💡 Problem Formulation: You have a DataFrame in Python that holds data in a tabular form. You wish to add a new row, but only if it does not already exist in the DataFrame to prevent duplicates. For instance, you may have data about books and want to add a book detail as a new row only if the book is not already listed. This article provides you with various methods to check for the existence of a row and add it if it’s indeed new.
Method 1: Using loc with Conditions
This method relies on the loc indexer to check in the DataFrame if the row already exists based on one or more unique criteria, such as an ‘id’ or a combination of columns. If the row doesn’t meet the conditions, it will be appended to the DataFrame.
Here’s an example:
import pandas as pd
data = {'id': [1, 2, 3], 'name': ['Book A', 'Book B', 'Book C']}
df = pd.DataFrame(data)
new_row = {'id': 4, 'name': 'Book D'}
if not df['id'].eq(new_row['id']).any():
df = df.append(new_row, ignore_index=True)Output:
id name 0 1 Book A 1 2 Book B 2 3 Book C 3 4 Book D
This snippet initializes a DataFrame with book IDs and names. The new row to be added contains ‘Book D’. It checks if the ‘id’ value of the new row exists in the DataFrame. If not, it appends the new row. The ignore_index=True argument is used to reassign the index.
Method 2: Using the merge function
The merge function is generally used for joining two DataFrames but can cleverly be repurposed to add a row if it doesn’t exist by doing a left merge with an indicator and adding the row only if the merge indicator signifies it’s a left-only row (meaning it’s not present in the original DataFrame).
Here’s an example:
import pandas as pd
df = pd.DataFrame({'id': [1, 2, 3], 'name': ['Book A', 'Book B', 'Book C']})
new_row_df = pd.DataFrame([{'id': 4, 'name': 'Book D'}])
merged_df = df.merge(new_row_df, how='left', indicator=True)
if '_merge' in merged_df and merged_df['_merge'].iloc[-1] == 'left_only':
df = pd.concat([df, new_row_df], ignore_index=True)Output:
id name 0 1 Book A 1 2 Book B 2 3 Book C 3 4 Book D
In this code, merged_df is the result of merging the original DataFrame with a DataFrame that consists of the new row only. The 'left_only' indicator means the new row doesn’t exist in the original DataFrame. When this condition is met, pd.concat is used to add the new row.
Method 3: Using Boolean Mask for Comparison
This method involves creating a boolean mask based on row values. You compare every row to the new row, and if there’s no match across the entire DataFrame, you add the new row. It’s an explicit method that showcases the concept clearly but may not be the most efficient for large DataFrames.
Here’s an example:
import pandas as pd
df = pd.DataFrame({'id': [1, 2, 3], 'name': ['Book A', 'Book B', 'Book C']})
new_row = {'id': 4, 'name': 'Book D'}
mask = (df == new_row)
if not mask.all(axis=1).any():
df.loc[df.index.max() + 1] = new_rowOutput:
id name 0 1 Book A 1 2 Book B 2 3 Book C 3 4 Book D
The code creates a mask comparing existing DataFrame rows with the new row. If the boolean mask mask.all(axis=1).any() evaluates to False (implying the row does not exist), the new row is added to the DataFrame using loc.
Method 4: Using not in with a List of Tuples
Another approach is converting the DataFrame rows and the new row into tuples, and then checking if the tuple representing the new row is not in the list of tuples from the DataFrame. This method is straightforward but might not scale well with very large DataFrames, as it involves creating a list of all row tuples in the DataFrame.
Here’s an example:
import pandas as pd
df = pd.DataFrame({'id': [1, 2, 3], 'name': ['Book A', 'Book B', 'Book C']})
new_row = {'id': 4, 'name': 'Book D'}
if tuple(new_row.values()) not in list(df.itertuples(index=False, name=None)):
df.loc[len(df)] = new_rowOutput:
id name 0 1 Book A 1 2 Book B 2 3 Book C 3 4 Book D
Here, itertuples is used to iterate over the DataFrame rows and convert them into tuples. The new row is also converted to a tuple. Then, a check is made to ascertain that the new row tuple is not already in the list of existing row tuples.
Bonus One-Liner Method 5: Using append with drop_duplicates
The most Pythonic way could be appending the row and then dropping duplicates afterward. This method’s strength is its simplicity, though it adds an overhead of checking and removing duplicates which might not be the most efficient in terms of performance for large datasets.
Here’s an example:
import pandas as pd
df = pd.DataFrame({'id': [1, 2, 3], 'name': ['Book A', 'Book B', 'Book C']})
new_row = pd.DataFrame({'id': [4], 'name': ['Book D']})
df = df.append(new_row).drop_duplicates(subset='id', keep='first').reset_index(drop=True)Output:
id name 0 1 Book A 1 2 Book B 2 3 Book C 3 4 Book D
This one-liner first appends the new row to the DataFrame and then removes any duplicates based on the ‘id’ column, keeping the first occurrence. It’s a quick method when performance is not of the essence or the DataFrame is not too large.
Summary/Discussion
- Method 1: Using
locwith Conditions. Straightforward and efficient for small to medium datasets. May be slower on very large datasets due to the need to search through the index. - Method 2: Using the
mergefunction. Robust and also great for complex merge operations. However, it may be more complicated and not as efficient as simpler methods for this specific task. - Method 3: Using Boolean Mask for Comparison. Extremely explicit and good for educational purposes. Not recommended for large DataFrames due to potential performance issues.
- Method 4: Using
not inwith a List of Tuples. Simple, but can be slow as it converts the entire DataFrame into a list of tuples. Not suitable for very large datasets. - Bonus Method 5: Using
appendwithdrop_duplicates. The simplest one-liner with a performance hit due to duplicate removal which makes it suboptimal for large datasets.