Pandas matplotlib : bar charts in percentage of the total-CodePudding

I am trying to group chart in a pivot table fashion way. I would like to chart my datas, using pandas/matplotlib in a percentage way stacked by ['post_datetime','claimed'], where the chart bar would result into a 100% divided between my True/False 'claimed' datas.

This is an extract from my database:

post_datetime,claimed,percent,claimed_pct,unclaimed_pct
2016,True,1.0,1.0,
2017,True,1.0,1.0,
2018,False,0.25342,,0.25342
2018,True,0.74658,0.74658,
2019,False,0.3971,,0.3971
2019,True,0.6029,0.6029,
2020,False,0.44128,,0.44128
2020,True,0.55872,0.55872,
2021,False,0.5594,,0.5594
2021,True,0.4406,0.4406,
2022,False,0.5316,,0.5316
2022,True,0.4684,0.4684,

So far the best I get using the following code is a next to next bar chart (I want it 100%, divided between the proporption or True and False 'claiemd', which is defined by 'percent' in my database.


    ax = year_claim_df.plot(x='post_datetime',
                       y='claimed_pct',
                       kind='bar',
                       color='red'

                       )
    year_claim_df.plot(x='post_datetime',
                       y='unclaimed_pct',
                       kind='bar',
                       color='blue',
                       ax=ax

                       )
    plt.show()

here is the chart, that I get:

But I would lile all bars to 1 or 100%, being split colored using my true/false 'claimed' column and by the proportion of my 'percent' column

I would like the chart to be in that fashion (not my data):

Tx for the help. best

CodePudding user response：

Use

CodePudding user response：

IIUC you want to have something like this. Using seaborn is really easy and straightforward here.

import seaborn as sns

sns.barplot(x='post_datetime',y='percent', hue='claimed', data=df, palette=('blue', 'red'))