Iris+dataset Csv + Python

Edgar Anderson's Iris Data: islands: Areas of the World's Major Landmasses-- J --. In this post I will build a simple machine learning model using the popular iris data set available in the csv format. It can be used to model the impact of marketing on customer acquisition, retention, and churn or to predict disease risk and susceptibility in patients. 0 Asa Ben-Hur June 2, 2008 myfi[email protected] Precisely, there are two data points (row number 34 and 37) in UCI's Machine Learning repository are different from the origianlly published Iris dataset. Here is a sample VB. To obtain this visualization, you supply the decision tree model. It is based on Bayes’ probability theorem. This dataset is stored as a comma-separated value (CSV) text file, with each line containing four different numerical values (petal measurements), followed by the flower type (here, there are three classes— Irissetosa, Irisversicolor, and Irisvirginica). csv" ) print (dataset. T hese datasets are useful to quickly illustrate the behavior of the various algorithms implemented in the scikit. values将数据集拆分为训练集和测试集fromsklearn. csvを確認 作業環境 今回の環境(前回同様) やってみる (v1,1) pkg> activate todayblog (todayblog) pkg…. Alternatively, you can click on each dataset separately to download it. You may view all data sets through our searchable interface. We import iris data by giving path of data file of “ iris. After you click OK, close the query window and the data will be loaded. These are the main topics that are included in our course. 5 or greater. datasets import load_iris >>> iris = load. It is a very powerful and versatile package which makes data cleaning and wrangling much easier and pleasant. Kolom kelima adalah spesies bunga yang. You can vote up the examples you like or vote down the ones you don't like. For each format R has a specific function and argument. Make your own Naive Bayes Algorithm. Load the dataset. The Iris Dataset¶ This data sets consists of 3 different types of irises’ (Setosa, Versicolour, and Virginica) petal and sepal length, stored in a 150x4 numpy. Intro: It is sometimes called Anderson’s Iris data set because Edgar Anderson collected the data to quantify the ('iris_dataset. In the second section you learn how to use python to classify output of your system with nonlinear structure. From Wikipedia - Principal component analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables (entities each of which takes on various numerical values) into a set of values of linearly uncorrelated variables called principal components. Seaborn comes handy when dealing with DataFrames, which is most. Analyzing Iris dataset. In order to help you gain experience performing machine learning in Python, we’ll be working with two separate datasets. This paper shows you how to get started with machine learning by applying decision trees using Python on an established dataset. I assume you at least basic have knowledge of Python, installing packaging and have available a running Python 3 distribution. Answer the following 4 questions in the file solutions. In this post we will implement K-Means algorithm using Python from scratch. We will be working with the well-known Iris dataset for this. System and Environment preparation-----Installing Python and Required Libraries (Anaconda) Basics of python and sci-py-----Python, Numpy , Matplotlib and Pandas Quick Courses. Iris Flower Data Set: The Iris flower data set is a specific set of information compiled by Ronald Fisher, a biologist, in the 1930s. We are interested in seeing how we use pandas in Jupyter, how well the script performs, and what information is stored in the metadata (especially if it. A few examples are spam filtration, sentimental analysis, and classifying news. Python Machine learning Iris Visualization: Exercise-19 with Solution. The dataset has 5 rows. iris data set (131 downloads) NumPy sample csv data (132 downloads) boston-house-price-dataset (136 downloads) pima-indians-diabetes (143 downloads) string matched country names (153 downloads) NumPy sample text data (157 downloads) live-graph-test (161 downloads) FMEL-Dataset (163 downloads). SepalLength,SepalWidth,PetalLength,PetalWidth,Name 5. read_csv('winemag-data-130k-v2. In this article, I'll build a couple of models using Python and its accompanying library Scikit-learn. SVMs are. We will be using the famous Iris Dataset, collected in the 1930’s by Edgar Anderson. Run this code so you can see the first five rows of the dataset. My first time using matplotlib September 21, 2015 February 19, 2016 tonyromarock Uncategorized I was interested in learning a little bit more about data science and machine learning algorithms. (Feb-26-2018, 12:48 PM) Oliver Wrote: There must be a simple way to read csv "data" without writing an entire method like that. urlretrieve function to save the train and test (already partitioned by the researchers) to a local file and read in the file as a pandas dataFrame. The following are code examples for showing how to use sklearn. 