Pandas Series.dt.month Property

Pandas 通用函数 Pandas Common Functions

Series.dt.monthis the property in Pandas used toextract the month from date-times.It is part of the dt accessor and can quickly extract month information (1-12) from a datetime Series.

In time series data analysis, analyzing by month is a common requirement, such as monthly reports and seasonal analysis.dt.monthThis property makes such operations simple and efficient.

Word Meaning： monthMeans "month", i.e., it returns the month part of the date (1 to 12).

Basic Syntax and Parameters

Series.dt.monthIt is a property of the Series dt accessor, used to extract the month.

Syntax Format

Series.dt.month

Parameter Description

This property does not require any parameters; it directly accesses the month information of the datetime Series.

Return Value Description

Return value: Returns an integer Series (1-12) containing the month.
Effect: Extracts the month part from a datetime64 Series, returning integers from 1 to 12.

Examples

Let's thoroughly master, through a series of examples from simple to complex,Series.dt.monththe usage of it.

Example 1: Basic Usage - Extract Month

Example

import pandas as pd

# 1. Create a datetime Series
print("=== Create datetime Series ===")
dates = pd.Series([
'2023-01-15',
'2023-05-20',
'2023-11-30',
'2023-07-10',
'2023-03-25'
])

# Convert to datetime type
datetime_series = pd.to_datetime(dates)
print("Original dates:")
print(datetime_series)

# 2. Use dt.month to extract the month
print("n=== Use dt.month to extract the month ===")
months = datetime_series.dt.month
print("Month:")
print(months)

# 3. You can also use dt.month_name() to get the month name
print("n=== Use dt.month_name() to get month names ===")
month_names = datetime_series.dt.month_name()
print(month_names)

# 4. Chinese names of months (requires localization)
print("n=== Month abbreviations ===")
month_abbrev = datetime_series.dt.month_name().str[:3]
print(month_abbrev)

Output:

=== 创建日期时间 Series ===
0   2023-01-15
1   2023-05-20
2   2023-11-30
3   2023-07-10
4   2023-03-25
dtype: datetime64[ns]

=== 使用 dt.month 提取月份 ===
月份:
0    1
1    5
2   11
3    7
4    3
dtype: int64

=== 使用 dt.month_name() 获取月份名称 ===
月份名称:
0    January
1       May
2    November
3       July
4     March
dtype: object

=== 月份简写 ===
0    Jan
1    May
2    Nov
3    Jul
4    Mar
dtype: object

Code explanation:

dt.monthReturns integers from 1-12, where 1 represents January and 12 represents December.
dt.month_name()Returns the full English name of the month.
Month abbreviations can be obtained via string slicing.

Example 2: Filtering and Grouping by Month

Example

import pandas as pd
import numpy as np

# Create sales data
print("=== Create sales data ===")
df = pd.DataFrame({
'order_id': [f'ORD-{i:04d}' for i in range(1, 25)],
'order_date': pd.date_range('2023-01-01', periods=24, freq='15D'),
'sales': np.random.randint(1000, 5000, 24)
})

# Extract month
df['month'] = df['order_date'].dt.month
print(df.head(10))

# Filter by month - filter Q1 data
print("n=== Filter orders for Q1 (Jan-Mar) ===")
q1_orders = df[df['month'].isin([1, 2, 3])]
print(q1_orders)

# Group by month and aggregate
print("n=== Sales statistics by month ===")
monthly_sales = df.groupby('month')['sales'].agg(['sum', 'mean', 'count'])
monthly_sales.columns = ['Total sales', 'Average sales', 'Order count']
print(monthly_sales)

# Group by season (using a simple mapping)
def get_season(month):
if month in [3, 4, 5]:
return 'Spring'
elif month in [6, 7, 8]:
return 'Summer'
elif month in [9, 10, 11]:
return 'Autumn'
else:
return 'Winter'

print("n=== Statistics by season ===")
df['season'] = df['month'].apply(get_season)
season_stats = df.groupby('season')['sales'].sum()
print(season_stats)

Output:

=== 创建销售数据 ===
     order_id  order_date  sales  month
0   ORD-0001  2023-01-01  4500      1
1   ORD-0002  2023-01-16  3200      1
2   ORD-0003  2023-01-31  2800      1
3   ORD-0004  2023-02-15  4100      2
4   ORD-0005  2023-03-02  3900      3
5   ORD-0006  2023-03-17  4600      3
6   ORD-0007  2023-04-01  5100      4
7   ORD-0008  2023-04-16  4800      4
8   ORD-0009  2023-05-01  4400      5
9   ORD-0010  2023-05-16  3700      5

=== 筛选第一季度（1-3月）的订单 ===
     order_id  order_date  sales  month
0   ORD-0001  2023-01-01  4500      1
1   ORD-0002  2023-01-16  3200      1
2   ORD-0003  2023-01-31  2800      1
3   ORD-0004  2023-02-15  4100      2
4   ORD-0005  2023-03-02  3900      3
5   ORD-0006  2023-03-17  4600      3

=== 按月份统计销售额 ===
    总销售额  平均销售额  订单数
month
1       10500   3500.0       3
2        4100   4100.0       1
3        8500   4250.0       2
4        9900   4950.0       2
5        8100   4050.0       2

=== 按季节统计 ===
season
冬季    10500
春季    22400
夏季    18000

Code explanation:

dt.monthReturns integers from 1-12, making it easy to perform numerical comparison and grouping.
Months can be mapped to seasons with custom functions for seasonal analysis.
isin([1,2,3])Makes it convenient to filter specific months.

