How do you work with dates and times in Pandas?

Time-series data is everywhere in data science — sales by day, sensor readings by second, user activity by hour. Pandas has first-class datetime support built on NumPy's datetime64 type and Python's datetime module.

import pandas as pd

df = pd.DataFrame({
    'date_str': ['2024-01-15', '2024-02-20', '2024-03-05'],
    'value':    [100, 200, 150],
})

# Parse string dates — always specify format for speed and correctness
df['date'] = pd.to_datetime(df['date_str'], format='%Y-%m-%d')

# Extract components via .dt accessor
df['year']    = df['date'].dt.year
df['month']   = df['date'].dt.month
df['day']     = df['date'].dt.day
df['weekday'] = df['date'].dt.day_name()  # 'Monday', 'Tuesday', ...
df['quarter'] = df['date'].dt.quarter

# Date arithmetic
df['days_since'] = (pd.Timestamp.today() - df['date']).dt.days
df['next_month'] = df['date'] + pd.DateOffset(months=1)

# Set as index for time-series resampling
ts = df.set_index('date')
ts.resample('M').sum()   # sum by month
ts.resample('W').mean()  # mean by week
ts.resample('Q').agg({'value': ['sum', 'count']})  # quarterly stats

# Filtering date ranges
df[df['date'] >= '2024-02-01']
df[df['date'].between('2024-01-01', '2024-03-01')]

Always parse dates explicitly with format= rather than relying on infer_datetime_format=True — the inferred path is slow and occasionally wrong for ambiguous formats like 01/02/03. For production pipelines, parse at read time using parse_dates=['date_col'] in pd.read_csv.

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