如何在DataFrame中基于球队列获取上一场比赛日期并计算休息天数

First up, let's make sure your Matchday column is formatted as a datetime—this is crucial for accurate date difference calculations:

import pandas as pd

# Convert Matchday to datetime if it's not already
df['Matchday'] = pd.to_datetime(df['Matchday'])

Next, sort your DataFrame by team and match date. This ensures we're comparing each match to the immediately preceding one for the same team, which is exactly what we need:

df = df.sort_values(by=['Team', 'Matchday'])

Now for the core calculation—we'll use groupby to isolate each team's match history, then diff() to find the gap between consecutive matches:

# Calculate days since the last match for each team
df['Rest_Days'] = df.groupby('Team')['Matchday'].diff().dt.days

Breakdown of how this works: #

• groupby('Team') splits the data so we only look at one team's matches at a time
• diff() computes the time difference between the current match's date and the previous match's date within each team's group
• .dt.days converts the raw timedelta result into a clean numeric day count
• The first match for every team will show NaN in Rest_Days (since there's no prior match to compare), which aligns perfectly with your requirement for first-time entries.

Example Output: #

If your input data looks like this:

After running the code, you'll get:

Which matches the example you provided!

If you prefer to replace NaN with a specific value (like 0 for clarity), just add:

# Optional: Replace NaN with 0 or another value
df['Rest_Days'] = df['Rest_Days'].fillna(0)

内容的提问来源于stack exchange，提问作者Morten Ravn