Here is the code that I have tried:
# import pandas as pd
import numpy as np
import plotly.graph_objects as go
from plotly.subplots import make_subplots
df = pd.read_csv("resultant_data.txt", index_col = 0, sep = ",")
display=df[["Velocity", "WinLoss"]]
pos = lambda col : col[col > 0].sum()
neg = lambda col : col[col < 0].sum()
Related_Display_Info = df.groupby("RacerCount").agg(Counts=("Velocity","count"),
WinLoss=("WinLoss","sum"),
Positives=("WinLoss", pos),
Negatives=("WinLoss", neg),
)
# Create figure with secondary y-axis
fig = make_subplots(specs=[[{"secondary_y": True}]])
# Add traces
fig.add_trace(
go.Scatter(x=display.index, y=display["Velocity"], name="Velocity", mode="markers"),
secondary_y=False
)
fig.add_trace(
go.Scatter(x=Related_Display_Info.index,
y=Related_Display_Info["WinLoss"],
name="Win/Loss",
mode="markers",
marker=dict(
color=(
(Related_Display_Info["WinLoss"] < 0)
).astype('int'),
colorscale=[[0, 'green'], [1, 'red']]
)
),
secondary_y=True,
)
# Add figure title
fig.update_layout(
title_text="Race Analysis"
)
# Set x-axis title
fig.update_xaxes(title_text="<b>Racer Counts</b>")
# Set y-axes titles
fig.update_yaxes(title_text="<b>Velocity</b>", secondary_y=False)
fig.update_yaxes(title_text="<b>Win/Loss/b>", secondary_y=True)
fig.update_layout(hovermode="x unified")
fig.show()
The output is:
But I was willing to display the following information when I hover on the point:
RaceCount = From Display dataframe value Number of the race corresponding to the dot I hover on.
Velocity = From Display Dataframe value Velocity at that point
Counts = From Related_Display_Info Column
WinLoss = From Related_Display_Info Column
Positives = From Related_Display_Info Column
Negatives = From Related_Display_Info Column
Please can anyone tell me what to do to get this information on my chart?
I have checked this but was not helpful since I got many errors: Python/Plotly: How to customize hover-template on with what information to show?
Data:
RacerCount,Velocity,WinLoss
111,0.36,1
141,0.31,1
156,0.3,1
141,0.23,1
147,0.23,1
156,0.22,1
165,0.2,1
174,0.18,1
177,0.18,1
183,0.18,1
114,0.32,1
117,0.3,1
120,0.29,1
123,0.29,1
126,0.28,1
129,0.27,1
120,0.32,1
144,0.3,1
147,0.3,1
159,0.27,1
165,0.26,1
168,0.25,1
156,0.29,1
165,0.26,1
168,0.26,1
165,0.28,1
213,0.17,1
243,0.15,1
249,0.14,1
228,0.54,1
177,0.67,1
180,0.66,1
183,0.65,1
192,0.66,1
195,0.62,1
198,0.6,1
180,0.66,1
222,0.56,1
114,0.41,1
81,0.82,1
102,0.56,1
111,0.55,1
90,1.02,1
93,1.0,1
90,1.18,1
90,1.18,1
93,1.1,1
96,1.07,1
99,1.04,1
102,0.99,1
105,0.94,1
108,0.92,1
111,0.9,1
162,0.66,1
159,0.63,1
162,0.65,-1
162,0.66,-1
168,0.64,-1
159,0.68,-1
162,0.67,-1
174,0.62,-1
168,0.65,-1
171,0.64,-1
198,0.55,-1
300,0.47,-1
201,0.56,-1
174,0.63,-1
180,0.61,-1
171,0.64,-1
174,0.62,-1
303,0.47,-1
312,0.48,-1
258,0.51,-1
261,0.51,-1
264,0.5,-1
279,0.47,-1
288,0.48,-1
294,0.47,-1
258,0.52,-1
261,0.51,-1
267,0.5,-1
222,0.53,-1
171,0.64,-1
177,0.63,-1
177,0.63,-1
Essentially, this code ungroups the data frame before plotting to create the hovertemplate you're looking for.
