Dataframe manipulation and Sharpe ratio calculation in Python using pandas
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😎 I have a dataframe with 3000 rows indexed and sorted by date. I want a new dataframe (without altering the original) with the last 255 rows
🤖😎 now I want to filter for a specific calendar year. For example, 2023python import pandas as pd # Assuming 'df' is your original dataframe # Create a new dataframe with the last 255 rows new_df = df.tail(255).copy() # new_df is the new dataframe with the last 255 rows
🤖😎 I want to add a condition that removes all columns where there are less than 250 valid numeric non-NA valuespython # Assuming the date is the index of the dataframe and is in datetime format filtered_df = new_df[new_df.index.year == 2023] # filtered_df is the dataframe filtered for the calendar year 2023
🤖😎 got this error: ” “You cannot set both the how and thresh arguments at the same time.””python # Filter columns with at least 250 valid numeric non-NA values final_df = filtered_df.dropna(axis=1, how='any', thresh=250) # final_df is the dataframe after applying all the conditions
🤖😎 are you sure we need that complexity?python # Correcting the approach to filter columns based on valid numeric non-NA values final_df = filtered_df.loc[:, filtered_df.apply(lambda x: pd.to_numeric(x, errors='coerce').notnull().sum() >= 250)] # final_df now correctly filters out columns not meeting the criteria
pandas.DataFrame.dropna
DataFrame.dropna(*, axis=0, how=_NoDefault.no_default, thresh=_NoDefault.no_default, subset=None, inplace=False, ignore_index=False)[source]
Remove missing values.
See the User Guide for more on which values are considered missing, and how to work with missing data.
Parameters:
axis{0 or ‘index’, 1 or ‘columns’}, default 0
Determine if rows or columns which contain missing values are removed.
0, or ‘index’ : Drop rows which contain missing values.
1, or ‘columns’ : Drop columns which contain missing value.
Only a single axis is allowed.
how{‘any’, ‘all’}, default ‘any’
Determine if row or column is removed from DataFrame, when we have at least one NA or all NA.
‘any’ : If any NA values are present, drop that row or column.
‘all’ : If all values are NA, drop that row or column.
threshint, optional
Require that many non-NA values. Cannot be combined with how.
subsetcolumn label or sequence of labels, optional
Labels along other axis to consider, e.g. if you are dropping rows these would be a list of columns to include.
inplacebool, default False
Whether to modify the DataFrame rather than creating a new one.
ignore_indexbool, default False
If True, the resulting axis will be labeled 0, 1, …, n – 1.
🤖😎 I have an annual risk-free rate. In order to include it in my shape ratio calculation, which includes average daily returns, I need to convert the risk-free rate from annual to daily:python # Simplifying the approach based on your correction final_df = filtered_df.dropna(axis=1, thresh=250) # final_df now filters out columns with less than 250 valid numeric non-NA values without the previously mentioned complexity
sharpe_ratio = (rent_diaria_promedio – rfr_diario ) / std_dev
🤖python # Assuming 'rent_diaria_promedio' is the average daily return, 'std_dev' is the standard deviation of daily returns # and 'annual_rfr' is the annual risk-free rate # Convert annual risk-free rate to daily risk-free rate # Assuming 252 trading days in a year rfr_diario = (1 + annual_rfr) ** (1/252) - 1 # Calculate Sharpe Ratio sharpe_ratio = (rent_diaria_promedio - rfr_diario) / std_dev # sharpe_ratio is the calculated Sharpe Ratio
Hugging Face Dataset Metrics
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