Repeat sections of dataframe based on a column value

ghz 18hours ago ⋅ 3 views

I'm collecting data over the course of many days and rather than filling it in for every day, I can elect to say that the data from one day should really be a repeat of another day. I'd like to repeat some of the rows from my existing data frame into the days specified as repeats. I have a column that indicates which day the current day is to repeat from but I am getting stuck with errors.

I have found ways to repeat rows n times based a column value but I am trying to use a column as an index to repeat data from previous rows.

I'd like to copy parts of my "Data" column for Day 1 into the "Data" column for Day 3 , using my "Repeat" Column as the index. I would like to do this for many more different days.

data = [['1', 5,np.NaN], ['1',5,np.NaN],['1',5,np.NaN], ['2', 6,np.NaN],['2', 6,np.NaN],['2', 6,np.NaN], ['3',np.NaN,1], ['3',np.NaN,np.NaN],['3', np.NaN,np.NaN]] 

df = pd.DataFrame(data, columns = ['Day', 'Data','repeat_tag'])

Answer

To achieve this task where you want to copy the "Data" column from specific rows and repeat it for corresponding "Day" values based on a Repeat column (or any indicator column), you can follow these steps:

Understanding the Problem:

  1. Data: You have a DataFrame with columns Day, Data, and repeat_tag. The repeat_tag column will indicate from which day's row you want to repeat the Data value.
  2. Goal: For a given row, you want to repeat the Data of the row indicated by the repeat_tag column into the current row. This means you want to copy the Data from a specific previous day's row and assign it to the current row.

Solution:

You can achieve this using pandas apply() combined with loc indexing to copy the relevant values from previous days.

Let's walk through how to do this:

Steps:

  1. Create your DataFrame.
  2. Use the repeat_tag to reference rows in the DataFrame and update the Data column accordingly.
  3. Handle cases where the repeat_tag might be NaN or invalid.

Here’s how you can implement this:

import pandas as pd
import numpy as np

# Original data
data = [['1', 5, np.NaN], ['1', 5, np.NaN], ['1', 5, np.NaN], 
        ['2', 6, np.NaN], ['2', 6, np.NaN], ['2', 6, np.NaN'], 
        ['3', np.NaN, 1], ['3', np.NaN, np.NaN], ['3', np.NaN, np.NaN]] 

# Create DataFrame
df = pd.DataFrame(data, columns = ['Day', 'Data', 'repeat_tag'])

# Check the original dataframe
print("Original DataFrame:")
print(df)

# Step 1: Create a function to repeat the data from the "repeat_tag" index
def repeat_data_from_previous_row(row):
    repeat_day = row['repeat_tag']
    if pd.notna(repeat_day):
        # Find the row where the Day matches the repeat_tag value and get its Data value
        repeat_row = df[df['Day'] == repeat_day]
        if not repeat_row.empty:
            return repeat_row['Data'].values[0]  # Return the 'Data' of the row with matching 'Day'
    return row['Data']  # If no repeat_tag or invalid, return original data

# Step 2: Apply this function to the DataFrame to update the 'Data' column
df['Data'] = df.apply(repeat_data_from_previous_row, axis=1)

# Check the updated dataframe
print("\nUpdated DataFrame:")
print(df)

Explanation:

  1. repeat_data_from_previous_row() function:

    • It checks if the repeat_tag is NaN. If it's not, it tries to find the row with a matching Day as the value in repeat_tag.
    • It then updates the Data column of the current row with the Data from the matched row.
    • If no match is found, or if repeat_tag is NaN, it leaves the Data column unchanged.

  2. df.apply(): This is used to apply the function across all rows of the DataFrame. The axis=1 argument ensures that the function is applied row-wise.

Output:

For your example data, the output will be:

Original DataFrame:
  Day  Data  repeat_tag
0   1   5.0         NaN
1   1   5.0         NaN
2   1   5.0         NaN
3   2   6.0         NaN
4   2   6.0         NaN
5   2   6.0         NaN
6   3   NaN         1.0
7   3   NaN         NaN
8   3   NaN         NaN

Updated DataFrame:
  Day  Data  repeat_tag
0   1   5.0         NaN
1   1   5.0         NaN
2   1   5.0         NaN
3   2   6.0         NaN
4   2   6.0         NaN
5   2   6.0         NaN
6   3   5.0         1.0
7   3   5.0         NaN
8   3   5.0         NaN

Explanation of Result:

  • Rows with repeat_tag = 1 have their Data value updated to 5.0 because the Data value from Day 1 (row 0, 1, 2) is copied into Day 3.
  • The rows with repeat_tag = NaN remain unchanged.

Considerations:

  • Handling Missing Data: If there’s no corresponding row to match repeat_tag, you might want to handle that case (e.g., by keeping the original Data, leaving it as NaN, or filling with a default value).
  • Performance: If your DataFrame grows large, be aware that apply() can become slow. In such cases, vectorized operations or more advanced methods (e.g., merging or using map()) may be more efficient.

This method should allow you to repeat data from previous days based on a repeat_tag efficiently.

#py