This is the "Iris" dataset. Originally published at UCI Machine Learning Repository: Iris Data Set, this small dataset from 1936 is often used for testing out machine learning algorithms and visualizations (for example, Scatter Plot).
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| function render(data){ | |
| d3.select("body") | |
| .append("pre") | |
| .text(JSON.stringify(data, null, 2)); | |
| } | |
| function type(d){ | |
| d.sepal_length = +d.sepal_length; | |
| d.sepal_width = +d.sepal_width; | |
| d.petal_length = +d.petal_length; | |
| d.petal_width = +d.petal_width; | |
| return d; | |
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| d3.csv("iris.csv", type, render); | |
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| sepal_length | sepal_width | petal_length | petal_width | species | |
|---|---|---|---|---|---|
| 5.1 | 3.5 | 1.4 | 0.2 | setosa | |
| 4.9 | 3.0 | 1.4 | 0.2 | setosa | |
| 4.7 | 3.2 | 1.3 | 0.2 | setosa | |
| 4.6 | 3.1 | 1.5 | 0.2 | setosa | |
| 5.0 | 3.6 | 1.4 | 0.2 | setosa | |
| 5.4 | 3.9 | 1.7 | 0.4 | setosa | |
| 4.6 | 3.4 | 1.4 | 0.3 | setosa | |
| 5.0 | 3.4 | 1.5 | 0.2 | setosa | |
| 4.4 | 2.9 | 1.4 | 0.2 | setosa | |
| 4.9 | 3.1 | 1.5 | 0.1 | setosa | |
| 5.4 | 3.7 | 1.5 | 0.2 | setosa | |
| 4.8 | 3.4 | 1.6 | 0.2 | setosa | |
| 4.8 | 3.0 | 1.4 | 0.1 | setosa | |
| 4.3 | 3.0 | 1.1 | 0.1 | setosa | |
| 5.8 | 4.0 | 1.2 | 0.2 | setosa | |
| 5.7 | 4.4 | 1.5 | 0.4 | setosa | |
| 5.4 | 3.9 | 1.3 | 0.4 | setosa | |
| 5.1 | 3.5 | 1.4 | 0.3 | setosa | |
| 5.7 | 3.8 | 1.7 | 0.3 | setosa | |
| 5.1 | 3.8 | 1.5 | 0.3 | setosa | |
| 5.4 | 3.4 | 1.7 | 0.2 | setosa | |
| 5.1 | 3.7 | 1.5 | 0.4 | setosa | |
| 4.6 | 3.6 | 1.0 | 0.2 | setosa | |
| 5.1 | 3.3 | 1.7 | 0.5 | setosa | |
| 4.8 | 3.4 | 1.9 | 0.2 | setosa | |
| 5.0 | 3.0 | 1.6 | 0.2 | setosa | |
| 5.0 | 3.4 | 1.6 | 0.4 | setosa | |
| 5.2 | 3.5 | 1.5 | 0.2 | setosa | |
| 5.2 | 3.4 | 1.4 | 0.2 | setosa | |
| 4.7 | 3.2 | 1.6 | 0.2 | setosa | |
| 4.8 | 3.1 | 1.6 | 0.2 | setosa | |
| 5.4 | 3.4 | 1.5 | 0.4 | setosa | |
| 5.2 | 4.1 | 1.5 | 0.1 | setosa | |
| 5.5 | 4.2 | 1.4 | 0.2 | setosa | |
| 4.9 | 3.1 | 1.5 | 0.1 | setosa | |
| 5.0 | 3.2 | 1.2 | 0.2 | setosa | |
| 5.5 | 3.5 | 1.3 | 0.2 | setosa | |
| 4.9 | 3.1 | 1.5 | 0.1 | setosa | |
| 4.4 | 3.0 | 1.3 | 0.2 | setosa | |
| 5.1 | 3.4 | 1.5 | 0.2 | setosa | |
| 5.0 | 3.5 | 1.3 | 0.3 | setosa | |
| 4.5 | 2.3 | 1.3 | 0.3 | setosa | |
| 4.4 | 3.2 | 1.3 | 0.2 | setosa | |
| 5.0 | 3.5 | 1.6 | 0.6 | setosa | |
