PANDAS
A short post about a useful function in Pandas, merge_asof. It’s one of the most used tools in Pandas when dealing with time series data.
Merging data frames is one of the most frequent manipulations in data science. Most of the data merging focuses on the exact merge, where a row from the left and that from the right data frames must have index/values in common. However, sometimes we don’t want the exact match but the nearest match in merging data frames, especially in the time series analysis.
For example, we have a data frame of the S&P 500 index per day and another data frame of the weather in New York City per day. We want to know whether the weather in NYC can affect the next day’s S&P 500 index.
Note that the market is closed on weekends and holidays, so we want to make sure that the weather info we collect for each day’s S&P 500 index is its most recent business day.
To finish the task described above, we need to use one Pandas function, merge_asof instead of merge.
In this short post, I’ll briefly go over how to use this function with codes in Python. Hope it’s helpful to you.
Basic Usage of merge_asof
Following the aforementioned example, we first create our toy datasets.
import pandas as pd# S&P 500 index data
sp500_data =
'Date': pd.to_datetime(['2023-01-03', '2023-01-04', '2023-01-05', '2023-01-06', '2023-01-09']),
'SP500': [3750, 3780, 3795, 3800, 3820]
sp500_df = pd.DataFrame(sp500_data)
# NYC weather data
weather_data =
'Date': pd.to_datetime(['2023-01-02', '2023-01-03', '2023-01-04', '2023-01-05', '2023-01-08']),
'Weather': ['Rainy', 'Sunny', 'Cloudy', 'Snow', 'Windy']
weather_df = pd.DataFrame(weather_data)
Then, we want to merge the two data frames where we want the match could be an exact match but also allow the nearest match when the exact match is not available. For example, ‘2023–01–03’ could be exactly matched between two datasets because both of them have that index, however, ‘2023–01–09’ in sp500_data has…