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Python Pandas: How to Check if Date is during Weekend or Weekday

Determining whether a given date falls on a weekend (Saturday or Sunday) or a weekday (Monday through Friday) is a common requirement in data analysis, especially when dealing with business days, scheduling, or time series data. Pandas provides convenient attributes and methods through its dt accessor for datetime Series to easily make this distinction.

This guide explains how to check if dates in a Pandas DataFrame column (or a single Timestamp) are weekends or weekdays using Series.dt.weekday and Series.dt.day_name().

The Goal: Identifying Weekends vs. Weekdays​

Given a date or a column of dates, we want to determine for each date if it's a Saturday/Sunday (weekend) or a Monday/Tuesday/Wednesday/Thursday/Friday (weekday).

Prerequisite: Ensuring Date Column is Datetime Type​

For Pandas datetime-specific operations, the column containing dates must be of datetime64[ns] type. If your dates are currently strings, convert them first.

import pandas as pd

df_sample = pd.DataFrame({'DateStr': ['2023-10-27', '2023-10-28', '2023-10-29']}) # Fri, Sat, Sun
print(f"Original Dtype: {df_sample['DateStr'].dtype}\n")

# ✅ Convert to datetime
df_sample['DateDT'] = pd.to_datetime(df_sample['DateStr'])
print(f"Converted Dtype: {df_sample['DateDT'].dtype}") # datetime64[ns]

Output:

Original Dtype: object

Converted Dtype: datetime64[ns]

Example DataFrame and Single Timestamp​

import pandas as pd

# DataFrame Example
data = {
    'Event': ['Meeting', 'Workshop', 'Holiday', 'Conference', 'Review'],
    'EventDate': [
        '2023-10-27', # Friday
        '2023-10-28', # Saturday
        '2023-10-29', # Sunday
        '2023-10-30', # Monday
        '2023-11-03'  # Friday
    ]
}
df = pd.DataFrame(data)
df['EventDate'] = pd.to_datetime(df['EventDate']) # Ensure datetime type
print("Original DataFrame:")
print(df)
print()

print("Data types:")
print(df.dtypes)

# Single Timestamp Example
single_date_weekend = pd.Timestamp('2023-10-28') # Saturday
single_date_weekday = pd.Timestamp('2023-10-27') # Friday

Output:

Original DataFrame:
        Event  EventDate
0     Meeting 2023-10-27
1    Workshop 2023-10-28
2     Holiday 2023-10-29
3  Conference 2023-10-30
4      Review 2023-11-03

Data types:
Event                object
EventDate    datetime64[ns]
dtype: object

Method 1: Using Series.dt.weekday (Numeric Day of Week)​

The Series.dt.weekday attribute (or Timestamp.weekday() method for single objects) returns the day of the week as an integer, where:

  • Monday = 0
  • Tuesday = 1
  • Wednesday = 2
  • Thursday = 3
  • Friday = 4
  • Saturday = 5
  • Sunday = 6

Therefore, a date is a weekend if its .dt.weekday is greater than 4 (i.e., 5 or 6).

Checking a DataFrame Column​

import pandas as pd

df_example = pd.DataFrame({
    'EventDate': pd.to_datetime(['2023-10-27', '2023-10-28', '2023-10-29', '2023-10-30'])
})

# ✅ Create a boolean column 'IsWeekend'
# df_example['EventDate'].dt.weekday returns a Series of integers (0-6)
df_example['IsWeekend_dt_weekday'] = df_example['EventDate'].dt.weekday > 4

print("DataFrame with 'IsWeekend' column (using .dt.weekday):")
print(df_example)

Output:

DataFrame with 'IsWeekend' column (using .dt.weekday):
   EventDate  IsWeekend_dt_weekday
0 2023-10-27                 False
1 2023-10-28                  True
2 2023-10-29                  True
3 2023-10-30                 False

Checking a Single pd.Timestamp Object​

For a single Timestamp object, use the .weekday() method (note the parentheses).

import pandas as pd

date_saturday = pd.Timestamp('2023-10-28') # Saturday
date_friday = pd.Timestamp('2023-10-27')   # Friday

is_saturday_weekend = date_saturday.weekday() > 4
print(f"Is {date_saturday.date()} a weekend? {is_saturday_weekend}")

is_friday_weekend = date_friday.weekday() > 4
print(f"Is {date_friday.date()} a weekend? {is_friday_weekend}")

Output:

Is 2023-10-28 a weekend? True
Is 2023-10-27 a weekend? False

Method 2: Using Series.dt.day_name() and isin()​

The Series.dt.day_name() method returns the name of the day of the week (e.g., "Monday", "Saturday"). You can then check if this name is in a list of weekend day names.

Checking a DataFrame Column​

This method is often more readable as it deals with explicit day names.

import pandas as pd

df_example = pd.DataFrame({
    'EventDate': pd.to_datetime(['2023-10-27', '2023-10-28', '2023-10-29', '2023-10-30'])
})

# Get the names of the days
day_names_series = df_example['EventDate'].dt.day_name()
print("Day names:")
print(day_names_series)
print()

# ✅ Check if the day name is 'Saturday' or 'Sunday'
weekend_days = ['Saturday', 'Sunday']
df_example['IsWeekend_day_name'] = day_names_series.isin(weekend_days)

print("DataFrame with 'IsWeekend' column (using .dt.day_name()):")
print(df_example)

Output:

Day names:
0      Friday
1    Saturday
2      Sunday
3      Monday
Name: EventDate, dtype: object

DataFrame with 'IsWeekend' column (using .dt.day_name()):
   EventDate  IsWeekend_day_name
0 2023-10-27               False
1 2023-10-28                True
2 2023-10-29                True
3 2023-10-30               False

Adding a Column for Day of Week Name or Number (Optional)​

It can be useful to add a column for the day of the week number or name for other analyses or easier filtering later.

import pandas as pd

df = pd.DataFrame({
    'EventDate': pd.to_datetime(['2023-10-27', '2023-10-28', '2023-10-29', '2023-10-30'])
})

# Add day of week number (0=Mon, 6=Sun)
df['DayOfWeek_Num'] = df['EventDate'].dt.weekday

# Add day of week name
df['DayOfWeek_Name'] = df['EventDate'].dt.day_name()

# Add IsWeekend flag
df['IsWeekend'] = df['DayOfWeek_Num'] > 4 # Or use the .dt.day_name().isin(...) method

print("DataFrame with additional day of week columns:")
print(df)

Output:

DataFrame with additional day of week columns:
   EventDate  DayOfWeek_Num DayOfWeek_Name  IsWeekend
0 2023-10-27              4         Friday      False
1 2023-10-28              5       Saturday       True
2 2023-10-29              6         Sunday       True
3 2023-10-30              0         Monday      False

Conclusion​

Pandas makes it easy to determine if a date falls on a weekend or a weekday:

  1. Ensure your date column is of datetime64[ns] type (use pd.to_datetime() if necessary).
  2. Using Series.dt.weekday:
    • your_datetime_series.dt.weekday > 4 returns a boolean Series (True for weekends).
    • For a single Timestamp object: timestamp_obj.weekday() > 4.
  3. Using Series.dt.day_name():
    • your_datetime_series.dt.day_name().isin(['Saturday', 'Sunday']) returns a boolean Series.

The .dt.weekday method is generally more concise for direct boolean checking, while .dt.day_name() can be more readable if you prefer working with day names. Both are effective for categorizing your dates.

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