5 Best Ways to Copy a Row from One DataFrame to Another in Python

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💡 Problem Formulation: Data manipulation is a frequent operation when working with tabular data in Python. Specifically, copying a row from one DataFrame to another can sometimes be necessary for data reorganization, summary, or analysis. Imagine having two DataFrames, df_source and df_target. We want to copy a row with index i from df_source to df_target, potentially adding to it or creating a new DataFrame with this row.

Method 1: Using loc[] indexer

The loc[] indexer can be used to select rows by label from the DataFrame. We then append the selected row to another DataFrame using the append() method. This approach is intuitive and mimics how you might manually extract and append data in other contexts.

Here’s an example:

import pandas as pd

# Create the source and target DataFrames
df_source = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})
df_target = pd.DataFrame(columns=['A', 'B'])

# Copy a row with index 1 from df_source to df_target
df_target = df_target.append(df_source.loc[1])

print(df_target)

Output:

     A    B
1  2.0  4.0

This code snippet creates two DataFrames df_source and df_target. df_source is populated with simple numerical data, while df_target initializes with the same columns but no data. The row with index 1 is selected from df_source using loc[] and appended to df_target. The output shows the target DataFrame with the copied row.

Method 2: Using iloc[] indexer

Similar to loc[], the iloc[] indexer selects rows by integer location instead of label, which is useful when we do not know the index label or if the DataFrame has a default integer index.

Here’s an example:

import pandas as pd

# Create the source and target DataFrames
df_source = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})
df_target = pd.DataFrame(columns=['A', 'B'])

# Copy the second row from df_source to df_target using integer index
df_target = df_target.append(df_source.iloc[1])

print(df_target)

Output:

     A    B
1  2.0  4.0

In this example, iloc[1] is used to select the second row from df_source by integer location and then appended to df_target. The resulting df_target shows the row with integer index 1 successfully copied from the source.

Method 3: Direct Assignment with loc[] or iloc[]

Direct assignment can be used to copy a specific row from the source DataFrame to the target DataFrame. First, you ensure the target DataFrame has enough rows to accommodate the new row and then assign directly to the specified index.

Here’s an example:

import pandas as pd

# Create the source DataFrame
df_source = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})
# Create the target DataFrame with the same structure but empty
df_target = pd.DataFrame(columns=['A', 'B'], index=[1])

# Directly copy the row with index 1 from df_source to df_target
df_target.loc[1] = df_source.loc[1]

print(df_target)

Output:

   A  B
1  2  4

This snippet first ensures that df_target has an index label that matches the source row to be copied. The direct assignment copies the row with index 1 from df_source into df_target. This method bypasses the use of the append method, allowing for direct placement of the row at a specific index.

Method 4: Using DataFrame.concat()

The concat() function in pandas concatenates along a particular axis. This method is particularly useful if you’re copying multiple rows at once or combining multiple DataFrames into one.

Here’s an example:

import pandas as pd

# Create the source DataFrame
df_source = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})
# Create the target DataFrame
df_target = pd.DataFrame(columns=['A', 'B'])

# Copy rows with specified index from df_source to df_target
df_target = pd.concat([df_target, df_source.loc[[1]]])

print(df_target)

Output:

   A  B
1  2  4

This code uses the concat() function to combine the empty df_target with the row indexed with 1 from df_source. The result is a new DataFrame that includes the specified row. concat() is a versatile function and can be easily expanded to concatenate more rows or even DataFrames.

Bonus One-Liner Method 5: Using DataFrame slice with append()

Combining the DataFrame slicing to isolate a row and the append() method allows for a concise one-liner row copy. This hands-on approach is straightforward when you need to quickly append a single row without the need for intermediate steps.

Here’s an example:

import pandas as pd

# Create source and target DataFrames
df_source = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})
df_target = pd.DataFrame(columns=['A', 'B'])

# Copy the last row from df_source to df_target using a one-liner
df_target = df_target.append(df_source[-1:])

print(df_target)

Output:

   A  B
1  2  4

Here, we’ve copied the last row from df_source into df_target using a concise one-liner that slices the last row and appends it to df_target. This method is effective for quickly copying rows by their integer location, especially the last row, with minimal setup.

Summary/Discussion

  • Method 1: Using loc[] indexer. Intuitive label-based selection. It can be less efficient if appending multiple rows sequentially due to the creation of a new DataFrame each time.
  • Method 2: Using iloc[] indexer. Position-based selection is useful when index labels are not known or are default integer values. Similar efficiency concerns as Method 1 when used in append loops.
  • Method 3: Direct Assignment with loc[] or iloc[]. Efficient direct placement of rows. Requires pre-existing rows in the target DataFrame or proper index alignment.
  • Method 4: Using DataFrame.concat(). Ideal for appending multiple rows or DataFrames. Efficient for larger operations but can be overkill for single row copies.
  • Method 5: One-Liner with slice and append(). Concise and handy for quick operations, especially when copying the last row or when avoiding the need to specify index labels.