Altair - Gradient above line - altair
I want to create an area chart, however the gradient should run from the line up to the top of the chart. Any ideas?
example of a regular gradient chart here https://altair-viz.github.io/gallery/area_chart_gradient.html
alt.Chart(source).transform_filter(
'datum.symbol==="GOOG"'
).mark_area(
line={'color':'darkgreen'},
color=alt.Gradient(
gradient='linear',
stops=[alt.GradientStop(color='white', offset=0),
alt.GradientStop(color='darkgreen', offset=1)],
x1=1,
x2=1,
y1=1,
y2=0
)
).encode(
alt.X('date:T'),
alt.Y('price:Q')
)
You can do this by setting the y2 encoding to alt.value(0) – the zero in this case measures pixels from the top of the chart axis:
import altair as alt
from vega_datasets import data
source = data.stocks()
alt.Chart(source).transform_filter(
'datum.symbol==="GOOG"'
).mark_area(
line={'color':'darkgreen'},
color=alt.Gradient(
gradient='linear',
stops=[alt.GradientStop(color='white', offset=0),
alt.GradientStop(color='darkgreen', offset=1)],
x1=1,
x2=1,
y1=1,
y2=0
)
).encode(
alt.X('date:T'),
alt.Y('price:Q'),
y2=alt.value(0)
)
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cg04497154,0,0,0,1 cg08146865,0,0,0,2 cg18589016,0,0,0,2 cg05397886,0,0,0,1 cg13679048,0,0,0,2 cg21946299,0,0,0,2 cg19788741,0,0,0,2 cg04323979,0,0,0,1 cg13580857,0,0,0,2 cg08016802,0,0,0,2 cg18319687,0,0,0,1 cg00257542,0,0,0,1 cg26512993,0,0,0,1 cg02117859,0,0,0,1 cg21622555,0,0,0,2 cg00540941,0,0,0,1 cg24332767,0,0,0,2 cg02052774,0,0,0,2 cg15627380,0,0,0,1 cg22562590,0,0,0,1 cg00871979,0,0,0,1 cg04012364,0,0,0,2 cg15952045,0,0,0,1 cg13576200,0,0,0,1 cg22264014,0,0,0,2 cg26673648,0,0,0,1 cg01381130,0,0,0,1 cg22294804,0,0,0,2 cg01727686,0,0,0,1 cg21932368,0,0,0,2 cg06536629,0,0,0,2 cg10915772,0,0,0,1 cg18449721,0,0,0,2 cg19697530,0,0,0,2 cg19253643,0,0,0,1 cg26635603,0,0,0,1 cg00517407,0,0,0,2 cg21291641,0,0,0,2 cg13914598,0,0,0,2 cg05516842,0,0,0,1 cg03187614,0,0,0,1 cg05272099,0,0,0,2 cg10661615,0,0,0,2 cg05601623,0,0,0,2 cg13118545,0,0,0,2 cg12690313,0,0,0,2 cg06369090,0,0,0,2 cg08743392,0,0,0,2 cg02276361,0,0,0,1 cg08915922,0,0,0,2 cg04169908,0,0,0,1 cg12440258,0,0,0,2 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cg14318942,0,0,0,2 cg11229715,0,0,0,2 cg11691189,0,0,0,1 cg12991830,0,0,0,2 cg22699052,0,0,1,1 cg09485472,0,0,0,1 cg14752227,0,0,1,1 cg04787343,0,0,0,2 cg11746846,0,0,0,1 cg17852021,0,0,0,2 cg15120477,0,0,0,1 cg24572400,0,0,0,2 cg00117869,0,0,0,1 cg01216607,0,0,0,2 cg17222164,0,0,0,2 cg01204964,0,0,1,1 cg07955004,1,1,1,1 cg04371440,0,0,0,2 cg15035364,0,0,0,2 cg19710662,0,0,0,1 cg16595365,0,0,0,2 cg03370106,0,0,0,1 cg18571419,0,0,0,2 cg20624137,0,0,0,1 cg15412736,0,0,0,2 cg00889769,0,0,0,2 cg14649140,0,0,0,2 cg25531618,0,0,0,1 cg07594031,0,0,0,2 cg05816239,0,0,0,2 cg00295604,0,0,0,2 cg04941721,0,0,0,2 cg11613164,0,0,0,2 cg02387679,0,0,0,2 cg22134372,0,0,0,2 cg27099166,0,0,0,2 cg09735674,0,0,0,1 cg23173517,0,0,0,2 cg20713333,0,0,0,1 cg01520402,0,0,0,1 cg00328593,0,0,0,2 cg17348479,0,0,0,1 cg26643142,0,0,0,2 cg14575053,0,0,0,2 cg05092885,0,0,0,1 cg08620751,0,0,0,1 cg21562321,0,0,0,1 cg22374901,0,0,0,2 cg27613976,0,0,0,2 cg06127885,0,0,1,1 cg14840664,0,0,0,1 cg25045242,0,0,0,1 cg12747844,0,0,0,1 cg14534464,0,0,0,2 cg21508023,0,0,0,2 cg13417559,0,0,0,2 cg14461650,0,0,0,1 cg03885264,0,0,0,2 cg02868338,0,0,0,2 cg08846467,0,0,0,2 cg27565938,0,0,0,1 cg08904363,0,0,0,2 cg12253071,0,0,0,1 cg06259664,0,1,1,1 cg18453904,0,0,0,2 cg19144392,0,0,0,1 cg16189596,0,0,0,2 And I want to create a seaborn heatmap like this: plt.figure(figsize=(470, 60)) sns.set(font_scale = 14) df=comparison.T # create a Boolean mask of df mask = df.ge(1).all() # use the mask to update a list of labels cols = [col if m else '' for (col, m) in zip(df.columns, mask)] # plot with custom labels ax = sns.heatmap(df, xticklabels=cols,cmap="crest_r") ax.set_xticklabels(labels=cols, fontsize=200) plt.show() However, sometimes due to the narrow space the xtick labels overlap. Is there any way to add more spacing while still providing a readable image (not too small so that it cannot be read) or to put them one below the other?
