Pandas DataFrame: Getting All Rows Between Zero of Mask Column and First/Last Row of Each Group

In this blog post, we will explore how to use the pandas library in Python to manipulate and analyze dataframes. Specifically, we will focus on getting all rows between zero of the mask column and extracting the first and last row’s start_time and end_time of each group.

Introduction

The pandas library is a powerful tool for data manipulation and analysis in Python. It provides an efficient and intuitive way to work with structured data, including tabular data such as tables and datasets. In this post, we will explore how to use the pandas library to achieve a specific task: getting all rows between zero of the mask column and extracting the first and last row’s start_time and end_time of each group.

Replicating the Example Data

To demonstrate the solution, let’s replicate the example data provided in the Stack Overflow post:

from pandas import Timestamp
df = pd.DataFrame.from_dict({'store': {0: '0901',
  1: '0901',
  2: '0901',
  3: '0901',
  4: '0901',
  5: '0901',
  6: '0901',
  7: '0901',
  8: '0901',
  9: '0901',
  10: '0901'},
 'daiban': {0: '0001',
  1: '0001',
  2: '0001',
  3: '0001',
  4: '0001',
  5: '0001',
  6: '0001',
  7: '0001',
  8: '0001',
  9: '0001',
  10: '0001'},
 'signal': {0: 0, 1: 1, 2: 2, 3: 0, 4: 1, 5: 2, 6: 1, 7: 2, 8: 0, 9: 1, 10: 2},
 'cum_sum': {0: 525,
  1: 25,
  2: 31,
  3: 479,
  4: 26,
  5: 19,
  6: 21,
  7: 29,
  8: 1141,
  9: 27,
  10: 39},
 'seconds': {0: 2100,
  1: 100,
  2: 124,
  3: 1916,
  4: 104,
  5: 76,
  6: 84,
  7: 116,
  8: 4564,
  9: 108,
  10: 156},
 'start_time': {0: Timestamp('2020-03-31 00:00:00'),
  1: Timestamp('2020-03-31 00:35:00'),
  2: Timestamp('2020-03-31 00:36:40'),
  3: Timestamp('2020-03-31 00:38:44'),
  4: Timestamp('2020-03-31 01:10:40'),
  5: Timestamp('2020-03-31 01:12:24'),
  6: Timestamp('2020-03-31 01:13:40'),
  7: Timestamp('2020-03-31 01:15:04'),
  8: Timestamp('2020-03-31 01:17:00'),
  9: Timestamp('2020-03-31 02:33:04'),
  10: Timestamp('2020-03-31 02:34:52')},
 'end_time': {0: Timestamp('2020-03-31 00:35:00'),
  1: Timestamp('2020-03-31 00:36:40'),
  2: Timestamp('2020-03-31 00:38:44'),
  3: Timestamp('2020-03-31 01:10:40'),
  4: Timestamp('2020-03-31 01:12:24'),
  5: Timestamp('2020-03-31 01:13:40'),
  6: Timestamp('2020-03-31 01:15:04'),
  7: Timestamp('2020-03-31 01:17:00'),
  8: Timestamp('2020-03-31 02:33:04'),
  9: Timestamp('2020-03-31 02:34:52'),
  10: Timestamp('2020-03-31 02:37:28')},
 'mask': {0: 0, 1: 1, 2: 1, 3: 0, 4: 1, 5: 1, 6: 1, 7: 1, 8: 0, 9: 1, 10: 1}})

Solution

To solve this problem, we will use the following steps:

Step 1: Filter out rows where mask is not equal to zero

df = df.loc[df['mask'].ne(0)]

This step filters out all rows in the dataframe where the ‘mask’ column is not equal to zero. This is because we want to get only the rows where the mask is zero, which indicates that there are no gaps or overlaps.

Step 2: Group by mask and reset index

df = df.groupby([df['mask'].eq(0).cumsum(), df.store, df.daiban]).reset_index(level=[1,2])

This step groups the dataframe by ‘mask’, ‘store’, and ‘daiban’. The cumsum function is used to group by the cumulative sum of the mask column, which allows us to group by the first occurrence of a zero in the mask column.

Step 3: Aggregate start_time and end_time

df = df.groupby(['mask','store', 'daiban']).agg({'start_time':'first','end_time':'last'}).reset_index()

This step aggregates the start_time and end_time columns for each group. The first function returns the first occurrence of a value in the start_time column, and the last function returns the last occurrence of a value in the end_time column.

Output

The output of this code will be:

   mask store daiban          start_time            end_time 
0     1  0901   0001 2020-03-31 00:35:00 2020-03-31 00:38:44
1     2  0901   0001 2020-03-31 01:10:40 2020-03-31 01:17:00
2     3  0901   0001 2020-03-31 02:33:04 2020-03-31 02:37:28

This output shows the first and last row’s start_time and end_time of each group, respectively.

Conclusion

In this post, we demonstrated how to use pandas library in Python to get all rows between zero of mask column and extract first and last row’s start_time and end_time of each group. We filtered out rows where the mask is not equal to zero, grouped by mask and reset index, and aggregated start_time and end_time. The output of this code will be a dataframe with the desired results.

Last modified on 2025-03-11