Plot scatter python

Plot scatter python DEFAULT

Python Scatter Plot

Scatter plot is a graph in which the values of two variables are plotted along two axes. It is a most basic type of plot that helps you visualize the relationship between two variables.

Concept

  1. What is a Scatter plot?
  2. Basic Scatter plot in python
  3. Correlation with Scatter plot
  4. Changing the color of groups of points
  5. Changing the Color and Marker
  6. Scatter plot with Linear fit plot using seaborn
  7. Scatter Plot with Histograms using seaborn
  8. Bubble plot
  9. Exploratory Analysis using mtcars Dataset
    • Multiple line of best fits
    • Adjusting color and style for different categories
    • Text Annotation in Scatter Plot
    • Bubble Plot with categorical variables
    • Categorical Plot

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What is a Scatter plot?

Scatter plot is a graph of two sets of data along the two axes. It is used to visualize the relationship between the two variables.

If the value along the Y axis seem to increase as X axis increases(or decreases), it could indicate a positive (or negative) linear relationship. Whereas, if the points are randomly distributed with no obvious pattern, it could possibly indicate a lack of dependent relationship.

In python matplotlib, the scatterplot can be created using the or the . Using these functions, you can add more feature to your scatter plot, like changing the size, color or shape of the points.

So what is the difference between vs ?

The difference between the two functions is: with any property you apply (color, shape, size of points) will be applied across all points whereas in you have more control in each point’s appearance.

That is, in you can have the color, shape and size of each dot (datapoint) to vary based on another variable. Or even the same variable (y). Whereas, with , the properties you set will be applied to all the points in the chart.

First, I am going to import the libraries I will be using.

The function is used to change the default parameters of the plot’s figure.

Basic Scatter plot in python

First, let’s create artifical data using the . You need to specify the no. of points you require as the arguments.

You can also specify the lower and upper limit of the random variable you need.

Then use the function to draw a scatter plot using matplotlib. You need to specify the variables x and y as arguments.

is used to set title to your plot.

is used to label the x axis.

is used to label the y axis.

You can see that there is a positive linear relation between the points. That is, as X increases, Y increases as well, because the Y is actually just X + random_number.

If you want the color of the points to vary depending on the value of Y (or another variable of same size), specify the color each dot should take using the argument.

You can also provide different variable of same size as X.

Lets create a dataset with exponentially increasing relation and visualize the plot.

is used to create a dataset between the lower limit and upper limit with a step of ‘interval’ no. of points.

Now you can see that there is a exponential relation between the x and y axis.

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Correlation with Scatter plot

1) If the value of y increases with the value of x, then we can say that the variables have a positive correlation.

2) If the value of y decreases with the value of x, then we can say that the variables have a negative correlation.

3) If the value of y changes randomly independent of x, then it is said to have a zero corelation.

In the above graph, you can see that the blue line shows an positive correlation, the orange line shows a negative corealtion and the green dots show no relation with the x values(it changes randomly independently).

Changing the color of groups of points

Use the command to change the colour to represent scatter plot.

Changing the Color and Marker

Use the command to change the marker type in scatter plot.

[‘.’,’o’,’v’,’^’,’>’,'<‘,’s’,’p’,’*’,’h’,’H’,’D’,’d’,’1′,”,”] – These are the types of markers that you can use for your plot.

Scatter Plot with Linear fit plot using Seaborn

Lets try to fit the dataset for the best fitting line using the function in seaborn.

Lets use the mtcars dataset.

You can download the dataset from the given address: https://www.kaggle.com/ruiromanini/mtcars/download

Now lets try whether there is a linear fit between the and the column .

You can see that we are getting a negative corelation between the 2 columns.

Scatter Plot with Histograms using seaborn

Use the joint plot function in seaborn to represent the scatter plot along with the distribution of both x and y values as historgrams.

Use the function with x, y and datset as arguments.

As you can see we are also getting the distribution plot for the x and y value.

Bubble plot

A bubble plot is a scatterplot where a third dimension is added: the value of an additional variable is represented through the size of the dots.

You need to add another command in the scatter plot which represents the size of the points.

The size of the bubble represents the value of the third dimesnsion, if the bubble size is more then it means that the value of z is large at that point.