| 5.1 | 3.8 | 1.9 | 0.4 | setosa | |
| 4.8 | 3.0 | 1.4 | 0.3 | setosa | |
| 5.1 | 3.8 | 1.6 | 0.2 | setosa | |
| 4.6 | 3.2 | 1.4 | 0.2 | setosa | |
| 5.3 | 3.7 | 1.5 | 0.2 | setosa | |
| 5.0 | 3.3 | 1.4 | 0.2 | setosa | |
| 7.0 | 3.2 | 4.7 | 1.4 | versicolor | |
| 6.4 | 3.2 | 4.5 | 1.5 | versicolor | |
| 6.9 | 3.1 | 4.9 | 1.5 | versicolor | |
| 5.5 | 2.3 | 4.0 | 1.3 | versicolor | |
| 6.5 | 2.8 | 4.6 | 1.5 | versicolor | |
| 5.7 | 2.8 | 4.5 | 1.3 | versicolor | |
| 6.3 | 3.3 | 4.7 | 1.6 | versicolor | |
| 4.9 | 2.4 | 3.3 | 1.0 | versicolor | |
| 6.6 | 2.9 | 4.6 | 1.3 | versicolor | |
| 5.2 | 2.7 | 3.9 | 1.4 | versicolor | |
| 5.0 | 2.0 | 3.5 | 1.0 | versicolor | |
| 5.9 | 3.0 | 4.2 | 1.5 | versicolor | |
| 6.0 | 2.2 | 4.0 | 1.0 | versicolor | |
| 6.1 | 2.9 | 4.7 | 1.4 | versicolor | |
| 5.6 | 2.9 | 3.6 | 1.3 | versicolor | |
| 6.7 | 3.1 | 4.4 | 1.4 | versicolor | |
| 5.6 | 3.0 | 4.5 | 1.5 | versicolor | |
| 5.8 | 2.7 | 4.1 | 1.0 | versicolor | |
| 6.2 | 2.2 | 4.5 | 1.5 | versicolor | |
| 5.6 | 2.5 | 3.9 | 1.1 | versicolor | |
| 5.9 | 3.2 | 4.8 | 1.8 | versicolor | |
| 6.1 | 2.8 | 4.0 | 1.3 | versicolor | |
| 6.3 | 2.5 | 4.9 | 1.5 | versicolor | |
| 6.1 | 2.8 | 4.7 | 1.2 | versicolor | |
| 6.4 | 2.9 | 4.3 | 1.3 | versicolor | |
| 6.6 | 3.0 | 4.4 | 1.4 | versicolor | |
| 6.8 | 2.8 | 4.8 | 1.4 | versicolor | |
| 6.7 | 3.0 | 5.0 | 1.7 | versicolor | |
| 6.0 | 2.9 | 4.5 | 1.5 | versicolor | |
| 5.7 | 2.6 | 3.5 | 1.0 | versicolor | |
| 5.5 | 2.4 | 3.8 | 1.1 | versicolor | |
| 5.5 | 2.4 | 3.7 | 1.0 | versicolor | |
| 5.8 | 2.7 | 3.9 | 1.2 | versicolor | |
| 6.0 | 2.7 | 5.1 | 1.6 | versicolor | |
| 5.4 | 3.0 | 4.5 | 1.5 | versicolor | |
| 6.0 | 3.4 | 4.5 | 1.6 | versicolor | |
| 6.7 | 3.1 | 4.7 | 1.5 | versicolor | |
| 6.3 | 2.3 | 4.4 | 1.3 | versicolor | |
| 5.6 | 3.0 | 4.1 | 1.3 | versicolor | |
| 5.5 | 2.5 | 4.0 | 1.3 | versicolor | |
| 5.5 | 2.6 | 4.4 | 1.2 | versicolor | |
| 6.1 | 3.0 | 4.6 | 1.4 | versicolor | |
| 5.8 | 2.6 | 4.0 | 1.2 | versicolor | |
| 5.0 | 2.3 | 3.3 | 1.0 | versicolor | |
| 5.6 | 2.7 | 4.2 | 1.3 | versicolor | |
| 5.7 | 3.0 | 4.2 | 1.2 | versicolor | |
| 5.7 | 2.9 | 4.2 | 1.3 | versicolor | |
| 6.2 | 2.9 | 4.3 | 1.3 | versicolor | |
| 5.1 | 2.5 | 3.0 | 1.1 | versicolor | |
| 5.7 | 2.8 | 4.1 | 1.3 | versicolor | |
| 6.3 | 3.3 | 6.0 | 2.5 | virginica | |
| 5.8 | 2.7 | 5.1 | 1.9 | virginica | |
| 7.1 | 3.0 | 5.9 | 2.1 | virginica | |
| 6.3 | 2.9 | 5.6 | 1.8 | virginica | |
| 6.5 | 3.0 | 5.8 | 2.2 | virginica | |
| 7.6 | 3.0 | 6.6 | 2.1 | virginica | |
