[FIXED] Plot stacked (100%) bar chart for multiple categories on multiple dates

Issue

I have following initial dataframe:

	Post ID	Submission_Date	Flair
0	row1	01.12.2020	NaN
1	row2	03.12.2020	Discussion
2	row3	03.12.2020	News
3	row4	03.12.2020	Discussion
4	row5	06.12.2020	Due Diligence
5	row6	07.12.2020	Discussion
6	row7	31.12.2020	Discussion
1	row8	01.01.2021	Hedge Fund Tears

• Multiple Dates with missing dates in between
• Multiple categories on dates

I grouped the dataframe with:

import pandas as pd
import numpy as np  # for test data

data = {'Post ID': ['row1', 'row2', 'row3', 'row4', 'row5', 'row6', 'row7', 'row8'], 'Submission_Date': ['01.12.2020', '03.12.2020', '03.12.2020', '03.12.2020', '06.12.2020', '07.12.2020', '31.12.2020', '01.01.2021'], 'Flair': [np.nan, 'Discussion', 'News', 'Discussion', 'Due Diligence', 'Discussion', 'Discussion', 'Hedge Fund Tears']}
df = pd.DataFrame(data)

df['Submission_Date'] = pd.to_datetime(df['Submission_Date'], format = "%Y-%m-%d %H:%M:%S").dt.strftime('%Y-%m-%d') 

df = df.groupby('Submission_Date')['Flair'].value_counts(normalize=True).unstack()

The result is this:

I want to fill the dates with "empty" bars and plot like this

something like this:

I tried already this:

fig, ax = plt.subplots(figsize=(20,10))
df.plot(kind='bar',ax=ax, stacked=True, width=1)
plt.xlabel('Submission_Date', fontsize=16)
plt.ylabel('Ratio of Flairs used', fontsize=16)

But the dates are incorrect since the empty days are not displayed

Solution

Assuming this input as df2 (the output of your groupby operation):

Flair            Discussion  Due Diligence  Hedge Fund Tears      News
Submission_Date                                                       
01.01.2021              NaN            NaN               1.0       NaN
03.12.2020         0.666667            NaN               NaN  0.333333
06.12.2020              NaN            1.0               NaN       NaN
07.12.2020         1.000000            NaN               NaN       NaN
31.12.2020         1.000000            NaN               NaN       NaN

You can reindex from pd.date_range:

df2.index = pd.to_datetime(df2.index, format='%d.%m.%Y')
df2 = df2.reindex(pd.date_range(df2.index.min(), df2.index.max()))
df2.index = df2.index.strftime('%Y-%m-%d') 

Flair       Discussion  Due Diligence  Hedge Fund Tears      News
2020-12-03    0.666667            NaN               NaN  0.333333
2020-12-04         NaN            NaN               NaN       NaN
2020-12-05         NaN            NaN               NaN       NaN
2020-12-06         NaN            1.0               NaN       NaN
2020-12-07    1.000000            NaN               NaN       NaN
...
2020-12-30         NaN            NaN               NaN       NaN
2020-12-31    1.000000            NaN               NaN       NaN
2021-01-01         NaN            NaN               1.0       NaN

graphical outcome (small size):

Answered By - mozway