How to Create a New Pandas Row at Every “\n” Instance

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What Will You Learn?

Discover how to enhance your pandas dataframe manipulation skills by dynamically adding new rows based on specific conditions.

Introduction to the Problem and Solution

Imagine having a pandas dataframe with text data where each cell contains multiple lines of information separated by “\n”. The challenge is to create a new row for every line within these cells.

To tackle this issue, we need to iterate through the dataframe, split the cell content using the “\n” delimiter, and then generate new rows for each element resulting from the split operation.

Code

import pandas as pd

# Sample DataFrame with multi-line text in one column
data = {'text': ["Hello\nWorld", "Python\nProgramming", "Data\nScience"]}
df = pd.DataFrame(data)

# Function to create new rows at every '\n' instance
def expand_rows(row):
    lines = row['text'].split('\n')
    return pd.Series(lines)

# Apply function and concatenate results back into a single DataFrame
new_df = df.apply(expand_rows, axis=1).stack().reset_index(level=1, drop=True).to_frame('text').reset_index(drop=True)

# Copyright PHD

Note: Ensure you have imported the pandas library before executing this code.

Explanation

In this solution: – We initialize a sample dataframe containing multi-line text in one of its columns. – We define a function expand_rows that splits the text at each “\n” instance and returns it as a separate series. – This function is then applied along axis=1 to process each row independently. – Finally, we stack these results vertically (creating multiple columns) and reset index levels to obtain our desired output.

  1. How can I install the pandas library?

  2. You can install pandas using pip: pip install pandas.

  3. Can I apply similar logic for data stored in CSV files?

  4. Yes, you can read your data into a pandas dataframe from CSV file and then apply similar operations.

  5. What if my text has different delimiters than ‘\n’?

  6. You can adjust the splitting logic inside the function according to your specific delimiter.

  7. Is there an alternative method without using functions?

  8. While functions offer modularity, achieving similar results without defining separate functions is possible but may lead to less readable code.

  9. Can I preserve other columns while expanding rows?

  10. Yes, ensure those columns are duplicated or handled correctly during row expansion so they align properly with expanded rows.

Conclusion

Expanding rows based on specific criteria like encountering “\n” instances provides us with more detailed control over our dataset’s organization. Breaking down multi-line entries into individual records within a DataFrame enhances analytical capabilities when dealing with intricate textual information. Dive deeper into Pandas possibilities at PythonHelpDesk.com.

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