Insert a row to pandas dataframe

There are several ways to insert a new row into a pandas DataFrame, depending on whether you want to append at the end or place the row at a specific position. Pandas provides flexible methods, but it’s important to choose the most efficient one for performance, especially with large datasets.

 Common Methods to Add a Row

1️ Append a Row (Recommended for small additions)

Using loc when adding at the bottom:

  df.loc[len(df)] = ["Alice", 25, "USA"]

2️ Insert at a Specific Index

You can use pd.concat() to place the new row anywhere:

new_row = pd.DataFrame([["Bob", 30, "UK"]], columns=df.columns)

df = pd.concat([df.iloc[:2], new_row, df.iloc[2:]]).reset_index(drop=True)

3️ Using .append() (Deprecated in newer pandas)

Older code may still use:

  df = df.append(new_row, ignore_index=True)

But avoid this method in recent pandas versions.

 Important Notes

• Concat operations are more expensive on larger DataFrames because they create new memory copies.
  
• For frequent row additions, collect rows in a list first → convert to DataFrame later.
  
• Always ensure columns match in order and number when inserting.
  

In summary, pandas allows multiple ways to insert rows, but the best method depends on performance needs: loc for quick bottom insertions, concat for more control, and batching rows for larger data operations.