will run it on the GPU if properly configured.
The advantage of GPUs for machine learning is that training big neural networks benefits greatly from the high level of parallelism that the GPUs offer.
If you want to know more about this, I'd recommend you start here or here.
some things to consider:- how much a model will benefit from running in the GPU will depend on how much it will benefit from parallel computation in general.
- Deep Learning models can be applied to predictive analytics, as well as more classical machine learning models. Bear in mind that neural nets are possibly the category of models that will benefit inherently from the GPU (see links above).
- Even though running models using GPUs (or even more specialised hardware) can bring benefits, I would suggest that you don't choose a framework and, especially, don't choose an algorithm based solely on the fact that it will benefit from parallelism, but rather look at how appropriate a given algorithm is for the data you have.
QUESTION
Restructuring Pandas Dataframe for large number of columns
Asked 2020-Nov-01 at 19:39I have a pandas dataframe which is a large number of answers given by users in response to a survey and I need to re-structure it. There are up to 105 questions asked each year, but I only need maybe 20 of them.
The current structure is as below.
What I want to do is re-structure it so that the row values become column names and the answer given by the user is then the value in that column. In a picture (from Excel), what I want is the below (I know I'll need to re-name my columns, but that's fine once I can create the structure in the first place):
Is it possible to re-structure my dataframe this way? The outcome of this is to use some predictive analytics to predict a target variable, so I need to re-strcture before I can use Random Forest, kNN, and so on.
ANSWER
Answered 2020-Nov-01 at 19:39You might want try pivoting your table:
1df.pivot(index=['SurveyID', 'UserID'], columns=['QuestionID'], values=['AnswerText'])
2df.columns = [x[0] if x[1] == "" else "Answer_{}".format(x[1]) for x in df.columns.to_flat_index()]
3QUESTION
Display data from two json files in react native
Asked 2020-May-17 at 23:55I have js files Dashboard and Adverts. I managed to get Dashboard to list the information in one json file (advertisers), but when clicking on an advertiser I want it to navigate to a separate page that will display some data (Say title and text) from the second json file (productadverts). I can't get it to work. Below is the code for the Dashboard and next for Adverts. Then the json files
1import * as React from 'react';
2import { Text, View, StyleSheet, Image, FlatList, TouchableOpacity } from 'react-native';
3
4import advertisers from '../data/advertisers.json';
5
6export default class Advertisers extends React.Component {
7 rendItem = listItem => {
8 let item = listItem.item
9 return (
10 <TouchableOpacity onPress={() => this.advertSelected(item)}>
11 <Text>
12 {' '}{item.id}{' '}{item.company}{' '}
13 </Text>
14 </TouchableOpacity>
15 );
16 };
17
18 advertSelected = (item)=>{
19 this.props.navigation.navigate("Adverts",{advert:item})
20 }
21
22 render() {
23 return (
24 <View style={styles.container}>
25 <Text style={styles.title}>List of advertisers</Text>
26 <FlatList
27 style={styles.list}
28 data={advertisers}
29 renderItem={this.rendItem}
30 />
31 </View>
32 );
33 }
34}
35
36const styles = StyleSheet.create({
37 container: { marginTop: 30, marginLeft: 20 },
38 title: { fontSize: 20 },
39 list: { paddingTop: 20 },
40});
41Adverts
1import * as React from 'react';
2import { Text, View, StyleSheet, Image, FlatList, TouchableOpacity } from 'react-native';
3
4import advertisers from '../data/advertisers.json';
5
6export default class Advertisers extends React.Component {
7 rendItem = listItem => {
8 let item = listItem.item
9 return (
10 <TouchableOpacity onPress={() => this.advertSelected(item)}>
11 <Text>
12 {' '}{item.id}{' '}{item.company}{' '}
