Distplot Fit a Studentas T Continuous Random Variable
In [1]:
import plotly.express as px df = px . data . tips () fig = px . histogram ( df , x = "total_bill" , y = "tip" , color = "sex" , marginal = "rug" , hover_data = df . columns ) fig . show ()
In [2]:
import plotly.express as px df = px . data . tips () fig = px . histogram ( df , x = "total_bill" , y = "tip" , color = "sex" , marginal = "box" , # or violin, rug hover_data = df . columns ) fig . show ()
Combined statistical representations in Dash¶
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Combined statistical representations with distplot figure factory¶
The distplot figure factory displays a combination of statistical representations of numerical data, such as histogram, kernel density estimation or normal curve, and rug plot.
Basic Distplot¶
A histogram, a kde plot and a rug plot are displayed.
In [4]:
import plotly.figure_factory as ff import numpy as np np . random . seed ( 1 ) x = np . random . randn ( 1000 ) hist_data = [ x ] group_labels = [ 'distplot' ] # name of the dataset fig = ff . create_distplot ( hist_data , group_labels ) fig . show ()
Plot Multiple Datasets¶
In [5]:
import plotly.figure_factory as ff import numpy as np # Add histogram data x1 = np . random . randn ( 200 ) - 2 x2 = np . random . randn ( 200 ) x3 = np . random . randn ( 200 ) + 2 x4 = np . random . randn ( 200 ) + 4 # Group data together hist_data = [ x1 , x2 , x3 , x4 ] group_labels = [ 'Group 1' , 'Group 2' , 'Group 3' , 'Group 4' ] # Create distplot with custom bin_size fig = ff . create_distplot ( hist_data , group_labels , bin_size = .2 ) fig . show ()
Use Multiple Bin Sizes¶
Different bin sizes are used for the different datasets with the bin_size argument.
In [6]:
import plotly.figure_factory as ff import numpy as np # Add histogram data x1 = np . random . randn ( 200 ) - 2 x2 = np . random . randn ( 200 ) x3 = np . random . randn ( 200 ) + 2 x4 = np . random . randn ( 200 ) + 4 # Group data together hist_data = [ x1 , x2 , x3 , x4 ] group_labels = [ 'Group 1' , 'Group 2' , 'Group 3' , 'Group 4' ] # Create distplot with custom bin_size fig = ff . create_distplot ( hist_data , group_labels , bin_size = [ .1 , .25 , .5 , 1 ]) fig . show ()
Customize Rug Text, Colors & Title¶
In [7]:
import plotly.figure_factory as ff import numpy as np x1 = np . random . randn ( 26 ) x2 = np . random . randn ( 26 ) + .5 group_labels = [ '2014' , '2015' ] rug_text_one = [ 'a' , 'b' , 'c' , 'd' , 'e' , 'f' , 'g' , 'h' , 'i' , 'j' , 'k' , 'l' , 'm' , 'n' , 'o' , 'p' , 'q' , 'r' , 's' , 't' , 'u' , 'v' , 'w' , 'x' , 'y' , 'z' ] rug_text_two = [ 'aa' , 'bb' , 'cc' , 'dd' , 'ee' , 'ff' , 'gg' , 'hh' , 'ii' , 'jj' , 'kk' , 'll' , 'mm' , 'nn' , 'oo' , 'pp' , 'qq' , 'rr' , 'ss' , 'tt' , 'uu' , 'vv' , 'ww' , 'xx' , 'yy' , 'zz' ] rug_text = [ rug_text_one , rug_text_two ] # for hover in rug plot colors = [ 'rgb(0, 0, 100)' , 'rgb(0, 200, 200)' ] # Create distplot with custom bin_size fig = ff . create_distplot ( [ x1 , x2 ], group_labels , bin_size = .2 , rug_text = rug_text , colors = colors ) fig . update_layout ( title_text = 'Customized Distplot' ) fig . show ()
Plot Normal Curve¶
In [8]:
