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I am plotting a ganttChart in python-plotly,can I set this axis style in plotly ganttChart?
Unfortunately, at the time of this answer, the plotly documentation does not have enough features to create what you are looking for. The best you can do is change the background color and text color which is extremely far from what you need.
import plotly.express as px
import pandas as pd
import plotly.io as pio
pio.renderers.default = 'browser'
df = pd.DataFrame([
dict(Task="Job A", Start='2009-01-01',
Finish='2009-02-28', Completion_pct=50),
dict(Task="Job B", Start='2009-03-05',
Finish='2009-04-15', Completion_pct=25),
dict(Task="Job C", Start='2009-02-20',
Finish='2009-05-30', Completion_pct=75)
])
fig = px.timeline(df, x_start="Start", x_end="Finish",
y="Task",
)
fig.update_yaxes(autorange="reversed")
fig.update_xaxes(color="#ffffff")
fig.update_yaxes(color="#ffffff")
fig.update_layout(paper_bgcolor="#48596f")
fig.show()
Related
I need to show gray color to the states which do not have any data in Plotly.
Sample csv file is: (This states have data)
States which are not having data are: (I have filled the missing values as -1
The current plots generated are: ( I need to show gray color to the states with missing data.
Thanks!
Your solution is to use custom colorscale in combination with
import plotly.express as px
px.choropleth_mapbox
The following is an example on how to use custom colorscale:
import plotly.plotly as py
import plotly.graph_objs as go
import numpy as np
import copy
import pandas as pd
# Read data from a csv
z_data = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/api_docs/mt_bruno_elevation.csv')
z=z_data.values.copy()
# Compute surface color with nan's
surfacecolor = z.copy()
surfacecolor[-10:, -10:] = np.nan
# Replace nans with -100
surfacecolor[np.isnan(surfacecolor)] = -100
# Build surface trace
data = [
go.Surface(
z=z,
surfacecolor=surfacecolor,
cmin = -5,
cmax = 350,
colorscale=[[0, 'gray'],
[0.01, 'gray'],
[0.01, 'blue'],
[1, 'red']]
)
]
# Build layout
layout = go.Layout(
title='Mt Bruno Elevation',
autosize=False,
width=500,
height=500,
margin=dict(
l=65,
r=50,
b=65,
t=90
)
)
fig = go.FigureWidget(data=data, layout=layout)
fig
A similar question has been solved by the plotly community forum.
Please find the plotly documentation on how to define custom colorscales.
Hope this solves your issue!
I have a problem figuring out how to have Seaborn show the right values in a logarithmic barplot. A value of mine should be, in the ideal case, be 1. My dataseries (5,2,1,0.5,0.2) has a set of values that deviate from unity and I want to visualize these in a logarithmic barplot. However, when plotting this in the standard log-barplot it shows the following:
But the values under one are shown to increase from -infinity to their value, whilst the real values ought to look like this:
Strangely enough, I was unable to find a Seaborn, Pandas or Matplotlib attribute to "snap" to a different horizontal axis or "align" or ymin/ymax. I have a feeling I am unable to find it because I can't find the terms to shove down my favorite search engine. Some semi-solutions I found just did not match what I was looking for or did not have either xaxis = 1 or a ylog. A try that uses some jank Matplotlib lines:
If someone knows the right terms or a solution, thank you in advance.
Here are the Jupyter cells I used:
{1}
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
data = {'X': ['A','B','C','D','E'], 'Y': [5,2,1,0.5,0.2]}
df = pd.DataFrame(data)
{2}
%matplotlib widget
g = sns.catplot(data=df, kind="bar", y = "Y", x = "X", log = True)
{3}
%matplotlib widget
plt.vlines(x=data['X'], ymin=1, ymax=data['Y'])
You could let the bars start at 1 instead of at 0. You'll need to use sns.barplot directly.
The example code subtracts 1 of all y-values and sets the bar bottom at 1.
import matplotlib.pyplot as plt
from matplotlib.ticker import NullFormatter
import seaborn as sns
import pandas as pd
import numpy as np
data = {'X': ['A', 'B', 'C', 'D', 'E'], 'Y': [5, 2, 1, 0.5, 0.2]}
df = pd.DataFrame(data)
ax = sns.barplot(y=df["Y"] - 1, x=df["X"], bottom=1, log=True, palette='flare_r')
ax.axhline(y=1, c='k')
# change the y-ticks, as the default shows too few in this case
ax.set_yticks(np.append(np.arange(.2, .8, .1), np.arange(1, 7, 1)), minor=False)
ax.set_yticks(np.arange(.3, 6, .1), minor=True)
ax.yaxis.set_major_formatter(lambda x, pos: f'{x:.0f}' if x >= 1 else f'{x:.1f}')
ax.yaxis.set_minor_formatter(NullFormatter())
ax.bar_label(ax.containers[0], labels=df["Y"])
sns.despine()
plt.show()
PS: With these specific values, the plot might go without logscale:
I would like o add a plot figure based on smileys like this one:
dat will come from a dataframe pandas : dataframe.value_counts(normalize=True)
Can some one give me some clues.