13 </Text>
14 </TouchableOpacity>
15 );
16 };
17
18 advertSelected = (item)=>{
19 this.props.navigation.navigate("Adverts",{advert:item})
20 }
21
22 render() {
23 return (
24 <View style={styles.container}>
25 <Text style={styles.title}>List of advertisers</Text>
26 <FlatList
27 style={styles.list}
28 data={advertisers}
29 renderItem={this.rendItem}
30 />
31 </View>
32 );
33 }
34}
35
36const styles = StyleSheet.create({
37 container: { marginTop: 30, marginLeft: 20 },
38 title: { fontSize: 20 },
39 list: { paddingTop: 20 },
40});
41import * as React from 'react';
42import { Text, View, StyleSheet, Image, FlatList, TouchableOpacity } from 'react-native';
43
44import adverts from '../data/productadverts.json';
45
46export default class Adverts extends React.Component {
47 rendItem = listItem => {
48 let item = listItem.item
49 let advert = this.props.navigation.getParam("advert")
50 let pic = advert.picture
51 let title = advert.title;
52 let id = advert.id;
53 };
54
55 render() {
56 return (
57 <View style={styles.container}>
58 <Text style={styles.title}>List of advertisers</Text>
59 <FlatList
60 style={styles.list}
61 data={adverts}
62 renderItem={this.rendItem}
63 />
64 </View>
65 );
66 }
67}
68
69const styles = StyleSheet.create({
70 container: { marginTop: 30, marginLeft: 20 },
71 title: { fontSize: 20 },
72 list: { paddingTop: 20 },
73});
74advertisers.json
1import * as React from 'react';
2import { Text, View, StyleSheet, Image, FlatList, TouchableOpacity } from 'react-native';
3
4import advertisers from '../data/advertisers.json';
5
6export default class Advertisers extends React.Component {
7 rendItem = listItem => {
8 let item = listItem.item
9 return (
10 <TouchableOpacity onPress={() => this.advertSelected(item)}>
11 <Text>
12 {' '}{item.id}{' '}{item.company}{' '}
13 </Text>
14 </TouchableOpacity>
15 );
16 };
17
18 advertSelected = (item)=>{
19 this.props.navigation.navigate("Adverts",{advert:item})
20 }
21
22 render() {
23 return (
24 <View style={styles.container}>
25 <Text style={styles.title}>List of advertisers</Text>
26 <FlatList
27 style={styles.list}
28 data={advertisers}
29 renderItem={this.rendItem}
30 />
31 </View>
32 );
33 }
34}
35
36const styles = StyleSheet.create({
37 container: { marginTop: 30, marginLeft: 20 },
38 title: { fontSize: 20 },
39 list: { paddingTop: 20 },
40});
41import * as React from 'react';
42import { Text, View, StyleSheet, Image, FlatList, TouchableOpacity } from 'react-native';
43
44import adverts from '../data/productadverts.json';
45
46export default class Adverts extends React.Component {
47 rendItem = listItem => {
48 let item = listItem.item
49 let advert = this.props.navigation.getParam("advert")
50 let pic = advert.picture
51 let title = advert.title;
52 let id = advert.id;
53 };
54
55 render() {
56 return (
57 <View style={styles.container}>
58 <Text style={styles.title}>List of advertisers</Text>
59 <FlatList
60 style={styles.list}
61 data={adverts}
62 renderItem={this.rendItem}
63 />
64 </View>
65 );
66 }
67}
68
69const styles = StyleSheet.create({
70 container: { marginTop: 30, marginLeft: 20 },
71 title: { fontSize: 20 },
72 list: { paddingTop: 20 },
73});
74[
75 {
76 "company": "Fujifilm",
77 "id": 1,
78 "address": "St Martins Business Centre, St Martins Way, Bedford MK42 0LF",
79 "contactperson": "Carrie Perrett",
80 "contactnumber": "01234572000",
81 "emailaddress": "carrie@fuji.co.uk",
82 "logo": "https://logos-download.com/wp-content/uploads/2016/04/Fujifilm_logo_slogan_value_from_innovation.jpg"
83 },
84 {
85 "company": "Boeing",
86 "id": 2,
87 "address": "25 Victoria St, Westminster, London SW1H 0EX",
88 "contactperson": "Joanne Cumner",
89 "contactnumber": "02073401900",
90 "emailaddress": "jo@boeing.co.uk",
91 "logo": "https://www.govconwire.com/wp-content/uploads/2013/06/boeing-logo.jpg"
92 },
93 {
94 "company": "IBM",
95 "id": 3,
96 "address": "Birmingham Rd, Warwick CV34 5AH",
97 "contactperson": "Allan Elborn",
