How to realize scatter plot by Python
This article mainly explains "how to achieve scatter plot in Python". Interested friends may wish to have a look at it. The method introduced in this paper is simple, fast and practical. Let's let the editor take you to learn "how to achieve scatter plot in Python".
What is an association graph?
An association graph is an image that looks for the relationship between two things. It can show us how one thing changes with the change of another.
Typical correlation graphs are: line chart, scatter chart, correlation matrix, etc.
When will we need an association diagram?
1. Data report & academic research
Show trends: such as how product sales change over time, how intelligence changes with education, etc.
Show status: the transaction rate of customers of different ages, the skill requirements of production staff corresponding to different production costs, etc.
2. Data exploration & data interpretation
Explore data relationships, help understand and try, and promote research
3. Statistics & Machine Learning
Explore data relations and guide data preprocessing and model selection
Library import numpy as np is required for scatter chart import
Import pandas as pd
Import matplotlib as mpl
Draw a simple scatter plot
Using pd.scatter function to draw scatter plot
A simple example of a scatter chart:
# define data, x1 take the number of random machines
X1 = np.random.randn (10)
X2 = x1 + x1x 2-10
# define the canvas. When only this picture is available, the following sentence is not required
Plt.figure (figsize= (8, 4))
# draw an image
Plt.scatter (x1, # Abscissa
X2, # ordinate
S = 50, # the size of the data point
C = "red", # Color of the data point
Label = "red points" # legend
)
# decorative graphics
# display the legend, and a warning appears in the following sentence with no label attribute in the above plt.scatter
Plt.legend ()
# display graphics
Plt.show ()
Draw scatter diagrams of multiple legend colors (take two kinds of examples)
To draw a graph, you need to find three elements:
1. Data for drawing, x1pl x2
2. List of tags
3. Color
Legend:
# generate a data table with 10 rows and 2 columns
X = np.random.randn (10jue 2)
Y = np.array ([0pc0pr 1pl 0pl 0pl 0pl 0p0p0p0])
Plt.figure (figsize= (8, 4))
Colors = ["red", "black"] # establish the color list
Label is a list of categories that establish the tag as = ["Zero", "One"] #
# overlay the images formed by multiple columns by looping through the x.shape
For i in range (x.shape [1]):
Plt.scatter (
X [yearly quotation 0]
X [yearly publication 1]
C=colors [i]
Label=labels [i]
)
# there are several categories in the tag, so we need to cycle through several times, drawing dots of one color at a time
Plt.legend ()
Plt.show ()
Draw a complex scatter plot
Creating our own data is too simple, we can use the simple dataset of the online god to learn to draw complex scatter maps.
Midwest = pd.read_csv ("https://raw.githubusercontent.com/selva86/datasets/master/midwest_filter.csv")
# filter tags to remove duplicate tags
Categories = np.unique (midwest ['category']) # remove all duplicates
Plt.figure (figsize= (160.10))
For i in range (len (categories)):
Plt.scatter (midwest.loc [midwest ["category"] = = categories [I], "area"]
, midwest.loc [midwest ["category"] = = categories [I], "poptotal"]
, swarm 20
, c=np.array (plt.cm.tab10 (i/len (categories) .reshape (1mam Murray 1)
, label=categories [i]
)
Plt.legend ()
Plt.show () so far, I believe you have a deeper understanding of "how to achieve scatter plot in Python". You might as well do it in practice. Here is the website, more related content can enter the relevant channels to inquire, follow us, continue to learn!
