3 ms·

Could you show how you write "calculate a monthly sum anchored on the last business day of the month" in pandas please?

sebg 2y ago
Not OP. But I'm guessing it's something like this: import pandas as pd def calculate_monthly_business_sum(df, date_column, value_column): """ Calculate monthly sums anchored to the last business day of each month Parameters: df: DataFrame with dates and values date_column: name of date column value_column: name of value column to sum Returns: DataFrame with sums anchored to last business day """ # Ensure date column is datetime df[date_column] = pd.to_datetime(df[date_column]) # Group by end of business month and sum monthly_sum = df.groupby(pd.Grouper( key=date_column, freq='BME' # Business Month End frequency ))[value_column].sum().reset_index() return monthly_sum # Example usage: df = pd.DataFrame({ 'date': ['2024-01-01', '2024-01-31', '2024-02-29'], 'amount': [100, 200, 300] }) result = calculate_monthly_business_sum(df, 'date', 'amount') print(result) Which you can run here => https://python-fiddle.com/examples/pandas?checkpoint=1732114144 https://python-fiddle.com/examples/pandas?checkpoint=1732114...
- short_sells_poo 2y ago
  It's actually much simpler than that. Assuming the index of the dataframe DF is composed of timestamps (which is normal for timeseries): df.resample("BME").sum() Done. One line of code and it is quite obvious what it is doing - with perhaps the small exception of BME, but if you want max readability you could do: df.resample(pd.offsets.BusinessMonthEnd()).sum() This is why people use pandas.
short_sells_poo 2y ago
Answered the child comment but let me copy paste here too. It's literally one (short) line: > df.resample("BME").sum() Assuming `df` is a dataframe (ie table) indexed by a timestamp index, which is usual for timeseries analysis. "BME" stands for BusinessMonthEnd, which you can type out if you want the code to be easier to read by someone not familiar with pandas.
- ies7 2y ago
  This one liner example is one of the reason why some people use pandas and some people despise it. It so easy for my analyst team because of daily uses but my developers probavly will never thought/know BME and decided to implement the code again.
tomrod 2y ago
A bit from memory as in transit, but something like df.groupby(df[date_col]+pd.offsets.MonthEnd(0))[agg_col].sum()

sebg 2y ago

Not OP. But I'm guessing it's something like this: import pandas as pd def calculate_monthly_business_sum(df, date_column, value_column): """ Calculate monthly sums anchored to the last business day of each month Parameters: df: DataFrame with dates and values date_column: name of date column value_column: name of value column to sum Returns: DataFrame with sums anchored to last business day """ # Ensure date column is datetime df[date_column] = pd.to_datetime(df[date_column]) # Group by end of business month and sum monthly_sum = df.groupby(pd.Grouper( key=date_column, freq='BME' # Business Month End frequency ))[value_column].sum().reset_index() return monthly_sum # Example usage: df = pd.DataFrame({ 'date': ['2024-01-01', '2024-01-31', '2024-02-29'], 'amount': [100, 200, 300] }) result = calculate_monthly_business_sum(df, 'date', 'amount') print(result) Which you can run here => https://python-fiddle.com/examples/pandas?checkpoint=1732114144 https://python-fiddle.com/examples/pandas?checkpoint=1732114...

short_sells_poo 2y ago
It's actually much simpler than that. Assuming the index of the dataframe DF is composed of timestamps (which is normal for timeseries): df.resample("BME").sum() Done. One line of code and it is quite obvious what it is doing - with perhaps the small exception of BME, but if you want max readability you could do: df.resample(pd.offsets.BusinessMonthEnd()).sum() This is why people use pandas.

short_sells_poo 2y ago

Answered the child comment but let me copy paste here too. It's literally one (short) line: > df.resample("BME").sum() Assuming `df` is a dataframe (ie table) indexed by a timestamp index, which is usual for timeseries analysis. "BME" stands for BusinessMonthEnd, which you can type out if you want the code to be easier to read by someone not familiar with pandas.

ies7 2y ago
This one liner example is one of the reason why some people use pandas and some people despise it. It so easy for my analyst team because of daily uses but my developers probavly will never thought/know BME and decided to implement the code again.

tomrod 2y ago

A bit from memory as in transit, but something like df.groupby(df[date_col]+pd.offsets.MonthEnd(0))[agg_col].sum()