I want to add horizontal line at 0.09 and -0.09 in every subplot I am generating in plotly. Following is my code to do that.
trace1 = go.Scatter(
x=df1['transaction_date'],
y=df1['difference'],
)
trace2 = go.Scatter(
x=df2['transaction_date'],
y=df2['difference'],
)
trace3 = go.Scatter(
x=df3['transaction_date'],
y=df3['difference'],
)
trace4 = go.Scatter(
x=df4['transaction_date'],
y=df4['difference'],
)
fig = tools.make_subplots(rows=2, cols=2,subplot_titles=('DF1 HS', DF2 HSD',
'DF3 HD', 'DF4 SD',
))
fig.append_trace(trace1, 1, 1)
fig.append_trace(trace2, 1, 2)
fig.append_trace(trace3, 2, 1)
fig.append_trace(trace4, 2, 2)
Then I want to save this 4 subplots as jpeg on disk. How can I do that in python
Try updating layout of fig object with shapes as below:
import plotly.graph_objs as go
from plotly import tools
from plotly.offline import init_notebook_mode, plot
df = pd.DataFrame(np.random.randint(0,100,size=(20,2)),
index=pd.date_range(start='2018-08-21',end='2018-09-09'),
columns=['A','B'])
trace1 = go.Scatter(x=df.index,y=df['A'],)
trace2 = go.Scatter(x=df.index,y=df['B'],)
fig = tools.make_subplots(rows=2, cols=1,subplot_titles=(['A','B']))
fig.append_trace(trace1, 1, 1)
fig.append_trace(trace2, 2, 1)
fig['layout'].update(shapes=[{'type': 'line','y0':50,'y1': 50,'x0':str(df.index[0]),
'x1':str(df.index[-1]),'xref':'x1','yref':'y1',
'line': {'color': 'red','width': 2.5}},
{'type': 'line','y0':50,'y1': 50,'x0':str(df.index[0]),
'x1':str(df.index[-1]),'xref':'x2','yref':'y2',
'line': {'color': 'red','width': 2.5}}])
plot(fig,show_link=False,image='jpeg',image_filename='Temp_plot')
The plot will be saved as Temp_plot.jpeg. Check the image below.
The downside of this method is we need to carefully give axes values to xref and yref with respect to subplots.
I'm pretty new to Plotly so maybe the API has just been updated, but it seems there is a much simpler solution, per the documentation here. One need only use the fig.add_hline() syntax while specifying which subplot (col and row) it should be drawn on, as such:
fig.add_hline(y=1, line_dash="dot", row=1, col=1, line_color="#000000", line_width=2)
This line will instruct Plotly to draw a horizontal line y = 1 on the subplot located at row = 1; col = 1.
Alternatively, as noted in the dox, the "all" keyword can be passed as a value for either the row or col argument to instruct plotly to draw the line on (wait for it...) all the subplots!
You mentioned that you were ok with a matplotlib solution:
Data:
dict = {
"a":np.random.randint(low=-10,high=10,size=20),
"b":np.random.randint(low=-10,high=10,size=20),
"c":np.random.randint(low=-10,high=10,size=20),
"d":np.random.randint(low=-10,high=10,size=20),
}
df = pd.DataFrame(dict)
Plot:
fig, axes = plt.subplots(2,2, figsize=(20,10), sharex=True, sharey=True)
for i,j in zip(axes.ravel(), list(df)):
i.plot(df.index, df[j], 'ro')
i.hlines(y=-3, xmin=0, xmax=22)
i.hlines(y=3, xmin=0, xmax=22)
fig.savefig("testplot.png")
Result:
Related
I am trying to use plotly to plot a graph similar to the one here below:
Unfortunately I am only able to plot something like this
What I would like is to have normal boundaries (upper and lower defined by two dataframe columns and only one entry in the legend.
import plotly.graph_objs as go
# Create a trace for the lower bound
trace1 = go.Scatter(x=df.index,
y=df['lower'],
name='Lower Bound',
fill='tonexty',
fillcolor='rgba(255,0,0,0.2)',
line=dict(color='blue'))
# Create a trace for the median
trace2 = go.Scatter(x=df.index,
y=df['median'],
name='median',
line=dict(color='blue', width=2))
# Create a trace for the upper bound
trace3 = go.Scatter(x=df.index,
y=df['upper'],
name='Upper Bound',
fill='tonexty',
fillcolor='rgba(255,0,0,0.2)',
line=dict(color='blue'))
# Create the layout
layout = go.Layout(xaxis=dict(title='Date'),
yaxis=dict(title='title'))
# Create the figure with the three traces and the layout
fig = go.Figure(data=[trace1, trace2, trace3], layout=layout)
context['pltyplot'] = pltyplot(fig, output_type="div")
I want to use plotly because I am integrating the resulting figure into a django web page and plotly enables, with the las line, to import the whole object in a clean, simple and interactive way into the poge.
Any ideas?
