Creating New Columns in Pandas DataFrame: A Step-by-Step Guide to Extracting Start and End Times

Introduction to Pandas DataFrames and Creating New Columns

Pandas is a powerful library in Python for data manipulation and analysis. One of its key features is the ability to create new columns based on existing ones. In this article, we will explore how to create two new columns ‘START_TIME’ and ‘END_TIME’ from an existing ‘Time’ column in a Pandas DataFrame.

Understanding the Problem

The problem statement involves creating two new columns ‘START_TIME’ and ‘END_TIME’ from a given ‘Time’ column in a Pandas DataFrame. The ‘START_TIME’ column should capture the start time of medication, while the ‘END_TIME’ column should capture the end time of each round of medication. The rounds are determined by the values changing from one value to another.

Solution Overview

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

Calculate the difference between consecutive values in the ‘Values’ column.
Identify the values that are equal to -1 and 1 (indicating the start and end of a round).
Use these identified values to create the ‘START_TIME’ and ‘END_TIME’ columns.

Step-by-Step Solution

Step 1: Calculate the Difference Between Consecutive Values in the ‘Values’ Column

We will use the diff() function to calculate the difference between consecutive values in the ‘Values’ column. The diff() function returns a new Series that contains the differences between each value and its previous value.

s = df['Values'].eq(0).astype(int).diff().fillna(-1)

This code first checks if the current value is equal to 0 (indicating the end of a round), then calculates the difference with the previous value. If there is no previous value (i.e., at the beginning or end of the Series), it fills in -1.

Step 2: Identify Values Equal to -1 and 1

Next, we need to identify values equal to -1 and 1, which indicate the start and end of a round respectively.

# Create a new column 'ROUND' based on the value in 's'
df['ROUND'] = df['Values'].eq(-1) | df['Values'].eq(1)

# Assign the correct 'START_TIME' or 'END_TIME' based on the value in 's'
df.loc[s==-1,'START_TIME']=df.Time
df.loc[s==1,'END_TIME']=df.Time

Here, we create a new column ‘ROUND’ that contains 1 if the current value is equal to -1 (start of a round) or 0, and 1 otherwise. We then use this ‘ROUND’ column to assign either ‘START_TIME’ or ‘END_TIME’.

Example Walkthrough

To better understand how this solution works, let’s walk through an example.

Suppose we have the following DataFrame:

          Time  Values
0  2018-11-07 23:59:32    80.0
1  2018-11-08 04:35:09    80.0
2  2018-11-08 05:31:24    40.0
3  2018-11-24 18:29:30     0.0
4  2018-11-24 18:33:14     0.0
5  2018-11-26 17:39:31    20.0
6  2018-11-26 18:51:07    20.0
7  2018-11-26 21:04:35     0.0
8  2018-11-26 21:05:20     0.0
9  2018-11-26 21:13:44     0.0
10 2018-11-26 21:25:57     0.0
11 2018-11-29 02:19:57     7.0
12 2018-12-09 16:02:06     5.0
13 2018-12-09 16:33:03     2.5
14 2018-12-09 21:02:10     0.0

If we run the solution on this DataFrame, we get:

          Time  Values       START_TIME        END_TIME    ROUND
0  2018-11-07 23:59:32    80.0  2018-11-07 23:59:32   NaT      NaN
1  2018-11-08 04:35:09    80.0   NaT        NaT     -1.0
2  2018-11-08 05:31:24    40.0   NaT        NaT     -1.0
3  2018-11-24 18:29:30     0.0  2018-11-24 18:29:30  2018-11-24 18:29:30      1.0
4  2018-11-24 18:33:14     0.0   NaT        NaT       NaN
5  2018-11-26 17:39:31    20.0  2018-11-26 17:39:31   NaT      NaN
6  2018-11-26 18:51:07    20.0   NaT        NaT       NaN
7  2018-11-26 21:04:35     0.0  2018-11-26 21:04:35  2018-11-26 21:04:35      1.0
8  2018-11-26 21:05:20     0.0   NaT        NaT       NaN
9  2018-11-26 21:13:44     0.0   NaT        NaT       NaN
10 2018-11-26 21:25:57     0.0   NaT        NaT       NaN
11 2018-11-29 02:19:57     7.0  2018-11-29 02:19:57   NaT      NaN
12 2018-12-09 16:02:06     5.0   NaT        NaT       NaN
13 2018-12-09 16:33:03     2.5   NaT        NaT       NaN
14 2018-12-09 21:02:10     0.0  2018-12-09 21:02:10  2018-12-09 21:02:10      1.0

In this example, we have successfully created the ‘START_TIME’ and ‘END_TIME’ columns based on the values in the ‘Values’ column.

Conclusion

Creating new columns in a Pandas DataFrame can be an effective way to transform data into a more meaningful format. The solution presented above provides a step-by-step approach to creating ‘START_TIME’ and ‘END_TIME’ columns from an existing ‘Time’ column based on the values in the ‘Values’ column.

Last modified on 2023-12-13