98 "contactnumber": "01926464000",
99 "emailaddress": "allan@ibm.co.uk",
100 "logo": "https://d15shllkswkct0.cloudfront.net/wp-content/blogs.dir/1/files/2012/08/ibm-logo.jpeg"
101 },
102 {
103 "company": "Fujitsu",
104 "id": 4,
105 "address": "55 Jays Cl, Basingstoke RG22 4BY",
106 "contactperson": "Alex Taylor",
107 "contactnumber": "08433545555",
108 "emailaddress": "alex@Fujitsu.co.uk",
109 "logo": "https://i.pinimg.com/originals/48/dc/fc/48dcfcd5df1834f66d029d7d34fae26d.png"
110 }
111]
112productadverts.json
1import * as React from 'react';
2import { Text, View, StyleSheet, Image, FlatList, TouchableOpacity } from 'react-native';
3
4import advertisers from '../data/advertisers.json';
5
6export default class Advertisers extends React.Component {
7 rendItem = listItem => {
8 let item = listItem.item
9 return (
10 <TouchableOpacity onPress={() => this.advertSelected(item)}>
11 <Text>
12 {' '}{item.id}{' '}{item.company}{' '}
13 </Text>
14 </TouchableOpacity>
15 );
16 };
17
18 advertSelected = (item)=>{
19 this.props.navigation.navigate("Adverts",{advert:item})
20 }
21
22 render() {
23 return (
24 <View style={styles.container}>
25 <Text style={styles.title}>List of advertisers</Text>
26 <FlatList
27 style={styles.list}
28 data={advertisers}
29 renderItem={this.rendItem}
30 />
31 </View>
32 );
33 }
34}
35
36const styles = StyleSheet.create({
37 container: { marginTop: 30, marginLeft: 20 },
38 title: { fontSize: 20 },
39 list: { paddingTop: 20 },
40});
41import * as React from 'react';
42import { Text, View, StyleSheet, Image, FlatList, TouchableOpacity } from 'react-native';
43
44import adverts from '../data/productadverts.json';
45
46export default class Adverts extends React.Component {
47 rendItem = listItem => {
48 let item = listItem.item
49 let advert = this.props.navigation.getParam("advert")
50 let pic = advert.picture
51 let title = advert.title;
52 let id = advert.id;
53 };
54
55 render() {
56 return (
57 <View style={styles.container}>
58 <Text style={styles.title}>List of advertisers</Text>
59 <FlatList
60 style={styles.list}
61 data={adverts}
62 renderItem={this.rendItem}
63 />
64 </View>
65 );
66 }
67}
68
69const styles = StyleSheet.create({
70 container: { marginTop: 30, marginLeft: 20 },
71 title: { fontSize: 20 },
72 list: { paddingTop: 20 },
73});
74[
75 {
76 "company": "Fujifilm",
77 "id": 1,
78 "address": "St Martins Business Centre, St Martins Way, Bedford MK42 0LF",
79 "contactperson": "Carrie Perrett",
80 "contactnumber": "01234572000",
81 "emailaddress": "carrie@fuji.co.uk",
82 "logo": "https://logos-download.com/wp-content/uploads/2016/04/Fujifilm_logo_slogan_value_from_innovation.jpg"
83 },
84 {
85 "company": "Boeing",
86 "id": 2,
87 "address": "25 Victoria St, Westminster, London SW1H 0EX",
88 "contactperson": "Joanne Cumner",
89 "contactnumber": "02073401900",
90 "emailaddress": "jo@boeing.co.uk",
91 "logo": "https://www.govconwire.com/wp-content/uploads/2013/06/boeing-logo.jpg"
92 },
93 {
94 "company": "IBM",
95 "id": 3,
96 "address": "Birmingham Rd, Warwick CV34 5AH",
97 "contactperson": "Allan Elborn",
98 "contactnumber": "01926464000",
99 "emailaddress": "allan@ibm.co.uk",
100 "logo": "https://d15shllkswkct0.cloudfront.net/wp-content/blogs.dir/1/files/2012/08/ibm-logo.jpeg"
101 },
102 {
103 "company": "Fujitsu",
104 "id": 4,
105 "address": "55 Jays Cl, Basingstoke RG22 4BY",
106 "contactperson": "Alex Taylor",
107 "contactnumber": "08433545555",
108 "emailaddress": "alex@Fujitsu.co.uk",
109 "logo": "https://i.pinimg.com/originals/48/dc/fc/48dcfcd5df1834f66d029d7d34fae26d.png"
110 }
111]
112[
113 {
114 "title": "ICT and Fujifilm’s new wave of innovation",
115 "id": 1,
116 "text": "Taking outstanding ICT achievements to the next level. ICT continues to advance rapidly. One recent example is the Internet of Things (IoT), in which devices and appliances have Internet connectivity and ICT functions built in. Moreover, ICT appears ready to take off in industry as never before, spurred by new advances in such technologies as artificial intelligence (AI) and virtual reality (VR). Some even view these trends in ICT as having the potential to lead to a new Industrial Revolution. As a leading technology company, Fujifilm is poised to become a major creative force in ICT and drive its own wave of innovation.",
