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How to extract the levels in a column variable into separate new columns;

In this technical blog post, we'll delve into the intricacies of extracting levels within a column variable and transforming them into separate, distinct columns. To achieve this, we'll utilize sequential counters to uniquely identify rows based on specific criteria. Let's dive right in!

The code snippet below demonstrates the steps involved in this data manipulation process:

df['ix'] = df.groupby(['Day', 'Diet']).cumcount()
result = (
    df
    .pivot(index=['ix', 'Diet'], columns='Day', values='weight')
    .add_prefix('Day')
    .reset_index(level=1)
)

Let's break down each step of the code:

  1. Creating a Sequential Counter:
  2. The code begins by creating a new column named 'ix' using the cumcount() function. This function groups the data frame by the 'Day' and 'Diet' columns and assigns a sequential counter to each row within each group. This counter uniquely identifies each row based on its day and diet.
  3. Reshaping the Data Frame:
  4. The pivot() function is then employed to reshape the data frame. It rearranges the data so that the 'ix' and 'Diet' columns become the index, while the 'Day' column becomes the column headers. The 'weight' column values are placed in the cells accordingly.
  5. Adding a Prefix to Column Names:
  6. The add_prefix() function is used to add the prefix 'Day' to the names of all the columns that were created during the pivoting operation. This helps in distinguishing the columns that correspond to different days.
  7. Resetting the Level 1 Index:
  8. Finally, the reset_index() function is applied with the level=1 argument. This removes the first level of the index (which corresponds to the 'ix' column) and elevates the second level (which corresponds to the 'Diet' column) to become the new index.
The resulting data frame, stored in the 'result' variable, now has the 'Diet' column as the index and the 'Day' columns as separate columns. The weight values for each day and diet are neatly organized within these columns.
Day Diet  Day1  Day2
ix                  
0      A    52    26
0      B    25    15
1      A    50    10
1      B    30    34

By following this step-by-step process, we've successfully extracted the levels from the 'Day' column and converted them into separate columns. This technique can be particularly useful when working with multi-level data or when you need to manipulate data in a specific format for further analysis or visualization.

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