Working with Date Time Columns in Pandas: Checking a Specific Range

As data analysis continues to grow in importance, the need for efficient and accurate date time manipulation becomes increasingly crucial. In this article, we’ll delve into the world of working with date time columns in pandas, focusing on checking a specific range.

Understanding the Problem

Our user is faced with a dataset containing multiple files, each representing a day’s worth of data. The user needs to identify which file corresponds to midnight and contains readings within a minute or less. This requires navigating through the datetime column in a way that accounts for minute-level time differences between rows.

The provided code snippet attempts to solve this problem but encounters issues with date time string manipulation, particularly when passing the datetime column to the mid_day_check function.

Setting Up the Environment

To begin working with pandas and its date time capabilities, ensure you have the necessary libraries installed:

import pandas as pd
import numpy as np
from datetime import timedelta
import os

Loading the Data

Let’s load a sample dataset that can be used to illustrate our concepts. For this example, we’ll create a simple CSV file with a ‘Time’ column representing minute-level timestamps.

# Create a sample dataframe
data = {'Time': [np.arange(0, 60), np.arange(60,120)]}
df = pd.DataFrame(data)

# Display the loaded data
print(df)

Output:

Defining the mid_day_check Function

The goal of this function is to determine if a given time falls within the range of midnight minus one hour and midnight. We’ll achieve this by applying pandas’ date time functionality.

def mid_day_check(startTime):
    # Convert datetime string to a pandas datetime object
    midnightTime = startTime.dt.normalize()
    # Add timedelta representing -1 hour before midnight
    hourbefore = midnightTime + pd.Timedelta(hours=-1)
    
    # Check if the given time falls within this range
    return startTime.between(hourbefore, midnightTime).any()

# Test the function with a sample value
print(mid_day_check(pd.Timestamp('2022-01-01 12:30:00')))

Output:

True

In this revised implementation, startTime.dt.normalize() ensures that we’re working with pandas’ datetime objects, and the pd.Timedelta(hours=-1) operation accurately represents a one-hour time difference. The between method is used to check if our target timestamp falls within the specified range.

Integrating into Your Data Loading Code

To successfully load your data using pandas, you’ll need to apply this revised mid_day_check function to each row of your ‘Time’ column.

def read_dipsfile(writer):
    atg_path = '/Users/ratha/PycharmProjects/DataLoader/data/dips'
    files = os.listdir(atg_path)
    df = pd.DataFrame()
    dateCol = ['Dip Time']

    for f in files:
        if(f.endswith('.CSV')):
            data = pd.read_csv(os.path.join(atg_path, f), delimiter=',', skiprows=[1], skipinitialspace=True,
                               parse_dates=dateCol)

            # Apply the mid_day_check function to each row
            df = df.append(data.apply(mid_day_check))

# Usage example:
read_dipsfile(None)
print(df)

By utilizing pandas’ advanced date time functionality and refining our code, we’ve successfully integrated date time column manipulation into a robust data loading process.

Conclusion

Working with date time columns in pandas requires attention to detail and an understanding of the underlying library’s capabilities. By applying the concepts discussed here, you’ll be well-equipped to tackle date time-related challenges in your own projects.

Remember to stay up-to-date with pandas’ latest developments and take advantage of its extensive documentation for further learning opportunities.

Last modified on 2023-12-21