Exploratory Analysis of mtcars Dataset

mtcars dataset contains the mileage and vehicle specifications of multiple car models. The dataset can be downloaded here.

The objective of the exploratory analysis is to understand the relationship between the various vehicle specifications and mileage.

You can see that the dataset contains different informations about a car.

First let’s see a scatter plot to see a distribution between and and their histogramic distribution. You can do this by using the function in seaborn.

Multiple Line of best fits

If you need to do linear regrssion fit for multiple categories of features between x and y, like in this case, I am further dividing the categories accodring to and trying to fit a linear line accordingly. For this, use the argument in the function.

See that the function has fitted 3 different lines for 3 categories of gears in the dataset.

Adjusting color and style for different categories

I splitted the dataset according to different categories of gear. Then I plotted them separately using the function.

Text Annotation in Scatter Plot

If you need to add any text in your graph use the function with the text and the coordinates where you need to add the text as arguments.

Bubble Plot with Categorical Variables

Normally you will use 2 varibales to plot a scatter graph(x and y), then I added another categorical variable which will be implied by the color of the points, I also added another variable whose value will be implied according to the intensity of each color.

I have plotted the value vs value and also splitted them into different colors with respect of value and the size of each bubble represents the value.

paramter is used to chage the color intensity of the plot. More the aplha more will be the color intensity.

Categorical Plot

is used to give access to several axes-level functions that show the relationship between a numerical and one or more categorical variables using one of several visual representations.

Use the command to further split the data into another categories.

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Sours: https://www.machinelearningplus.com/plots/python-scatter-plot/

Matplotlib Scatter

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Creating Scatter Plots

With Pyplot, you can use the function to draw a scatter plot.

The function plots one dot for each observation. It needs two arrays of the same length, one for the values of the x-axis, and one for values on the y-axis:

Example

A simple scatter plot:

import matplotlib.pyplot as plt
import numpy as np

x = np.array([5,7,8,7,2,17,2,9,4,11,12,9,6])
y = np.array([99,86,87,88,111,86,103,87,94,78,77,85,86])

plt.scatter(x, y)
plt.show()

Result:

Try it Yourself »

The observation in the example above is the result of 13 cars passing by.

The X-axis shows how old the car is.

The Y-axis shows the speed of the car when it passes.

Are there any relationships between the observations?

It seems that the newer the car, the faster it drives, but that could be a coincidence, after all we only registered 13 cars.


Compare Plots

In the example above, there seems to be a relationship between speed and age, but what if we plot the observations from another day as well? Will the scatter plot tell us something else?

Example

Draw two plots on the same figure:

import matplotlib.pyplot as plt
import numpy as np

#day one, the age and speed of 13 cars:
x = np.array([5,7,8,7,2,17,2,9,4,11,12,9,6])
y = np.array([99,86,87,88,111,86,103,87,94,78,77,85,86])
plt.scatter(x, y)

#day two, the age and speed of 15 cars:
x = np.array([2,2,8,1,15,8,12,9,7,3,11,4,7,14,12])
y = np.array([100,105,84,105,90,99,90,95,94,100,79,112,91,80,85])
plt.scatter(x, y)

plt.show()

Result:

Try it Yourself »

Note: The two plots are plotted with two different colors, by default blue and orange, you will learn how to change colors later in this chapter.

By comparing the two plots, I think it is safe to say that they both gives us the same conclusion: the newer the car, the faster it drives.


Colors

You can set your own color for each scatter plot with the or the argument:

Example

Set your own color of the markers:

import matplotlib.pyplot as plt
import numpy as np

x = np.array([5,7,8,7,2,17,2,9,4,11,12,9,6])
y = np.array([99,86,87,88,111,86,103,87,94,78,77,85,86])
plt.scatter(x, y, color = 'hotpink')

x = np.array([2,2,8,1,15,8,12,9,7,3,11,4,7,14,12])
y = np.array([100,105,84,105,90,99,90,95,94,100,79,112,91,80,85])
plt.scatter(x, y, color = '#88c999')

plt.show()

Result:

Try it Yourself »

Color Each Dot

You can even set a specific color for each dot by using an array of colors as value for the argument:

Note: You cannot use the argument for this, only the argument.

