I am trying to generally recreate this graph and struggling with adding a column to the hovertemplate of a plotly Scatter. Here is a working example:
import pandas as pd
import chart_studio.plotly as py
import plotly.graph_objects as go
dfs = pd.read_html('https://coronavirus.jhu.edu/data/mortality', header=0)
df = dfs[0]
percent = df['Case-Fatality'] # This is my closest guess, but isn't working
fig = go.Figure(data=go.Scatter(x=df['Confirmed'],
y = df['Deaths'],
mode='markers',
hovertext=df['Country'],
hoverlabel=dict(namelength=0),
hovertemplate = '%{hovertext}<br>Confirmed: %{x}<br>Fatalities: %{y}<br>%{percent}',
))
fig.show()
I'd like to get the column Cast-Fatality to show under {percent}
I've also tried putting in the Scatter() call a line for text = [df['Case-Fatality']], and switching {percent} to {text} as shown in this example, but this doesn't pull from the dataframe as hoped.
I've tried replotting it as a px, following this example but it throws the error dictionary changed size during iteration and I think using go may be simpler than px but I'm new to plotly.
Thanks in advance for any insight for how to add a column to the hover.
As the question asks for a solution with graph_objects, here are two that work-
Method (i)
Adding %{text} where you want the variable value to be and passing another variable called text that is a list of values needed in the go.Scatter() call. Like this-
percent = df['Case-Fatality']
hovertemplate = '%{hovertext}<br>Confirmed: %{x}<br>Fatalities: %{y}<br>%{text}',text = percent
Here is the complete code-
import pandas as pd
import plotly.graph_objects as go
dfs = pd.read_html('https://coronavirus.jhu.edu/data/mortality', header=0)
df = dfs[0]
percent = df['Case-Fatality'] # This is my closest guess, but isn't working
fig = go.Figure(data=go.Scatter(x=df['Confirmed'],
y = df['Deaths'],
mode='markers',
hovertext=df['Country'],
hoverlabel=dict(namelength=0),
hovertemplate = '%{hovertext}<br>Confirmed: %{x}<br>Fatalities: %{y}<br>%{text}',
text = percent))
fig.show()
Method (ii)
This solution requires you to see the hoverlabel as when you pass x unified to hovermode. All you need to do then is pass an invisible trace with the same x-axis and the desired y-axis values. Passing mode='none' makes it invisible. Here is the complete code-
import pandas as pd
import plotly.graph_objects as go
dfs = pd.read_html('https://coronavirus.jhu.edu/data/mortality', header=0)
df = dfs[0]
percent = df['Case-Fatality'] # This is my closest guess, but isn't working
fig = go.Figure(data=go.Scatter(x=df['Confirmed'],
y = df['Deaths'],
mode='markers',
hovertext=df['Country'],
hoverlabel=dict(namelength=0)))
fig.add_scatter(x=df.Confirmed, y=percent, mode='none')
fig.update_layout(hovermode='x unified')
fig.show()
The link you shared is broken. Are you looking for something like this?
import pandas as pd
import plotly.express as px
px.scatter(df,
x="Confirmed",
y="Deaths",
hover_name="Country",
hover_data={"Case-Fatality":True})
Then if you need to use bold or change your hover_template you can follow the last step in this answer
Drawing inspiration from another SO question/answer, I find that this is working as desired and permits adding multiple cols to the hover data:
import pandas as pd
import plotly.express as px
fig = px.scatter(df,
x="Confirmed",
y="Deaths",
hover_name="Country",
hover_data=[df['Case-Fatality'], df['Deaths/100K pop.']])
fig.show()
Related
I want to create a multi layer graph with the same data frame from pandas.
One should be a boxplot and the other a scatter to see where the company is located.
Is there a way to combine both plots?
