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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cg19493601,0,0,0,2
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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
5.642541143884205468e-02
4.930903227540417433e-03
1.942745077277890919e-02
2.158310326121676281e-02
1.049712334090066590e-02
1.754053058915017171e-02
-2.353744351610204122e-02
-8.208313574505410326e-03
5.744397964275682611e-02
-1.211723809639734806e-01
-8.992113883794172757e-02
1.061556222415527273e-02
-2.920395846191836675e-02
1.037069716181004964e-03
-3.161371576086063895e-02
-3.612043404641407385e-02
-1.809334659086286878e-02
3.662870373600723983e-02
1.391056336430290807e-02
-1.172024701805174929e-01
3.251886867868275521e-02
2.396867235334465551e-02
3.837332827102124533e-02
6.056630689588365923e-02
8.321671975043376523e-02
1.115621200150082593e-01
8.420054612809740879e-02
5.823374107493473062e-02
7.729794103241893755e-02
2.477495629227444152e-02
2.972088991663957014e-02
3.742060783581174777e-02
-1.110223024625156540e-16
2.587337514429000063e-02
1.527836714091357351e-02
1.958896955617406288e-02
-1.832210450473931163e-02
-1.926657560263266011e-02
2.339325805152586701e-02
-6.324903516012525539e-02
-6.690299051450193657e-02
-2.482072685296415893e-02
3.064499196744452370e-02
3.355998562941886476e-02
1.788746275401903452e-02
4.613919286422546451e-03
-8.927918985562194321e-03
1.028462556431128383e-02
5.532095657291985491e-02
6.604320581321337924e-02
3.669973111374175145e-02
2.277477855616832914e-02
4.808227903197481723e-02
4.832747444828244809e-02
7.656143177495855490e-03
1.245515608493746873e-02
2.349500690106676171e-02
4.072972204376856808e-03
2.516504860752621919e-02
2.003738740895273651e-02
-2.613223707890222069e-02
4.011136596889314232e-03
2.548231927644559192e-02
1.330938824527588826e-02
2.404439993147697296e-02
6.907130907663830421e-03
6.979484635157226502e-03
-3.290848338052448918e-02
-1.755191743006401239e-02
-3.366674089662069491e-04
5.945578987875954624e-05
-1.632427050508156174e-02
-8.658771138098070308e-02
-4.820886804917889901e-02
-8.546677486103770871e-03
-6.925724668856880761e-03
-9.925127350824403116e-04
-2.449622387186950467e-02
3.123051142945909575e-02
5.795788098893345230e-02
3.605543122998344785e-02
3.302943330800275912e-02
7.059531249141720588e-02
5.853930993753908574e-02
3.905874422983779404e-02
4.843263815672965711e-02
4.147627035435175191e-02
8.400424535113787394e-02
9.844201764532001242e-02
9.294548499676424935e-02
6.497483032833101246e-02
1.144645348202695256e-01
2.999608390738961461e-02
3.085053739416804275e-02
2.276726259836320265e-02
0.000000000000000000e+00
-2.510456168410146738e-02
2.116637291906409146e-02
-3.321700185738685196e-02
3.982853772878908183e-03
1.363191485535983349e-02
1.564968650026804520e-02
1.478997225428510531e-02
-3.131685025064945282e-03
-3.806954212751323396e-02
-8.159214863622976655e-03
-7.186257889362090978e-02
-9.500427775385178464e-04
-3.395150378118699797e-02
7.110268582201983989e-03
-2.162228010763439512e-02
-5.384418762160989025e-02
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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()

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