Example

Set your own color of the markers:

import matplotlib.pyplot as plt
import numpy as np

x = np.array([5,7,8,7,2,17,2,9,4,11,12,9,6])
y = np.array([99,86,87,88,111,86,103,87,94,78,77,85,86])
colors = np.array(["red","green","blue","yellow","pink","black","orange","purple","beige","brown","gray","cyan","magenta"])

plt.scatter(x, y, c=colors)

plt.show()

Result:

Try it Yourself »

ColorMap

The Matplotlib module has a number of available colormaps.

A colormap is like a list of colors, where each color has a value that ranges from 0 to 100.

Here is an example of a colormap:

This colormap is called 'viridis' and as you can see it ranges from 0, which is a purple color, and up to 100, which is a yellow color.

How to Use the ColorMap

You can specify the colormap with the keyword argument with the value of the colormap, in this case which is one of the built-in colormaps available in Matplotlib.

In addition you have to create an array with values (from 0 to 100), one value for each of the point in the scatter plot:

Example

Create a color array, and specify a colormap in the scatter plot:

import matplotlib.pyplot as plt
import numpy as np

x = np.array([5,7,8,7,2,17,2,9,4,11,12,9,6])
y = np.array([99,86,87,88,111,86,103,87,94,78,77,85,86])
colors = np.array([0, 10, 20, 30, 40, 45, 50, 55, 60, 70, 80, 90, 100])

plt.scatter(x, y, c=colors, cmap='viridis')

plt.show()

Result:

Try it Yourself »

You can include the colormap in the drawing by including the statement:

Example

Include the actual colormap:

import matplotlib.pyplot as plt
import numpy as np

x = np.array([5,7,8,7,2,17,2,9,4,11,12,9,6])
y = np.array([99,86,87,88,111,86,103,87,94,78,77,85,86])
colors = np.array([0, 10, 20, 30, 40, 45, 50, 55, 60, 70, 80, 90, 100])

plt.scatter(x, y, c=colors, cmap='viridis')

plt.colorbar()

plt.show()

Result:

Try it Yourself »

Available ColorMaps

You can choose any of the built-in colormaps:


Size

You can change the size of the dots with the argument.

Just like colors, make sure the array for sizes has the same length as the arrays for the x- and y-axis:

Example

Set your own size for the markers:

import matplotlib.pyplot as plt
import numpy as np

x = np.array([5,7,8,7,2,17,2,9,4,11,12,9,6])
y = np.array([99,86,87,88,111,86,103,87,94,78,77,85,86])
sizes = np.array([20,50,100,200,500,1000,60,90,10,300,600,800,75])

plt.scatter(x, y, s=sizes)

plt.show()

Result:

Try it Yourself »

Alpha

You can adjust the transparency of the dots with the argument.

Just like colors, make sure the array for sizes has the same length as the arrays for the x- and y-axis:

Example

Set your own size for the markers:

import matplotlib.pyplot as plt
import numpy as np

x = np.array([5,7,8,7,2,17,2,9,4,11,12,9,6])
y = np.array([99,86,87,88,111,86,103,87,94,78,77,85,86])
sizes = np.array([20,50,100,200,500,1000,60,90,10,300,600,800,75])

plt.scatter(x, y, s=sizes, alpha=0.5)

plt.show()

Result:

Try it Yourself »

Combine Color Size and Alpha

You can combine a colormap with different sizes on the dots. This is best visualized if the dots are transparent:

Example

Create random arrays with 100 values for x-points, y-points, colors and sizes:

import matplotlib.pyplot as plt
import numpy as np

x = np.random.randint(100, size=(100))
y = np.random.randint(100, size=(100))
colors = np.random.randint(100, size=(100))
sizes = 10 * np.random.randint(100, size=(100))

plt.scatter(x, y, c=colors, s=sizes, alpha=0.5, cmap='nipy_spectral')

plt.colorbar()

plt.show()

Result:

Try it Yourself »

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Sours: https://www.w3schools.com/python/matplotlib_scatter.asp
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Matplotlib - Scatter Plot



Scatter plots are used to plot data points on horizontal and vertical axis in the attempt to show how much one variable is affected by another. Each row in the data table is represented by a marker the position depends on its values in the columns set on the X and Y axes. A third variable can be set to correspond to the color or size of the markers, thus adding yet another dimension to the plot.