117 "picture": "https://2df223ae-a-62cb3a1a-s-sites.googlegroups.com/site/eportfolioduaa/home/advantages-and-disadvantages-of-i-c-t/ict%202.png?attachauth=ANoY7cpUeQC5IlBqWx_cSW5wq5f4lDOPpWph4cfUpWUbE5h-fxfKatvv_ztmibYt834f8GHLpHcgZ6yA3wmc7c7veFhbf5NMke0MAkprLtZZHdllza0Q62BOEj3SHvZMg4rGKJegcIwfb6zW8a4OqAdgqFYvU1BCtNm25YqpngDRRN0HPqt8PmulWjVk2TS4jDWOt4KZfAd9pznmf8fi3Vw-zZJ0Ne_yFRON763E-2v8YzwRFc3yui_HfDE3HsqxcF3JIOizhQSVnqnJStlxeyzTDH_1yL8iZg%3D%3D&attredirects=0",
118 "advertiser": "Fujifilm"
119 },
120 {
121 "title": "Technologies",
122 "id": 2,
123 "text": "Fujifilm is a technology company. A photography company. Although quite a few people still have this image of Fujifilm, today it’s so much more. By leveraging the technologies it originally developed for the photography industry and continuously and proactively pursuing advanced R&D, Fujifilm has created businesses in multiple high-tech fields and become a technology-oriented company. ",
124 "picture": "https://logos-download.com/wp-content/uploads/2016/04/Fujifilm_logo_slogan_value_from_innovation.jpg",
125 "advertiser": "Fujifilm"
126 },
127
128 {
129 "title": "2020 Call for Code Global Challenge",
130 "id": 3,
131 "text": "Get inspired. Join the fight. Impact the world. Congratulations to the initial COVID-19 solutions that are now receiving deployment support. They show how technology can help small businesses find assistance after a crisis, redefine the queuing experience and guide us to the right medical advice. Developers and problem solvers, remember you have until July 31 to submit your open source solutions.",
132 "picture": "https://res.cloudinary.com/practicaldev/image/fetch/s--YBeZKs5E--/c_limit%2Cf_auto%2Cfl_progressive%2Cq_auto%2Cw_880/https://dev-to-uploads.s3.amazonaws.com/i/c5zqnlp91mjy1v4uqaog.png",
133 "advertiser": "IBM"
134 },
135 {
136 "title": "It’s not about the data – it's what you do with it!",
137 "id": 4,
138 "text": "Power your operations and gain valuable insights using data analytics. Boeing AnalytX utilizes our aerospace expertise with data-based information to give you empowered decision support to optimize your operation and mission. Applications using Boeing predictive analytics give customers a glimpse into the near-future; more time to evaluate, plan and manage solutions. Boeing AnalytX offers three interrelated categories of analytics enabled products and services customers may easily mix and match to meet needs and goals. Digital Solutions – a set of analytics enabled software applications addressing the needs of crew and fleet scheduling, flight/mission planning and operations, maintenance planning and management, and inventory and logistics management. Analytics Consulting Services – a group of aviation, business, and analytics professionals who are ready to help customers improve their operational performance, efficiency, and economy. Self-Service Analytics – our newest category, that opens up the data behind the digital solutions for customers to explore and discover new insights and opportunities using Boeing provided analytics tools. ",
139 "picture": "https://www.govconwire.com/wp-content/uploads/2013/06/boeing-logo.jpg",
140 "advertiser": "Boeing"
141 },
142
143 {
144 "title": "Rethinking business and society in times of crisis",
145 "id": 5,
146 "text": "The continued spread and effects of the Coronavirus (COVID-19) are disrupting the everyday lives of people, society and businesses alike, and triggering inevitable and reasonable concerns among us all. Alongside our ongoing commitment to supporting many of the critical systems on which the UK relies every day, we have made it a priority to look at where Fujitsu technology and innovation can support the response to COVID-19. ",