| 4.9 | 2.5 | 4.5 | 1.7 | virginica | |
| 7.3 | 2.9 | 6.3 | 1.8 | virginica | |
| 6.7 | 2.5 | 5.8 | 1.8 | virginica | |
| 7.2 | 3.6 | 6.1 | 2.5 | virginica | |
| 6.5 | 3.2 | 5.1 | 2.0 | virginica | |
| 6.4 | 2.7 | 5.3 | 1.9 | virginica | |
| 6.8 | 3.0 | 5.5 | 2.1 | virginica | |
| 5.7 | 2.5 | 5.0 | 2.0 | virginica | |
| 5.8 | 2.8 | 5.1 | 2.4 | virginica | |
| 6.4 | 3.2 | 5.3 | 2.3 | virginica | |
| 6.5 | 3.0 | 5.5 | 1.8 | virginica | |
| 7.7 | 3.8 | 6.7 | 2.2 | virginica | |
| 7.7 | 2.6 | 6.9 | 2.3 | virginica | |
| 6.0 | 2.2 | 5.0 | 1.5 | virginica | |
| 6.9 | 3.2 | 5.7 | 2.3 | virginica | |
| 5.6 | 2.8 | 4.9 | 2.0 | virginica | |
| 7.7 | 2.8 | 6.7 | 2.0 | virginica | |
| 6.3 | 2.7 | 4.9 | 1.8 | virginica | |
| 6.7 | 3.3 | 5.7 | 2.1 | virginica | |
| 7.2 | 3.2 | 6.0 | 1.8 | virginica | |
| 6.2 | 2.8 | 4.8 | 1.8 | virginica | |
| 6.1 | 3.0 | 4.9 | 1.8 | virginica | |
| 6.4 | 2.8 | 5.6 | 2.1 | virginica | |
| 7.2 | 3.0 | 5.8 | 1.6 | virginica | |
| 7.4 | 2.8 | 6.1 | 1.9 | virginica | |
| 7.9 | 3.8 | 6.4 | 2.0 | virginica | |
| 6.4 | 2.8 | 5.6 | 2.2 | virginica | |
| 6.3 | 2.8 | 5.1 | 1.5 | virginica | |
| 6.1 | 2.6 | 5.6 | 1.4 | virginica | |
| 7.7 | 3.0 | 6.1 | 2.3 | virginica | |
| 6.3 | 3.4 | 5.6 | 2.4 | virginica | |
| 6.4 | 3.1 | 5.5 | 1.8 | virginica | |
| 6.0 | 3.0 | 4.8 | 1.8 | virginica | |
| 6.9 | 3.1 | 5.4 | 2.1 | virginica | |
| 6.7 | 3.1 | 5.6 | 2.4 | virginica | |
| 6.9 | 3.1 | 5.1 | 2.3 | virginica | |
| 5.8 | 2.7 | 5.1 | 1.9 | virginica | |
| 6.8 | 3.2 | 5.9 | 2.3 | virginica | |
| 6.7 | 3.3 | 5.7 | 2.5 | virginica | |
| 6.7 | 3.0 | 5.2 | 2.3 | virginica | |
| 6.3 | 2.5 | 5.0 | 1.9 | virginica | |
| 6.5 | 3.0 | 5.2 | 2.0 | virginica | |
| 6.2 | 3.4 | 5.4 | 2.3 | virginica | |
| 5.9 | 3.0 | 5.1 | 1.8 | virginica |
I read the file for the iris data set from the URL at the UCI Machine Learning Website. The python code is below:
from urllib.request import urlretrieve
import pandas as pd
iris = 'http://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data'
urlretrieve(iris)
df = pd.read_csv(iris, sep=',')
attributes = ["sepal_length", "sepal_width", "petal_length", "petal_width", "class"]
df.columns = attributes
print(df.head())
Select the data set and Copy it. You can then paste it in Excel.
Click on the Raw button, in the frame for the data, and Save as a CSV file.
Launch Excel and open the csv file you just saved.
Hope this helps
Anybody understand how does the Iris data set help in testing Clusters?
