I've searched for days and didn't find an answer. How can I plot a time series data in Dash Plotly as a linegraph with selectable lines?
My data (pandas dataframe) describes GDP of different countrys. Index is country, column is years.
I don't find a solution to pass the data to Dash Plotly linegraph. What are my x and y values?
fig = px.line(df, x=?, y=?)
By the looks of it, the solution in your example should be:
fig = px.line(df, x=df.index, y = df.columns)
Plot 1 - plot by columns as they appear in your dataset
From here, if you'd like to display countries in the legend and have time on the x-axis, you can just add df = df.T into the mix and get:
Plot 2 - transposed dataframe to show time on the x-axis
Details
There's a multitude of possibilites when it comes to plotting time series with plotly. Your example displays a dataset of a wide format. With the latest versions, plotly handles both datasets of long and wide format elegantly straight out of the box. If you need specifics of long and wide data in plotly you can also take a closer look here.
The code snippet below uses the approach described above, but in order for this to work for you exactly the same way, your countries will have to be set as the dataframe row index. But you've stated that they are, so give it a try and let me know how it works out for you. And one more thing: you can freely select which traces to display by clicking the years in the plot legend. The figure produced by the snippet below can also be directly implemented in Dash by following the steps under the section What About Dash? here.
Complete code:
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
import plotly.io as pio
import plotly.io as pio
# sample dataframe of a wide format
np.random.seed(5); cols = ['Canada', 'France', 'Germany']
X = np.random.randn(6,len(cols))
df=pd.DataFrame(X, columns=cols)
df.iloc[0]=0;df=df.cumsum()
df['Year'] = pd.date_range('2020', freq='Y', periods=len(df)).year.astype(str)
df = df.T
df.columns = df.iloc[-1]
df = df.head(-1)
df.index.name = 'Country'
# Want time on the x-axis? ###
# just include:
# df = df.T
##############################
# plotly
fig = px.line(df, x=df.index, y = df.columns)
fig.update_layout(template="plotly_dark")
fig.show()
Related
I am having trouble eliminating datetime gaps within a dataset that i'm trying to create a very simple line chart in plotly express and I have straight lines on the graph connecting datapoints over a gap in the data (weekends).
Dataframe simply has an index of datetime (to the hour) called sale_date, and cols called NAME, COST with approximately 30 days worth of data.
df['sale_date'] = pd.to_datetime(df['sale_date'])
df = df.set_index('sale_date')
px.line(df, x=df.index, y='COST', color='NAME')
I've seen a few posts regarding this issue and one recommended setting datetime as the index, but it still yields the gap lines.
The data in the example may not be the same as yours, but the point is that you can change the x-axis data to string data instead of date/time data, or change the x-axis type to category, and add a scale and tick text.
import pandas as pd
import plotly.express as px
import numpy as np
np.random.seed(2021)
date_rng = pd.date_range('2021-08-01','2021-08-31', freq='B')
name = ['apple']
df = pd.DataFrame({'sale_date':pd.to_datetime(date_rng),
'COST':np.random.randint(100,3000,(len(date_rng),)),
'NAME':np.random.choice(name,size=len(date_rng))})
df = df.set_index('sale_date')
fig= px.line(df, x=[d.strftime('%m/%d') for d in df.index], y='COST', color='NAME')
fig.show()
xaxis update
fig= px.line(df, x=df.index, y='COST', color='NAME')
fig.update_xaxes(type='category',
tickvals=np.arange(0,len(df)),
ticktext=[d.strftime('%m/%d') for d in df.index])
I have a data frame plot of a time series along with a list of numeric values at which I'd like to draw vertical lines. The plot is an interactive one created using the cufflinks package. Here is an example of three time series in 1000 time values, I'd like to draw vertical lines at 500 and 800. My attempt using "axvlinee" is based upon suggestions I've seen for similar posts:
import numpy as np
import pandas as pd
import cufflinks
np.random.seed(123)
X = np.random.randn(1000,3)
df=pd.DataFrame(X, columns=['a','b','c'])
fig=df.iplot(asFigure=True,xTitle='time',yTitle='values',title='Time Series Plot')
fig.axvline([500,800], linewidth=5,color="black", linestyle="--")
fig.show()
The error message states 'Figure' object has no attribute 'axvline'.
I'm not sure whether this message is due to my lack of understanding about basic plots or stems from a limitation of using igraph.
The answer:
To add a line to an existing plotly figure, just use:
fig.add_shape(type='line',...)