5 or greater. A small demo of Machine Learning in Python has already been elaborated in the above-given article, you can check it out yourself and see if you want to go for it or not. In this post you discovered that the scikit-learn method comes with packaged data sets including the iris flowers dataset. Loading data in python environment is the most initial step of analyzing data. read_csv Our iris dataset is now stored in form of a $150 \times 4$ matrix where the. We have stored iris data set in CSV file as iris. Load CSV files into Python to create Pandas Dataframes using the read_csv function. Fisher's paper is a classic in the field and is referenced frequently to this day. Iris Plants Database This is perhaps the best known database to be found in the pattern recognition literature. world Feedback. In order to help you gain experience performing machine learning in Python, we’ll be working with two separate datasets. The dataset we will be using for this is a commonly available dataset called the iris dataset that has information about flower species and their petal and sepal dimensions. Use A "for" Loop To Repeat Steps 1-5 5 Times And Calculate Average Accuracy To Be Your Result 3. Categories. StatsModels Datasets Package. The Iris flower data set or Fisher’s Iris data set is a multivariate data set introduced by the British statistician and biologist Ronald Fisher in his 1936 paper. pandas is a NumFOCUS sponsored project. We can extract data from this DataFrame into Numpy arrays. For pie plots it's best to use square figures, i. We will use Numpy arrays as input to our Neural Network. Tensorflow has an implementation for the neural network included, which we’ll use to on csv data (the iris dataset). Now that we've set up Python for machine learning, let's get started by loading an example dataset into scikit-learn! We'll explore the famous "iris" dataset, learn some important machine learning. Specifically the IPython Notebook (which works inside a browser). Bar plots (or “bar graphs”) are a type of data visualization that are used to display and compare the number, frequency or other measure (e. Discover how to prepare data with pandas, fit and evaluate models with scikit-learn, and more in my new book, with 16 step-by-step tutorials, 3 projects, and full python code. I found it! Just upload the file in the same folder of the project, and then you can refer to it just with its name and extension, without any other address specification. Introduction. g2cba174 The Blaze Ecosystem provides Python users high-level access to efficient computation on inconveniently large data. When building classification models in H2O, you will get to see the variable importance table at the FLOW UI. These labels can be in the form of words or numbers. Contributing. We will plot the loss and accuracy during the training process. The text is released under the CC-BY-NC-ND license, and code is released under the MIT license. #Also there are native bindings that require Python 2. load_iris(). Data Retriever using Python A simple download for the iris dataset can be done we are installing the dataset wine-composition as a CSV file in our current. Iris: Perhaps the best known database to be found in the pattern recognition literature, R. If you find this content useful, please consider supporting the work by buying the book!. T hese datasets are useful to quickly illustrate the behavior of the various algorithms implemented in the scikit. It’s not uncommon for this data to have missing values, inconsistencies, errors, weird characters, or uninteresting columns. To get a better feel for the problem, let's create a simple example using CSV file: to get a better grasp of the problem: The StringIO() function allows us to read the string assigned to csv_data into a pandas DataFrame via the read_csv() function as if it was a regular CSV file on our hard drive. In this chapter, we will discuss how to import Datasets and Libraries. Learn vocabulary, terms, and more with flashcards, games, and other study tools. This dataset is famous because it is used as the "hello world" dataset in machine learning and statistics by pretty much everyone. To begin with let’s try to load the Iris dataset. For seeing the outliers in the Iris dataset use the following code. py, which is not the most recent version. There are four types of kernels in SVM which we will implement in this article:. csv function with each element in the dataset as. It has 5 attributes, the first one is sepal length (Numeric), second is sepal width (Numeric) third one is petal length (Numeric), the fourth one is petal width (Numeric) and the last one is the class itself. print(‘Python: {}’. This famous (Fisher's or Anderson's) iris data set gives the measurements in centimeters of the variables sepal length and width and petal length and width, respectively, for 50 flowers from each of 3 species of iris. This is a fast, scalable, highly optimized way to read data. csv" ) print (dataset. If you are using D3 or Altair for your project, there are builtin functions to load these files into your project. CSV format was used for many years prior to attempts to describe the format in a standardized way in RFC 41. : Import all files that have the pattern iris_. ai with APIs in Python and R. Python-Jupyter (+R) basics tutorial for DM 2017 course¶ This notebook contains some information and examples for getting started with Jupyter and Python. 