147 "picture": "https://i.pinimg.com/originals/48/dc/fc/48dcfcd5df1834f66d029d7d34fae26d.png",
148 "advertiser": "Fujitsu"
149 }
150]
151ANSWER
Answered 2020-May-17 at 23:55The new object to get params in React Navigation 5 is:
1import * as React from 'react';
2import { Text, View, StyleSheet, Image, FlatList, TouchableOpacity } from 'react-native';
3
4import advertisers from '../data/advertisers.json';
5
6export default class Advertisers extends React.Component {
7 rendItem = listItem => {
8 let item = listItem.item
9 return (
10 <TouchableOpacity onPress={() => this.advertSelected(item)}>
11 <Text>
12 {' '}{item.id}{' '}{item.company}{' '}
13 </Text>
14 </TouchableOpacity>
15 );
16 };
17
18 advertSelected = (item)=>{
19 this.props.navigation.navigate("Adverts",{advert:item})
20 }
21
22 render() {
23 return (
24 <View style={styles.container}>
25 <Text style={styles.title}>List of advertisers</Text>
26 <FlatList
27 style={styles.list}
28 data={advertisers}
29 renderItem={this.rendItem}
30 />
31 </View>
32 );
33 }
34}
35
36const styles = StyleSheet.create({
37 container: { marginTop: 30, marginLeft: 20 },
38 title: { fontSize: 20 },
39 list: { paddingTop: 20 },
40});
41import * as React from 'react';
42import { Text, View, StyleSheet, Image, FlatList, TouchableOpacity } from 'react-native';
43
44import adverts from '../data/productadverts.json';
45
46export default class Adverts extends React.Component {
47 rendItem = listItem => {
48 let item = listItem.item
49 let advert = this.props.navigation.getParam("advert")
50 let pic = advert.picture
51 let title = advert.title;
52 let id = advert.id;
53 };
54
55 render() {
56 return (
57 <View style={styles.container}>
58 <Text style={styles.title}>List of advertisers</Text>
59 <FlatList
60 style={styles.list}
61 data={adverts}
62 renderItem={this.rendItem}
63 />
64 </View>
65 );
66 }
67}
68
69const styles = StyleSheet.create({
70 container: { marginTop: 30, marginLeft: 20 },
71 title: { fontSize: 20 },
72 list: { paddingTop: 20 },
73});
74[
75 {
76 "company": "Fujifilm",
77 "id": 1,
78 "address": "St Martins Business Centre, St Martins Way, Bedford MK42 0LF",
79 "contactperson": "Carrie Perrett",
80 "contactnumber": "01234572000",
81 "emailaddress": "carrie@fuji.co.uk",
82 "logo": "https://logos-download.com/wp-content/uploads/2016/04/Fujifilm_logo_slogan_value_from_innovation.jpg"
83 },
84 {
85 "company": "Boeing",
86 "id": 2,
87 "address": "25 Victoria St, Westminster, London SW1H 0EX",
88 "contactperson": "Joanne Cumner",
89 "contactnumber": "02073401900",
90 "emailaddress": "jo@boeing.co.uk",
91 "logo": "https://www.govconwire.com/wp-content/uploads/2013/06/boeing-logo.jpg"
92 },
93 {
94 "company": "IBM",
95 "id": 3,
96 "address": "Birmingham Rd, Warwick CV34 5AH",
97 "contactperson": "Allan Elborn",
98 "contactnumber": "01926464000",
99 "emailaddress": "allan@ibm.co.uk",
100 "logo": "https://d15shllkswkct0.cloudfront.net/wp-content/blogs.dir/1/files/2012/08/ibm-logo.jpeg"
101 },
102 {
103 "company": "Fujitsu",
104 "id": 4,
105 "address": "55 Jays Cl, Basingstoke RG22 4BY",
106 "contactperson": "Alex Taylor",
107 "contactnumber": "08433545555",
108 "emailaddress": "alex@Fujitsu.co.uk",
109 "logo": "https://i.pinimg.com/originals/48/dc/fc/48dcfcd5df1834f66d029d7d34fae26d.png"
110 }
111]
112[
113 {
114 "title": "ICT and Fujifilm’s new wave of innovation",
115 "id": 1,
116 "text": "Taking outstanding ICT achievements to the next level. ICT continues to advance rapidly. One recent example is the Internet of Things (IoT), in which devices and appliances have Internet connectivity and ICT functions built in. Moreover, ICT appears ready to take off in industry as never before, spurred by new advances in such technologies as artificial intelligence (AI) and virtual reality (VR). Some even view these trends in ICT as having the potential to lead to a new Industrial Revolution. As a leading technology company, Fujifilm is poised to become a major creative force in ICT and drive its own wave of innovation.",