As stated in the comments, the data has to have the same number of rows to be shown in the hovertemplate. At the end of my answer, I added the code all in one chunk.
Since you have hovermode as x unified, you probably only want one of these traces to have hover content.
I slightly modified the creation of Related_Display_Info. Instead of WinLoss, which is already in the parent data frame, I modified it to WinLoss_sum, so there wouldn't be a naming conflict when I ungrouped.
Related_Display_Info = df.groupby("RacerCount").agg(
Counts=("Velocity","count"), WinLoss_sum=("WinLoss","sum"),
Positives=("WinLoss", pos), Negatives=("WinLoss", neg))
Now it's time to ungroup the data you grouped. I created dui (stands for display info ungrouped).
dui = pd.merge(df, Related_Display_Info, how = "outer", on="RacerCount",
suffixes=(False, False))
I created the hovertemplate for both traces. I passed the entire ungrouped data frame to customdata. It looks like the only column that isn't in the template is the original WinLoss.
# create hover template for all traces
ht="<br>".join(["<br>RacerCount: %{customdata[0]}",
"Velocity: %{customdata[1]:.2f}",
"Counts: %{customdata[3]}",
"Winloss: %{customdata[4]}",
"Positives: %{customdata[5]}",
"Negatives: %{customdata[6]}<br>"])
The creation of fig is unchanged. However, the traces are both based on dui. Additionally, the index isn't RacerCount, so I used the literal field instead.
# Create figure with secondary y-axis
fig = make_subplots(specs=[[{"secondary_y": True}]])
# Add traces
fig.add_trace(go.Scatter(x=dui["RacerCount"], y=dui["Velocity"],
name="Velocity", mode="markers",
customdata=dui, hovertemplate=ht),
secondary_y=False)
fig.add_trace(
go.Scatter(x = dui["RacerCount"], y=dui["WinLoss_sum"], customdata=dui,
name="Win/Loss", mode="markers",
marker=dict(color=((dui["WinLoss_sum"] < 0)).astype('int'),
colorscale=[[0, 'green'], [1, 'red']]),
hovertemplate=ht),
secondary_y=True)
All the code altogether (for easier copy + paste)
import pandas as pd
import numpy as np
import plotly.graph_objects as go
from plotly.subplots import make_subplots
df = pd.read_clipboard(sep = ',')
display=df[["Velocity", "WinLoss"]]
pos = lambda col : col[col > 0].sum()
neg = lambda col : col[col < 0].sum()
Related_Display_Info = df.groupby("RacerCount").agg(
Counts=("Velocity","count"), WinLoss_sum=("WinLoss","sum"),
Positives=("WinLoss", pos), Negatives=("WinLoss", neg))
# ungroup the data for the hovertemplate
dui = pd.merge(df, Related_Display_Info, how = "outer", on="RacerCount",
suffixes=(False, False))
# create hover template for all traces
ht="<br>".join(["<br>RacerCount: %{customdata[0]}",
"Velocity: %{customdata[1]:.2f}",
"Counts: %{customdata[3]}",
"Winloss: %{customdata[4]}",
"Positives: %{customdata[5]}",
"Negatives: %{customdata[6]}<br>"])
# Create figure with secondary y-axis
fig = make_subplots(specs=[[{"secondary_y": True}]])
# Add traces
fig.add_trace(go.Scatter(x=dui["RacerCount"], y=dui["Velocity"],
name="Velocity", mode="markers",
customdata=dui, hovertemplate=ht),
secondary_y=False)
fig.add_trace(
go.Scatter(x = dui["RacerCount"], y=dui["WinLoss_sum"], customdata=dui,
name="Win/Loss", mode="markers",
marker=dict(color=((dui["WinLoss_sum"] < 0)).astype('int'),
colorscale=[[0, 'green'], [1, 'red']]),
hovertemplate=ht),
secondary_y=True)
# Add figure title
fig.update_layout(
title_text="Race Analysis"
)
# Set x-axis title
fig.update_xaxes(title_text="<b>Racer Counts</b>")
# Set y-axes titles
fig.update_yaxes(title_text="<b>Velocity</b>", secondary_y=False)
fig.update_yaxes(title_text="<b>Win/Loss/b>", secondary_y=True)
fig.update_layout(hovermode="x unified")
fig.show()
I couldn't find the way to add text labels to plotly/dash box plot like you could add it to a scatterplot. In the example below, for ScatterPlot x=qty, y=price and you can then add Salesperson to the graph when the cursor is on Marker. For adding this I use the 'text' argument.