2 Iris Data Set Iris Data Set from UCI Machine Learning Repository 1 [3] is used in the second experiment. Edgar Anderson's Iris Data Description. Here's the procedure: Open a new Python interactive shell session. iris_train. Naive Bayes Algorithm in python. Some things to take note of though: k-means clustering is very sensitive to scale due to its reliance on Euclidean distance so be sure to normalize data if there are likely to be scaling problems. Basic Analysis of the Iris Data set Using Python. BigMLer - A command-line tool for BigML’s API¶. Getting H2O & Documentation 2. Iris: Perhaps the best known database to be found in the pattern recognition literature, R. We'll utilize some useful packages:. Use A "for" Loop To Repeat Steps 1-5 5 Times And Calculate Average Accuracy To Be Your Result 3. We will program our classifier in Python language and will use its sklearn library. Flexible Data Ingestion. Use the model to make predictions about unknown data. Since we will be using the used cars dataset, you will need to download this dataset. import sys. Learn vocabulary, terms, and more with flashcards, games, and other study tools. The Pandas library has a great contribution to the python community and it makes python as one of the top programming…. We will read the data file name mtcats. Alternatively, you can click on each dataset separately to download it. Blaze can refer to both a particular library as well as an ecosystem of related projects that have spun off of Blaze development. The Iris dataset is not easy to graph for predictive analytics in its original form. In this tutorial, We will see how to get started with Data Analysis in Python. For each format R has a specific function and argument. They are extracted from open source Python projects. Data Set Information: This is perhaps the best known database to be found in the pattern recognition literature. ai with APIs in Python and R. We'll extract two features of two flowers form Iris data sets. 2,Iris-setosa 4. Note that more elaborate visualization of this dataset is detailed in the Statistics in Python chapter. This dataset is already packaged and available for an easy download from the dataset page or directly from here Used Cars Dataset – usedcars. read_csv Our iris dataset is now stored in form of a $150 \times 4$ matrix where the. #separate training and validation datsests with 80/20 split array=dataset. a powerful interactive development environment for the Python language with advanced editing, interactive testing, debugging and introspection features and a numerical computing environment thanks to the support of IPython (enhanced interactive Python interpreter) and popular Python libraries such as NumPy (linear algebra), SciPy (signal and. csv(dataset, "filename. Each sample has four features (or variables) that are the length and the width of sepal and petal, in centimeters. Iris Data Set, along with the MNIST dataset, is probably one of the best-known datasets to be found in the pattern recognition literature. Download Link. At Analytics Vidhya, we love Python. In the following example, we will use multiple linear regression to predict the stock index price (i. The first dataset we will use for ML will be the Iris dataset which can be obtained here. A Complete Guide to K-Nearest-Neighbors with Applications in Python and R using is the Iris Flower Dataset do is load the data set. metrics import. R and Python codebase is typically contained in complex script files which gets data from a variety of sources and applies. zip and uncompress it in. The system is a bayes classifier and calculates (and compare) the decision based upon conditional probability of the decision options. Import TensorFlow and the other required Python modules. Data analysis with Python¶. Let us start by importing Pandas, which is a great library for managing relational (table-format) datasets. csv file is a 150-row file, with comma-separated values. Integer based indexing using iloc. Download Link. input_file variable will be different of course depending on where you store the iris. Along the way, we’ll learn about euclidean distance and figure out which NBA players are the most similar to Lebron James. Now we shall use the decision tree with a sklearn library for better understanding. We can extract data from this DataFrame into Numpy arrays. Here, we will use the Iris flower dataset, which is a multivariate and one of the famous datasets available at the UCI machine learning repository. In this lesson, we will look at the basics of reading a CSV file, using TensorFlow, and using that data in a graph. Last updated on September 11, 2018. IRIS Flowers. Intuitively we'd expect to find some correlation between price and. This is a log of one day only (if you are a JDS course participant, you will get much more of this data set on the last week of the course ;-)). Apply EM algorithm to cluster a set of data stored in a. 