Plotly library of Python can be very useful for data visualization and understanding the data simply and easily. Plotly graph objects are a high-level interface to plotly which are easy to use.
plotly.express.line_3d() function
This function is used to create a 3D line plot and can be used with pandas dataframes. Each row of dataframe is represented by a symbol mark in 3D space in the line plot.
Syntax: plotly.express.line_3d(data_frame=None, x=None, y=None, z=None, color=None, line_dash=None, text=None, line_group=None, hover_name=None, hover_data=None, title=None, template=None, width=None, height=None)
Parameters:
data_frame: DataFrame or array-like or dict needs to be passed for column names.
x, y, z: This parameters is either a name of a column in data_frame, or a pandas Series or array_like object. Values from this column or array_like are used to position marks along the x, y and z axis in cartesian coordinates respectively.
color: This parameters assign color to marks.
line_dash: This parameter is used to assign dash-patterns to lines.
line_group: This parameter is used to group rows of data_frame into lines.
hover_name: Â Values from this column or array_like appear in bold in the hover tooltip.
hover_data: This parameter is used to appear in the hover tooltip or tuples with a bool or formatting string as first element, and list-like data to appear in hover as second element Values from these columns appear as extra data in the hover tooltip.
Example 1:
Python3
import plotly.express as px   df = px.data.tips()   plot = px.line_3d(df, x = 'time',                      y = 'day',                      z = 'sex') plot.show() |
Output:
Example 2:
Python3
import plotly.express as px   df = px.data.tips()   plot = px.line_3d(df, x = 'time',                   y = 'day',                   z = 'sex',                   color = 'time') plot.show() |
Output:
Example 3:
Python3
# Python program to demonstrate scatter # plot   import plotly.express as px   df = px.data.tips()   plot = px.line_3d(df, x = 'day',                      y = 'total_bill',                      z = 'sex',                      color = 'time',                      line_group = 'sex') plot.show() |
Output:
