Question: Program Here's a small Python program that gets tax data via the treasury.gov API: import pandas as pd import treasury_gov_pandas # ---------------------------------------------------------------------- df = treasury_gov_pandas.update_records( url = 'https://api.fiscaldata.treasury.gov/services/api/fiscal_service/v1/accounting/dts/deposits_withdrawals_operating_cash') df['record_date'] = pd.to_datetime(df['record_date']) df['transaction_today_amt'] = pd.to_numeric(df['transaction_today_amt']) tmp = df[(df['transaction_type'] == 'Deposits') & ((df['transaction_catg'].str.contains('Tax')) | (df['transaction_catg'].str.contains('FTD'))) ]  The program is using the following library to download the data: https://github.com/dharmatech/treasury-gov-pandas.py Dataframe Here's what the resulting data looks like: >>> tmp.tail(20).drop(columns=['table_nbr', 'table_nm', 'src_line_nbr', 'record_fiscal_year', 'record_fiscal_quarter', 'record_calendar_year', 'record_calendar_quarter', 'record_calendar_month', 'record_calendar_day', 'transaction_mtd_amt', 'transaction_fytd_amt', 'transaction_catg_desc', 'account_type', 'transaction_type']) record_date transaction_catg transaction_today_amt 371266 2024-04-03 DHS - Customs and Certain Excise Taxes 84 371288 2024-04-03 Taxes - Corporate Income 237 371289 2024-04-03 Taxes - Estate and Gift 66 371290 2024-04-03 Taxes - Federal Unemployment (FUTA) 10 371291 2024-04-03 Taxes - IRS Collected Estate, Gift, misc 23 371292 2024-04-03 Taxes - Miscellaneous Excise 41 371293 2024-04-03 Taxes - Non Withheld Ind/SECA Electronic 1786 371294 2024-04-03 Taxes - Non Withheld Ind/SECA Other 2315 371295 2024-04-03 Taxes - Railroad Retirement 3 371296 2024-04-03 Taxes - Withheld Individual/FICA 12499 371447 2024-04-04 DHS - Customs and Certain Excise Taxes 82 371469 2024-04-04 Taxes - Corporate Income 288 371470 2024-04-04 Taxes - Estate and Gift 59 371471 2024-04-04 Taxes - Federal Unemployment (FUTA) 8 371472 2024-04-04 Taxes - IRS Collected Estate, Gift, misc 127 371473 2024-04-04 Taxes - Miscellaneous Excise 17 371474 2024-04-04 Taxes - Non Withheld Ind/SECA Electronic 1905 371475 2024-04-04 Taxes - Non Withheld Ind/SECA Other 1092 371476 2024-04-04 Taxes - Railroad Retirement 1 371477 2024-04-04 Taxes - Withheld Individual/FICA 2871  The dataframe has data that goes back to 2005: >>> tmp.drop(columns=['table_nbr', 'table_nm', 'src_line_nbr', 'record_fiscal_year', 'record_fiscal_quarter', 'record_calendar_year', 'record_calendar_quarter', 'record_calendar_month', 'record_calendar_day', 'transaction_mtd_amt', 'transaction_fytd_amt', 'transaction_catg_desc', 'account_type', 'transaction_type']) record_date transaction_catg transaction_today_amt 2 2005-10-03 Customs and Certain Excise Taxes 127 7 2005-10-03 Estate and Gift Taxes 74 10 2005-10-03 FTD's Received (Table IV) 2515 12 2005-10-03 Individual Income and Employment Taxes, Not Wi... 353 21 2005-10-03 FTD's Received (Table IV) 15708 ... ... ... ... 371473 2024-04-04 Taxes - Miscellaneous Excise 17 371474 2024-04-04 Taxes - Non Withheld Ind/SECA Electronic 1905 371475 2024-04-04 Taxes - Non Withheld Ind/SECA Other 1092 371476 2024-04-04 Taxes - Railroad Retirement 1 371477 2024-04-04 Taxes - Withheld Individual/FICA 2871  Question I'd like to plot this data as a stacked bar chart.  x-axis should be 'record_date'. y-axis should be the 'transaction_today_amt'. The 'transaction_catg' values should be used for the stacked items.  I'm open to any plotting library. I.e. matplotlib, bokeh, plotly, etc. What's a good way to implement this?

Answer: I have create dummy df and tested it worked This code creates a DataFrame with random transaction data grouped by date and category. It then pivots the data to display a stacked bar chart where each bar represents a date, and the stack segments represent transaction amounts for different categories. I hope that solution help your project. And final code is shown below. import pandas as pd import numpy as np import matplotlib.pyplot as plt from matplotlib.dates import DayLocator data = { 'record_date': pd.to_datetime(['2023-10-01', '2023-10-02', '2023-10-03','2023-10-04', '2023-10-05', '2023-10-06','2023-10-07', '2023-10-08', '2023-10-09'] * 5), 'transaction_catg': ['A', 'B', 'C', 'D', 'E'] * 9, 'transaction_today_amt': np.random.randint(100, 1000, 45) } tmp = pd.DataFrame(data) tmp_agg = tmp.groupby(['record_date', 'transaction_catg'])['transaction_today_amt'].sum().reset_index() tmp_agg['record_date'] = tmp_agg['record_date'].dt.date pivot_df = tmp_agg.pivot(index='record_date', columns='transaction_catg', values='transaction_today_amt').fillna(0) ax = pivot_df.plot(kind='bar', stacked=True, figsize=(10, 6)) ax.xaxis.set_major_locator(DayLocator(interval=3)) plt.xticks(rotation=45) plt.xlabel('Record Date') plt.ylabel('Transaction Today Amount') plt.title('Stacked Bar Chart of Transaction Amounts by Category and Date') plt.show()  Output is like :