本篇文章主要介绍了python实现随机森林random forest的原理及方法,详细的介绍了随机森林的原理和python实现,非常具有参考价值,有兴趣的可以了解一下. Along the way, we’ll learn about euclidean distance and figure out which NBA players are the most similar to Lebron James. Just like with any new machine learning problem, you should always start with some analysis of the data made available to you. read_csv(‘Boston. read_csv('winemag-data-130k-v2. Kita akan menggunakan dataset bunga iris. The Python packages that we use in this notebook are: numpy, pandas, matplotlib, and seaborn. Python Machine learning Scikit-learn, K Nearest Neighbors - Exercises, Practice and Solution: Write a Python program using Scikit-learn to split the iris dataset into 70% train data and 30% test data. Write a program to implement the naïve Bayesian classifier for a sample training. The dataset consists of 50 samples from three species of Iris flowers (Iris setosa, Iris virginica and Iris versicolor). Use the model to make predictions about unknown data. If you are learning Python for Data Science, this test was created to help you assess your skill in Python. sepal_length,sepal_width,petal_length,petal_width,species 5. csv file $ pip install numpy $ pip install pickle; Save below code into trainsave. Bar Plots in Python using Seaborn. Lots of examples of ways to use one of the most versatile data structures in the whole Python data analysis stack. In the latter part, we will translate our understanding into code and implement it on the famous ‘iris’ dataset for classifying flowers into one of three categories. The text is released under the CC-BY-NC-ND license, and code is released under the MIT license. The rows being the samples and the columns being: Sepal Length, Sepal Width, Petal Length and Petal Width. Categories. Precisely, there are two data points (row number 34 and 37) in UCI's Machine Learning repository are different from the origianlly published Iris dataset. evaluate(x=test_set. In this part I discuss classification with Support Vector Machines (SVMs), using both a Linear and a Radial basis kernel, and Decision Trees. Scikit-learn is used for the min-max Scaling, test-train set splitting and one-hot encoding for categorical data/output. R and Python codebase is typically contained in complex script files which gets data from a variety of sources and applies. So, let’s take a dataset first. The following are code examples for showing how to use sklearn. csv file is a 150-row file, with comma-separated values. Seaborn requires data to plot the histogram and this data can be loaded using the below code. Fork, write tests and create a pull request! Misc. of rows from this data, one way is to achieve it by using iloc operation. In this post I will cover decision trees (for classification) in python, using scikit-learn and pandas. Implement simple linear regression in Python on this ‘Boston’ dataset where the dependent variable is ‘medv’ and the independent variable is ‘lstat. of kNN implemented from Scratch in Python 3 6 import CSV. The specified path must be a client-side path. # # Licensed under the Apache License, Version 2. This is not scalable and is only intended for smaller data sizes. Download the training dataset file using the tf. Takes in a csv file that supports an iterator and whose __next__ method returns a str. It will cover how to do basic analysis of a dataset using pandas functions and how to transform a dataset by mapping functions. You can simulate this by splitting the dataset in training and test data. RDatasetsからirisのデータセットを取得 2. So, we would import the data in CSV format from Power BI. This is a collection of rich examples supported by Hydrogen. Let's implement SVM in Python using sklearn The Dataset. The following python script will use the iris data set and the following python modules to build and run the model: Numpy, scikit-learn and TensorFlow. csvをサンプルのデータセットとして,pandas, seabornで可視化の練習をした際のメモ.あくまで自分用メモなので図の種類やカラムの選び方など恣意的な箇所があると思いますが,ご了承ください(. And then select the appropiate columns of your choice. The most basic method for reading data is to simply read it with standard python code. read_csv('data. To select some fixed no. The data was read into python with the urllib and the request. A debate about which language is better suited for Datascience, R or Python, can set off diehard fans of these languages into a tizzy. In this chapter, we will discuss how to import Datasets and Libraries. Alternatively, you can click on each dataset separately to download it. Free download page for Project Iris's IRIS. csv() It opens help window of read. We are going to use a famous iris dataset which is available on the UCI repository. K Nearest Neighbours is one of the most commonly implemented Machine Learning clustering algorithms. used Pandas do load a dataset from a CSV file. Most of us use Python as our preferred tool for machine learning. Here is a sample VB. As the file is in an csv format, we also specify the format of the file while reading it using the SparkSession object: Now load this dataset file into a Spark dataset object. csv できれば、もうちょっと大きいものを例にした方が良いと思うんだけど、とりあえずということで。 