Standardizing Mixed Date Format in Pandas DataFrame That Includes Strings

Introduction

In this article, we’ll discuss the challenges of dealing with mixed date formats and strings in a pandas DataFrame. We’ll explore the different approaches to standardize these dates and provide a step-by-step guide on how to do it.

Understanding Date Formats

There are several date formats that can be used in pandas DataFrames, including:

ISO 8601 (YYYY-MM-DD)
US date format (MM/DD/YYYY)
European date format (DD/MM/YYYY)
Time zone-ambiguous dates (e.g., Mon Mar 20 11:03:10 UTC 2023)

Each of these formats has its own advantages and disadvantages. Some may be more suitable for certain use cases than others.

Converting Non-NaN Floats to Integers

The first step in cleaning the date column is to convert non-NaN float values to integers. This ensures that all values in the column are either integers or strings, which makes it easier to standardize the dates later on.

from datetime import datetime

s = df['date'].copy()
mask = (s.apply(type) == float) & ~s.isna()
s.loc[mask] = s.loc[mask].astype(int)

Converting the Whole Column to Strings

Next, we convert the entire column to strings. This is necessary because pandas cannot directly work with mixed date formats and strings.

s = s.astype(str)

Using `pd.to_datetime` with Error Handling

Now that our column is entirely in string format, we can use the pd.to_datetime function to standardize the dates. We’ll pass errors='coerce' to handle any invalid or missing values.

s2 = pd.to_datetime(s, errors='coerce')

Checking for Time Zone-Aware Datetimes

After using pd.to_datetime, we need to check if there are any time zone-ambiguous datetimes in our DataFrame. If such dates exist, we’ll convert them to a common timezone (UTC) and make all datetimes tz-naive.

has_tz = (
    s2[~is_bad]
    .apply(datetime.tzname).astype(bool)
    .reindex(s.index, fill_value=False)
)

if has_tz.any():
    # Convert second time to get all datetimes to a common tz: utc,
    # then make all tz-naive
    s3 = pd.to_datetime(
        s, errors='coerce', utc=True).dt.tz_localize(None)
else:
    s3 = s2

Keeping Only Dates

Finally, we’ll keep only the dates from our standardized DataFrame and assign them to a new column.

newdf = df.assign(date=s3.dt.date)

Code Example

Here’s the complete code example that demonstrates how to standardize mixed date formats in pandas DataFrames:

import numpy as np
import pandas as pd

# Create a DataFrame with mixed date formats and strings
df = pd.DataFrame({'date':[20110912.0, 20230102, '10/10/17', '4/8/14',
                         '7/28/2020', '20121001', 2023.01.02,
                         '2019-04-23 0:00:00', '2011-12-21 0:00:00', 
                         '07/28/14', '', 'NaN' ]})

from datetime import datetime

# Step 1: Convert non-NaN floats to integers
s = df['date'].copy()
mask = (s.apply(type) == float) & ~s.isna()
s.loc[mask] = s.loc[mask].astype(int)

# Step 2: Convert the whole column to strings
s = s.astype(str)

# Step 3: Use pd.to_datetime with error handling
s2 = pd.to_datetime(s, errors='coerce')

# Step 4: Check for time zone-aware datetimes
has_tz = (
    s2[~is_bad]
    .apply(datetime.tzname).astype(bool)
    .reindex(s.index, fill_value=False)
)

if has_tz.any():
    # Convert second time to get all datetimes to a common tz: utc,
    # then make all tz-naive
    s3 = pd.to_datetime(
        s, errors='coerce', utc=True).dt.tz_localize(None)
else:
    s3 = s2

# Step 5: Keep only dates and assign them to a new column
newdf = df.assign(date=s3.dt.date)

print(newdf)

Conclusion

In this article, we discussed the challenges of dealing with mixed date formats and strings in pandas DataFrames. We explored different approaches to standardizing these dates and provided a step-by-step guide on how to do it. By following these steps, you can ensure that your dates are properly standardized, making it easier to work with them.

Note

This article assumes that you have a basic understanding of pandas and its data manipulation capabilities. If you’re new to pandas, I recommend checking out the official pandas documentation for more information on how to use the library.

Last modified on 2024-07-20