Resampling Within a Pandas MultiIndex
Introduction
Pandas is a powerful library for data manipulation and analysis. One of its key features is the ability to work with hierarchical data, which can be represented using MultiIndexes. In this article, we will explore how to resample within a pandas MultiIndex.
Background
A pandas MultiIndex is a data structure that allows you to store multiple levels of labels for each row or column in a DataFrame. This makes it ideal for representing hierarchical data, such as time series data with multiple variables. However, when working with MultiIndexes, we often need to perform resampling operations, which can be challenging.
Resampling within a pandas MultiIndex
In the given Stack Overflow question, we have a DataFrame df with a MultiIndex that looks like this:
State City Date
Georgia Atlanta 2012-01-01 0 10
2012-01-02 1 11
2012-01-03 2 12
2012-01-04 3 13
Savanna 2012-01-01 4 14
2012-01-02 5 15
2012-01-03 6 16
2012-01-04 7 17
Alabama Mobile 2012-01-01 8 18
2012-01-02 9 19
2012-01-03 10 20
2012-01-04 11 21
Montgomery 2012-01-01 12 22
2012-01-02 13 23
2012-01-03 14 24
2012-01-04 15 25
We want to perform resampling per city, so something like df.resample("2D", how="sum") would output:
State City Date
Georgia Atlanta 2012-01-01 1 21
2012-01-03 5 25
Savanna 2012-01-01 9 29
2012-01-03 13 33
Alabama Mobile 2012-01-01 17 37
2012-01-03 21 41
Montgomery 2012-01-01 25 45
2012-01-03 29 49
However, df.resample('2D', how='sum') gets us a TypeError: Only valid with DatetimeIndex or PeriodIndex.
Solution using pd.Grouper
The solution to this problem lies in using the pd.Grouper function, which allows you to specify a “groupby instruction for a target object”. In particular, we can use it to group by dates even if df.index is not a DatetimeIndex.
Here’s how we can do it:
df.groupby(pd.Grouper(freq='2D', level=-1))
The level=-1 tells pd.Grouper to look for the dates in the last level of the MultiIndex.
Moreover, we can use this in conjunction with other level values from the index. Here’s an example:
level_values = df.index.get_level_values
result = (df.groupby([level_values(i) for i in [0,1]] +[pd.Grouper(freq='2D', level=-1)]).sum())
This solution is not only correct but also much faster than my original suggestion using_reset_index.
Solution using stack, unstack and swaplevel
Another way to solve this problem is by using the stack, unstack and swaplevel functions.
Here’s an example:
def using_stack(df):
# http://stackoverflow.com/a/15813787/190597
return (df.unstack(level=[0,1])
.resample('2D').sum()
.stack(level=[2,1])
.swaplevel(2,0))
This solution is a bit more involved but can be useful in certain situations.
Solution using reset_index and groupby
Finally, we can also use the reset_index function to reset the index of the DataFrame before grouping by the city level:
def using_reset_index(df):
df = df.reset_index(level=[0, 1])
return df.groupby(['State','City']).resample('2D').sum()
However, this solution is much slower than the first one.
Benchmarking the solutions
To compare the performance of these solutions, we can use the timeit function:
In [30]: df = make_df(10)
In [34]: len(df)
Out[34]: 5000
In [32]: %timeit using_Grouper(df)
100 loops, best of 3: 6.03 ms per loop
In [33]: %timeit using_stack(df)
10 loops, best of 3: 22.3 ms per loop
In [31]: %timeit using_reset_index(df)
1 loop, best of 3: 659 ms per loop
Conclusion
Resampling within a pandas MultiIndex can be challenging, but there are several ways to solve this problem. In this article, we explored three different solutions using pd.Grouper, stack, unstack and swaplevel, as well as reset_index. We also benchmarked the performance of these solutions to determine which one is the fastest.
By understanding how to use these functions and techniques, you can easily resample within a pandas MultiIndex and perform complex data analysis tasks.
Last modified on 2024-05-03