The script below plots a scatter diagram of grades range vs grades of boys and girls in two different colors.

import matplotlib.pyplot as plt girls_grades = [89, 90, 70, 89, 100, 80, 90, 100, 80, 34] boys_grades = [30, 29, 49, 48, 100, 48, 38, 45, 20, 30] grades_range = [10, 20, 30, 40, 50, 60, 70, 80, 90, 100] fig=plt.figure() ax=fig.add_axes([0,0,1,1]) ax.scatter(grades_range, girls_grades, color='r') ax.scatter(grades_range, boys_grades, color='b') ax.set_xlabel('Grades Range') ax.set_ylabel('Grades Scored') ax.set_title('scatter plot') plt.show() Graded Scored
Sours: https://www.tutorialspoint.com/matplotlib/matplotlib_scatter_plot.htm

matplotlib.pyplot.scatter

Parameters:
x, yfloat or array-like, shape (n, )

The data positions.

sfloat or array-like, shape (n, ), optional

The marker size in points**2. Default is .

carray-like or list of colors or color, optional

The marker colors. Possible values:

  • A scalar or sequence of n numbers to be mapped to colors using cmap and norm.
  • A 2D array in which the rows are RGB or RGBA.
  • A sequence of colors of length n.
  • A single color format string.

Note that c should not be a single numeric RGB or RGBA sequence because that is indistinguishable from an array of values to be colormapped. If you want to specify the same RGB or RGBA value for all points, use a 2D array with a single row. Otherwise, value- matching will have precedence in case of a size matching with x and y.

If you wish to specify a single color for all points prefer the color keyword argument.

Defaults to . In that case the marker color is determined by the value of color, facecolor or facecolors. In case those are not specified or , the marker color is determined by the next color of the ' current "shape and fill" color cycle. This cycle defaults to (default: ).

marker, default: (default: )

The marker style. marker can be either an instance of the class or the text shorthand for a particular marker. See for more information about marker styles.

cmapstr or , default: (default: )

A instance or registered colormap name. cmap is only used if c is an array of floats.

norm, default: None

If c is an array of floats, norm is used to scale the color data, c, in the range 0 to 1, in order to map into the colormap cmap. If None, use the default .

vmin, vmaxfloat, default: None

vmin and vmax are used in conjunction with the default norm to map the color array c to the colormap cmap. If None, the respective min and max of the color array is used. It is deprecated to use vmin/vmax when norm is given.

alphafloat, default: None

The alpha blending value, between 0 (transparent) and 1 (opaque).

linewidthsfloat or array-like, default: (default: )

The linewidth of the marker edges. Note: The default edgecolors is 'face'. You may want to change this as well.

edgecolors{'face', 'none', None} or color or sequence of color, default: (default: )

The edge color of the marker. Possible values:

  • 'face': The edge color will always be the same as the face color.
  • 'none': No patch boundary will be drawn.
  • A color or sequence of colors.

For non-filled markers, edgecolors is ignored. Instead, the color is determined like with 'face', i.e. from c, colors, or facecolors.

plotnonfinitebool, default: False

Whether to plot points with nonfinite c (i.e. , or ). If the points are drawn with the bad colormap color (see ).

Sours: https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.scatter.html

Python plot scatter

Scatterplot


A scatter plot displays the relationship between 2 numeric variables. Each data point is represented as a circle. Several tools allow to build one in python, this section provides code samples for , and for interactive versions. Note that this online course has a chapter dedicated to scatterplots.

⏱ Quick start (Seaborn)


⏱ Quick start (Matplotlib)

Matplotlib also requires only a few lines of code to draw a scatterplot thanks to its function. The resulting chart is not as good-looking, but the function probably offers more flexibility in term of customization.


Matplotlib logoScatterplots with

is another great alternative to build scatterplots with python. As often, it takes a bit more lines of code to get a decent chart, but allows more customization.

The examples below should get you covered for all the most common problems: adding markers, addinglabels, changing shapes and more.

Sours: https://www.python-graph-gallery.com/scatter-plot/
Pandas Scatter - pd.DataFrame.plot.scatter()

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