Adventures in Machine Learning

Mastering Time-Series Data Merging with Pandas Merge_asof Function

Pandas Merge_asof Function: An Essential Tool for Data Analysts

Are you struggling with merging data frames based on time stamps in your data analysis? Pandas Merge_asof function is the perfect solution for you.

This function is a powerful data manipulation tool that allows you to merge data frames on non-exact match criteria like time stamps. It delivers a fast and easy solution for merging data frames effectively.

In this article, we will explore the Pandas Merge_asof function in detail.and Syntax

The Pandas merge_asof function is a time-series-based merge function in the Pandas package. It matches two data frames based on the nearest key values without matching them precisely.

This function finds the closest match by searching the Nearest timestamps in the right data frame.

The syntax of the Pandas merge_asof function is straightforward.

It takes two data frames, the left and right data frames, as its parameters. Both data frames must have at least one column with a valid time stamp or a key column.

An example of the syntax is:

pandas.merge_asof(left, right, on=None, left_on=None, right_on=None, left_index=False, right_index=False, by=None, left_by=None, right_by=None, suffixes=(‘_x’, ‘_y’), tolerance=None, allow_exact_matches=True, direction=’backward’,)

Parameters

The Pandas merge_asof function includes several parameters that help in specifying the type of merge operation. Here are some of the significant parameters to consider:

1.

left – This parameter takes the left data frame that holds the primary data. 2.

right – This parameter takes the right data frame that holds the secondary data. 3.

on – This parameter takes a key column name used in both frames to merge on. 4.

left_on, right_on – This takes the key column name for the left data frame and right data frame separately, when both data frames’ respective key column names are different. 5.

left_index, right_index – This parameter is used to specify whether to use index for merge operation for both dataframes. 6.

by, left_by, right_by – This parameter can be used when the key column is not in both data frames, it sets the new key columns for the merge. 7.

suffixes – This parameter is used as a string or sequence to append corresponding suffixes to column names to avoid duplicate columns. 8.

tolerance – This parameter controls the acceptable distance between the left and right data frame key value. 9.

allow_exact_matches – This parameter controls whether to allow exact matches or not. 10.

direction – This parameter controls the direction in which to search for matching values, backward by default.

Examples of Pandas Merge_asof

Now that we have an idea of what Pandas Merge_asof function entails let’s consider some examples. Example 1: Suppose you want to merge two data frames based on timestamps.

Data frame on the left named left_df:

timestamp value

2018-01-01 00:00:58 ‘A’

2018-01-01 00:01:25 ‘B’

2018-01-01 00:03:09 ‘C’

Data frame on the right named right_df:

timestamp category price

2018-01-01 00:00:15 ‘cat1’ 10

2018-01-01 00:02:13 ‘cat2’ 20

2018-01-01 00:04:22 ‘cat3’ 30

Code to perform Pandas Merge_asof:

merge_df = pd.merge_asof(left_df, right_df, on=’timestamp’, direction=’nearest’, allow_exact_matches=False)

Output:

timestamp value category price

2018-01-01 00:00:58 ‘A’ ‘cat1’ 10

2018-01-01 00:01:25 ‘B’ ‘cat1’ 10

2018-01-01 00:03:09 ‘C’ ‘cat2’ 20

The newly created data frame merge_df include the closest data-points from the right data frame based on time. There are no exact matches allowed in this example because the allow_exact_matches parameter is set to False.

Example 2: Another example is merging two data frames based on timestamps, but this time with exact matches allowed. Code to perform Pandas Merge_asof:

merge_df = pd.merge_asof(left_df, right_df, on=’timestamp’, direction=’nearest’, allow_exact_matches=True)

Output:

timestamp value category price

2018-01-01 00:00:58 ‘A’ ‘cat1’ 10

2018-01-01 00:01:25 ‘B’ NAN NAN

2018-01-01 00:03:09 ‘C’ ‘cat2’ 20

In this example, the output data frame includes all points from the left data frame with the exact matches on the timestamp from the right data frame, and the NaN values represent the non-matching timestamps in the right data frame.

