Converting and Replacing ‘%Y%m%d%H%M’ to a Datetime in a Dictionary of Dataframes

Introduction

The problem presented involves converting a specific format of timestamp, '%Y%m%d%H%M', into a datetime object within a dictionary of dataframes. This task requires handling both the conversion and replacement processes efficiently.

Background

The %Y%m%d%H%M format is commonly used to represent timestamps in milliseconds.
Pandas, a popular Python library for data manipulation and analysis, provides powerful tools for handling date and time-related operations.
We will utilize the pd.to_datetime() function for converting the timestamp into datetime objects.

The Solution

To achieve the desired outcome, we can follow these steps:

1. Convert the Timestamp to Datetime Objects

We’ll use the time.strptime() function to convert each timestamp string in col1 of all dataframes (df1 and df2) into datetime objects.

import pandas as pd
import time

# Define sample dataframes
df1 = pd.DataFrame(data= {'col1':['201706202359' , '201706220510'], "col2" : ['0', '1']})
df2 = pd.DataFrame(data= {'col1':['201707202300' , '201706230600'],"col2" : ['0', '1']})


dfs = {'df1' : df1, 'df2' : df2}

# Iterate through each dataframe and convert col1 to datetime objects
for name, df in dfs.items():
    for i in range(len(df)):
        timestamp = time.strptime(str(df[["col1"]].iloc[i][0]), '%Y%m%d%H%M')
        datetime = pd.to_datetime((str(timestamp[2])+"-"+str(timestamp[1])+"-"+str(timestamp[0])+" "+ str(timestamp[3])+":"+ str(timestamp[4])+":"+ str(timestamp[5])))
        # Store the converted datetime in a new column
        df.loc[i, 'col1_datetime'] = datetime

2. Replace Original Timestamps with Datetime Objects

After converting all timestamps to datetime objects, we can replace the original timestamp strings in col1 of each dataframe with their corresponding datetime objects.

# Iterate through each dataframe and replace col1 with its datetime version
for name, df in dfs.items():
    # Create a copy of the original dataframe to avoid modifying it directly
    df_copy = df.copy()
    
    # Replace original timestamps with their converted datetime versions
    df_copy.loc[:, 'col1'] = df_copy['col1_datetime'].dt.strftime('%Y%m%d%H%M')

3. Update Dictionary Entries

With the conversion and replacement processes complete, we’ll update our dictionary entries (dfs) to reflect the new dataframes with datetime objects in col1.

# Iterate through each dataframe again to update dfs
for name, df in dfs.items():
    # Convert col1 to datetime format
    dfs[name]['col1'] = pd.to_datetime(dfs[name]['col1'], format='%Y%m%d%H%M').dt.strftime('%Y%m%d%H%M')

Example Use Cases

This approach can be applied to any dictionary of dataframes where timestamp values are present in a specific format. Some possible scenarios include:

Handling time-based data in scientific research or engineering applications.
Integrating with systems that require datetime-specific processing, such as financial analysis or scheduling.

Code Refactoring and Best Practices

While the example code demonstrates the conversion process clearly, we can further refine it for better readability and maintainability. Here are some suggestions:

Extract a separate function to perform the conversion and replacement process.
Use more descriptive variable names throughout the code.
Consider adding error handling mechanisms for potential issues during execution.

Example Refactored Code

import pandas as pd
import time

def convert_timestamps(dataframes):
    """
    Convert '%Y%m%d%H%M' timestamp format to datetime objects and replace them in dataframes.
    
    Args:
        dataframes (dict): Dictionary containing dataframes with timestamp values.
        
    Returns:
        dict: Updated dictionary with converted datetime objects.
    """

    for name, df in dataframes.items():
        # Convert col1 to datetime objects
        df['col1_datetime'] = pd.to_datetime(df['col1'].str.replace('%Y%m%d%H%M', ''), format='%Y%m%d%H%M')
        
        # Replace original timestamps with their converted datetime versions
        df['col1'] = df['col1_datetime'].dt.strftime('%Y%m%d%H%M')

    return dataframes

# Define sample dataframes
df1 = pd.DataFrame(data= {'col1':['201706202359' , '201706220510'], "col2" : ['0', '1']})
df2 = pd.DataFrame(data= {'col1':['201707202300' , '201706230600'],"col2" : ['0', '1']})


dfs = {'df1' : df1, 'df2' : df2}

# Update dataframes with converted datetime objects
dfs = convert_timestamps(dfs)

print(dfs)

Best Practices Summary

Follow the pd.to_datetime() function to efficiently handle date and time-related operations.
Use meaningful variable names for improved code readability.
Consider adding error handling mechanisms to ensure robustness in your codebase.

Last modified on 2025-05-06