As is visible from the image, the scaffolding for the rangeslider is generated but the trace inside it is not. It is also fully functional otherwise. With some experiment, I found that only if you set the no. of rows to 500 or less, it displays correctly. Is there a way to display it for rows more than that? Here is the code to reproduce-
size = 501 #change this to change no. of rows
import numpy as np
import pandas as pd
import plotly.express as px
df = {'date': pd.date_range(start='2021-01-01', periods=size, freq='D'),
'new_cases': np.random.random(size=size),
'new_cases_smoothed': np.random.random(size=size)}
df = pd.DataFrame(df)
fig = px.line(df, x='date', y=['new_cases','new_cases_smoothed'])
fig.update_layout(xaxis=dict(rangeslider=dict(visible=True),type="date"))
fig.show()
For others using plotly.express, I had luck setting the kwarg render_mode='webg1':
size = 501 #change this to change no. of rows
import numpy as np
import pandas as pd
import plotly.express as px
df = {'date': pd.date_range(start='2021-01-01', periods=size, freq='D'),
'new_cases': np.random.random(size=size),
'new_cases_smoothed': np.random.random(size=size)}
df = pd.DataFrame(df)
fig = px.line(df, x='date', y=['new_cases','new_cases_smoothed'], render_mode='webg1')
fig.update_layout(xaxis=dict(rangeslider=dict(visible=True),type="date"))
fig.show()
This works in graph_objects
size = 501 #change this to change no. of rows
import numpy as np
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
df = {'date': pd.date_range(start='2021-01-01', periods=size, freq='D'),
'new_cases': np.random.random(size=size),
'new_cases_smoothed': np.random.random(size=size)}
df = pd.DataFrame(df)
# fig = px.line(df, x='date', y=['new_cases','new_cases_smoothed'])
fig = go.Figure(data=[go.Scatter(x=df["date"], y=df[c], name=c) for c in ['new_cases','new_cases_smoothed']])
fig.update_layout(xaxis={"rangeslider":{"visible":True},"type":"date",
"range":[df.tail(50)["date"].min(),df.tail(50)["date"].max()]})
fig.show()
Interesting, you typed WEBG1 instead of WEBGL and it worked.
If you input WEBGL it doesn't work.
In fact if you type anything that should not be accepted as valid such as just blank (render_mode='') it works as well.
Go figure...
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 am trying to write a for loop that for distplot subplots.
I have a dataframe with many columns of different lengths. (not including the NaN values)
fig = make_subplots(
rows=len(assets), cols=1,
y_title = 'Hourly Price Distribution')
i=1
for col in df_all.columns:
fig = ff.create_distplot([[df_all[[col]].dropna()]], col)
fig.append()
i+=1
fig.show()
I am trying to run a for loop for subplots for distplots and get the following error:
PlotlyError: Oops! Your data lists or ndarrays should be the same length.
UPDATE:
This is an example below:
df = pd.DataFrame({'2012': np.random.randn(20),
'2013': np.random.randn(20)+1})
df['2012'].iloc[0] = np.nan
fig = ff.create_distplot([df[c].dropna() for c in df.columns],
df.columns,show_hist=False,show_rug=False)
fig.show()
I would like to plot each distribution in a different subplot.
Thank you.
Update: Distribution plots
Calculating the correct values is probably both quicker and more elegant using numpy. But I often build parts of my graphs using one plotly approach(figure factory, plotly express) and then use them with other elements of the plotly library (plotly.graph_objects) to get what I want. The complete snippet below shows you how to do just that in order to build a go based subplot with elements from ff.create_distplot. I'd be happy to give further explanations if the following suggestion suits your needs.
