Pandas is a versatile tool for data manipulation in Python, offering multiple functions to combine datasets effectively. Recently, I used the combine method from pandas.DataFrame for the first time in my many years of programming with Python. Although there are numerous online articles about concat, merge, and join, very few discuss combine. I’m curious as to why this is the case. Each function—combine, concat, merge, and join—serves unique purposes and suits different scenarios. In this blog post, we’ll explore these functions, highlight their differences, and guide you on choosing the right method for your data tasks.

Combine

The combine function is less commonly used but incredibly useful for performing element-wise custom operations on two DataFrames with the same shape. It allows for the application of a custom function to two overlapping DataFrames, providing flexibility in handling data with complex logic. This method is ideal when you need to resolve conflicts between data sources by applying specific rules to each element.

import pandas as pd
df1 = pd.DataFrame({'A': [1, np.nan, 3], 'B': [4, 5, np.nan]})
df2 = pd.DataFrame({'A': [5, 6, 7], 'B': [np.nan, 9, 10]})
result = df1.combine(df2, func=lambda s1, s2: s1.fillna(s2))

combine_first is also useful when two DataFrame objects by filling null values in one DataFrame with non-null values from other DataFrame.

For example,

df1 = pd.DataFrame({'A': [None, 0], 'B': [None, 4]})
df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3]})
df1.combine_first(df2)
     A    B
0  1.0  3.0
1  0.0  4.0

Concat

concat is straightforward: it stitches together multiple sequences of DataFrames or Series either vertically (stacking rows) or horizontally (aligning columns). This function is perfect when you need to append or combine datasets without considering keys or indexes that might need alignment.

pd.concat([df1, df2], axis=0)  # Vertical concatenation
pd.concat([df1, df2], axis=1)  # Horizontal concatenation

Merge

Similar to SQL joins, merge links two DataFrames based on one or more keys, resembling a database join operation. It is incredibly powerful for merging datasets by columns or indexes with a variety of join options available (inner, outer, left, right).

merged_df = pd.merge(df1, df2, on='key_column', how='inner')

Join

join is a convenience method for merging by indexes (default) or joining on keys if specified. It is inherently aligned with database join operations but specifically designed to leverage DataFrame indexes.

joined_df = df1.join(df2, how='left')

Conclusion

Choosing the right pandas function for combining data depends largely on the context of your data and the specific requirements of your operation. combine allows for intricate, custom logic; concat is ideal for direct concatenations; merge provides powerful SQL-like joining capabilities; and join focuses on index-based merging. By understanding these differences, you can harness the full potential of pandas to manipulate and analyze your data effectively.