use colorscale in normal way for a heatmap
use anotation_text to assign an emoji to a value
import plotly.figure_factory as ff
import plotly.graph_objects as go
import pandas as pd
import numpy as np
df = pd.DataFrame([[j*10+i for i in range(10)] for j in range(10)])
e=["đ","đ","đ","âšī¸"]
fig = go.Figure(ff.create_annotated_heatmap(
z=df.values, colorscale="rdylgn", reversescale=False,
annotation_text=np.select([df.values>75, df.values>50, df.values>25, df.values>=0], e),
))
fig.update_annotations(font_size=25)
# allows emoji to use background color
fig.update_annotations(opacity=0.7)
update coloured emoji
fundamentally you need emojicons that can accept colour styling
for this I switched to Font Awesome. This then also requires switching to dash, plotly's cousin so that external CSS can be used (to use FA)
then build a dash HTML table applying styling logic for picking emoticon and colour
from jupyter_dash import JupyterDash
import dash_html_components as html
import pandas as pd
import branca.colormap
# Load Data
df = pd.DataFrame([[j*10+i for i in range(10)] for j in range(10)])
external_stylesheets = [{
'href': 'https://use.fontawesome.com/releases/v5.8.1/css/all.css',
'rel': 'stylesheet', 'crossorigin': 'anonymous',
'integrity': 'sha384-50oBUHEmvpQ+1lW4y57PTFmhCaXp0ML5d60M1M7uH2+nqUivzIebhndOJK28anvf',
}]
# possibly could use a a different library for this - simple way to map a value to a colormap
cm = branca.colormap.LinearColormap(["red","yellow","green"], vmin=0, vmax=100, caption=None)
def mysmiley(v):
sm = ["far fa-grin", "far fa-smile", "far fa-meh", "far fa-frown"]
return html.Span(className=sm[3-(v//25)], style={"color":cm(v),"font-size": "2em"})
# Build App
app = JupyterDash(__name__, external_stylesheets=external_stylesheets)
app.layout = html.Div([
html.Table([html.Tr([html.Td(mysmiley(c)) for c in r]) for r in df.values])
])
# Run app and display result inline in the notebook
app.run_server(mode='inline')
how can I plot the dataframe-info below onto the geopandas map? Bubble size should be dependant on case-numbers!
import geopandas
import geoplot
import pandas
d = {"Germany": 5, "United Kingdom" : 3, "Finland" : 1, "United States of America" : 4}
df = pandas.DataFrame.from_dict(d,orient='index')
df.columns = ["Cases"]
def WorldCaseMap():
world = geopandas.read_file(geopandas.datasets.get_path("naturalearth_lowres"))
ex = geoplot.polyplot(world)
WorldCaseMap()
Make a second df containing centroid geometry and plot it over the first one. Working example below.
import geopandas as gpd
world = gpd.read_file(gpd.datasets.get_path("naturalearth_lowres"))
centroids = world.copy()
centroids.geometry = world.centroid
centroids['size'] = centroids['pop_est'] / 1000000 # to get reasonable plotable number
ax = world.plot(facecolor='w', edgecolor='k')
centroids.plot(markersize='size', ax=ax)
I'm not sure geopandas gives you a bubble-map that easily. Their best example is a choropleth:
gpd_per_person = world['gdp_md_est'] / world['pop_est']
scheme = mapclassify.Quantiles(gpd_per_person, k=5)
# Note: this code sample requires geoplot>=0.4.0.
geoplot.choropleth(
world, hue=gpd_per_person, scheme=scheme,
cmap='Greens', figsize=(8, 4)
)
Source
I found another example of bubble-maps using geopandas here: https://residentmario.github.io/geoplot/gallery/plot_usa_city_elevations.html however I prefer the look of the plotly example. (See below).
Otherwise have a look at plotly's examples they have a bubble map: https://plot.ly/python/bubble-maps/
import matplotlib.pyplot as plt
import pandas as pd
df = pd.DataFrame({'y':['a','b','c','d','e','f','g','h','i']\
,'x':[10,9,9,8,7,6,10,6,7]})
df.sort_values(by='x',inplace=True,ascending = True)
plt.barh(bottom=list(range(1,10)), width=df.x, height = 0.15, align='center',color = 'blue')
plt.xlim([0,11])
plt.yticks(list(range(1,10)),skills.y)
plt.show()
This code gives me a horizontal bar graph.
I want to add a circular dot at the edge of each bars.
Can someone please help me with that.
Tableau graph
I did this in tableau, I want to replicate the same in python.
Also, please let me know if there a better way of coding the same.
I am using Anaconda Python 3.5, Matplotlib library, Windows 10, Idlex IDE
You could just add a scatterplot on top of your bars, using matplotlib scatter function.
Also, note that you could use the numpy.arange function to generate your x values, instead of your current list(range(1,10)).
See example below
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
df = pd.DataFrame({'y':['a','b','c','d','e','f','g','h','i'],
'x':[10,9,9,8,7,6,10,6,7]})
df.sort_values(by='x',inplace=True,ascending = True)
plt.barh(bottom=np.arange(len(df)), width=df.x, height = 0.15, align='center',color = 'blue')
plt.scatter(df.x.values, y=np.arange(df.shape[0]), color='b', s=40)
plt.xlim([0,11])
plt.yticks(np.arange(len(df)),df.y)
plt.show()