In the second example for BoxPlot when x=date, y=price I want to add salesperson in the same way. It would be very useful in case of outliers to see immediately who was the salesperson for that purchase. I looked in the documentation, but there is no clue. I assume it's not possible but still decided to try my luck here.
scatterplot:
import plotly.offline as pyo
import plotly.graph_objs as go
purchase={'date':['11/03/2021','12/03/2021','14/03/2021','11/03/2021'],
'price':[300, 400,200, 200],
'currency':['eur', 'usd','usd','usd'],
'qty':[200, 300, 400, 500],
'salesman':['AC', 'BC', "CC", 'DC']}
pur=pd.DataFrame(purchase)
pur
data = [go.Scatter(
x = pur['qty'],
y = pur['price'],
mode = 'markers',
text=pur['salesman'],
marker = dict(
size = 12,
color = 'rgb(51,204,153)',
symbol = 'pentagon',
line = dict(
width = 2,
)
)
)]
layout = go.Layout(
title = 'Random Data Scatterplot',
xaxis = dict(title = 'Some random x-values'),
yaxis = dict(title = 'Some random y-values'),
hovermode ='closest'
)
fig = go.Figure(data=data, layout=layout)
fig.show()
boxplot:
import plotly.offline as pyo
import plotly.graph_objs as go
x = pur['date']
y = pur['price']
data = [
go.Box(
y=y,
x=x,
text=pur['salesman']
)
]
layout = go.Layout(
title = 'box_plot'
)
fig = go.Figure(data=data, layout=layout)
fig.show()
The data you currently have is not suitable for boxplot. If you try to plot a boxplot with your data, the list [300, 400,200, 200] is used only once for the first date. For the other dates, there is no data.
I will show a simpler example with my own data.
dataset.csv
salesman,sales
alan,1.8
bary,2.3
copa,4.2
dac,1.19
eila,2.3
foo,2.5
gary,0.1
holland,10
code
import plotly.graph_objs as go
import pandas as pd
import plotly.io as pio
pio.renderers.default = 'browser'
df = pd.read_csv("deletelater")
fig = go.Figure()
fig.add_trace(go.Box(
y=df["sales"],
name='12/12/22',
customdata=df["salesman"],
hovertemplate='<b>sales: %{y}</b><br>salesperson: %{customdata}'
))
fig.show()
Diagram
As you can see, the name of the outlier salesperson is displayed on the hover label.
See Image Here
I am trying to make Plotly graphs for anomaly detection in time series using Isolation Forest. The problem is: only the plot of the last iteration in for loop apprears. Please help.
