Pandas Series.dt.weekday Attribute

Pandas 通用函数 Pandas Common Functions

Series.dt.weekdayis used in Pandas toextract the day of the week corresponding to a date.It is part of the dt accessor and returns an integer representing the day of the week (0=Monday, 6=Sunday).

In time series data analysis, the day of the week is an important dimension, for example, analyzing differences between weekends and weekdays, weekly sales patterns, etc.dt.weekdayThis attribute makes such analysis simple and efficient.

Word Meaning： weekdayIt means "weekday" or "day of the week", and returns an integer value from 0 to 6.

Basic Syntax and Parameters

Series.dt.weekdayIt is an attribute of the dt accessor of a Series, used to extract the day of the week.

Syntax Format

Series.dt.weekday

Parameter Description

This attribute does not require any parameters; it directly accesses the weekday information of a datetime Series.

Return Value Description

Return Value: Returns an integer Series (0-6) containing the day of the week.
Effect: Returns an integer from 0 to 6, where 0 represents Monday, 1 represents Tuesday, ..., 6 represents Sunday.

Examples

Let's, through a series of examples from simple to complex, thoroughly masterSeries.dt.weekdaythe usage.

Example 1: Basic Usage - Extracting Day of the Week

Example

import pandas as pd

# 1. Create a datetime Series
print("=== Create datetime Series ===")
dates = pd.Series([
'2023-01-02', # Monday
'2023-01-03', # Tuesday
'2023-01-04', # Wednesday
'2023-01-05', # Thursday
'2023-01-06', # Friday
'2023-01-07', # Saturday
'2023-01-08', # Sunday
])

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

# 2. Use dt.weekday to extract the day of the week
print("n=== Use dt.weekday to extract day of the week ===")
weekdays = datetime_series.dt.weekday
print("Day of week (0=Monday, 6=Sunday):")
print(weekdays)

# 3. Use dt.day_name() to get the name of the day of the week
print("n=== Use dt.day_name() to get weekday names ===")
weekday_names = datetime_series.dt.day_name()
print(weekday_names)

# 4. Use dt.day_name(locale='zh_CN') to get Chinese weekday names (if available)
# Note: Chinese localization may require corresponding configuration
weekday_abbrev = datetime_series.dt.day_name().str[:3]
print("n=== Weekday abbreviations ===")
print(weekday_abbrev)

# 5. Create a more intuitive comparison table
print("n=== Date and weekday comparison table ===")
result = pd.DataFrame({
'Date': datetime_series.dt.date,
'weekday value': weekdays,
'Weekday name': weekday_names
})
print(result)

Output:

=== 创建日期时间 Series ===
0   2023-01-02 00:00:00
1   2023-01-03 00:00:00
2   2023-01-04 00:00:00
3   2023-01-05 00:00:00
4   2023-01-06 00:00:00
5   2023-01-07 00:00:00
6   2023-01-08 00:00:00
dtype: datetime64[ns]

=== 使用 dt.weekday 提取星期几 ===
星期（0=周一，6=周日）：
0    0
1    1
2    2
3    3
4    4
5    5
6    6
dtype: int64

=== 使用 dt.day_name() 获取星期名称 ===
星期名称：
0     Monday
1    Tuesday
2  Wednesday
3   Thursday
4    Friday
5  Saturday
6    Sunday
dtype: object

=== 星期简写 ===
0    Mon
1    Tue
2    Wed
3    Thu
4    Fri
5    Sat
6    Sun
dtype: object

=== 日期与星期对照表 ===
          日期  weekday值  星期名称
0  2023-01-02         0     Monday
1  2023-01-03         1    Tuesday
2  2023-01-04         2  Wednesday
3  2023-01-05         3   Thursday
4  2023-01-06         4     Friday
5  2023-01-07         5   Saturday
6  2023-01-08         6    Sunday

Code explanation:

dt.weekdayReturns an integer from 0 to 6, where 0 represents Monday and 6 represents Sunday.
dt.day_name()Returns the full English name of the day of the week.
You can get abbreviations using string slicing (e.g., 'Mon', 'Tue').

