Python Data Skills 12: Addressing Issues when combining DataFrames
Combining DataFrames Vertically, One-To-One, Multiple Merge-By Columns, One-To-Many Merges
This post will be about how to perform merges in Python and some of the issues that can go wrong. It will also be about some of the solutions to solve the problems here. It has some insights on things like why you generally shouldn’t use a specific merge type.
Table of Contents
Combining DataFrames Vertically
One-To-One
Multiple Merge-By Columns
One-To-Many
1 - Combining DataFrames Vertically
In the world of data wrangling, merging or combining DataFrames is a recurring task. Sometimes, we need to combine DataFrames side-by-side (horizontally). Other times, we need to stack them one on top of the other (vertically). When vertically combining, the primary focus is to align data along the same columns. In this section, we discuss vertically combining DataFrames in Pandas. We will discuss the process and challenges for this.
1.1 Vertical Combination
Vertically combining DataFrames means stacking them on top of one another. We are essentially appending the rows of one DataFrame to another. Imagine you have sales data for January in one DataFrame and February in another. To analyze Q1 sales as a whole, you'd need to stack February's data beneath January's.
1.2 Using shape to check the number of rows
It's prudent to check the number of rows each DataFrame has before combining them. The shape attribute is perfect for this, as it returns a tuple (rows, columns). You can ensure no data loss or duplicated during the process. Note the number of rows before and after combining.
print(df_jan.shape[0])
print(df_feb.shape[0])
1.3 Combining DataFrames Vertically with concat
Pandas provides the concat
function. This function is versatile and can handle both horizontal and vertical combinations. To vertically combine:
combined_df = pd.concat([df_jan, df_feb], axis=0)
Note: Make sure to check the combined DataFrame's shape. Ensure it matches the sum of the rows from the two original DataFrames:
assert combined_df.shape[0] == df_jan.shape[0] + df_feb.shape[0]
1.4 Combining a list of DataFrames quickly & Issues
Sometimes, you might have more than two DataFrames. In these cases, combining them one-by-one can be tedious. Instead, you can pass a list of DataFrames to the concat
function:
all_dfs = [df_jan, df_feb, df_mar, ...]
combined_df = pd.concat(all_dfs, axis=0)
Addressing Data Issues When Combining:
Combining DataFrames isn't always straightforward:
Mismatched Columns: If DataFrames have different columns, the result will have NaN values. This occurs when the data is missing. It's essential to align the columns before combining. If not, it is important to decide how to handle the disparities.
Index Overlap: The original DataFrames preserve their indices by default. This can lead to duplicate indices, which might be problematic. You can reset the index or set the
ignore_index
parameter to True.
combined_df = pd.concat(all_dfs, axis=0, ignore_index=True)
Data Type Consistency: A mix of data types can lead to unexpected results or errors. Ensure that the columns you're combining across DataFrames have the same data type.
Sorting: By default,
concat
will keep the original order of rows. To sort the combined DataFrame by a specific column, you need to do it after combining.
2 - One-to-One Merges
Merging is a cornerstone of data manipulation, enabling integrated datasets. Yet, it's not without its pitfalls. This is particularly when ensuring the integrity and consistency of combined data. Here, we'll delve into one-to-one merges. We'll also explain the distinct types of join operations. Furthermore, we'll introduce a utility function, checkmerge
. This function validates the accuracy of our merge operations.
2.1 Understanding one-to-one merges
A one-to-one merge is akin to pairing socks from two separate baskets based on their patterns. Each dataset (or basket) has unique entries (socks). We want to combine them based on a shared attribute (pattern).
Note: Always ensure the merging column has unique values in both datasets. Utilize df['column_name'].is_unique
to verify.
2.2 Join Operators
INNER JOIN: Fetches only the rows with matching keys in both dataframes. If a key exists in one dataframe but not in the other, it's excluded.
result = pd.merge(df1, df2, on='key_column', how='inner')
LEFT JOIN: This retains all rows from the left dataframe (df1). If there's no match for a particular row in the right dataframe (df2), those cells will be filled with NaN.
result = pd.merge(df1, df2, on='key_column', how='left')
RIGHT JOIN: Analogous to LEFT JOIN, but this retains all rows from the right dataframe (df2).
result = pd.merge(df1, df2, on='key_column', how='right')
OUTER JOIN (or FULL JOIN): This operation retains all rows from both dataframes. If there's no match between the dataframes for a specific key, the missing side will contain NaN.
result = pd.merge(df1, df2, on='key_column', how='outer')
Note: Naming conflicts (identically named columns in both dataframes) can confuse the merge. Use the suffixes
parameter to handle this.
