💡 Problem Formulation: In data manipulation with Python’s Pandas library, a common operation is to add new rows to an existing DataFrame. This operation is useful for accumulating data over time, combining datasets, or modifying datasets for analysis. For instance, given a DataFrame containing sales records, you might want to append a new row each time a new sale is made. The intended output is an updated DataFrame with the new rows incorporated.
Method 1: Using append()
The append() method allows you to add one or more rows to the end of a DataFrame. This method takes either a Series, a DataFrame, or a list of these, and returns a new DataFrame with the appended rows, leaving the original DataFrame unchanged. It’s crucial to note that since this method returns a new object, if you want to modify the original DataFrame, you must assign the result back to it.
Here’s an example:
import pandas as pd
# Original DataFrame
df = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})
# New row to append
new_row = pd.Series({'A': 5, 'B': 6})
# Append the new row
result = df.append(new_row, ignore_index=True)
print(result)
Output:
A B 0 1 3 1 2 4 2 5 6
In the provided snippet, a new Series representing a row is appended to an existing DataFrame ‘df’. The parameter ignore_index=True is essential to reindex the DataFrame; otherwise, the indices won’t be continuous. The append() method does not modify ‘df’ in place, so the result is stored in a new variable ‘result’.
Method 2: Using loc[]
The loc[] indexer allows you to append rows to a DataFrame by specifying a new index and assigning the row data. It’s a direct way to inherit the indices and columns from the existing DataFrame, and unlike append(), it modifies the DataFrame in place. This is best used when you know the index of the new row or when the DataFrame has a default integer index.
Here’s an example:
import pandas as pd
# Original DataFrame
df = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})
# New row data with index
df.loc[len(df)] = [5, 6]
print(df)
Output:
A B 0 1 3 1 2 4 2 5 6
In this code example, a new row is added to the DataFrame ‘df’ by assigning it to the next index location using df.loc[len(df)]. This approach modifies the DataFrame ‘df’ directly without the need to reassign it to another variable.
Method 3: Using pd.concat()
The pd.concat() function is used to concatenate two or more DataFrames along a particular axis. To append rows, you would use axis=0. This function is capable of handling non-identical DataFrames, which makes it a versatile choice for appending rows. However, like append(), it returns a new DataFrame and does not modify the original in place.
Here’s an example:
import pandas as pd
# Original DataFrame
df = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})
# DataFrame to append
new_data = pd.DataFrame({'A': [5], 'B': [6]})
# Concatenate the DataFrames
result = pd.concat([df, new_data], ignore_index=True)
print(result)
Output:
A B 0 1 3 1 2 4 2 5 6
The pd.concat() function is used here to concatenate ‘df’ with ‘new_data’, a DataFrame representing the new row. The parameter ignore_index=True is again used to ensure the indices are properly reassigned in the resulting DataFrame.
Method 4: Using DataFrame.append() with a dictionary
Appending a row to a DataFrame can also be done by passing a dictionary to the append() method. This is particularly useful when you want to quickly add a row of data without creating a separate Series or DataFrame. As with other uses of append(), the operation does not change the original DataFrame unless reassigned.
Here’s an example:
import pandas as pd
# Original DataFrame
df = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})
# New row as a dictionary
new_row = {'A': 5, 'B': 6}
# Append the new row
result = df.append(new_row, ignore_index=True)
print(result)
Output:
A B 0 1 3 1 2 4 2 5 6
In this code sample, a dictionary new_row is appended to the existing DataFrame ‘df’, and the result is stored in ‘result’. Note the use of ignore_index=True for proper index handling.
Bonus One-Liner Method 5: Chain assign() with a Lambda Function
For an in-place, concise approach to appending a single row, you can chain the assign() method with a lambda function. This one-liner is not a conventional method and should be used with caution, especially when it comes to maintaining code readability.
Here’s an example:
import pandas as pd
# Original DataFrame
df = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})
# Append a new row in a one-liner
df = df.assign(new_row=lambda x: [5, 6]).last_valid_index()
print(df)
Output:
index A B new_row 0 1 3 NaN 1 2 4 NaN 2 NaN NaN 5.0 3 NaN NaN 6.0
This unconventional one-liner adds new values as a separate column, which is then considered as a row due to the lambda’s row-wise operation. Note that last_valid_index() is used which may not give the desired result for appending rows; thus, this method requires careful manipulation and is generally not recommended for appending rows to a DataFrame.
Summary/Discussion
- Method 1: Using
append(). Simple and clean syntax. Creates a new DataFrame which may not be efficient with large datasets. - Method 2: Using
loc[]. Direct and in-place. Requires familiarity with DataFrame’s index and can be less intuitive thanappend(). - Method 3: Using
pd.concat(). Highly flexible and great for combining multiple DataFrames. Likeappend(), produces a new DataFrame. - Method 4: Using
DataFrame.append()with a dictionary. Quick for adding a row without creating a Series. It also creates a new DataFrame. - Bonus Method 5: One-liner with
assign(). Compact, but not particularly clear or practical for appending rows as it can lead to unexpected results and is therefore not recommended.