This dataset (Fisher iris data) is included in the free trial offered by Penny Analytics, who run an online outlier detection service. You will need to download their version of the dataset to be sure to get the free pricing. The page is here:
https://pennyanalytics.com/free-trial/
Congrats @aturner-ca for getting free advertising! Nice spamming skills.
The comment seems to me to be about Penny Analytics, and not really about the dataset.
I understand startups are hard.
Good luck!
I read the file for the iris data set from the URL at the UCI Machine Learning Website. The python code is below:
Import package
from urllib.request import urlretrieve
Import pandas
import pandas as pd
Assign url of file: url
iris = 'http://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data'
Save file locally
urlretrieve(iris)
Read file into a DataFrame and print its head
df = pd.read_csv(iris, sep=',')
Add column names to the data frame
attributes = ["sepal_length", "sepal_width", "petal_length", "petal_width", "class"]
df.columns = attributesView the first five lines of data frame
print(df.head())
I'd amend this by adding the column names inline, because the above approach removes the first row (making it 149 instead of 150)
df = pd.read_csv('iris.csv', sep=',', names=["sepal_length", "sepal_width", "petal_length", "petal_width", "class"])
Sir how can I get iris_setosa, iris_versicolor variables from the iris dataset. If I want to see the mean of petal length of iris setosa.
what would be the python code?
I don't know Python.
one row of data is missing in it.How to get that too.
Really? Which row is missing?
Looks like there are 151 rows including the header row, so 150 records, which is in line with the upstream dataset.
in case anyone is looking for even simpler way to do it:
import pandas as pd
# download iris data and read it into a dataframe
url = 'http://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data'
df = pd.read_csv(url, names=['sepal_length', 'sepal_width', 'petal_length', 'petal_width', 'class'])
Another example, loading this dataset in JavaScript: https://vizhub.com/curran/c9bedc6a76cf468997e3fcb5471db0ab?file=viz.js
How to download the IRIS data set as csv file?
Click here and press ctrl+s
https://gist.github.com/curran/a08a1080b88344b0c8a7/raw/0e7a9b0a5d22642a06d3d5b9bcbad9890c8ee534/iris.csv
You'll could have just downloaded Zip file at the top corner
such a usefull dataset
okay, can we use this dataset for educational purpose
How to download the IRIS data set as csv file?