Python Pandas Group By Consecutive Multiple Columns

ghz 8hours ago ⋅ 4 views

I need to do a consecutive group-by involving multiple columns in a pandas dataframe. I've found answers on how to do a consecutive group-by with a single column, but I'm not sure how to extend it to multiple columns.

For exmaple, my data looks like:

    Time     Object   Status                           
    1        A       On 
    2        A       Off
    3        A       On
    4        B       On
    5        B       On

I want to count consecutive observations with the same Object and Status.

    Object    Status    Duration
1   A         On        1
2   A         Off       1
3   A         On        1                       ,
4   B         On        2

Answer

To count consecutive observations with the same Object and Status in a pandas DataFrame, you can use the shift() function along with groupby() to detect changes in consecutive rows. Here's how you can extend the solution to handle multiple columns (i.e., Object and Status).

Solution Outline:

  1. Shift the Object and Status columns to detect when a change occurs between consecutive rows.
  2. Create a group identifier that increments whenever a change happens in either the Object or Status.
  3. Group by Object, Status, and the group identifier, and count the size of each group to get the duration.

Full Code:

import pandas as pd

# Sample data
data = {
    'Time': [1, 2, 3, 4, 5],
    'Object': ['A', 'A', 'A', 'B', 'B'],
    'Status': ['On', 'Off', 'On', 'On', 'On']
}

# Create DataFrame
df = pd.DataFrame(data)

# Shift the 'Object' and 'Status' columns to identify when they change
df['Object_shift'] = df['Object'].shift()
df['Status_shift'] = df['Status'].shift()

# Create a new column for consecutive groupings, where the group changes whenever Object or Status change
df['Group'] = (df['Object'] != df['Object_shift']) | (df['Status'] != df['Status_shift'])

# Convert the boolean 'Group' column to an integer, incrementing whenever the group changes
df['Group'] = df['Group'].cumsum()

# Now group by 'Object', 'Status', and 'Group' to calculate the duration
result = df.groupby(['Object', 'Status', 'Group']).agg(Duration=('Time', 'size')).reset_index()

# Display the result
print(result[['Object', 'Status', 'Duration']])

Explanation:

  1. Shifting columns: df['Object_shift'] and df['Status_shift'] hold the previous row's values for Object and Status. This helps to identify when a change occurs.
  2. Detecting group changes: The expression (df['Object'] != df['Object_shift']) | (df['Status'] != df['Status_shift']) returns True when there’s a change in either Object or Status. The cumsum() function is used to generate a unique group ID for each consecutive sequence of the same Object and Status.
  3. Grouping and counting: We then group by Object, Status, and the generated Group column, and calculate the size of each group (Duration), which gives the count of consecutive rows with the same Object and Status.

Output:

  Object Status  Duration
0      A     On         1
1      A    Off         1
2      A     On         1
3      B     On         2

Key Points:

  • The shift() function is useful for comparing the current row with the previous one, helping to identify changes.
  • The cumsum() function helps in generating a consecutive group ID.
  • groupby() is used to count the consecutive occurrences of Object and Status.

This approach works for multiple columns and can be extended further if you need to detect changes based on more columns.

#py