When working with data in Python, there might be scenarios where you need to insert a new row into an existing Pandas DataFrame at a specific position. For instance, you may have a DataFrame holding student grades, and you want to insert a new student’s grade at a precise index without overwriting the existing entries. This article demonstrates how to achieve this, ensuring the integrity of your data remains intact.
Method 1: Using DataFrame.loc[]
The DataFrame.loc[] method provides a label-based way to insert a row at a given position. You can use slicing to create two separate DataFrames and concatenate them with the new row in between. This method is simple and straightforward but may be less efficient with larger DataFrames due to the copying involved.
Here’s an example:
import pandas as pd
# Assume we have an existing DataFrame
df = pd.DataFrame({'A': [1, 3], 'B': [2, 4]})
# New row to be inserted
new_row = pd.Series({'A': 2, 'B': 3})
# Insert the row at index 1
df1 = df.iloc[:1]
df2 = df.iloc[1:]
df = pd.concat([df1, pd.DataFrame([new_row]), df2]).reset_index(drop=True)
print(df)Output:
A B 0 1 2 1 2 3 2 3 4
In the code snippet above, we split the original DataFrame into two parts, df1 which holds the rows before the insertion point, and df2 which holds the rows after the insertion point. We then create a new DataFrame from the new row, and use pd.concat to concatenate the three parts together, reset the index to maintain the correct indexing.
Method 2: Using DataFrame.append() and slicing
Another way to insert a row at a specific index involves using the append() method along with slicing. This method appends a row at the end and then reorders the DataFrame to place the row at the desired position. It is suitable for quick inserts but can be inefficient if you constantly reorder large DataFrames.
Here’s an example:
import pandas as pd
df = pd.DataFrame({'A': [1, 3], 'B': [2, 4]})
new_row = pd.DataFrame({'A': [2], 'B': [3]})
# Append and reorder
df = df.append(new_row, ignore_index=True)
df = pd.concat([df.iloc[:1], df.iloc[-1:], df.iloc[1:-1]]).reset_index(drop=True)
print(df)Output:
A B 0 1 2 1 2 3 2 3 4
The provided code appends the new row to the end of the DataFrame and then rearranges the DataFrame’s rows to simulate inserting the row at the specified index. This involves selecting slices of the DataFrame and concatenating them in the correct order.
Method 3: Using pd.concat() with dictionaries
This method allows you to insert a row at the desired position by creating a dictionary with the new row and the split parts of the original DataFrame, and then using pd.concat() to combine them. It is intuitive and Pythonic, avoiding the explicit handling of indices.
Here’s an example:
import pandas as pd
df = pd.DataFrame({'A': [1, 3], 'B': [2, 4]})
new_row = pd.DataFrame({'A': [2], 'B': [3]}, index=[1])
# Compile dictionary and concatenate
pieces = {0: df.iloc[:1], 1: new_row, 2: df.iloc[1:]}
df = pd.concat(pieces).reset_index(drop=True)
print(df)Output:
A B 0 1 2 1 2 3 2 3 4
In this method, we create a dictionary assigning keys to the DataFrame pieces and the new row based on their desired final positions. We use pd.concat() which recognizes dictionary keys as indices, then concatenate the pieces in order, and finally reset the index to tidy up the DataFrame.
Method 4: Reindexing and filling the new row data
Reindexing is a powerful feature in Pandas that can be used to insert rows at specific positions by expanding the existing index, and then you can fill the data for the new row. This is direct and efficient, especially with indices that are easily manipulable.
Here’s an example:
import pandas as pd
df = pd.DataFrame({'A': [1, 3], 'B': [2, 4]})
new_row = {'A': 2, 'B': 3}
index = [0, 'new', 1] # 'new' is the placeholder for the new index
# Reindex and fill
df = df.reindex(index).reset_index(drop=True)
df.loc['new'] = new_row
df = df.sort_index().reset_index(drop=True)
print(df)Output:
A B 0 1 2 1 2 3 2 3 4
We started by extending the DataFrame’s index with a placeholder for the new row. After reindexing, this creates a row with NaN values, which we then fill with the new row data. Finally, we sort the index and reset it to have a clean DataFrame.
Bonus One-Liner Method 5: Using iloc[] and list comprehension
For a fast one-liner solution, you can use iloc[] and list comprehension to create a new list of rows which includes the new row at the desired index. This method is very concise, but it’s less readable and harder to debug or extend.
Here’s an example:
import pandas as pd
df = pd.DataFrame({'A': [1, 3], 'B': [2, 4]})
new_row = [2, 3]
index = 1
# One-Liner insertion
df = pd.DataFrame([df.iloc[i] if i < index else new_row if i == index else df.iloc[i-1] for i in range(len(df)+1)])
print(df)Output:
A B 0 1 2 1 2 3 2 3 4
The code uses a list comprehension to construct a list of rows where the new row is inserted at the correct position based on the loop index, and this new list is used to create the updated DataFrame.
Summary/Discussion
- Method 1: Using
DataFrame.loc[]. Strengths: Simple, easy to understand. Weaknesses: Inefficient for larger DataFrames. - Method 2: Using
DataFrame.append()and slicing. Strengths: Straightforward. Weaknesses: Can be inefficient with frequent use or on large DataFrames due to constant reordering. - Method 3: Concatenating with dictionaries. Strengths: Pythonic, easy to read. Weaknesses: May encounter performance issues with very large DataFrames.
- Method 4: Reindexing and filling. Strengths: Effective for indices that are numeric or easily modified. Weaknesses: Requires careful handling when dealing with complex indices.
- Bonus Method 5: Using
iloc[]and list comprehension. Strengths: Compact, efficient. Weaknesses: Less readable, not suitable for complex conditions.