import plotly.figure_factory as ff import numpy as np x1 = np . random . randn ( 200 ) x2 = np . random . randn ( 200 ) + 2 group_labels = [ 'Group 1' , 'Group 2' ] colors = [ 'slategray' , 'magenta' ] # Create distplot with curve_type set to 'normal' fig = ff . create_distplot ([ x1 , x2 ], group_labels , bin_size = .5 , curve_type = 'normal' , # override default 'kde' colors = colors ) # Add title fig . update_layout ( title_text = 'Distplot with Normal Distribution' ) fig . show ()
Plot Only Curve and Rug¶
In [9]:
import plotly.figure_factory as ff import numpy as np x1 = np . random . randn ( 200 ) - 1 x2 = np . random . randn ( 200 ) x3 = np . random . randn ( 200 ) + 1 hist_data = [ x1 , x2 , x3 ] group_labels = [ 'Group 1' , 'Group 2' , 'Group 3' ] colors = [ '#333F44' , '#37AA9C' , '#94F3E4' ] # Create distplot with curve_type set to 'normal' fig = ff . create_distplot ( hist_data , group_labels , show_hist = False , colors = colors ) # Add title fig . update_layout ( title_text = 'Curve and Rug Plot' ) fig . show ()
Plot Only Hist and Rug¶
In [10]:
import plotly.figure_factory as ff import numpy as np x1 = np . random . randn ( 200 ) - 1 x2 = np . random . randn ( 200 ) x3 = np . random . randn ( 200 ) + 1 hist_data = [ x1 , x2 , x3 ] group_labels = [ 'Group 1' , 'Group 2' , 'Group 3' ] colors = [ '#835AF1' , '#7FA6EE' , '#B8F7D4' ] # Create distplot with curve_type set to 'normal' fig = ff . create_distplot ( hist_data , group_labels , colors = colors , bin_size = .25 , show_curve = False ) # Add title fig . update_layout ( title_text = 'Hist and Rug Plot' ) fig . show ()
Plot Hist and Rug with Different Bin Sizes¶
In [11]:
import plotly.figure_factory as ff import numpy as np x1 = np . random . randn ( 200 ) - 2 x2 = np . random . randn ( 200 ) x3 = np . random . randn ( 200 ) + 2 hist_data = [ x1 , x2 , x3 ] group_labels = [ 'Group 1' , 'Group 2' , 'Group 3' ] colors = [ '#393E46' , '#2BCDC1' , '#F66095' ] fig = ff . create_distplot ( hist_data , group_labels , colors = colors , bin_size = [ 0.3 , 0.2 , 0.1 ], show_curve = False ) # Add title fig . update ( layout_title_text = 'Hist and Rug Plot' ) fig . show ()
Plot Only Hist and Curve¶
In [12]:
import plotly.figure_factory as ff import numpy as np x1 = np . random . randn ( 200 ) - 2 x2 = np . random . randn ( 200 ) x3 = np . random . randn ( 200 ) + 2 hist_data = [ x1 , x2 , x3 ] group_labels = [ 'Group 1' , 'Group 2' , 'Group 3' ] colors = [ '#A56CC1' , '#A6ACEC' , '#63F5EF' ] # Create distplot with curve_type set to 'normal' fig = ff . create_distplot ( hist_data , group_labels , colors = colors , bin_size = .2 , show_rug = False ) # Add title fig . update_layout ( title_text = 'Hist and Curve Plot' ) fig . show ()
Distplot with Pandas¶
In [13]:
import plotly.figure_factory as ff import numpy as np import pandas as pd df = pd . DataFrame ({ '2012' : np . random . randn ( 200 ), '2013' : np . random . randn ( 200 ) + 1 }) fig = ff . create_distplot ([ df [ c ] for c in df . columns ], df . columns , bin_size = .25 ) fig . show ()
Reference¶
What About Dash?¶
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.add_trace( ... ) # 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 ) ]) app . run_server ( debug = True , use_reloader = False ) # Turn off reloader if inside Jupyter 
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Source: https://plotly.com/python/distplot/
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