Python NumPy: How to Generate All Dates for a Month in NumPy

Use NumPy's datetime64 with np.arange() to generate complete monthly date ranges, handling leap years and varying month lengths automatically with minimal code.

NumPy's datetime64 type combined with np.arange() provides an efficient way to generate date ranges. This approach handles month lengths and leap years automatically without external dependencies.

Basic Date Range Generation

Use np.arange() with datetime64[D] to generate all days in a month:

import numpy as np

dates = np.arange('2026-07', '2026-08', dtype='datetime64[D]')

print(f"Number of days: {len(dates)}")  # 31
print(f"First day: {dates[0]}")         # 2026-07-01
print(f"Last day: {dates[-1]}")         # 2026-07-31

Output:

Number of days: 31
First day: 2026-07-01
Last day: 2026-07-31

Understanding the Syntax

import numpy as np


dates = np.arange('2026-07', '2026-08', dtype='datetime64[D]')

print(dates[:5])

Output:

['2026-07-01' '2026-07-02' '2026-07-03' '2026-07-04' '2026-07-05']

Automatic Leap Year Handling

NumPy correctly handles February in leap and non-leap years:

import numpy as np

feb_2026 = np.arange('2026-02', '2026-03', dtype='datetime64[D]')
print(f"Feb 2026: {len(feb_2026)} days")  # 29 days

feb_2023 = np.arange('2023-02', '2023-03', dtype='datetime64[D]')
print(f"Feb 2023: {len(feb_2023)} days")  # 28 days

print(f"Last day 2026: {feb_2026[-1]}")  # 2026-02-29
print(f"Last day 2023: {feb_2023[-1]}")  # 2023-02-28

Output:

Feb 2026: 28 days
Feb 2023: 28 days
Last day 2026: 2026-02-28
Last day 2023: 2023-02-28

Different Time Resolutions

The datetime64 type supports various time units:

import numpy as np

days = np.arange('2026-07', '2026-08', dtype='datetime64[D]')
print(f"Days in July: {len(days)}")  # 31

months = np.arange('2026-01', '2025-01', dtype='datetime64[M]')
print(f"Months in 2026: {len(months)}")  # 12
print(months[:3])  # ['2026-01' '2026-02' '2026-03']

years = np.arange('2020', '2025', dtype='datetime64[Y]')
print(f"Years: {years}")  # ['2020' '2021' '2022' '2023' '2026']

hours = np.arange('2026-07-01', '2026-07-02', dtype='datetime64[h]')
print(f"Hours in a day: {len(hours)}")  # 24

Output:

Days in July: 31
Months in 2026: 0
[]
Years: ['2020' '2021' '2022' '2023' '2024']
Hours in a day: 24

Time Unit Reference

Unit	Code	Example
Year	`[Y]`	`'datetime64[Y]'`
Month	`[M]`	`'datetime64[M]'`
Week	`[W]`	`'datetime64[W]'`
Day	`[D]`	`'datetime64[D]'`
Hour	`[h]`	`'datetime64[h]'`
Minute	`[m]`	`'datetime64[m]'`
Second	`[s]`	`'datetime64[s]'`

Date Arithmetic

Perform vectorized operations on date arrays:

import numpy as np

dates = np.arange('2026-01', '2026-02', dtype='datetime64[D]')

next_week = dates + 7
print(f"Jan 1 + 7: {next_week[0]}")     # 2026-01-08

last_week = dates - 7
print(f"Jan 1 - 7: {last_week[0]}")     # 2023-12-25

diff = np.datetime64('2026-01-15') - np.datetime64('2026-01-01')
print(f"Difference: {diff}")            # 14 days

Output:

Jan 1 + 7: 2026-01-08
Jan 1 - 7: 2025-12-25
Difference: 14 days

Adding Months and Years

import numpy as np

months = np.arange('2026-01', '2026-04', dtype='datetime64[M]')

future = months + 6
print(future)  # ['2026-07' '2026-08' '2026-09']

first_days = future.astype('datetime64[D]')
print(first_days)  # ['2026-07-01' '2026-08-01' '2026-09-01']

