Write a program in Python to filter valid dates in a given series

Filtering valid dates from a Pandas Series involves identifying date strings that follow a specific format. We'll explore two approaches: using regular expressions with loops and using Pandas filtering methods.

Sample Data

Let's start with a Series containing date strings in various formats ?

import pandas as pd

dates = ['2010-03-12', '2011-3-1', '2020-10-10', '11-2-2']
data = pd.Series(dates)
print("Original Series:")
print(data)

Original Series:
0    2010-03-12
1       2011-3-1
2    2020-10-10
3        11-2-2
dtype: object

Using Regular Expression with Loop

This method iterates through each element and checks if it matches the YYYY-MM-DD pattern ?

import pandas as pd
import re

dates = ['2010-03-12', '2011-3-1', '2020-10-10', '11-2-2']
data = pd.Series(dates)

print("Valid dates (Method 1):")
for i, j in data.items():
    if(re.match(r"\d{4}\W\d{2}\W\d{2}", j)):
        print(i, j)

Valid dates (Method 1):
0 2010-03-12
2 2020-10-10

Using Filter and isin() Methods

This approach uses Python's filter() function with lambda and Pandas isin() method for cleaner code ?

import pandas as pd
import re

dates = ['2010-03-12', '2011-3-1', '2020-10-10', '11-2-2']
data = pd.Series(dates)

# Filter valid dates using lambda and regular expression
result = pd.Series(filter(lambda x: re.match(r"\d{4}\W\d{2}\W\d{2}", x), data))

# Use isin() to get original indices
valid_dates = data[data.isin(result)]
print("Valid dates (Method 2):")
print(valid_dates)

Valid dates (Method 2):
0    2010-03-12
2    2020-10-10
dtype: object

Using Pandas str.match() Method

A more Pandas-native approach using the str.match() method ?

import pandas as pd

dates = ['2010-03-12', '2011-3-1', '2020-10-10', '11-2-2']
data = pd.Series(dates)

# Use Pandas string methods for filtering
valid_mask = data.str.match(r"\d{4}\W\d{2}\W\d{2}")
valid_dates = data[valid_mask]

print("Valid dates (Method 3):")
print(valid_dates)

Valid dates (Method 3):
0    2010-03-12
2    2020-10-10
dtype: object

Comparison

Conclusion

Use str.match() for the most efficient and Pandas-native approach to filter valid dates. The regular expression r"\d{4}\W\d{2}\W\d{2}" ensures dates follow the YYYY-MM-DD format with proper digit counts.