2.3 Validating your merge with a custom function called checkmerge
Given the many ways to combine data, it's vital to verify the integrity of our merges. We introduce the checkmerge
function to do that:
def checkmerge(df1, df2, result):
assert len(df1) + len(df2) >= len(result), "Merged dataframe shouldn't be longer than combined input dataframes."
print(f"df1: {len(df1)} rows")
print(f"df2: {len(df2)} rows")
print(f"Resultant dataframe: {len(result)} rows")
Usage:
result = pd.merge(df1, df2, on='key_column', how='inner')
checkmerge(df1, df2, result)
This function checks if the merged dataframe's length isn't unexpectedly long. It then provides a concise report on the number of rows in each dataframe. It's a simple way to ensure the merge operation behaved as expected.
3 - Multiple Merge-By Columns
Merging on a single column is powerful, but often datasets are more complex. When a unique key spans multiple columns, you must merge on those columns. This approach, while potent, comes with its unique set of challenges. Let’s explore how to execute multi-column merges and address potential issues.
3.1 Merging on Multiple Columns
Merging on multiple columns is an extension of the regular merge operation. But instead of specifying one column, you provide a list of columns. These columns should jointly define uniqueness.
merged_df = pd.merge(df1, df2, on=['column1', 'column2', 'column3'], how='inner')
Where column1
, column2
, and column3
are the shared columns. Together they define a unique key across both dataframes.
3.2 Common Issues You’ll Encounter (with solutions)
Mismatched Datatypes: This is important. The columns you're merging on must have the same datatype in both dataframes. The merge won't identify matches correctly. One dataframe cannot has a numeric datatype and another has it as a string.
Solution: Before merging, ensure consistent datatypes:
df1['column1'] = df1['column1'].astype(str)
df2['column1'] = df2['column1'].astype(str)
Subtle Data Variations: Issues can arise from spaces, capitalization inconsistencies, or special characters.
Solution: Stripping spaces and standardizing case can often solve these problems:
df1['column2'] = df1['column2'].str.strip().str.lower()
df2['column2'] = df2['column2'].str.strip().str.lower()
Missing Combos: An 'inner' merge will exclude those rows. Outer joins will include them with NaN values. This is if one dataframe has a combination of key columns that doesn’t exist in the other.
Solution: Always inspect the merged dataframe for NaN values. Trace back to see if the original dataframes contained that combination.
missing_combos = merged_df[merged_df['relevant_column'].isnull()]
Unintended Duplicates: Sometimes, the columns you thought were unique identifiers aren't unique when combined.
Solution: Check for duplicates in your key columns before merging:
duplicate_keys = df1[df1.duplicated(subset=['column1', 'column2'], keep=False)]
The keep=False
argument ensures the display of all duplicates. This way, you can inspect and handle them.
4 - One-To-Many Merges
Merging dataframes is a staple in data processing tasks. While one-to-one merges are straightforward, one-to-many merges introduce complexities. This can lead to unintentional data duplications and bloating. Let's delve into the intricacies of one-to-many merges. We will also look at how to navigate the potential pitfalls effectively.
4.1 Understanding One-To-Many Merges
A one-to-many merge occurs during the combining of two dataframes. It occurs when one of the dataframes has multiple rows that match a single row in the other dataframe. In this scenario, the single row in the first dataframe duplicates. This aligns each row in the second dataframe.
For example, imagine a dataframe of authors and another dataframe of books. One author can write multiple books. This would lead to a one-to-many relationship between the author and the books.
Note: Before performing a one-to-many merge, thoroughly understand the nature of the data to anticipate how many duplications might occur.
4.2 One-To-One vs One-To-Many Merges
Here we will talk about a one-to-one merge. Each row from the first dataframe corresponds to one and only one row in the second dataframe. But a single row in the first dataframe corresponds to multiple rows in the second dataframe. This distinction is crucial. The latter can increase the size of the resulting dataframe. This can lead to memory issues and skewed analyses.
Note: AAlways gauge the nature of your data relationships. Do this before selecting the merge type. Being aware of the expected outcome aids in minimizing unforeseen issues.
4.3 Why you generally shouldn’t use One-To-Many Merges
One-to-many merges aren't inherently bad, but they can introduce complications:
Data Duplication: The biggest challenge is the inflation of rows. This is an expected. It can lead to oversized dataframes that are challenging to handle and store.
Memory Issues: Especially with large datasets, the duplication can consume significant memory. This may lead to performance challenges.
Skewed Analysis: Analyses performed on a dataframe. It results from a one-to-many merge. It can be misleading if the duplicated rows are not accounted for.
Note: When you expect a one-to-many merge, plan ahead. Ensure you have the computational resources to handle the merged data. Also make sure to adjust your analytical techniques to account for the nature of the merged data.
4.4 Performing One-To-Many Merges
The process of executing a one-to-many merge isn't different from a regular merge. This is in terms of syntax. But the preparation and post-merge checks are critical.
You use the same code here, as you would for a one-to-one merge:
merged_df = pd.merge(df1, df2, on='shared_column', how='inner')
Post-merge, always inspect the merged dataframe:
Check the size with
merged_df.shape
to ensure it aligns with expectations.Examine the first few rows with
merged_df.head()
to verify the merge logic.
Note: Look for unexpected data patterns. If necessary, trace them back to the source dataframes to diagnose and rectify any issues.