💡 Problem Formulation: When working with data in Python, it’s common to use Pandas DataFrames for data manipulation and analysis. A frequent operation involves adding a new row of data from a list to an existing DataFrame. This article will walk you through several methods to append a list as a new row in a DataFrame. For example, given a DataFrame with named columns, we want to append a list that represents a new data entry that fits within the schema of the existing DataFrame.
Method 1: Using DataFrame.append() Method
Appending a new row to a DataFrame can be done using the DataFrame.append() method. This method takes a dictionary or a Series representing the new row and returns a new DataFrame with the item appended. The original DataFrame remains unchanged because this method does not modify the DataFrame in place.
Here’s an example:
import pandas as pd
# Existing DataFrame
df = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})
# List to be added as a new row
new_row = [5, 6]
# Append as a Series, convert the list to a Series, use the DataFrame columns
new_df = df.append(pd.Series(new_row, index=df.columns), ignore_index=True)
print(new_df)Output:
A B 0 1 3 1 2 4 2 5 6
This code snippet creates a new DataFrame new_df with the list new_row appended as the last row. By converting the list to a pandas Series and specifying the DataFrame’s columns, the values are matched correctly, and ignore_index=True is used to reindex the new DataFrame.
Method 2: Using DataFrame.loc[] for Indexing
Another way to append a list as a row is to use the DataFrame.loc[] indexer to add a new row at a specified index. This method updates the DataFrame in place, which may be desirable for memory efficiency when dealing with large DataFrames.
Here’s an example:
import pandas as pd
# Existing DataFrame
df = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})
# List to be added as a new row
new_row = [5, 6]
# Append using loc
df.loc[len(df)] = new_row
print(df)Output:
A B 0 1 3 1 2 4 2 5 6
This snippet directly appends a new row to the DataFrame df by assigning the list new_row to the next row index of the DataFrame, modifying it in place. The length of the DataFrame is used to determine the new row’s index.
Method 3: Using DataFrame.concat() for Concatenation
The pd.concat() function is useful when you need to append multiple rows at once or combine several DataFrames. It provides more flexibility than the append() method and can append a list as a new row by first converting it to a DataFrame.
Here’s an example:
import pandas as pd
# Existing DataFrame
df = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})
# List to be added as a new row
new_row = [[5, 6]]
# Convert list to DataFrame and concatenate
new_df = pd.concat([df, pd.DataFrame(new_row, columns=df.columns)], ignore_index=True)
print(new_df)Output:
A B 0 1 3 1 2 4 2 5 6
In this method, we first convert new_row into a DataFrame, ensuring it has the same columns as df. Then we concatenate the new row to the original DataFrame, creating a new DataFrame new_df that includes the appended row.
Method 4: Using DataFrame.append() with a Dictionary
If you don’t want to deal with Series or additional DataFrames, you can simply append the list as a dictionary where the keys match the DataFrame’s columns. This method is straightforward and directly utilizes the existing append() functionality.
Here’s an example:
import pandas as pd
# Existing DataFrame
df = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})
# List to be added as a new row
new_row = [5, 6]
# Convert list to dictionary and append
new_df = df.append(dict(zip(df.columns, new_row)), ignore_index=True)
print(new_df)Output:
A B 0 1 3 1 2 4 2 5 6
This code converts the list new_row into a dictionary with column names as keys, then appends it to df. By using ignore_index=True, the indices are adjusted, and a new DataFrame new_df is returned.
Bonus One-Liner Method 5: Appending Inline with List Expansion
For a concise, one-liner solution, you can directly append the list as a row to the DataFrame by using the loc[] indexer with a one-liner that expands the list into the row.
Here’s an example:
import pandas as pd
# Existing DataFrame
df = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})
# List to be added as a new row
new_row = [5, 6]
# One-liner appending the list
df.loc[len(df)] = new_row
print(df)Output:
A B 0 1 3 1 2 4 2 5 6
This one-liner effectively appends the list new_row directly to df without the need for additional variables or conversions.
Summary/Discussion
- Method 1: DataFrame.append() Method. Simple to use but creates a new DataFrame. Might not be memory-efficient for very large DataFrames.
- Method 2: DataFrame.loc[] for Indexing. Updates the DataFrame in place. It’s more memory-efficient, though the in-place modification may not be desired in all cases.
- Method 3: DataFrame.concat() for Concatenation. Offers great flexibility and is useful for multiple row additions at once. However, it can be more verbose than necessary for single-row cases.
- Method 4: DataFrame.append() with a Dictionary. Straightforward and neatly uses the DataFrame’s column names. Still returns a new DataFrame, which may not be ideal in some situations.
- Method 5: Bonus One-Liner. Quick and handy for script writing. The simplicity of the syntax comes at the cost of readability for those new to Pandas.