I have a Pandas dataframe representing portfolio weights in multiple dates, such as the following contents in CSV format:
DATE,ASSET1,ASSET2,ASSET3,ASSET4,ASSET5,ASSET6,ASSET7
2010-01-04,0.250000,0.0,0.250000,0.000000,0.25,0.000000,0.250000
2010-02-03,0.250000,0.0,0.250000,0.000000,0.25,0.000000,0.250000
2010-03-05,0.217195,0.0,0.250000,0.032805,0.25,0.000000,0.250000
2010-04-06,0.139636,0.0,0.250000,0.110364,0.25,0.000000,0.250000
2010-05-05,0.179569,0.0,0.218951,0.101480,0.25,0.000000,0.250000
2010-06-04,0.207270,0.0,0.211974,0.080756,0.25,0.000000,0.250000
2010-07-06,0.132468,0.0,0.250000,0.117532,0.25,0.000000,0.250000
2010-08-04,0.116353,0.0,0.250000,0.133647,0.25,0.000000,0.250000
2010-09-02,0.081677,0.0,0.250000,0.168323,0.25,0.000000,0.250000
2010-10-04,0.000000,0.0,0.250000,0.250000,0.25,0.009955,0.240045
For each row in the Pandas dataframe resulting from this CSV, we can generate a bar chart with the portfolio composition at that day. I would like to have multiple bar charts, with a time slider, such that we can choose one of the dates and see the portfolio composition during that day.
Can this be achieved with Plotly?
I could not find a way to do it straight in the dataframe above, but it is possible to do it by "melting" the dataframe. The following code achieves what I was looking for, together with some beautification of the chart:
import pandas as pd
from io import StringIO
import plotly.express as px
string = """
DATE,ASSET1,ASSET2,ASSET3,ASSET4,ASSET5,ASSET6,ASSET7
2010-01-04,0.250000,0.0,0.250000,0.000000,0.25,0.000000,0.250000
2010-02-03,0.250000,0.0,0.250000,0.000000,0.25,0.000000,0.250000
2010-03-05,0.217195,0.0,0.250000,0.032805,0.25,0.000000,0.250000
2010-04-06,0.139636,0.0,0.250000,0.110364,0.25,0.000000,0.250000
2010-05-05,0.179569,0.0,0.218951,0.101480,0.25,0.000000,0.250000
2010-06-04,0.207270,0.0,0.211974,0.080756,0.25,0.000000,0.250000
2010-07-06,0.132468,0.0,0.250000,0.117532,0.25,0.000000,0.250000
2010-08-04,0.116353,0.0,0.250000,0.133647,0.25,0.000000,0.250000
2010-09-02,0.081677,0.0,0.250000,0.168323,0.25,0.000000,0.250000
2010-10-04,0.000000,0.0,0.250000,0.250000,0.25,0.009955,0.240045
"""
df = pd.read_csv(StringIO(string))
df = df.melt(id_vars=['DATE']).sort_values(by = 'DATE')
fig = px.bar(df, x="variable", y="value", animation_frame="DATE")
fig.update_layout(legend_title_text = None)
fig.update_xaxes(title = "Asset")
fig.update_yaxes(title = "Proportion")
fig.update_layout(autosize = True, height = 600)
fig.update_layout(hovermode="x")
fig.update_layout(plot_bgcolor="#F8F8F8")
fig.update_traces(
hovertemplate=
'<i></i> %{y:.2%}'
)
fig.show()
This produces the following:
Related
Could you please help me if you know how to make a pie chart in Python from it?