Basic usage of Pandas Merge_asof

To get started with the Pandas Merge_asof function, consider loading two dataframes that need to get merged based on timestamps. The timestamps in both data frames don’t have to match precisely; the function will handle the mismatch.

In this case, the first step is to import the Pandas package, which can be done using the command:

import pandas as pd

Assuming you have two data frames- df1 and df2, the parameters to set up your merge will look like this:

merged_df = pd.merge_asof(df1, df2, on=’timestamp’, tolerance=pd.Timedelta(‘2s’))

In the example above, the command uses the timestamp column to merge the data frames. The ‘tolerance’ parameter instructs the function to look for data not matching exactly by allowing a two-second window for each time stamp.

Conclusion

In conclusion, the Pandas merge_asof function provides data analysts with a powerful, fast, and straightforward time-series-based data manipulation tool. By merging data frames based on nearest timestamps, this function takes the edge off the complexities of exact matches in merging data.

The function also supports a wide range of parameters that allow analysts to specify the type of merge operation needed for their data. End-users can make full use of merge_asof function to join multiple time-series data frames for effective analysis purposes.

3) Reversing the Order of Input Values

One of the unique features of the Pandas merge_asof function is its ability to merge two data frames based on nearness in time or space. Typically, the function merges the data frames in a backwards direction, looking for the nearest key value from the right data frame for each key value in the left data frame.

However, you can reverse the order of input values to find the nearest key value from the left data frame and merge it with the nearest key value in the right data frame. To reverse the order of the input values, you can simply switch the positions of the data frames in the merge_asof() function as follows:

merge_df = pd.merge_asof(df2, df1, on=’timestamp’)

In the example above, we switch the order of the input data frames, df2 is the primary data frame, whereas df1 is the secondary data frame.

The result is a merged data frame that contains the nearest key value in the right data frame for each key value in the left data frame. Consider the following data frames as examples:

df1:

| timestamp | value |

|———————-|——–|

| 2021-01-01 00:00:00 | 1 |

| 2021-01-02 00:00:00 | 2 |

| 2021-01-03 00:00:00 | 3 |

df2:

| timestamp | price |

|———————-|——-|

| 2021-01-01 00:00:01 | 5 |

| 2021-01-02 00:00:02 | 4 |

| 2021-01-03 00:00:04 | 2 |

If we were to perform a merge_asof() with df1 as the left data frame and df2 as the right data frame, we’d get the following output:

merge_df = pd.merge_asof(df1, df2, on=’timestamp’)

| timestamp | value | price |

|———————-|——–|——–|

| 2021-01-01 00:00:00 | 1 | 5 |

| 2021-01-02 00:00:00 | 2 | 4 |

| 2021-01-03 00:00:00 | 3 | 2 |

Notice that the merge is done based on the nearest key value from the right data frame, given each key value in the left data frame.

However, if we switch the order of the data frames in the merge_asof() function as follows:

merge_df = pd.merge_asof(df2, df1, on=’timestamp’)

| timestamp | price | value |

|———————-|——-|——–|

| 2021-01-01 00:00:01 | 5 | 1 |

| 2021-01-02 00:00:02 | 4 | 2 |

| 2021-01-03 00:00:04 | 2 | 3 |

Notice the difference in output. The merge is now based on the nearest key value from the left data frame given each key value in the right data frame.

4) Setting Direction = Forward

By default, the Pandas merge_asof() function matches key values in the left data frame with the nearest key value in the right data frame, looking backward from the key value in the left data frame. However, you can change the direction of the search by setting the direction parameter to ‘forward’.

This direction searches for the nearest key value in the right data frame given each key value in the left data frame but this time looking from the front. To achieve this, you can use the following code:

merge_df = pd.merge_asof(df1, df2, on=’timestamp’, direction=’forward’)

Consider the following example of data frames:

df1:

| timestamp | value |

|———————-|——–|

| 2021-01-01 00:00:00 | 1 |

| 2021-01-02 00:00:00 | 2 |

| 2021-01-03 00:00:00 | 3 |

df2:

| timestamp | price |

|———————-|——-|

| 2021-01-01 00:00:01 | 5 |

| 2021-01-02 00:00:02 | 4 |

| 2021-01-04 00:00:04 | 2 |

If we were to perform a merge_asof() with df1 as the left data frame and df2 as the right data frame and setting the direction parameter to ‘forward’, we’d get the following output:

merge_df = pd.merge_asof(df1, df2, on=’timestamp’, direction=’forward’)

| timestamp | value | price |

|———————-|——–|——–|

| 2021-01-01 00:00:00 | 1 | 5 |

| 2021-01-02 00:00:00 | 2 | 4 |

| 2021-01-03 00:00:00 | 3 | NaN |

Notice that the merge operation is based on the nearest key value in the right data frame, but this time looking forward from the key value in the left data frame.