The details:
I gather this is the post you've seen since you're mixing in matplotlib. And as it has been stated in the comments, axvline has got nothing to do with plotly. That was only used as an example for how you could have done it using matplotlib. Using plotly, I'd either go for fig.add_shape(go.layout.Shape(type="line"). But before you try it out for yourself, please b aware that cufflinks has been deprecated. I really liked cufflinks, but now there are better options for building both quick and detailed graphs. If you'd like to stick to one-liners similat to iplot, I'd suggest using plotly.express. The only hurdle in your case is changing your dataset from a wide to a long format that is preferred by plotly.express. The snippet below does just that to produce the following plot:
Code:
import numpy as np
import pandas as pd
import plotly.express as px
from plotly.offline import iplot
#
np.random.seed(123)
X = np.random.randn(1000,3)
df=pd.DataFrame(X, columns=['a','b','c'])
df['id'] = df.index
df = pd.melt(df, id_vars='id', value_vars=df.columns[:-1])
# plotly line figure
fig = px.line(df, x='id', y='value', color='variable')
# lines to add, specified by x-position
lines = {'a':500,'c':700,'a':900,'b':950}
# add lines using absolute references
for k in lines.keys():
#print(k)
fig.add_shape(type='line',
yref="y",
xref="x",
x0=lines[k],
y0=df['value'].min()*1.2,
x1=lines[k],
y1=df['value'].max()*1.2,
line=dict(color='black', width=3))
fig.add_annotation(
x=lines[k],
y=1.06,
yref='paper',
showarrow=False,
text=k)
fig.show()
Not sure if this is what you want, adding two scatter seems to work:
np.random.seed(123)
X = np.random.randn(1000,3)
df=pd.DataFrame(X, columns=['a','b','c'])
fig = df.iplot(asFigure=True,xTitle='time',yTitle='values',title='Time Series Plot')
fig.add_scatter(x=[500]*100, y=np.linspace(-4,4,100), name='lower')
fig.add_scatter(x=[800]*100, y=np.linspace(-4,4,100), name='upper')
fig.show()
Output:
I was wondering how pandas formats the x-axis date exactly. I am using the same script on a bunch of data results, which all have the same pandas df format. However, pandas formats each df date differently. How could this be more consistently?
Each df has a DatetimeIndex like this, dtype='datetime64[ns]
>>> df.index
DatetimeIndex(['2014-10-02', '2014-10-03', '2014-10-04', '2014-10-05',
'2014-10-06', '2014-10-07', '2014-10-08', '2014-10-09',
'2014-10-10', '2014-10-11',
...
'2015-09-23', '2015-09-24', '2015-09-25', '2015-09-26',
'2015-09-27', '2015-09-28', '2015-09-29', '2015-09-30',
'2015-10-01', '2015-10-02'],
dtype='datetime64[ns]', name='Date', length=366, freq=None)
Eventually, I plot with df.plot() where the df has two columns.
But the axes of the plots have different styles, like this:
I would like all plots to have the x-axis style of the first plot. pandas should do this automatically, so I'd rather not prefer to begin with xticks formatting, since I have quite a lot of data to plot. Could anyone explain what to do? Thanks!
EDIT:
I'm reading two csv-files from 2015. The first has the model results of about 200 stations, the second has the gauge measurements of the same stations. Later, I read another two csv-files from 2016 with the same format.
import pandas as pd
df_model = pd.read_csv(path_model, sep=';', index_col=0, parse_dates=True)
df_gauge = pd.read_csv(path_gauge, sep=';', index_col=0, parse_dates=True)
df = pd.DataFrame(columns=['model', 'gauge'], index=df_model.index)
df['model'] = df_model['station_1'].copy()
df['gauge'] = df_gauge['station_1'].copy()
df.plot()
I do this for each year, so the x-axis should look the same, right?
I do not think this possible unless you make modifications to the pandas library. I looked around a bit for options that one may set in Pandas, but couldn't find one. Pandas tries to intelligently select the type of axis ticks using logic implemented here (I THINK). So in my opinion, it would be best to define your own function to make the plots and than overwrite the tick formatting (although you do not want to do that).
There are many references around the internet which show how to do this. I used this one by "Simone Centellegher" and this stackoverflow answer to come up with a function that may work for you (tested in python 3.7.1 with matplotlib 3.0.2, pandas 0.23.4):
import pandas as pd
import numpy as np
import matplotlib.dates as mdates
import matplotlib.pyplot as plt
## pass df with columns you want to plot
def my_plotter(df, xaxis, y_cols):
fig, ax = plt.subplots()
plt.plot(xaxis,df[y_cols])
ax.xaxis.set_minor_locator(mdates.MonthLocator())
ax.xaxis.set_major_locator(mdates.YearLocator())
ax.xaxis.set_minor_formatter(mdates.DateFormatter('%b'))
ax.xaxis.set_major_formatter(mdates.DateFormatter('%b\n%Y'))
# Remove overlapping major and minor ticks
majticklocs = ax.xaxis.get_majorticklocs()
minticklocs = ax.xaxis.get_minorticklocs()
minticks = ax.xaxis.get_minor_ticks()
for i in range(len(minticks)):
cur_mintickloc = minticklocs[i]
if cur_mintickloc in majticklocs:
minticks[i].set_visible(False)
return fig, ax
df = pd.DataFrame({'values':np.random.randint(0,1000,36)}, \
index=pd.date_range(start='2014-01-01', \
end='2016-12-31',freq='M'))
fig, ax = my_plotter(df, df.index, ["values"])
I dynamically generate a pandas dataframe where columns are months, index is day-of-month, and values are cumulative revenue. This is fairly easy, b/c it just pivots a dataframe that is month/dom/rev.