Plot
Complete code
import numpy as np
import pandas as pd
import plotly.express as px
import plotly.figure_factory as ff
from plotly.subplots import make_subplots
import plotly.graph_objects as go
df = pd.DataFrame({'2012': np.random.randn(20),
'2013': np.random.randn(20)+1})
df['2012'].iloc[0] = np.nan
df = df.reset_index()
dfm = pd.melt(df, id_vars=['index'], value_vars=df.columns[1:])
dfm = dfm.dropna()
dfm.rename(columns={'variable':'year'}, inplace = True)
cols = dfm.year.unique()
nrows = len(cols)
fig = make_subplots(rows=nrows, cols=1)
for r, col in enumerate(cols, 1):
dfs = dfm[dfm['year']==col]
fx1 = ff.create_distplot([dfs['value'].values], ['distplot'],curve_type='kde')
fig.add_trace(go.Scatter(
x= fx1.data[1]['x'],
y =fx1.data[1]['y'],
), row = r, col = 1)
fig.show()
First suggestion
You should:
1. Restructure your data with pd.melt(df, id_vars=['index'], value_vars=df.columns[1:]),
2. and the use the occuring column 'variable' to build subplots for each year through the facet_row argument to get this:
In the complete snippet below you'll see that I've changed 'variable' to 'year' in order to make the plot more intuitive. There's one particularly convenient side-effect with this approach, namely that running dfm.dropna() will remove the na value for 2012 only. If you were to do the same thing on your original dataframe, the corresponding value in the same row for 2013 would also be removed.
import numpy as np
import pandas as pd
import plotly.express as px
df = pd.DataFrame({'2012': np.random.randn(20),
'2013': np.random.randn(20)+1})
df['2012'].iloc[0] = np.nan
df = df.reset_index()
dfm = pd.melt(df, id_vars=['index'], value_vars=df.columns[1:])
dfm = dfm.dropna()
dfm.rename(columns={'variable':'year'}, inplace = True)
fig = px.histogram(dfm, x="value",
facet_row = 'year')
fig.show()
I am trying to draw a bar chart from the CSV data I transform using pivot_table. The bar chart should have the count on the y-axis and companystatus along the x-axis.
I am getting this instead:
Ultimately, I want to stack the bar by CompanySizeId.
I have been following this video.
import plotly.graph_objects as go
import plotly.offline as pyo
import pandas as pd
countcompany = pd.read_csv(
'https://raw.githubusercontent.com/redbeardcr/Plotly/master/Data/countcompany.csv')
df = pd.pivot_table(countcompany, index='CompanyStatusLabel',
values='n', aggfunc=sum)
print(df)
data = [go.Bar(
x=df.index,
y=df.values,
)]
layout = go.Layout(title='Title')
fig = go.Figure(data=data, layout=layout)
pyo.plot(fig)
Code can be found here
Thanks for any help
If you flatten the array with the y values, i.e. if you replace y=df.values with y=df.values.flatten(), your code will work as expected.
import plotly.graph_objects as go
import plotly.offline as pyo
import pandas as pd
countcompany = pd.read_csv('https://raw.githubusercontent.com/redbeardcr/Plotly/master/Data/countcompany.csv')
df = pd.pivot_table(countcompany, index='CompanyStatusLabel', values='n', aggfunc=sum)
data = [go.Bar(
x=df.index,
y=df.values.flatten(),
)]
layout = go.Layout(title='Title')
fig = go.Figure(data=data, layout=layout)
pyo.plot(fig)
I'm trying to create a bar plot from a DataFrame with Datetime Index.
This is an example working code:
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
sns.set()
index = pd.date_range('2012-01-01', periods=48, freq='M')
data = np.random.randint(100, size = (len(index),1))
df = pd.DataFrame(index=index, data=data, columns=['numbers'])
fig, ax = plt.subplots()
ax.bar(df.index, df['numbers'])
The result is:
As you can see the white bars cannot be distinguished well with respect of the background (why?).
I tried using instead:
df['numbers'].plot(kind='bar')
import matplotlib.ticker as ticker
ticklabels = df.index.strftime('%Y-%m')
ax.xaxis.set_major_formatter(ticker.FixedFormatter(ticklabels))
with this result:
But in this way I lose the automatic xticks labels (and grid) 6-months spacing.
Any idea?
You can just change the style:
import matplotlib.pyplot as plt
index = pd.date_range('2012-01-01', periods=48, freq='M')
data = np.random.randint(100, size = (len(index),1))
df = pd.DataFrame(index=index, data=data, columns=['numbers'])
plt.figure(figsize=(12, 5))
plt.style.use('default')
plt.bar(df.index,df['numbers'],color="red")
You do not actually use seaborn. Replace ax.bar(df.index, df['numbers'])
with
sns.barplot(df.index, df['numbers'], ax=ax)
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()