You can try this code:
import plotly.graph_objs as go
x = [1, 2, 3, 4, 5]
y = [2, 4, 5, 3, 6]
# Define the confidence interval
interval = 0.6 * np.std(y) / np.mean(y)
fig = go.Figure()
fig.add_trace(go.Scatter(x=x, y=y, mode='lines', name='Line'))
fig.add_trace(go.Scatter(x=x+x[::-1],
y=y+[i + interval for i in y[::-1]],
fill='toself',
fillcolor='rgba(0,100,80,0.2)',
line=dict(width=0),
showlegend=False))
fig.add_trace(go.Scatter(x=x+x[::-1],
y=y+[i - interval for i in y[::-1]],
fill='toself',
fillcolor='rgba(0,100,80,0.2)',
line=dict(width=0),
showlegend=False))
fig.show()
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()
My input:
names_col = ['Count','Percentage']
dat = [['Matching', 63],['Mismatching', 37]]
plot_df = pd.DataFrame(data=dat,columns=names_col)
I just trying plot within plotly simple bar char where stacked.
my code:
fig = px.bar(p_df, x='Count', y='Percentage', color='Count' ,title='My plot', barmode='stack')
fig.show();
And what I get:
That not what I expected. I want something like this:
Here code within seaborn:
p=p_df.set_index('Count').T.plot(kind='bar', stacked=True, figsize=(12,8),rot=0)
p.set_title('BBPS.2')
for x in p.containers:
p.bar_label(x, label_type='edge', weight='bold')
p.bar_label(x, label_type='center', weight='bold', color='white')
plt.show();
By setting the x axis to 'Count' you are defining the bars to not be stacked.
You could either find a different parameter for the x axis or add a dummy column with the same value for both rows so they have the same x value:
import pandas as pd
import plotly.express as px
names_col = ['Count','Percentage', 'dummy']
dat = [['Matching', 63, 0],['Mismatching', 37, 0]]
plot_df = pd.DataFrame(data=dat,columns=names_col)
fig = px.bar(plot_df, x='dummy', y='Percentage', color='Count' ,title='My plot')
fig.show()
The result:
You need to set the base to the first bar in order to stack them. Right now you have merely defined two separate bars. Take a look at this code from a dev.to post:
fig3 = go.Figure(
data=[
go.Bar(
name="Original",
x=data["labels"],
y=data["original"],
offsetgroup=0,
),
go.Bar(
name="Model 1",
x=data["labels"],
y=data["model_1"],
offsetgroup=1,
),
go.Bar(
name="Model 2",
x=data["labels"],
y=data["model_2"],
offsetgroup=1,
base=data["model_1"],
)
],
layout=go.Layout(
title="Issue Types - Original and Models",
yaxis_title="Number of Issues"
)
)
fig3.show()
That resulted in a plot that looks like this:
I made a line graph with the code below and I'm trying to add a horizontal line at y=1. I tried following the instructions on the plotly site but it is still not showing. Does anyone know why?
date = can_tot_df.date
growth_factor = can_tot_df.growth_factor
trace0 = go.Scatter(
x=date,
y=growth_factor,
mode = 'lines',
name = 'growth_factor'
)
fig = go.Figure()
fig.add_shape(
type='line',
x0=date.min(),
y0=1,
x1=date.max(),
y1=1,
line=dict(
color='Red',
)
)
data = [trace0]
iplot(data)
Short answer, and a general solution:
fig.add_shape(type='line',
x0=0,
y0=40,
x1=8,
y1=40,
line=dict(color='Red',),
xref='x',
yref='y'
)
Details and specifics about OP's question
It's hard to tell exactly what's wrong without a sample of your data.
What I can tell for sure is that you're missing the arguments xref and yref to specify that the line is drawn as units of your y and x axis. Judging by your sample code, this is what you'd like to do since you're specifying your x-values in terms of dates.
Also, you don't need to worry about iplot for newer versions of plotly. You can display your chart just as easily by just running fig.show(). The figure and code sample below will show you how to use fig.show() and how to define your lines in terms of axis units.
Plot:
Code:
import plotly.graph_objects as go
import numpy as np
x = np.arange(10)
fig = go.Figure(data=go.Scatter(x=x, y=x**2))
fig.add_shape(type='line',
x0=0,
y0=40,
x1=8,
y1=40,
line=dict(color='Red',),
xref='x',
yref='y'
)
fig.show()
An alternative to xref='x' is xref='paper'. Now you can specify x0 as a float between 0 and 1 spanning from the start and end of the plot.