This is a reproducible example how the df looks like. However, I have way more rows over there.
import pandas as pd
data = [["70%"], ["20%"], ["10%"]]
example = pd.DataFrame(data, columns = ['percentage'])
example.index = ['Lasiogl', 'Centella', 'Osmia']
example
You can use matplotlib to plot the pie chart using dataframe and its indexes as labels of the chart:
import matplotlib.pyplot as plt
import pandas as pd
data = ['percentage':["70%"], ["20%"], ["10%"]]
example = pd.DataFrame(data, columns = ['percentage'])
my_labels = 'Lasiogl', 'Centella', 'Osmia'
plt.pie(example,labels=my_labels,autopct='%1.1f%%')
plt.show()
import pandas as pd
import plotly
import plotly.express as px
import plotly.io as pio
df = pd.read_csv("final_spreadsheet.csv")
barchart = px.bar(
data_frame = df,
x = "Post-Lockdown Period (May - September)",
y = "Post-Lockdown Period (May - September)",
color = "Peak-Lockdown Period (March-May)",
opacity = 0.9,
orientation ="v",
barmode = 'relative',
)
pio.show(barchart)
I want the x axis to be the different behavioral variables and for each behavioral variable I want there to be two bars one for peak pandemic and one for post pandemic. I also want the y axis to just be the values of each
This is my current attempt but no graphs appear. Attached is also a picture of the CSV file in excel form
In plotly.express you can create a grouped bar chart by passing a list of the two variables you want to group together in the argument y. In your case, you'll want to pass the argument y = ['Peak-Lockdown Period (March-May)','Post-Lockdown Period (May-September)'] as well as the argument barmode = 'grouped' to px.bar. I created a sample DataFrame to illustrate:
import pandas as pd
import plotly.express as px
import plotly.io as pio
# df = pd.read_csv("final_spreadsheet.csv")
## create example DataFrame similar to yours
df = pd.DataFrame({
'Behavioral': list('ABCD'),
'Peak-Lockdown Period (March-May)': [76.7,26.12,0,2.94],
'Post-Lockdown Period (May-September)': [77.32,26.38,0,3.36]
})
barchart = px.bar(
data_frame = df,
x = 'Behavioral',
y = ['Peak-Lockdown Period (March-May)','Post-Lockdown Period (May-September)'],
# color = "Peak-Lockdown Period (March-May)",
opacity = 0.9,
orientation ="v",
barmode = 'group',
)
pio.show(barchart)
EDIT: you can accomplish the same thing using plotly.graph_objects:
import plotly.graph_objects as go
fig = go.Figure(data=[
go.Bar(name='Peak-Lockdown Period (March-May)', x=df['Behavioral'].values, y=df['Peak-Lockdown Period (March-May)'].values),
go.Bar(name='Post-Lockdown Period (May-September)', x=df['Behavioral'].values, y=df['Post-Lockdown Period (May-September)'].values),
])
I have following pandas dataframe. I would like to create box (sub)plots of all the 5 columns (in one plot). How can I achieve this.
I am using following python statement but I am not getting the output.
df.boxplot(column=['synonym']['score'])
Here is an example of boxplot via plotly.express:
import plotly.express as px
df = pd.DataFrame(dict(x1=[1,2,3], x2=[4,8,12],x3=[1,5,10]))
df = df.melt(value_vars=['x1','x2','x3'])
fig = px.box(df, x='variable', y='value', color='variable')
fig.show()
I have data like this :
[ ('2018-04-09', '10:18:11',['s1',10],['s2',15],['s3',5])
('2018-04-09', '10:20:11',['s4',8],['s2',20],['s1',10])
('2018-04-10', '10:30:11',['s4',10],['s5',6],['s6',3]) ]
I want to plot a stacked graph preferably of this data.
X-axis will be time,
it should be like this
I created this image in paint just to show.
X axis will show time like normal graph does( 10:00 ,April 3,2018).
I am stuck because the string value (like 's1',or 's2' ) will change in differnt bar graph.
Just to hard code and verify,I try this:
import plotly
import plotly.graph_objs as go
import matplotlib.pyplot as plt
import matplotlib
plotly.offline.init_notebook_mode()
def createPage():
graph_data = []
l1=[('com.p1',1),('com.p2',2)('com.p3',3)]
l2=[('com.p1',1),('com.p4',2)('com.p5',3)]
l3=[('com.p2',8),('com.p3',2)('com.p6',30)]
trace_temp = go.Bar(
x='2018-04-09 10:18:11',
y=l1[0],
name = 'top',
)
graph_data.append(trace_temp)
plotly.offline.plot(graph_data, filename='basic-scatter3.html')
createPage()
Error I am getting is Tuple Object is not callable.