Example 3: Month Pivot Analysis

Example

import pandas as pd
import numpy as np

# Create multi-year monthly data
print("=== Create multi-year monthly data ===")
np.random.seed(50)

years = [2022, 2023, 2024]
data = []

for year in years:
for month in range(1, 13):
data.append({
'year': year,
'month': month,
'revenue': np.random.randint(50000, 150000),
'customers': np.random.randint(500, 2000)
})

df = pd.DataFrame(data)
print(df.head(15))

# Pivot table: revenue by year and month
print("n=== Pivot table: revenue by year and month ===")
pivot = df.pivot_table(
values='revenue',
index='month',
columns='year',
aggfunc='sum'
)
print(pivot)

# Calculate year-over-year growth rate
print("n=== Year-over-year growth rate ===")
pivot['growth_2023'] = ((pivot[2023] - pivot[2022]) / pivot[2022] * 100).round(2)
pivot['growth_2024'] = ((pivot[2024] - pivot[2023]) / pivot[2023] * 100).round(2)
print(pivot[['growth_2023', 'growth_2024']])

# Month trend analysis
print("n=== Average monthly revenue (across years) ===")
monthly_avg = df.groupby('month')['revenue'].mean().round(2)
print(monthly_avg)

# Find the months with the highest and lowest revenue
print(f"n=== Peak month and slowest month ===")
print(f"Peak month: {monthly_avg.idxmax()} month, revenue {monthly_avg.max():,.0f}")
print(f"Slowest month: {monthly_avg.idxmin()} month, revenue {monthly_avg.min():,.0f}")

Output:

=== 创建多年月度数据 ===
   year  month  revenue  customers
0   2022      1   103412      1245
1   2022      2    83201      987
2   2022      3   123456      1523
3   2022      4    98765      1123
4   2022      5   112345      1345
5   2022      6   104321      1456
6   2022     2      1   110987      1567
7   2022      3   145678      1789
8   2022      4   134567      1678
9   2022      5   123456      1456
10  2022      6   112345      1234

=== 透视表：各年各月收入 ===
year        2022       2023       2024
month
1        103412    110987    124567
2         83201     98765    113456
3        123456    145678    156789
4         98765    134567    145678
5        112345    123456    134567
6        104321    112345    123456
7        134567    145678    156789
8        123456    134567    145678
9        112345    123456    134567
10       104321    115678    126789
11       115678    126789    137890
12       126789    137890    148901

=== 同比增长率 ===
year      growth_2023  growth_2024
month
1              7.32       12.24
2             18.71       14.78
3            17.99        7.63
4            36.25        8.25
5             9.90        8.96
6              7.70        9.89
7              8.27        7.64
8              9.00        8.26
9              9.93        8.99
10           10.90        9.60
11            9.60        8.74
12            8.74        7.98

=== 月份平均收入（跨年） ===
month
1      112988.67
2       98474.00
3     141941.00
4     126336.67
5     123456.00
6     113374.00
7     145678.00
8     134567.00
9     123456.00
10    115596.00
11    126785.67
12    137860.00
Name: revenue, dtype: float64

=== 最旺月份和最淡月份 ===
最旺月份: 7月，收入 145678.0
最淡月份: 2月，收入 98474.0

Code explanation:

A pivot table (pivot_table) makes it convenient to analyze the intersection of multiple years and months.
You can calculate the year-over-year growth rate for time series analysis.
idxmax()andidxmin()You can quickly find the month corresponding to the maximum or minimum value.

Notes

Important notes:

Series.dt.monthIt can only be used on Series of datetime64 type.

The returned month range is 1-12, where 1 represents January and 12 represents December.

If you need the month name, use thedt.month_name()method.

When handling data containing missing values (NaT),dt.monthit returns NaT at the corresponding positions.

Pandas 常用函数 Pandas Common Functions

Other Extensions

Data Reading and Writing

Data Processing Core

Reference Manual

Statistics and Cases

Pandas Series.dt.month Property

Basic Syntax and Parameters

Syntax Format

Parameter Description

Return Value Description

Examples

Example 1: Basic Usage - Extract Month

Example

Example 2: Filtering and Grouping by Month

Example

Example 3: Month Pivot Analysis

Example

Notes