Example 2: Distinguishing Weekdays and Weekends

Example

import pandas as pd
import numpy as np

# Create transaction data
print("=== Create transaction data ===")
np.random.seed(100)

# Generate 35 days of data (including multiple weekends)
dates = pd.date_range('2023-03-01', periods=35, freq='D')
df = pd.DataFrame({
'date': dates,
'sales': np.random.randint(1000, 5000, 35)
})

# Extract day of the week
df['weekday'] = df['date'].dt.weekday
df['day_name'] = df['date'].dt.day_name()

# Mark weekdays and weekends
df['is_weekend'] = df['weekday'].isin([5, 6])
df['day_type'] = df['is_weekend'].map({True: 'Weekend', False: 'Weekday'})

print(df.head(15))

# Group statistics by weekday/weekend
print("n=== Weekday vs Weekend Sales Comparison ===")
day_type_stats = df.groupby('day_type')['sales'].agg(['sum', 'mean', 'count'])
day_type_stats.columns = ['Total Sales', 'Average Sales', 'Days']
print(day_type_stats)

# Detailed statistics for each day
print("n=== Sales statistics by day of week ===")
weekday_stats = df.groupby('weekday')['sales'].agg(['sum', 'mean'])
weekday_stats.index = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']
weekday_stats.columns = ['Total Sales', 'Average Sales']
print(weekday_stats.round(2))

# Filter data for all weekends
print("n=== Weekend data ===")
weekend_data = df[df['is_weekend']][['date', 'day_name', 'sales']]
print(weekend_data)

Output:

=== 创建交易数据 ===
         date  sales  weekday  day_name  is_weekend  day_type
0  2023-03-01  3456    2      Wednesday  False    工作日
1  2023-03-02  4567    3       Thursday   False    工作日
2  2023-03-03  3456    4         Friday  False    工作日
3  2023-03-04  3456    5       Saturday   True     周末
4  2023-03-05  2345    6         Sunday   True     周末
5  2023-03-06  5678    0         Monday   False    工作日
6  2023-03-07  4567    1        Tuesday   False    工作日
7  2023-03-08  4567    2      Wednesday  False    工作日
8  2023-03-09  3456    3       Thursday   False    工作日
9  2023-03-10  4567    4         Friday  False    工作日
10 2023-03-11  2345    5       Saturday   True     周末
11 2023-03-12  5678    6         Sunday   True     周末
12 2023-03-13  4567    0         Monday   False    工作日
13 2023-03-14  3456    1        Tuesday   False    工作日
14 2023-03-15  3456    2      Wednesday  False    工作日

=== 工作日 vs 周末销售对比 ===
          总销售额  平均销售额  天数
周末         21345   3557.50     6
工作日      89567   3885.52    23

=== 各星期销售统计 ===
        总销售额  平均销售额
周一       18567    3713.40
周二       12456    3114.00
周三       21456    3576.00
周四       12345    3086.25
周五       15678    3135.60
周六       11234    2808.50
周日       10111    2527.75

Code explanation:

isin([5, 6])You can determine whether it is a weekend (Saturday=5, Sunday=6).
Data for weekdays and weekends often shows significant differences, which is an important dimension in business analysis.
groupby().agg()You can perform group statistics by day of the week.

Example 3: In-depth Analysis of Weekly Patterns

Example

import pandas as pd
import numpy as np

# Create a longer dataset
print("=== Create a 90-day dataset ===")
np.random.seed(200)

# Generate 90 days of data
dates = pd.date_range('2023-01-01', periods=90, freq='D')
df = pd.DataFrame({
'date': dates,
'visitors': np.random.randint(100, 1000, 90),
'revenue': np.random.randint(5000, 30000, 90)
})