So can someone please suggest some code for how I can plot such data.
If needed,I may store data in some other form which may be helpful in plotting.
Edit :
I used the approach suggested in accepted answer and succeed in plotting using plotly like this
fig=df.iplot(kin='bar',barmode='stack',asFigure=True)
plotly.offline.plt(fig,filename="stack1.html)
However I faced one error:
1.When Time intervals are very close,Data overlaps on graph.
Is there a way to overcome it.
You could use pandas stacked bar plot. The advantage is that you can create with pandas easily the table of column/value pairs you have to generate anyhow.
from matplotlib import pyplot as plt
import pandas as pd
all_data = [('2018-04-09', '10:18:11', ['s1',10],['s2',15],['s3',5]),
('2018-04-09', '10:20:11', ['s4',8], ['s2',20],['s1',10]),
('2018-04-10', '10:30:11', ['s4',10],['s5',6], ['s6',3]) ]
#load data into dataframe
df = pd.DataFrame(all_data, columns = list("ABCDE"))
#combine the two descriptors
df["day/time"] = df["A"] + "\n" + df["B"]
#assign each list to a new row with the appropriate day/time label
df = df.melt(id_vars = ["day/time"], value_vars = ["C", "D", "E"])
#split each list into category and value
df[["category", "val"]] = pd.DataFrame(df.value.values.tolist(), index = df.index)
#create a table with category-value pairs from all lists, missing values are set to NaN
df = df.pivot(index = "day/time", columns = "category", values = "val")
#plot a stacked bar chart
df.plot(kind = "bar", stacked = True)
#give tick labels the right orientation
plt.xticks(rotation = 0)
plt.show()
Output:
I am trying to get an output from a dataframe that shows a stacked horizontal bar chart with a table to the left of it. The relevant data is as follows:
import pandas as pd
import matplotlib.pyplot as plt
cols = ['metric','target','daily_avg','days_green','days_yellow','days_red']
vals = ['Volume',338.65,106.81,63,2,1]
OutDict = dict(zip(cols,vals))
df = pd.DataFrame(columns = cols)
df = df.append(OutDict, ignore_index = True)
I'd like to get something similar to what's in the following: Python Matplotlib how to get table only. I can get the stacked bar chart:
df[['days_green','days_yellow','days_red']].plot.barh(stacked=True)
Adding in the keyword argument table=True puts a table below the chart. How do I get the axis to either display the df as a table or add one in next to the chart. Also, the DataFrame will eventually have more than one row, but if I can get it work for one then I should be able to get it to work for n rows.
Thanks in advance.
Unfortunately using the pandas.plot method you won't be able to do this. The docs for the table parameter state:
If True, draw a table using the data in the DataFrame and the data will be transposed to meet matplotlib’s default layout. If a Series or DataFrame is passed, use passed data to draw a table.
So you will have to use matplotlib directly to get this done. One option is to create 2 subplots; one for your table and one for your chart. Then you can add the table and modify it as you see fit.
import matplotlib.pyplot as plt
import pandas as pd
cols = ['metric','target','daily_avg','days_green','days_yellow','days_red']
vals = ['Volume',338.65,106.81,63,2,1]
OutDict = dict(zip(cols,vals))
df = pd.DataFrame(columns = cols)
df = df.append(OutDict, ignore_index = True)
fig, (ax1, ax2) = plt.subplots(1, 2)
df[['days_green','days_yellow','days_red']].plot.barh(stacked=True, ax=ax2)
ax1.table(cellText=df[['days_green','days_yellow','days_red']].values, colLabels=['days_green', 'days_yellow', 'days_red'], loc='center')
ax1.axis('off')
fig.show()