Merging and Updating DataFrames

Introduction

In this article, we’ll explore how to merge two dataframes from different tables and update values in one of them based on a common column.

When working with pandas DataFrames, it’s not uncommon to have multiple tables containing related data. In such cases, you may need to perform operations like searching for specific records across both tables and updating the values in one table based on matching criteria.

Understanding Pandas Merging

Pandas provides an efficient way to merge dataframes using various merge types, including inner joins, left joins, right joins, and outer joins. The choice of merge type depends on the desired outcome, as each type has its own strengths and weaknesses.

Inner Join

An inner join is used when you want to keep only the records that have matches in both tables. This means that if there’s no match, the record will be dropped from the result.

Syntax

df1 = pd.DataFrame({'fruit': ['apple', 'banana', 'orange'],
                    'attribute': ['red', 'yellow', 'orange']})

df2 = pd.DataFrame({'fruit': ['apple', 'peach', 'banana'],
                    'size': ['large', 'small', 'medium'],
                    'abbrev.': ['app.', 'pea.', 'ban.']})

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

Left Join

A left join is used when you want to keep all the records from the first table (left) and matching records from the second table (right). If there’s no match, the result will contain null values.

Syntax

df1 = pd.DataFrame({'fruit': ['apple', 'banana', 'orange'],
                    'attribute': ['red', 'yellow', 'orange']})

df2 = pd.DataFrame({'fruit': ['apple', 'peach', 'banana'],
                    'size': ['large', 'small', 'medium'],
                    'abbrev.': ['app.', 'pea.', 'ban.']})

merged_df = pd.merge(df1, df2, on='fruit', how='left')

Replacing Values in a DataFrame

Once you’ve merged the dataframes, you can update values in one of them using various pandas operations.

Using loc and isin

table_a = pd.DataFrame({'fruit': ['apple', 'banana', 'orange'],
                        'attribute': ['red', 'yellow', 'orange']})

table_b = pd.DataFrame({'fruit': ['apple', 'peach', 'banana'],
                        'size': ['large', 'small', 'medium'],
                        'abbrev.': ['app.', 'pea.', 'ban.']})

# Find the index of rows where table_a.fruit is in table_b.fruit
index_to_update = table_a.loc[table_a['fruit'].isin(table_b['fruit']), 'attribute']

# Update the attribute column in table_a with values from table_b
table_a["attribute"] = table_a.loc[index_to_update, 'abbrev.']

However, this approach is not recommended because it uses loc to select rows based on a condition and then updates the value using another index selection.

Recommended Approach: Using merge and Assignment

table_a = pd.DataFrame({'fruit': ['apple', 'banana', 'orange'],
                        'attribute': ['red', 'yellow', 'orange']})

table_b = pd.DataFrame({'fruit': ['apple', 'peach', 'banana'],
                        'size': ['large', 'small', 'medium'],
                        'abbrev.': ['app.', 'pea.', 'ban.']})

# Merge the dataframes on the 'fruit' column and assign the values from table_b
table_a['attribute'] = pd.merge(table_a, table_b, on='fruit')['abbrev.'].values

print(table_a)

This approach is more efficient because it uses the merge operation to join both tables based on the common column and assigns the resulting columns directly.

Handling Missing Values

When working with dataframes that may contain missing values, you should be aware of how these operations handle them. In this case, since we’re using an inner join, any rows without a match in table_b will be dropped from the result.

If you want to include all rows from table_a even if there’s no match in table_b, you can use a left join instead.

# Using a left join
merged_df = pd.merge(table_a, table_b, on='fruit', how='left')

Conclusion

In this article, we explored the process of merging two dataframes and updating values based on a common column. We discussed various merge types (inner joins, left joins) and provided examples to illustrate each approach.

When working with pandas DataFrames, it’s essential to understand the different operations available, including merges, filtering, grouping, sorting, and more.

We also covered some potential pitfalls to avoid when working with missing values in your dataframes.

Last modified on 2023-06-08