117 "picture": "https://2df223ae-a-62cb3a1a-s-sites.googlegroups.com/site/eportfolioduaa/home/advantages-and-disadvantages-of-i-c-t/ict%202.png?attachauth=ANoY7cpUeQC5IlBqWx_cSW5wq5f4lDOPpWph4cfUpWUbE5h-fxfKatvv_ztmibYt834f8GHLpHcgZ6yA3wmc7c7veFhbf5NMke0MAkprLtZZHdllza0Q62BOEj3SHvZMg4rGKJegcIwfb6zW8a4OqAdgqFYvU1BCtNm25YqpngDRRN0HPqt8PmulWjVk2TS4jDWOt4KZfAd9pznmf8fi3Vw-zZJ0Ne_yFRON763E-2v8YzwRFc3yui_HfDE3HsqxcF3JIOizhQSVnqnJStlxeyzTDH_1yL8iZg%3D%3D&attredirects=0",
118 "advertiser": "Fujifilm"
119 },
120 {
121 "title": "Technologies",
122 "id": 2,
123 "text": "Fujifilm is a technology company. A photography company. Although quite a few people still have this image of Fujifilm, today it’s so much more. By leveraging the technologies it originally developed for the photography industry and continuously and proactively pursuing advanced R&D, Fujifilm has created businesses in multiple high-tech fields and become a technology-oriented company. ",
124 "picture": "https://logos-download.com/wp-content/uploads/2016/04/Fujifilm_logo_slogan_value_from_innovation.jpg",
125 "advertiser": "Fujifilm"
126 },
127
128 {
129 "title": "2020 Call for Code Global Challenge",
130 "id": 3,
131 "text": "Get inspired. Join the fight. Impact the world. Congratulations to the initial COVID-19 solutions that are now receiving deployment support. They show how technology can help small businesses find assistance after a crisis, redefine the queuing experience and guide us to the right medical advice. Developers and problem solvers, remember you have until July 31 to submit your open source solutions.",
132 "picture": "https://res.cloudinary.com/practicaldev/image/fetch/s--YBeZKs5E--/c_limit%2Cf_auto%2Cfl_progressive%2Cq_auto%2Cw_880/https://dev-to-uploads.s3.amazonaws.com/i/c5zqnlp91mjy1v4uqaog.png",
133 "advertiser": "IBM"
134 },
135 {
136 "title": "It’s not about the data – it's what you do with it!",
137 "id": 4,
138 "text": "Power your operations and gain valuable insights using data analytics. Boeing AnalytX utilizes our aerospace expertise with data-based information to give you empowered decision support to optimize your operation and mission. Applications using Boeing predictive analytics give customers a glimpse into the near-future; more time to evaluate, plan and manage solutions. Boeing AnalytX offers three interrelated categories of analytics enabled products and services customers may easily mix and match to meet needs and goals. Digital Solutions – a set of analytics enabled software applications addressing the needs of crew and fleet scheduling, flight/mission planning and operations, maintenance planning and management, and inventory and logistics management. Analytics Consulting Services – a group of aviation, business, and analytics professionals who are ready to help customers improve their operational performance, efficiency, and economy. Self-Service Analytics – our newest category, that opens up the data behind the digital solutions for customers to explore and discover new insights and opportunities using Boeing provided analytics tools. ",
139 "picture": "https://www.govconwire.com/wp-content/uploads/2013/06/boeing-logo.jpg",
140 "advertiser": "Boeing"
141 },
142
143 {
144 "title": "Rethinking business and society in times of crisis",
145 "id": 5,
146 "text": "The continued spread and effects of the Coronavirus (COVID-19) are disrupting the everyday lives of people, society and businesses alike, and triggering inevitable and reasonable concerns among us all. Alongside our ongoing commitment to supporting many of the critical systems on which the UK relies every day, we have made it a priority to look at where Fujitsu technology and innovation can support the response to COVID-19. ",
147 "picture": "https://i.pinimg.com/originals/48/dc/fc/48dcfcd5df1834f66d029d7d34fae26d.png",
148 "advertiser": "Fujitsu"