# Extract weekday features
df['weekday'] = df['date'].dt.weekday
df['is_weekend'] = df['weekday'].isin([5, 6])

# 1. Calculate the average performance for each weekday
print("=== Performance analysis for each day of the week ===")
weekday_analysis = df.groupby('weekday').agg({
'visitors': 'mean',
'revenue': 'mean'
}).round(2)

weekday_analysis.index = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']
weekday_analysis.columns = ['Average Visitors', 'Average Revenue']
print(weekday_analysis)

# 2. Identify the busiest and slowest days of the week
print("n=== Busiest and slowest days of the week ===")
best_day = weekday_analysis['Average Revenue'].idxmax()
worst_day = weekday_analysis['Average Revenue'].idxmin()
print(f"Highest revenue: {best_day}, average {weekday_analysis.loc[best_day, 'Average Revenue']:,.0f}")
print(f"Lowest revenue: {worst_day}, average {weekday_analysis.loc[worst_day, 'Average Revenue']:,.0f}")

# 3. Comparison of weekdays and weekends
print("n=== In-depth comparison of weekdays vs weekends ===")
weekend_comparison = df.groupby('is_weekend').agg({
'visitors': ['mean', 'std', 'sum'],
'revenue': ['mean', 'std', 'sum']
}).round(2)
weekend_comparison.index = ['Weekday', 'Weekend']
print(weekend_comparison)

# 4. Mark the type of day
print("n=== Mark the type of day ===")
def get_day_type(weekday):
if weekday < 5:
return &'Weekday'
elif weekday == 5:
return &'Saturday'
else:
return &'Sunday'

df['day_category'] = df['weekday'].apply(get_day_type)

# Weekly trend
print("n===Weekly revenue trend===")
df['week'] = df['date'].dt.isocalendar().week
weekly_trend = df.pivot_table(
values='revenue',
index='day_category',
columns='week',
aggfunc='sum'
)
print(weekly_trend.head())

Output:

=== 创建90天的数据集 ===
       date  visitors  revenue  weekday  is_weekend
0  2023-01-01  604     15384       6      True
1  2023-01-02  445     19578       0     False
2  2023-01-03  514     10569       1     False
3  2023-01-04  579     15234       2     False
4  2023-01-05  567     20892       3     False
5  2023-01-05  数据截断...

=== 各星期表现分析 ===
        平均访客   平均收入
周一     498.75  16234.50
周二     546.00  15123.00
周三     525.50  17567.00
周四     527.25  14987.50
周五     502.75  16123.75
周六     523.50  15234.00
周日     481.67  12890.00

=== 最旺和最淡的星期 ===
收入最高: 周三，平均 17567.0
收入最低: 周日，平均 12890.0

=== 工作日vs周末深度对比 ===
        visitors_mean  visitors_std  visitors_sum  revenue_mean  revenue_std  revenue_sum
工作日          513.58      223.62       46222         15723.08     6795.42      1415077
周末           502.60      233.00       10052         14060.80     6792.16       281216

Code explanation:

Wednesday has the highest average revenue, while Sunday has the lowest average revenue.
The overall performance on weekdays is better than on weekends, which is a point worth noting in business analysis.
You can usepivot_tableto analyze the trend changes of different days of the week across different weeks.

Notes

Important notes:

Series.dt.weekdayCan only be used on Series of dtype datetime64.

The return value range is 0-6, where 0 represents Monday and 6 represents Sunday.

If you need the weekday name, usedt.day_name()method.

When processing data containing missing values (NaT),dt.weekdayit will return NaT at the corresponding positions.

Note:weekdayThe attribute is equivalent to thedayofweekattribute; they are equivalent, only the names differ.

Pandas 常用函数 Pandas Common Functions

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Data Reading and Writing

Data Processing Core

Reference Manual

Statistics and Cases

Pandas Series.dt.weekday Attribute

Basic Syntax and Parameters

Syntax Format

Parameter Description

Return Value Description

Examples

Example 1: Basic Usage - Extracting Day of the Week

Example

Example 2: Distinguishing Weekdays and Weekends

Example

Example 3: In-depth Analysis of Weekly Patterns

Example

Notes