boxplot
scatterplot
import pandas as pd
import plotly.express as px
df = pd.read_csv("company_index.csv", sep=";", decimal=",")
print(df)
df_u9 = df.loc[df["company"].isin(["U9"])]
fig_1 = px.box(
df,
x="period",
y="index"
)
fig_2 = px.scatter(
df_u9,
x="period",
y="index"
)
fig_1.show()
fig_2.show()
company_index.csv
period;index;company
1;202,4;U1
1;226,69;U10
1;235,18;U9
1;236,49;U4
1;238,13;U2
1;244,05;U6
1;252,08;U3
1;256,68;U8
1;294,99;U5
1;299,391;U7
2;243,78;U1
2;264,26;U10
2;270,6;U2
2;272,89;U9
2;285,26;U5
2;289,29;U4
2;291,15;U6
2;291,19;U3
2;305,92;U7
2;314,65;U8
3;271,82;U1
3;278,65;U2
3;296,16;U10
3;297,21;U4
3;305,93;U6
3;308,96;U5
3;323,74;U9
3;335,93;U3
3;354,13;U8
3;381,2;U7
4;281,26;U5
4;308,5;U2
4;311,61;U1
4;334,03;U4
4;335,72;U9
4;344,32;U8
4;345,27;U6
4;355,44;U3
4;373,54;U7
4;381,68;U10
5;288,6;U1
5;305,66;U5
5;323,2;U2
5;358,46;U8
5;365,57;U3
5;366,96;U10
5;368,38;U7
5;371,23;U6
5;373,63;U4
5;422,93;U9
6;285,32;U5
6;291,65;U1
6;308,68;U2
6;372,04;U8
6;376,64;U3
6;403,55;U6
6;407,38;U4
6;420,65;U10
6;423,68;U9
6;453,09;U7
Found this solution. Works rather well.
Im still struggling to understand the ".data[0]" but i believe its referring to the first fig in use. Maybe if you have multiple graphs.
import pandas as pd
import plotly.express as px
df = pd.read_csv("company_index.csv", sep=";", decimal=",")
print(df)
df_u9 = df.loc[df["company"].isin(["U9"])].copy()
df_u9["size"] = 1
fig = px.box(
df,
x="period",
y="index"
)
fig.add_trace(px.scatter(
df_u9,
x="period",
y="index",
size="size",
size_max=15,
color_discrete_sequence=(203,153,201)
).data[0])
fig.show()
I'm trying to set different colors for some bars in a plotly express bar graph:
import plotly.express as px
import pandas as pd
data = {'Name':['2020/01', '2020/02', '2020/03', '2020/04',
'2020/05', '2020/07', '2020/08'],
'Value':[34,56,66,78,99,55,22]}
df = pd.DataFrame(data)
color_discrete_sequence = ['#ec7c34']*len(df)
color_discrete_sequence[5] = '#609cd4'
fig=px.bar(df,x='Name',y='Value',color_discrete_sequence=color_discrete_sequence)
fig.show()
My expectations were that one (the sixth one) bar had a different color, however I got this result:
What am I doing wrong?
This happens because color in px.bar is used to name a category to illustrate traits or dimensions of a dataset using a colorscale. Or in you your case, rather a color cycle since you're dealing with a categorical / discrete case. color_discrete_sequence is then used to specify which color sequence to follow. One way to achieve your goal using your setup here, is to simply define a string variable with unique values, for example like df['category'] [str(i) for i in df.index], and then use:
fig=px.bar(df,x='Name',y='Value',
color = 'category',
color_discrete_sequence=color_discrete_sequence,
)
Plot:
If df['category'] is a numerical value, color_discrete_sequence will be ignored, and a default continuous sequence will be applied:
If anything else is unclear, don't hesitate to let me know.
Complete code:
import plotly.express as px
import pandas as pd
data = {'Name':['2020/01', '2020/02', '2020/03', '2020/04',
'2020/05', '2020/07', '2020/08'],
'Value':[34,56,66,78,99,55,22]}
df = pd.DataFrame(data)
df['category'] = [str(i) for i in df.index]
# df['category'] = df.index
color_discrete_sequence = ['#ec7c34']*len(df)
color_discrete_sequence[5] = '#609cd4'
fig=px.bar(df,x='Name',y='Value',
color = 'category',
color_discrete_sequence=color_discrete_sequence,
)
fig.show()
I am new to plotly and wanted to visualize some data. I got this plot. see here
But I want to get this in 2 or more column based so that it can be seen better.
Can someone help me with that. Here is my source code what I have tried:
import pandas as pd
import plotly.express as px
fig = px.scatter(data2, x = "Total_System_Cost", y= "Total_CO2_Emissions",
color="Pol_Inst", symbol="Pol_Inst",
facet_row='Technologie',width=600, height=3500)
fig.show()
And the data looks like this.here
In this case you should use facet_col and facet_col_wrap as in this example
import pandas as pd
import plotly.express as px
fig = px.scatter(data2,
x="Total_System_Cost",
y="Total_CO2_Emissions",
color="Pol_Inst",
symbol="Pol_Inst",
facet_col='Technologie',
facet_col_wrap=2, #eventually change this
)
fig.show()
If you then want to use width and height do it so according to data2['Technologie'].nunique() and the value you picked for facet_col_wrap.
