# Quiver Plots in Python

How to make a quiver plot in Python. A quiver plot displays velocity vectors a arrows.

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#### Basic Quiver Plot¶

In :
import plotly.figure_factory as ff

import numpy as np

x,y = np.meshgrid(np.arange(0, 2, .2), np.arange(0, 2, .2))
u = np.cos(x)*y
v = np.sin(x)*y

fig = ff.create_quiver(x, y, u, v)
fig.show()


#### Quiver Plot with Points¶

In :
import plotly.figure_factory as ff
import plotly.graph_objects as go

import numpy as np

x,y = np.meshgrid(np.arange(-2, 2, .2),
np.arange(-2, 2, .25))
z = x*np.exp(-x**2 - y**2)
v, u = np.gradient(z, .2, .2)

# Create quiver figure
fig = ff.create_quiver(x, y, u, v,
scale=.25,
arrow_scale=.4,
name='quiver',
line_width=1)

mode='markers',
marker_size=12,
name='points'))

fig.show()


Cone plot for the 3D equivalent of quiver plots.

#### Reference¶

For more info on ff.create_quiver(), see the full function reference

Dash is an open-source framework for building analytical applications, with no Javascript required, and it is tightly integrated with the Plotly graphing library.

Learn about how to install Dash at https://dash.plot.ly/installation.

Everywhere in this page that you see fig.show(), you can display the same figure in a Dash application by passing it to the figure argument of the Graph component from the built-in dash_core_components package like this:

import plotly.graph_objects as go # or plotly.express as px
fig = go.Figure() # or any Plotly Express function e.g. px.bar(...)
# fig.update_layout( ... )

import dash
import dash_core_components as dcc
import dash_html_components as html

app = dash.Dash()
app.layout = html.Div([
dcc.Graph(figure=fig)
]) 