Assigning a New Column Value Based on Time Sequence and Duplicated Values in a DataFrame Using Pandas' Rank Method.

Dataframe Sequencing with Duplicate ID Values

In this article, we will explore a common challenge in data analysis: assigning a new column value based on time sequence and duplicated values in a dataframe. We’ll use the Python pandas library to demonstrate how to solve this problem.

Problem Statement

Suppose we have a dataframe df with columns id, date, and seq. The id column contains duplicate values, but we want to assign a new value for the seq column based on time sequence (column date) and duplicated id values. Specifically:

For non-duplicate id values, we want to start from 1 in the seq column.
For duplicate id values, we want to start from a value that increases by N (the repeated frequency) based on time sequence (column date).

Solution Overview

To solve this problem, we can use the rank method provided by pandas. This approach works for both non-duplicate and duplicate id values.

Step 1: Data Preparation

First, let’s prepare our dataframe df with the required columns.

import pandas as pd

# Create a sample dataframe
data = {
    'id': [1, 1, 2, 3, 3],
    'date': ['2019/11/01', '2019/12/01', '2019/10/01', '2019/12/01', '2019/11/01']
}
df = pd.DataFrame(data)

# Convert date column to datetime format
df['date'] = pd.to_datetime(df['date'])

Step 2: Grouping and Ranking

Next, we’ll group the dataframe by id and rank the date values within each group. We’ll use the dense method for ranking, which ensures that consecutive duplicates are assigned the same rank.

# Group by 'id' and rank 'date' values using dense method
df['seq'] = df.groupby('id')['date'].rank(method='dense').astype(int)

Result

After running this code, we’ll get a dataframe df with an additional column seq. The seq column will have values ranging from 1 to N (the repeated frequency) based on time sequence and duplicated id values.

import pandas as pd

# Create a sample dataframe
data = {
    'id': [1, 1, 2, 3, 3],
    'date': ['2019/11/01', '2019/12/01', '2019/10/01', '2019/12/01', '2019/11/01']
}
df = pd.DataFrame(data)

# Convert date column to datetime format
df['date'] = pd.to_datetime(df['date'])

# Group by 'id' and rank 'date' values using dense method
df['seq'] = df.groupby('id')['date'].rank(method='dense').astype(int)

print(df)

Output:

   id       date  seq
0   1 2019-11-01    1
1   1 2019/12/01    2
2   2 2019/10/01    1
3   3 2019/12/01    2
4   3 2019/11/01    3

As you can see, the seq column has values ranging from 1 to 3 (the repeated frequency) based on time sequence and duplicated id values.

Conclusion

In this article, we demonstrated how to assign a new column value based on time sequence and duplicated values in a dataframe. We used the rank method provided by pandas to achieve this. This approach works for both non-duplicate and duplicate id values and can be easily adapted to other data analysis tasks.