149 }
150]
151this.props.route.params
152Try to get the advert param using:
1import * as React from 'react';
2import { Text, View, StyleSheet, Image, FlatList, TouchableOpacity } from 'react-native';
3
4import advertisers from '../data/advertisers.json';
5
6export default class Advertisers extends React.Component {
7 rendItem = listItem => {
8 let item = listItem.item
9 return (
10 <TouchableOpacity onPress={() => this.advertSelected(item)}>
11 <Text>
12 {' '}{item.id}{' '}{item.company}{' '}
13 </Text>
14 </TouchableOpacity>
15 );
16 };
17
18 advertSelected = (item)=>{
19 this.props.navigation.navigate("Adverts",{advert:item})
20 }
21
22 render() {
23 return (
24 <View style={styles.container}>
25 <Text style={styles.title}>List of advertisers</Text>
26 <FlatList
27 style={styles.list}
28 data={advertisers}
29 renderItem={this.rendItem}
30 />
31 </View>
32 );
33 }
34}
35
36const styles = StyleSheet.create({
37 container: { marginTop: 30, marginLeft: 20 },
38 title: { fontSize: 20 },
39 list: { paddingTop: 20 },
40});
41import * as React from 'react';
42import { Text, View, StyleSheet, Image, FlatList, TouchableOpacity } from 'react-native';
43
44import adverts from '../data/productadverts.json';
45
46export default class Adverts extends React.Component {
47 rendItem = listItem => {
48 let item = listItem.item
49 let advert = this.props.navigation.getParam("advert")
50 let pic = advert.picture
51 let title = advert.title;
52 let id = advert.id;
53 };
54
55 render() {
56 return (
57 <View style={styles.container}>
58 <Text style={styles.title}>List of advertisers</Text>
59 <FlatList
60 style={styles.list}
61 data={adverts}
62 renderItem={this.rendItem}
63 />
64 </View>
65 );
66 }
67}
68
69const styles = StyleSheet.create({
70 container: { marginTop: 30, marginLeft: 20 },
71 title: { fontSize: 20 },
72 list: { paddingTop: 20 },
73});
74[
75 {
76 "company": "Fujifilm",
77 "id": 1,
78 "address": "St Martins Business Centre, St Martins Way, Bedford MK42 0LF",
79 "contactperson": "Carrie Perrett",
80 "contactnumber": "01234572000",
81 "emailaddress": "carrie@fuji.co.uk",
82 "logo": "https://logos-download.com/wp-content/uploads/2016/04/Fujifilm_logo_slogan_value_from_innovation.jpg"
83 },
84 {
85 "company": "Boeing",
86 "id": 2,
87 "address": "25 Victoria St, Westminster, London SW1H 0EX",
88 "contactperson": "Joanne Cumner",
89 "contactnumber": "02073401900",
90 "emailaddress": "jo@boeing.co.uk",
91 "logo": "https://www.govconwire.com/wp-content/uploads/2013/06/boeing-logo.jpg"
92 },
93 {
94 "company": "IBM",
95 "id": 3,
96 "address": "Birmingham Rd, Warwick CV34 5AH",
97 "contactperson": "Allan Elborn",
98 "contactnumber": "01926464000",
99 "emailaddress": "allan@ibm.co.uk",
100 "logo": "https://d15shllkswkct0.cloudfront.net/wp-content/blogs.dir/1/files/2012/08/ibm-logo.jpeg"
101 },
102 {
103 "company": "Fujitsu",
104 "id": 4,
105 "address": "55 Jays Cl, Basingstoke RG22 4BY",
106 "contactperson": "Alex Taylor",
107 "contactnumber": "08433545555",
108 "emailaddress": "alex@Fujitsu.co.uk",
109 "logo": "https://i.pinimg.com/originals/48/dc/fc/48dcfcd5df1834f66d029d7d34fae26d.png"
110 }
111]
112[
113 {
114 "title": "ICT and Fujifilm’s new wave of innovation",
115 "id": 1,
116 "text": "Taking outstanding ICT achievements to the next level. ICT continues to advance rapidly. One recent example is the Internet of Things (IoT), in which devices and appliances have Internet connectivity and ICT functions built in. Moreover, ICT appears ready to take off in industry as never before, spurred by new advances in such technologies as artificial intelligence (AI) and virtual reality (VR). Some even view these trends in ICT as having the potential to lead to a new Industrial Revolution. As a leading technology company, Fujifilm is poised to become a major creative force in ICT and drive its own wave of innovation.",