Output:

['2026-07' '2026-08' '2026-09']
['2026-07-01' '2026-08-01' '2026-09-01']

Reusable Function

Create a function to generate dates for any month:

import numpy as np

def get_month_dates(year, month):
    """Generate all dates for a given month."""
    start = f'{year}-{month:02d}'
    
    if month == 12:
        end = f'{year + 1}-01'
    else:
        end = f'{year}-{month + 1:02d}'
    
    return np.arange(start, end, dtype='datetime64[D]')

july_2026 = get_month_dates(2026, 7)
print(f"July 2026: {len(july_2026)} days")

dec_2026 = get_month_dates(2026, 12)
print(f"December 2026: {len(dec_2026)} days")

feb_2026 = get_month_dates(2026, 2)
print(f"February 2026: {len(feb_2026)} days")  # 29 (leap year)

Output:

July 2026: 31 days
December 2026: 31 days
February 2026: 28 days

Filtering Dates

Filter date arrays based on conditions:

import numpy as np

dates = np.arange('2026-07', '2026-08', dtype='datetime64[D]')

import datetime

weekdays = np.array([
    datetime.datetime.strptime(str(d), '%Y-%m-%d').weekday() 
    for d in dates
])

business_days = dates[weekdays < 5]
print(f"Business days in July: {len(business_days)}")

weekends = dates[weekdays >= 5]
print(f"Weekend days in July: {len(weekends)}")

Output:

Business days in July: 23
Weekend days in July: 8

Using NumPy's busday Functions

import numpy as np

start = np.datetime64('2026-07-01')
end = np.datetime64('2026-08-01')

business_days = np.busday_count(start, end)
print(f"Business days in July 2026: {business_days}")  # 23

all_days = np.arange('2026-07', '2026-08', dtype='datetime64[D]')
is_business = np.is_busday(all_days)
business_only = all_days[is_business]
print(f"First 5 business days: {business_only[:5]}")

Output:

Business days in July 2026: 23
First 5 business days: ['2026-07-01' '2026-07-02' '2026-07-03' '2026-07-06' '2026-07-07']

Converting to Other Formats

import numpy as np
import datetime

dates = np.arange('2026-07', '2026-08', dtype='datetime64[D]')

py_dates = dates.astype('datetime64[D]').astype(datetime.datetime)
print(type(py_dates[0]))  # <class 'datetime.datetime'>

str_dates = dates.astype(str)
print(str_dates[:3])  # ['2026-07-01' '2026-07-02' '2026-07-03']

import pandas as pd
pd_dates = pd.to_datetime(dates)
print(type(pd_dates))  # <class 'pandas.core.indexes.datetimes.DatetimeIndex'>

Output:

<class 'datetime.date'>
['2026-07-01' '2026-07-02' '2026-07-03']
<class 'pandas.core.indexes.datetimes.DatetimeIndex'>

Generating Date Ranges Across Multiple Months

import numpy as np

q3_dates = np.arange('2026-07', '2026-10', dtype='datetime64[D]')
print(f"Q3 2026: {len(q3_dates)} days")  # 92 days

year_dates = np.arange('2026-01', '2025-01', dtype='datetime64[D]')
print(f"2026: {len(year_dates)} days")  # 366 days (leap year)

range_dates = np.arange('2026-07-15', '2026-08-15', dtype='datetime64[D]')
print(f"Jul 15 - Aug 14: {len(range_dates)} days")  # 31 days

Output:

Q3 2026: 92 days
2026: 0 days
Jul 15 - Aug 14: 31 days

Summary

Component	Example	Description
Start	`'2026-07'`	First month (inclusive)
Stop	`'2026-08'`	Next month (exclusive)
dtype	`'datetime64[D]'`	Daily resolution
Leap years	Automatic	Handled correctly
Arithmetic	`dates + 7`	Add/subtract days

Use np.arange('YYYY-MM', 'YYYY-MM+1', dtype='datetime64[D]') to generate all dates in a month with automatic handling of varying month lengths and leap years. This approach is memory-efficient and integrates seamlessly with NumPy's vectorized operations.