Adding vertical average lines on top of a layered histogram in Altair
I am trying to add vertical lines indicating the average of datasets in a layered histogram in Altair (based on their example). My attempt below is failing: base = alt.Chart(outcomes) bar = base.transform_fold( ['Push','Dealer Win','Player Win','Ace High Push'], as_=['Outcome','Outcomes out of 1000'] ).mark_bar( opacity=0.3, binSpacing=0 ).encode( alt.X('Outcomes out of 1000:Q', bin=alt.Bin(maxbins=100)), alt.Y('count()', stack=None), alt.Color('Outcome:N') ) rule = base.transform_fold( ['Push','Dealer Win','Player Win','Ace High Push'], as_=['Count','Outcome'] ).mark_rule( color='red' ).encode( alt.X('mean(Outcome):Q'), size=alt.value(2) ) bar + rule which results in: When I do just bar though the layered histogram renders just fine: Basically what I'm looking for is: Thanks🙏 Update (less than an hour after original post): Thanks #debbes for the speedy guidance! I was able to use your example to get this working via: base = alt.Chart(outcomes).transform_fold( ['Push','Dealer Win','Player Win','Ace High Push'], as_=['Outcome','Outcomes out of 1000'] ).transform_bin( field='Outcomes out of 1000', as_='bin_meas', bin=alt.Bin(maxbins=100) ).encode( color='Outcome:N' ) hist = base.mark_bar( opacity=0.3, binSpacing=0 ).encode( alt.X('bin_meas:Q'), alt.Y('count()', stack=None) ) rule = base.mark_rule( size=2 ).encode( alt.X('mean(Outcomes out of 1000):Q') ) hist + rule which results in:
In this case you have to use the transform_bin instead of doing the binning in the X encoding: base = alt.Chart(source).transform_fold( ['Trial A', 'Trial B', 'Trial C'], as_=['Experiment', 'Measurement'] ).transform_bin( field='Measurement', as_='bin_meas', bin=alt.Bin(maxbins=100) ).encode( color='Experiment:N' ) hist = base.mark_bar(opacity=0.3,binSpacing=0).encode( alt.X('bin_meas:Q'), alt.Y('count()', stack=None), ) rule = base.mark_rule(size=2).encode(alt.X('mean(Measurement):Q'),) hist + rule
Remove repeating values from X axis label in Altair
I am having trouble with Altair repeating X axis label values. Data: rule_abbreaviation flagged_claim bill_month 0 CONCIDPROC 1 Apr2022 1 CONTUSMAT1 1 Apr2022 2 COVID05 1 Jun2021 3 FILTROTUB2 1 Sep2021 4 MEPIARTRO1 1 Mar2022 #Code to generate Altair Bar Chart bar = alt.Chart(Data).mark_bar().encode( x=alt.X('flagged_claim:Q', axis=alt.Axis(title='Flagged Claims', format= ',.0f'), stack='zero'), y=alt.Y('rule_abbreaviation:N', axis=alt.Axis(title='Component Abbreviation'), sort=alt.SortField(field=measure, order='descending')), tooltip=[alt.Tooltip('max(ClaimRuleName):N', title='Claim Component'), alt.Tooltip('flagged_claim:Q', title='Flagged Claims', format= ',.0f')], color=alt.Color('bill_month', legend=None) ).properties(width=485, title = alt.TitleParams(text = 'Bottom Components', font = 'Arial', fontSize = 16, color = '#000080', ) ).interactive() X axis label generated by this chart contains repeated 0 and 1 Image of Visualization: https://i.stack.imgur.com/0XdWB.png