You could also use fig.add_hline(y=1) --> see https://plotly.com/python/horizontal-vertical-shapes/
import plotly.graph_objects as go
import numpy as np
x = np.arange(10)
fig = go.Figure(data=go.Scatter(x=x, y=x**2))
fig.add_hline(y=40, line_width=3, line_dash="dash", line_color="green")
fig.show()
If you use subplots, then this is the easiest way I found to add an other line to a subplot. this example draws a horizontal line at y=80 for all x values
from plotly.subplots import make_subplots
fig = make_subplots(rows=2, cols=1,
shared_xaxes=True,
vertical_spacing=0.02)
[some graph]
fig.add_trace(go.Scatter(
name='Y=80',
x = [df['date'].min(), df['date'].max()],
y = [80, 80],
mode = "lines",
marker = dict(color = 'rgba(80, 26, 80, 0.8)')
),row=1, col=1)
i found the solution on github :
df = df
fig = px.scatter(df, x="date", y="growth_factor", mode = 'lines',
hover_name=df['growth_factor'] )
fig.update_layout(shapes=[
dict(
type= 'line',
yref= 'y', y0= 1, y1= 1, # adding a horizontal line at Y = 1
xref= 'paper', x0= 0, x1= 1
)
])
fig.show()
You’re adding the line to your fig object, but fig is not getting passed into the iplot() function, only your data. So only the trace is getting plotted.
If you're using a late version of plotly, the new syntax allows you to create this plot simply using the fig object, like:
from plotly import graph_objects as go
fig = go.Figure()
# Contrived dataset for example.
x = [1, 2, 3, 4]
y = [i**2 for i in x]
fig.add_trace(go.Scatter(
x=x,
y=y,
mode = 'lines',
name = 'growth_factor'))
fig.add_shape(type='line',
x0=min(x),
y0=5,
x1=max(x),
y1=5,
line=dict(color='Red'))
fig.update_shapes(dict(xref='x', yref='y'))
fig.show()
Here are the plotly docs for convenience.
I want to create a lollipop plot with several horizontal line segments like this - https://python-graph-gallery.com/184-lollipop-plot-with-2-group. I'd like to use plotly since I prefer the graphics (and easy interactivity) but can't find a succint way.
There's both line graphs (https://plot.ly/python/line-charts/) and you can add lines in the layout (https://plot.ly/python/shapes/#vertical-and-horizontal-lines-positioned-relative-to-the-axes), but both of these solutions require each line segment to be added separately, with about 4-8 lines of code each. While I could just for-loop this, would appreciate if anyone can point me to anything with inbuilt vectorization, like the matplotlib solution (first link)!
Edit: Also tried the following code, to first make the plot ala matplotlib, then convert to plotly. The line segments disappear in the process. Starting to think it's just impossible.
mpl_fig = plt.figure()
# make matplotlib plot - WITH HLINES
plt.rcParams['figure.figsize'] = [5,5]
ax = mpl_fig.add_subplot(111)
ax.hlines(y=my_range, xmin=ordered_df['value1'], xmax=ordered_df['value2'],
color='grey', alpha=0.4)
ax.scatter(ordered_df['value1'], my_range, color='skyblue', alpha=1,
label='value1')
ax.scatter(ordered_df['value2'], my_range, color='green', alpha=0.4 ,
label='value2')
ax.legend()
# convert to plotly
plotly_fig = tls.mpl_to_plotly(mpl_fig)
plotly_fig['layout']['xaxis1']['showgrid'] = True
plotly_fig['layout']['xaxis1']['autorange'] = True
plotly_fig['layout']['yaxis1']['showgrid'] = True
plotly_fig['layout']['yaxis1']['autorange'] = True
# plot: hlines disappear :/
iplot(plotly_fig)
You can use None in the data like this:
import plotly.offline as pyo
import plotly.graph_objs as go
fig = go.Figure()
x = [1, 4, None, 2, 3, None, 3, 4]
y = [0, 0, None, 1, 1, None, 2, 2]
fig.add_trace(
go.Scatter(x=x, y=y))
pyo.plot(fig)
Plotly doesn't provide a built in vectorization for such chart, because it can be done easily by yourself, see my example based on your provided links:
import pandas as pd
import numpy as np
import plotly.offline as pyo
import plotly.graph_objs as go
# Create a dataframe
value1 = np.random.uniform(size = 20)
value2 = value1 + np.random.uniform(size = 20) / 4
df = pd.DataFrame({'group':list(map(chr, range(65, 85))), 'value1':value1 , 'value2':value2 })
my_range=range(1,len(df.index)+1)
# Add title and axis names
data1 = go.Scatter(
x=df['value1'],
y=np.array(my_range),
mode='markers',
marker=dict(color='blue')
)
data2 = go.Scatter(
x=df['value2'],
y=np.array(my_range),
mode='markers',
marker=dict(color='green')
)
# Horizontal line shape
shapes=[dict(
type='line',
x0 = df['value1'].loc[i],
y0 = i + 1,
x1 = df['value2'].loc[i],
y1 = i + 1,
line = dict(
color = 'grey',
width = 2
)
) for i in range(len(df['value1']))]
layout = go.Layout(
shapes = shapes,
title='Lollipop Chart'
)
# Plot the chart
fig = go.Figure([data1, data2], layout)
pyo.plot(fig)
With the result I got: