In Python, you can visualize images with the Plotly IMshow function.


#Python #pythonlearning #datascience #datavisualization

You can use Plotly IMshow for a few uses.

You can use it to plot heatmaps ...

But you can also use it to plot images.

The syntax for Plotly IMshow is pretty simple.

You call the function as px.imshow and then provide the name of the image file you want to visualize.

(This assumes you've imported Plotly express as px)

#Python #pythoncode Image

Typically, when you plot an image this way ...

The input to px.imshow will be a .jpg or .png file ...

But You'll need to read the file in first using Plotly imread Image

You can learn more about Plotly IMread here:…

#Python #learnpython

Here's a quick example of how to use Plotly imshow.

We'll read in the file with IMRead ...

And then visualize with IMshow

#Python #pythoncode #pythonlearning #datascience ImageImageImageImage

Plotly IMRead is pretty easy to use, and it's useful if you're working with image data.

(For example, some machine learning systems operate on image data)

#Python #pythonlearning #machinelearning

If you want to learn more about data science and data visualization, then follow me here: @Josh_Ebner

Every day, I post tutorials and threads about data science and machine learning in Python and R.

#datascience #machinelearning #Python #rstats

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More from @Josh_Ebner

30 Dec
Merging two or more datasets is extremely important in data science.

Here's a quick thread that covers the basics of data merges in Python.


#Python #datascience #DataAnalytics ImageImageImageImage

In Python ...

You can combine two Pandas dataframes using the "merge" function.

You can also use the "join" function (which defaults to joining on the index)

#Python #datascience #DataAnalytics

When you merge dataframes, you'll typically have a so-called "key" variable.

This is the variable upon which you'll join the dataframes.

#Python #datascience
Read 19 tweets
29 Dec
In Python ...

You can combine Numpy arrays vertically or horizontally using np.concatenate

#Python #pythoncode #datascience ImageImage
The first argument to the function is a list (or collection) of arrays that you want to combine.

You can actually combine many arrays ...just put them inside the list.
The axis parameter controls the direction along which you combine the arrays.

For 2D arrays ...

'axis = 0' combines vertically
'axis = 1' combines horizontally
Read 6 tweets
29 Dec
In Matplotlib ...

You can get the RGBA representation of a color with the to_rgba() function.

#Python #pythoncode #datavisualization ImageImage
You'll notice that the output of to_rgba is a tuple with four floats: (%red, %green, %blue, alpha)

In RGBA, the alpha channel represents the opacity of the color, where:

– 0.0 is fully transparent
– 1.0 is fully opaque
Read 4 tweets
28 Dec
In Python ...

You can use the Pandas dropna method to drop rows with missing values.

#Python #pythoncode #datascience ImageImageImageImage
As seen above, you can limit dropna to specific columns with the 'subset=' parameter.

With 'subset=', you can specify the columns in which dropna will look for missing values

#Python #pythonlearning #datascience
If it finds missing values in any of those columns, it will drop the row.

But it will ignore missing values in other columns.
Read 6 tweets
28 Dec
In Python ...

You can use Numpy all to test conditions about the properties of a Numpy array.

#Python #pythoncode #datascience ImageImageImageImage

So for instance, in the example above, I test if all of the values are greater than 2, by column.

To do this, you need to know how np.all works ...

But you also need to know how to use axes.

#Python #pythonlearning
Read 5 tweets
31 Aug
The big thing that I'd change here is the color palette.

This color palette is hard to interpret and frankly, just look a little ugly.

#datascience #DataVisualization


The fix here is pretty simple.

The data are sequential in nature. There's a low and a high.

When you have sequential data, you should almost always look at sequential color palettes.


More specifically:

For sequential data, your go-to palettes should almost always be perceptually uniform sequential palettes like viridis or magma.

Read 11 tweets

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