配布するときは圧縮をかけることも考えられるので tar. The iris dataset is split in two files: the training set and the test set. The scikit-learn embeds some small toy datasets, which provide data scientists a playground to experiment a new algorithm and evaluate the correctness of their code before applying it to a real world sized data. sepal_length,sepal_width,petal_length,petal_width,species 5. ’ Simple Linear Regression. Iris A python package for the analysis and visualisation of Meteorological data Philip Elson 30th Sept 2015. Together with the team at Kaggle, we have developed a free interactive Machine Learning tutorial in Python that can be used in your Kaggle competitions! Step by step, through fun coding challenges, the tutorial will teach you how to predict survival rate for Kaggle's Titanic competition using Python and Machine Learning. Three types of storages are supported by the first release: NetCDF files, CSV text files and volatile in-memory datasets. Hello All, This is my first machine learning experiment in Python and I am really excited to explore this path. org distribution. The Iris flower dataset is one of the most famous databases for classification. StatsModels Datasets Package. The first one, the Iris dataset, is the machine learning practitioner’s equivalent of “Hello, World!” (likely one of the first pieces of software you wrote when learning how to program). Machine learning using python ## Check the versions of: # Python version. The following examples train a tree to recognize digits (0 - 9) from the MNIST dataset of images of handwritten digits and then displays the tree. For a general overview of the Repository, please visit our About page. This post contains a review of the cl. Fisher (1936). 6GB which made me a bit reluctant to train a model on it. At Analytics Vidhya, we love Python. If you are using D3 or Altair for your project, there are builtin functions to load these files into your project. For instructions on downloading data from tables, please see our Python documentation here. In fact, a lot of data scientists argue that the initial steps of obtaining and cleaning data constitute 80% of the job. # -*- coding: utf-8 -*- # Load libraries import pandas from Python - exporting results to. Calculate a p-value for SVM-plots on Iris dataset (2D-data) [closed] a data processing and CSV file I/O library import pandas as pd # We'll also import seaborn, a. The Data Retriever is written in Python and run using a command line interface or an associated R package. load_dataset (name, cache=True, data_home=None, **kws) ¶ Load a dataset from the online repository (requires internet). CSV stands for Comma Separated Values. Flexible Data Ingestion. 2,Iris-setosa 5. Iris Plants Database This is perhaps the best known database to be found in the pattern recognition literature. The preview of Microsoft Azure Machine Learning Python client library can enable secure access to your Azure Machine Learning datasets from a local Python environment and enables the creation and management of datasets in a workspace. Since we will be using the used cars dataset, you will need to download this dataset. read_csv('data. x, make sure if you're using Andaconda, you build with your Python 2. sepal_length,sepal_width,petal_length,petal_width,species 5. It is in CSV format. csv extension are similar to plain text files. We specify 'target_column' argument to indicate that our labels (survived or not) are located in the first column (id: 0). Interactive plots using Plotly. It is a multi-class classification problem and it only has 4 attributes and 150 rows. Related Course: Deep Learning for Computer Vision with Tensor Flow and Keras. Getting them into a pandas DataFrame is often an overkill if we just want to quickly try out some machine-learning algorithm or a visualization. A dimensionality reduction and learning tasks can be performed by the mlpy library with just a few number of commands. In the Datasets Section you can learn how customize the parsing rules and other options when converting a datasource to a dataset. Now that we've set up Python for machine learning, let's get started by loading an example dataset into scikit-learn! We'll explore the famous "iris" dataset, learn some important machine learning. It will also cover a working example to show you how to read and write data to a CSV file in Python. This will open a new notebook, with the results of the query loaded in as a dataframe. Instead of transferring large and sensitive data over the network or losing accuracy with sample csv files, you can have your R/Python code execute within your database. We can pass different values to the head function for viewing more or less values. cleaned column names of a Pandas dataframe; learned multivariate analysis by a MANOVA statsmodels example; Resources Analysis of Variance using Python: One-Way ANOVA in Python; Two-Way ANOVA in Python; Repeated Measures Analysis of Variance using Python: One-Way Repeated Measures ANOVA in R and Python. We import iris data by giving path of data file of " iris. I finally got around to finishing up this tutorial on how to use pandas DataFrames and SciPy together to handle any and all of your statistical needs in Python. I