117 "picture": "https://2df223ae-a-62cb3a1a-s-sites.googlegroups.com/site/eportfolioduaa/home/advantages-and-disadvantages-of-i-c-t/ict%202.png?attachauth=ANoY7cpUeQC5IlBqWx_cSW5wq5f4lDOPpWph4cfUpWUbE5h-fxfKatvv_ztmibYt834f8GHLpHcgZ6yA3wmc7c7veFhbf5NMke0MAkprLtZZHdllza0Q62BOEj3SHvZMg4rGKJegcIwfb6zW8a4OqAdgqFYvU1BCtNm25YqpngDRRN0HPqt8PmulWjVk2TS4jDWOt4KZfAd9pznmf8fi3Vw-zZJ0Ne_yFRON763E-2v8YzwRFc3yui_HfDE3HsqxcF3JIOizhQSVnqnJStlxeyzTDH_1yL8iZg%3D%3D&attredirects=0",
118 "advertiser": "Fujifilm"
119 },
120 {
121 "title": "Technologies",
122 "id": 2,
123 "text": "Fujifilm is a technology company. A photography company. Although quite a few people still have this image of Fujifilm, today it’s so much more. By leveraging the technologies it originally developed for the photography industry and continuously and proactively pursuing advanced R&D, Fujifilm has created businesses in multiple high-tech fields and become a technology-oriented company. ",
124 "picture": "https://logos-download.com/wp-content/uploads/2016/04/Fujifilm_logo_slogan_value_from_innovation.jpg",
125 "advertiser": "Fujifilm"
126 },
127
128 {
129 "title": "2020 Call for Code Global Challenge",
130 "id": 3,
131 "text": "Get inspired. Join the fight. Impact the world. Congratulations to the initial COVID-19 solutions that are now receiving deployment support. They show how technology can help small businesses find assistance after a crisis, redefine the queuing experience and guide us to the right medical advice. Developers and problem solvers, remember you have until July 31 to submit your open source solutions.",
132 "picture": "https://res.cloudinary.com/practicaldev/image/fetch/s--YBeZKs5E--/c_limit%2Cf_auto%2Cfl_progressive%2Cq_auto%2Cw_880/https://dev-to-uploads.s3.amazonaws.com/i/c5zqnlp91mjy1v4uqaog.png",
133 "advertiser": "IBM"
134 },
135 {
136 "title": "It’s not about the data – it's what you do with it!",
137 "id": 4,
138 "text": "Power your operations and gain valuable insights using data analytics. Boeing AnalytX utilizes our aerospace expertise with data-based information to give you empowered decision support to optimize your operation and mission. Applications using Boeing predictive analytics give customers a glimpse into the near-future; more time to evaluate, plan and manage solutions. Boeing AnalytX offers three interrelated categories of analytics enabled products and services customers may easily mix and match to meet needs and goals. Digital Solutions – a set of analytics enabled software applications addressing the needs of crew and fleet scheduling, flight/mission planning and operations, maintenance planning and management, and inventory and logistics management. Analytics Consulting Services – a group of aviation, business, and analytics professionals who are ready to help customers improve their operational performance, efficiency, and economy. Self-Service Analytics – our newest category, that opens up the data behind the digital solutions for customers to explore and discover new insights and opportunities using Boeing provided analytics tools. ",
139 "picture": "https://www.govconwire.com/wp-content/uploads/2013/06/boeing-logo.jpg",
140 "advertiser": "Boeing"
141 },
142
143 {
144 "title": "Rethinking business and society in times of crisis",
145 "id": 5,
146 "text": "The continued spread and effects of the Coronavirus (COVID-19) are disrupting the everyday lives of people, society and businesses alike, and triggering inevitable and reasonable concerns among us all. Alongside our ongoing commitment to supporting many of the critical systems on which the UK relies every day, we have made it a priority to look at where Fujitsu technology and innovation can support the response to COVID-19. ",
147 "picture": "https://i.pinimg.com/originals/48/dc/fc/48dcfcd5df1834f66d029d7d34fae26d.png",
148 "advertiser": "Fujitsu"
149 }
150]
151this.props.route.params
152const { advert } = this.props.route.params;
153Community Discussions contain sources that include Stack Exchange Network
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