As there are no values for timestamp 2021-01-03 00:00:00 in the right data frame, it returns NaN.

Summary

In summary, the Pandas merge_asof() function has a lot of flexibility in how we can extend its functionality to effectively merge two data frames. Two options for expanding its capabilities are reversing the order of the input values and setting the direction parameter to ‘forward’.

These provide alternate options to control the merge result to match our specific data analysis requirements. 5) Setting Direction = Nearest

The Pandas merge_asof function allows you to specify the direction of the search when merging data frames based on nearness to key values.

One of the available options is the ‘nearest’ direction. When you set the direction parameter to ‘nearest’, the function looks for the nearest key value to the left or right from each key value in the other data frame, depending on which data frame has a closer key value.

To achieve this, you can use the following code:

merge_df = pd.merge_asof(df1, df2, on=’timestamp’, direction=’nearest’)

Consider the following example data frames:

df1:

| timestamp | value |

|———————-|——–|

| 2021-01-01 00:00:00 | 1 |

| 2021-01-02 00:00:00 | 2 |

| 2021-01-03 00:00:00 | 3 |

df2:

| timestamp | price |

|———————-|——-|

| 2021-01-01 00:00:01 | 5 |

| 2021-01-02 00:00:02 | 4 |

| 2021-01-04 00:00:04 | 2 |

If we were to perform a merge_asof() with df1 as the left data frame and df2 as the right data frame and setting the direction parameter to ‘nearest’, we’d get the following output:

merge_df = pd.merge_asof(df1, df2, on=’timestamp’, direction=’nearest’)

| timestamp | value | price |

|———————-|——–|——–|

| 2021-01-01 00:00:00 | 1 | 5 |

| 2021-01-01 00:00:00 | 1 | NaN |

| 2021-01-02 00:00:00 | 2 | 4 |

| 2021-01-03 00:00:00 | 3 | 2 |

Notice that the merge operation is based on the nearest key value from either the left or right data frame, depending on which data frame has a closer key value.

6) Using the allow_exact_matches Parameter of Pandas merge_asof

Another useful parameter in the Pandas merge_asof function is the allow_exact_matches parameter. This parameter allows you to control whether the key values must be an exact match for the merge operation, or if the function should search for the nearest key value when an exact match is not found.

To achieve this, you can use the following code:

merge_df = pd.merge_asof(df1, df2, on=’timestamp’, allow_exact_matches=False)

If we were to perform a merge_asof() with df1 as the left data frame and df2 as the right data frame and setting the allow_exact_matches to False, we’d get the following output:

merge_df = pd.merge_asof(df1, df2, on=’timestamp’, allow_exact_matches=False)

| timestamp | value | price |

|———————-|——–|——–|

| 2021-01-01 00:00:00 | 1 | NaN |

| 2021-01-01 00:00:01 | NaN | 5 |

| 2021-01-02 00:00:00 | 2 | 4 |

| 2021-01-03 00:00:00 | 3 | NaN |

Notice that the output contains the nearest key value from the right data frame when an exact match is not available. Alternatively, if we were to perform a merge_asof() with the allow_exact_matches parameter set to True, we’d get the following output:

merge_df = pd.merge_asof(df1, df2, on=’timestamp’, allow_exact_matches=True)

| timestamp | value | price |

|———————-|——–|——–|

| 2021-01-01 00:00:00 | 1 | NaN |

| 2021-01-02 00:00:00 | 2 | 4 |

| 2021-01-03 00:00:00 | 3 | NaN |

Notice that the output only contains exact matches between the key values in the left and right data frames.

Conclusion

The

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