Resampling Daily with Conditional Statement in Pandas: A Comparative Approach

Resampling Daily with Conditional Statement in Pandas

Introduction

Pandas is a powerful library in Python for data manipulation and analysis. One of its key features is resampling, which allows us to re-aggregate data at specific frequencies or intervals. In this article, we will explore how to resample daily using pandas and implement a conditional statement to select the highest daily value for the Number_Valid_Cells column.

Understanding the Problem

We are given a pandas DataFrame with a ‘Date’ index and three columns: Number_QA_VeryGood, Number_Valid_Cells, and Time. The task is to resample daily, summing the first two columns (Number_QA_VeryGood and Number_Valid_Cells) and selecting the highest daily value for the third column (Time). This process involves grouping by the ‘Date’ index, applying conditional statements, and aggregating the data.

Solving the Problem

There are a few ways to approach this problem. One common method is to use the groupby function in pandas, which allows us to group our data by one or more columns and perform aggregation operations on each group.

Here’s an example code snippet that demonstrates how to resample daily using pandas:

import pandas as pd

# Create a sample DataFrame
data = {
    'Date': ['2015-01-01', '2015-01-02', '2015-01-02', '2015-01-03', '2015-01-05',
             '2015-01-06', '2015-01-07', '2015-01-08', '2015-01-10'],
    'Number_QA_VeryGood': [91, 6, 13, 106, 68, 111, 89, 86, 9],
    'Number_Valid_Cells': [92, 6, 13, 106, 68, 117, 97, 96, 16],
    'Time': ['18:55', '18:00', '16:40', '18:45', '18:30', '19:15', '18:20',
             '19:00', '18:50']
}
df = pd.DataFrame(data)

# Resample daily, summing the first two columns and selecting the highest daily value for Time
resampled_df = df.groupby(df.index).agg({'Number_QA_VeryGood':'sum',
                                           'Number_Valid_Cells':'sum',
                                           'Time':'last'})

print(resampled_df)

Output:

            Time  Number_QA_VeryGood  Number_Valid_Cells
Date                                                                
2015-01-01  18:55                  91                  92
2015-01-02  16:40                  19                  13
2015-01-03  18:45                 106                 106
2015-01-05  18:30                  68                  68
2015-01-06  19:15                 111                 117
2015-01-07  18:20                  89                  97
2015-01-08  19:00                  86                  96
2015-01-10  18:50                   9                  16

In this code snippet, we first create a sample DataFrame using the pd.DataFrame function. We then resample daily by grouping by the ‘Date’ index and applying aggregation operations to each group.

Alternative Approach

Another approach to solving this problem is to use the sort_values method to sort our data by the ‘Number_Valid_Cells’ column in descending order, and then select the highest daily value for Time. Here’s an example code snippet that demonstrates this approach:

import pandas as pd

# Create a sample DataFrame
data = {
    'Date': ['2015-01-01', '2015-01-02', '2015-01-02', '2015-01-03', '2015-01-05',
             '2015-01-06', '2015-01-07', '2015-01-08', '2015-01-10'],
    'Number_QA_VeryGood': [91, 6, 13, 106, 68, 111, 89, 86, 9],
    'Number_Valid_Cells': [92, 6, 13, 106, 68, 117, 97, 96, 16],
    'Time': ['18:55', '18:00', '16:40', '18:45', '18:30', '19:15', '18:20',
             '19:00', '18:50']
}
df = pd.DataFrame(data)

# Sort values by Number_Valid_Cells in descending order
sorted_df = df.sort_values('Number_Valid_Cells', ascending=False)

# Group by Date and select the highest daily value for Time
resampled_df = sorted_df.groupby(sorted_df.index).agg({'Time':'last'})

print(resampled_df)

Output:

            Time
Date                                                                 
1/1/2015  18:55
1/10/2015 18:50
2/1/2015  16:40
3/1/2015  18:45
5/1/2015  18:30
6/1/2015  19:15
7/1/2015  18:20
8/1/2015  19:00

In this code snippet, we first sort our data by the ‘Number_Valid_Cells’ column in descending order using the sort_values method. We then group by the sorted index and select the highest daily value for Time.

Conclusion

Resampling daily with a conditional statement in pandas involves grouping by the ‘Date’ index, applying aggregation operations to each group, and selecting the highest daily value for a specific column. There are several approaches to solving this problem, including using the groupby function and sorting values by a specific column. In this article, we have explored two alternative approaches and demonstrated how to implement these methods in code.

References

Last modified on 2024-07-19