Bar chart with 2 data series with Pandas Dataframe and Plotly

Question

I have a dataframe with the following data (example):

year	month	count
2020	11	100
	12	50
2021	01	80
	02	765
	03	100
	04	265
	05	500

I would like to plot this with plotly on a bar chart where I would have 2 vertical bars for each month, one for 2020 and another for 2021. I would like the axis to be defined automatically based on the existing values on the dataset which could change. today is only for year 2020 and 2021 but it could be different.

I have searched for information but is always mentioning hardcoded dataset series names and data and I'm not understanding how I could dynamically input these in ploty.

I was expecting something like this but it is not working:

import plotly.express as px

...

px.bar(df, x=['year','month'], y='count')
fig.show()

Thank you,

vestland · Accepted Answer · 2022-01-09 19:53:36Z

To get two vertical bar for each month, I'm guessing the traces should represent each individual year. In that case you can use:

for y in df.year.unique():
    dfy = df[df.year == y]
    fig.add_bar(x = dfy.month, y = dfy.value, name = str(y))

Plot 1

That's the result for your limited dataset, though. If you expand the dataset a bit you'll get a better impression of how it will look:

Plot 2

Complete code:

import plotly.graph_objects as go
import pandas as pd

df = pd.DataFrame({'year': {0: 2020, 1: 2020, 2: 2021, 3: 2021, 4: 2021, 5: 2021, 6: 2021},
                     'month': {0: 11, 1: 12, 2: 1, 3: 2, 4: 3, 5: 4, 6: 5},
                     'value': {0: 100, 1: 50, 2: 80, 3: 765, 4: 100, 5: 265, 6: 500}})

df = pd.DataFrame({'year': {0: 2020,
                  1: 2020,
                  2: 2020,
                  3: 2020,
                  4: 2020,
                  5: 2020,
                  6: 2020,
                  7: 2020,
                  8: 2020,
                  9: 2020,
                  10: 2020,
                  11: 2020,
                  12: 2021,
                  13: 2021,
                  14: 2021,
                  15: 2021,
                  16: 2021,
                  17: 2021,
                  18: 2021,
                  19: 2021,
                  20: 2021,
                  21: 2021,
                  22: 2021,
                  23: 2021},
                 'month': {0: 1,
                  1: 2,
                  2: 3,
                  3: 4,
                  4: 5,
                  5: 6,
                  6: 7,
                  7: 8,
                  8: 9,
                  9: 10,
                  10: 11,
                  11: 12,
                  12: 1,
                  13: 2,
                  14: 3,
                  15: 4,
                  16: 5,
                  17: 6,
                  18: 7,
                  19: 8,
                  20: 9,
                  21: 10,
                  22: 11,
                  23: 12},
                 'value': {0: 100,
                  1: 50,
                  2: 265,
                  3: 500,
                  4: 80,
                  5: 765,
                  6: 100,
                  7: 265,
                  8: 500,
                  9: 80,
                  10: 765,
                  11: 100,
                  12: 80,
                  13: 765,
                  14: 100,
                  15: 265,
                  16: 500,
                  17: 80,
                  18: 765,
                  19: 100,
                  20: 265,
                  21: 500,
                  22: 80,
                  23: 765}})

fig = go.Figure()
for y in df.year.unique():
    dfy = df[df.year == y]
    fig.add_bar(x = dfy.month, y = dfy.value, name = str(y))

fig.show()

Thank you @vestland. This was exactly what I was looking for. In my case because of the way I had my dataset I had to flatten the column names with this: df.reset_index(inplace=True)
@DanielBelémDuarte You are welcome! And thanks for the feedback!

Rob Raymond · Accepted Answer · 2022-01-09 19:48:11Z

1

have modified your data to demonstrate
this is an example of https://plotly.com/python/categorical-axes/#multicategorical-axes Hence need to use go

import pandas as pd
import io
import plotly.express as px
import plotly.graph_objects as go

df = pd.read_csv(
    io.StringIO(
        """year,month,count
2020,1,50
2020,2,50
2020,3,50
2020,4,50
2020,11,100
2020,12,50
2021,1,80
2021,2,765
2021,3,100
2021,4,265
2021,5,500"""
    )
)

go.Figure(go.Bar(x=[df["month"].tolist(), df["year"].tolist()], y=df["count"]))

answered Jan 9, 2022 at 19:48

Rob Raymond

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1 Comment

Daniel Belém Duarte Over a year ago

thank you @Rob Raymond. This is a great answer but I'n not marking as the correct answer as it is not creating different data series.

DougR · Accepted Answer · 2022-01-10 22:45:38Z

Using Plotly Express and updated with multi-categorical x-axis:

import pandas as pd
import io
import plotly.express as px

df = pd.read_csv(
    io.StringIO(
        """year,month,count
2020,1,50
2020,2,50
2020,3,50
2020,4,50
2020,11,100
2020,12,50
2021,1,80
2021,2,765
2021,3,100
2021,4,265
2021,5,500"""
    )
)

# convert year to string so you get a catergorical scale
df['year'] = df['year'].astype(str)

channel_top_Level = "year"
channel_2nd_Level = "month"

fig = px.bar(df, x = channel_2nd_Level, y = 'count', color = channel_top_Level)
for num,channel_top_Level_val in enumerate(df[channel_top_Level].unique()):
    temp_df = df.query(f"{channel_top_Level} == {channel_top_Level_val !r}")
    fig.data[num].x = [
            temp_df[channel_2nd_Level].tolist(), 
            temp_df[channel_top_Level].tolist() 
          ]
fig.layout.xaxis.title.text = f"{channel_top_Level} / { channel_2nd_Level}"
fig

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