But now I want to plot it in plotly. Since every month the columns will expand, I don't want to manually add a trace per month. But I can't seem to have a single trace incorporate multiple columns. I could've sworn this was possible.
revs = Scatter(
x=df.index,
y=[df['2016-Aug'], df['2016-Sep']],
name=['rev', 'revvv'],
mode='lines'
)
data=[revs]
fig = dict( data=data)
iplot(fig)
This generates an empty graph, no errors. Ideally I'd just pass df[df.columns] to y. Is this possible?
You were probably thinking about cufflinks. You can plot a whole dataframe with Plotly using the iplot function without data replication.
An alternative would be to use pandas.plot to get an matplotlib object which is then converted via plotly.tools.mpl_to_plotly and plotted. The whole procedure can be shortened to one line:
plotly.plotly.plot_mpl(df.plot().figure)
The output is virtually identical, just the legend needs tweaking.
import plotly
import pandas as pd
import random
import cufflinks as cf
data = plotly.tools.OrderedDict()
for month in ['2016-Aug', '2016-Sep']:
data[month] = [random.randrange(i * 10, i * 100) for i in range(1, 30)]
#using cufflinks
df = pd.DataFrame(data, index=[i for i in range(1, 30)])
fig = df.iplot(asFigure=True, kind='scatter', filename='df.html')
plot_url = plotly.offline.plot(fig)
print(plot_url)
#using mpl_to_plotly
plot_url = plotly.offline.plot(plotly.tools.mpl_to_plotly(df.plot().figure))
print(plot_url)
I have a data frame that looks like this:
type price1 price2
0 A 5450.0 31980.0
1 B 5450.0 20000.0
2 C 15998.0 18100.0
What I want is a clustered bar chart that plots "type" against "price". The end goal is a chart that has two bars for each type, one bar for "price1" and the other for "price2". Both columns are in the same unit ($). Using Bokeh I can group by type, but I cant seem to group by a generic "price" unit. I have this code so far:
import pandas as pd
import numpy as np
from bokeh.charts import Bar, output_file, show
from bokeh.palettes import Category20 as palette
from bokeh.models import HoverTool, PanTool
p = Bar(
df,
plot_width=1300,
plot_height=900,
label='type',
values='price2',
bar_width=0.4,
legend='top_right',
agg='median',
tools=[HoverTool(), PanTool()],
palette=palette[20])
But that only gets me one column for each type.
How can I modify my code to get two bars for each type?
What you are searching for is a grouped Bar plot.
But you have to reorganise your data a little bit, so that bokeh (or better Pandas) is able to group the data correctly.
df2 = pd.DataFrame(data={'type': ['A','B','C', 'A', 'B', 'C'],
'price':[5450, 5450, 15998, 3216, 20000, 15000],
'price_type':['price1', 'price1', 'price1', 'price2', 'price2', 'price2']})
p = Bar(
df2,
plot_width=1300,
plot_height=900,
label='type',
values='price',
bar_width=0.4,
group='price_type',
legend='top_right')
show(p)
Your table is "wide" format. you want to melt it to a long format first using pd.melt() function. For visualization,I suggest you use the "Seaborn" package and make your life easier. you can visualize every thing in one line.
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
your_df = pd.DataFrame(data={'type': ['A','B','C'],
'price1':[5450, 5450, 15998],
'price2' : [3216, 20000, 15000]})
long_df = pd.melt(your_df,id_vars = ['type'],value_vars =['price1','price2'])
print long_df
my_plot = sns.barplot(x="type", y="value",hue = "variable", data=long_df)
sns.plt.show()
A good post on long and wide formats can be found here:
Reshape Long Format Multivalue Dataframes with Pandas
if you insist on using bokeh here is how you do it as renzop pointed out :
p = Bar(long_df,
plot_width=1000,
plot_height=800,
label='type',
values='value',
bar_width=0.4,
group='variable',
legend='top_right')
show(p)