import plotly.express as px
import plotly.graph_objs as go
from plotly.subplots import make_subplots
start = 0.01
stop = 0.26
step = 0.05
float_range_array = np.arange(start, stop, step)
float_range_list = list(float_range_array)
fig = make_subplots(
rows=len(float_range_list), cols=1)
for x1,i in enumerate(float_range_list):
iforest1 = create_model('pca', fraction = i)
iforest_results = assign_model(iforest1)
fig = px.line( iforest_results, x="timestamp", y="value",
title='Principal Component Analysis: Fraction={}'.format(round(i,2)),template =
'plotly',labels={"timestamp": "Stay Date","value": "Number of Bookings"})
outlier_dates = iforest_results[iforest_results['Anomaly'] == 1].index
outlier_dates1=iforest_results.iloc[outlier_dates]['timestamp']
y_values = [iforest_results.loc[i]['value'] for i in outlier_dates]
fig.add_trace(go.Scatter(x=outlier_dates1, y=y_values, mode = 'markers',
name = 'Anomaly', marker=dict(color='red',size=10)),row=x1+1,col=1)
fig.show()
have coded placeholders for two functions used in your code create_model() and assign_model()
you create fig = make_subplots(rows=len(float_range_list), cols=1) then in loop overwrite it with fig = px.line(). Changed to use variable name fig_ for figure created within loop
also then added traces from fig_ to fig within loop
import plotly.express as px
import plotly.graph_objs as go
from plotly.subplots import make_subplots
import pandas as pd
import numpy as np
def create_model(a,fraction=.1):
return 1
def assign_model(n):
return pd.DataFrame({"timestamp":pd.date_range("1-mar-2022", freq="1H", periods=100),
"value":np.random.uniform(1,10,100),
"Anomaly":np.full(100, 1)})
start = 0.01
stop = 0.26
step = 0.05
float_range_array = np.arange(start, stop, step)
float_range_list = list(float_range_array)
fig = make_subplots(rows=len(float_range_list), cols=1)
for x1, i in enumerate(float_range_list):
iforest1 = create_model("pca", fraction=i)
iforest_results = assign_model(iforest1)
fig_ = px.line(
iforest_results,
x="timestamp",
y="value",
title="Principal Component Analysis: Fraction={}".format(round(i, 2)),
template="plotly",
labels={"timestamp": "Stay Date", "value": "Number of Bookings"},
)
outlier_dates = iforest_results[iforest_results["Anomaly"] == 1].index
outlier_dates1 = iforest_results.iloc[outlier_dates]["timestamp"]
y_values = [iforest_results.loc[i]["value"] for i in outlier_dates]
fig.add_trace(
go.Scatter(
x=outlier_dates1,
y=y_values,
mode="markers",
name="Anomaly",
marker=dict(color="red", size=6),
),
row=x1 + 1,
col=1,
)
for t in fig_.data:
fig.add_trace(t, row=x1+1,col=1)
fig.show()
I have 2 csv files, my codes are as below.
df = pd.read_csv("test.csv",
sep='\t',skiprows=range(9),names=['A', 'B', 'C','D'])
df2 = pd.read_csv("LoadMatch_Limit.csv",skiprows=range(1),names=['X','Y'])
fig = px.line([df,df2], x=['A','X'] , y=['D','Y'])
I would like my line chart, x-axis to take from (columns 'A' and 'X') and my y-axis to take from (columns 'D' and 'Y').
Is there anyway I can plot these 2 charts as one figure?
You could create the two plots and combine them with plotly graph objects
import plotly.express as px
import plotly.graph_objects as go
fig1 = px.line(df, x='A', y='D')
fig2 = px.line(df2, x='X', y='Y')
fig = go.Figure(data = fig1.data + fig2.data)
fig.show()
Plotly.offline has no attribute line. You need to use the graph object to plot two graphs in a single figure. A simple example is shown below(run the code below and see the output graph)
import numpy as np
import pandas as pd
import plotly.offline as py
import plotly.graph_objs as go
from plotly import tools
df1 = pd.DataFrame({"X":np.linspace(0,30,10), "Y":np.random.rand(10)})
df2 = pd.DataFrame({"A":np.linspace(0,40,10), "B":np.random.rand(10)})
# plotting the graphs...
# 'rgb(128, 0, 128)'
# color=sns.color_palette()
def scatter_chart(x, y, color, name):
trace = go.Scatter(
x=x.values,
y=y.values,
name=name,
marker=dict(
color=color,
line=dict(
color=color,
width=1)
),
)
return trace
trace1 = scatter_chart(df2["A"], df2["B"], 'rgb(128, 0, 128)', "df2")
trace2 = scatter_chart(df1["X"], df1["Y"], 'rgba(50, 171, 96, 0.6)', "df1")
fig = tools.make_subplots(rows=1,cols=1, vertical_spacing=0.5)
fig.add_trace(trace1)
fig.add_trace(trace2)
fig.update_layout(
title="df2 and df1 plot",
height=600,
width=600,
# annotations=annotations,
xaxis=dict(tickangle=-45),
legend=dict(x=0.029, y=1.038, font_size=10),
margin=dict(l=100, r=20, t=70, b=70),
paper_bgcolor='rgb(248, 248, 255)',
plot_bgcolor='rgb(248, 248, 255)',)
py.iplot(fig, filename='pageviews_plots_4')