The reason this is happening is because you have format= ',.0f' which tells Altair to include 0 decimals in the axis labels. Remove it or change to 1f to see decimals in the labels. In general, a good way to troubleshoot problems like this is to remove part of your code at a time to identify which part is causing the unexpected behavior. To reduce the number of ticks you can use alt.Axis(title='Flagged Claims', format='d', tickCount=1) or alt.Axis(title='Flagged Claims', format='d', values=[0, 1]). See also Changing Number of y-axis Ticks in Altair
What is the proper way to employ date2num for timestamps using candlestick_ohlc
My data looks like this (Date, Open, High, Low, Close): ohlc = [ [1502929058, 1.2652, 1.2653, 1.265, 1.2653], [1502929059, 1.267, 1.267, 1.267, 1.267], [1502929060, 1.2655, 1.2656, 1.2655, 1.2656], [1502929061, 1.2652, 1.2653, 1.2652, 1.2653], [1502929062, 1.2631, 1.2631, 1.263, 1.2631], [1502929063, 1.2625, 1.2625, 1.2625, 1.2625], [1502929064, 1.2619, 1.2619, 1.2619, 1.2619], [1502929065, 1.2622, 1.2623, 1.2622, 1.2623], [1502929066, 1.2622, 1.2623, 1.2622, 1.2623], [1502929067, 1.2617, 1.262, 1.2617, 1.262] ] and I'm using the code blow to plot the candlesticks: for row in ohlc: row[0] = mdates.date2num(datetime.datetime.fromtimestamp(row[0])) fig = plt.figure() ax1 = plt.subplot2grid((1,1), (0,0)) candlestick_ohlc(ax1,ohlc,width=0.1) fig.subplots_adjust(bottom=0.3) ax1.xaxis.set_major_formatter(mdates.DateFormatter('%y-%m-%d %H:%M:%S')) for label in ax1.xaxis.get_ticklabels(): label.set_rotation(45) plt.xlabel('Date') plt.ylabel('Price') plt.show() but the candlesticks being drawn on top of each other: as I checked the code further, I noticed that mdates.date2num(datetime.datetime.fromtimestamp(row[0])) is actually generating dates with very minute differences (and therefore candlesticks being drawn on top of each other): 736558.1997453704 736558.1997569444 736558.1997685186 736558.1997800926 736558.1997916667 736558.1998032407 736558.1998148148 736558.1998263889 736558.199837963 736558.199849537 what is the solution to this problem?
plot with hist function get wrong normalized probability density figure
I want drow a simple hist plot, with density=True, the data saved in the data.txt file, I put here: -3.159589616094560238e-02 -2.238517986981702901e-02 3.685513259832978727e-02 5.551115123125782702e-17 3.855829197377269590e-02 1.106747485805819609e-01 -1.343976685751569478e-01 4.594910529316376113e-03 4.908490856818659154e-04 3.419692699076753994e-02 -1.000969353410680052e-02 -3.022899285774027778e-02 -5.537409517542163373e-02 -4.602923607484266100e-02 3.284859133476397686e-02 -1.023686626680164746e-02 -3.664415656649880337e-02 8.171815787883440763e-02 2.999203027116759124e-02 5.004522795043858663e-02 7.388383006436807787e-02 6.113278573286806683e-02 6.386857025829173473e-02 3.591723008598152189e-02 6.549840356253561202e-02 1.253856364955455160e-02 6.041119437265624059e-02 6.908608718235104140e-02 1.134341604638248180e-01 -1.576914025344122727e-02 -2.417229901971842954e-02 -3.899153022868465102e-02 4.689096941761000670e-02 5.841198683607556896e-02 8.641413395100966399e-02 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5.906830317137512498e-04 7.382336514353160517e-03 8.410538789687949102e-03 1.535361143379543325e-02 9.231776277094427829e-03 3.122655500871762690e-03 -3.942937463410095544e-03 7.929557208005727498e-03 3.413561032196532619e-03 enter image description herejust got wrong figure[enter image description here][2], using python 3.7. import matplotlib.pyplot as plt import pandas as pd data = pd.read_csv('data.txt', header=-1) data.columns =['A'] data.hist('A', bins=20, density=True) plt.show()