I'm making a line chart below. I want to make the lines colored by a variable Continent. I know it can be done easily using plotly.express
Does anyone know how I can do that with plotly.graph_objects? I tried to add color=gapminder['Continent'], but it did not work.
Thanks a lot for help in advance.
import plotly.express as px
gapminder = px.data.gapminder()
import plotly.graph_objects as go
fig = go.Figure()
fig.add_trace(go.Scatter(x=gapminder['year'], y=gapminder['lifeExp'],
mode='lines+markers'))
fig.show()
Using an approach like color=gapminder['Continent'] normally applies to scatterplots where you define categories to existing points using a third variable. You're trying to make a line plot here. This means that not only will you have a color per continent, but also a line per continent. If that is in fact what you're aiming to do, here's one approach:
Plot:
Code:
import plotly.graph_objects as go
import plotly.express as px
# get data
df_gapminder = px.data.gapminder()
# manage data
df_gapminder_continent = df_gapminder.groupby(['continent', 'year']).mean().reset_index()
df = df_gapminder_continent.pivot(index='year', columns='continent', values = 'lifeExp')
df.tail()
# plotly setup and traces
fig = go.Figure()
for col in df.columns:
fig.add_trace(go.Scatter(x=df.index, y=df[col].values,
name = col,
mode = 'lines'))
# format and show figure
fig.update_layout(height=800, width=1000)
fig.show()
I need to create a line chart from multiple columns of a dataframe. In pandas, you can draw a multiple line chart using a code as follows:
df.plot(x='date', y=['sessions', 'cost'], figsize=(20,10), grid=True)
How can this be done using plotly_express?
With version 4.8 of Plotly.py, the code in the original question is now supported almost unmodified:
pd.options.plotting.backend = "plotly"
df.plot(x='date', y=['sessions', 'cost'])
Previous answer, as of July 2019
For this example, you could prepare the data slightly differently.
df_melt = df.melt(id_vars='date', value_vars=['sessions', 'cost'])
If you transpose/melt your columns (sessions, cost) into additional rows, then you can specify the new column 'variable' to partition by in the color parameter.
px.line(df_melt, x='date' , y='value' , color='variable')
Example plotly_express output
With newer versions of plotly, all you need is:
df.plot()
As long as you remember to set pandas plotting backend to plotly:
pd.options.plotting.backend = "plotly"
From here you can easily adjust your plot to your liking, for example setting the theme:
df.plot(template='plotly_dark')
Plot with dark theme:
One particularly awesome feature with newer versions of plotly is that you no longer have to worry whether your pandas dataframe is of a wide or long format. Either way, all you need is df.plot(). Check out the details in the snippet below.
Complete code:
# imports
import plotly.express as px
import pandas as pd
import numpy as np
# settings
pd.options.plotting.backend = "plotly"
# sample dataframe of a wide format
np.random.seed(4); cols = list('abc')
X = np.random.randn(50,len(cols))
df=pd.DataFrame(X, columns=cols)
df.iloc[0]=0; df=df.cumsum()
# plotly figure
df.plot(template = 'plotly_dark')
Answer for older versions:
I would highly suggest using iplot() instead if you'd like to use plotly in a Jupyter Notebook for example:
Plot:
Code:
import plotly
import cufflinks as cf
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
import pandas as pd
import numpy as np
# setup
init_notebook_mode(connected=True)
np.random.seed(123)
cf.set_config_file(theme='pearl')
# Random data using cufflinks
df1 = cf.datagen.lines()
df2 = cf.datagen.lines()
df3 = cf.datagen.lines()
df = pd.merge(df1, df2, how='left',left_index = True, right_index = True)
df = pd.merge(df, df3, how='left',left_index = True, right_index = True)
fig = df1.iplot(asFigure=True, kind='scatter',xTitle='Dates',yTitle='Returns',title='Returns')
iplot(fig)
Its also worth pointing out you can combine plotly express with graph_objs. This is a good route when the lines have different x points.
import numpy as np
import pandas as pd
import plotly.graph_objs as go
import plotly.express as px
# data set 1
x = np.linspace(0, 9, 10)
y = x
# data set 2
df = pd.DataFrame(np.column_stack([x*0.5, y]), columns=["x", "y"])
fig = go.Figure(px.scatter(df, x="x", y="y"))
fig.add_trace(go.Scatter(x=x, y=y))
fig.show()