believe its towards the end of the code when using append its returning None and i am not sure how to fix that. Eckstrand 2. Training and test data. Bar plots (or “bar graphs”) are a type of data visualization that are used to display and compare the number, frequency or other measure (e. To Explore Data using Python - Lemonade. Pandas being one of the most popular package in Python is widely used for data manipulation. Having common datasets is a good way of making sure that different ideas can be tested and compared in a meaningful way - because the data they are tested against is the same. Introduction. Get the Data First, let’s download the Iris dataset from the UC Irvine Machine Learning Online Repository using python as shown below into a file we name raw. title ("Iris Data Set", fontsize. Now that we've set up Python for machine learning, let's get started by loading an example dataset into scikit-learn! We'll explore the famous "iris" dataset, learn some important machine learning. The dataset has: 4 input features (the length and width of the sepals, and the length and width of the petals). In what is often called supervised learning, the goal is to estimate or predict an output based on one or more inputs. Download Open Datasets on 1000s of Projects + Share Projects on One Platform. k-means clustering is a method of vector quantization, that can be used for cluster analysis in data mining. Assuming that each line of a CSV text file is a new row is hugely naive because of all the edge cases that arise in real-world dirty data. The k-means Clustering Algorithm. Such is the beauty of R that we got the pair-plots and correlation matrix both on the same plot. All Rights Reserved. BigMLer wraps BigML’s API Python bindings to offer a high-level command-line script to easily create and publish datasets and models, create ensembles, make local predictions from multiple models, clusters and simplify many other machine learning tasks. Dataset ini terkenal karena digunakan sebagai dataset “hello world” dalam Machine learning dan statistik oleh hampir semua orang yang memulai belajar machine learning. iris_r_pairplot. total_bill,bins=20) plt. Look at the. Have to follow the HTTP protocol and how framework dealing with files over net. read_csv (r'Path where the CSV file is stored\File name. and Python 3. Here is a sample VB. This dataset is already packaged and available for an easy download from the dataset page or directly from here Used Cars Dataset – usedcars. DataSets¶ In Scala and Java, Spark 1. This system currently classify 3 groups of flowers from the iris dataset depending upon a few selected features. Chapter 5 Scrubbing Data. Load data set from csv / url-----Load CSV data with Python, NumPY and Pandas. pyplot as plt import bs4 tips=sb. The goal is to project a dataset onto a lower-dimensional space with good class-separability in order avoid. sepal_length,sepal_width,petal_length,petal_width,species 5. Three types of storages are supported by the first release: NetCDF files, CSV text files and volatile in-memory datasets. Copy and paste this code snippet into a notebook cell:. The below plot uses the first two features. The iris dataset is a classic and very easy multi-class classification dataset. Now, in this tutorial, we will learn how to split a CSV file into Train and Test Data in Python Machine Learning. It describes particular biological characteristics of various types of Iris flowers, specifically, the length and width of both pedals and the sepals, which are part of the flower’s reproductive system. It mainly reported differences on grammars of these languages, which I found interesting when I translated my codes. using the Remote Desktop and opening a terminal OR using SSH). The Iris flower dataset is one of the most famous databases for classification. Download the iris flowers dataset CSV file to the local directory. Let’s train a neural network on this sample data step by step in Python : Step 1 : Prepare the iris data set. In this post, I give an overview of "built-in" datasets that are provided by popular python data science packages, such as statsmodels, scikit-learn, and seaborn. We'll run through a quick tutorial covering the basics of selecting rows, columns and both rows and columns. import seaborn as sb import matplotlib. Applied Machine Learning using R - Classification with IRIS Dataset by WACAMLDS Get access for free Step-by-Step Applied Machine Learning & Data Science Recipes for Students, Beginners & Business Analysts!. One of the questions may be arising in your mind that what IRIS Dataset is actually? About IRIS Dataset:-It is also known as Toy Dataset as it is easy to understand as all work is done in only a single CSV file. Sometimes a categorical variable is present by strings and also by numeric value. This is the 4th installment of my ‘Practical Machine Learning with R and Python’ series. Read the CSV data into a DataFrame and write out in Delta Lake format. The network has a training phase. Each instance contains 4 features, "sepal length", "sepal width", "petal length" and "petal width". We can import iris data set by using read.