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11. Pandas Series: Time Series Handling

Status

Done

TABLE OF CONTENT

Time Series Handling

1. DateTime Conversion

df['datetime_series'].to_period(freq) → Convert to Period (e.g., 'M' for monthly)
df['period_series'].to_timestamp() → Convert Period back to Timestamp

2. Date Component Accessors (via .dt)

Basic Components:

.dt.year → Extract year
.dt.month → Extract month (1-12)
.dt.day → Extract day (1-31)
.dt.hour → Extract hour (0-23)
.dt.minute → Extract minute (0-59)
.dt.second → Extract second (0-59)

Advanced Components:

.dt.dayofweek → Day of week (0=Monday, alias: .weekday)
.dt.dayofyear → Day of year (1-366)
.dt.quarter → Quarter (1-4)
.dt.is_month_end → Bool for month-end dates
.dt.is_quarter_end → Bool for quarter-end dates
.dt.is_year_end → Bool for year-end dates

3. DateTime Formatting & Rounding

.dt.strftime(format) → Format as string (e.g., "%Y-%m-%d")
.dt.round(freq) → Round to nearest frequency (e.g., 'H' for hour)
.dt.floor(freq) → Round down to frequency
.dt.ceil(freq) → Round up to frequency
.dt.normalize() → Set time to midnight (00:00:00)

4. TimeDelta Operations (for timedelta Series)

.dt.total_seconds() → Convert entire duration to seconds

Example Usage:

# Create datetime Series
dates = pd.Series(pd.date_range('2023-01-01', periods=3, freq='M'))

# Access components
dates.dt.month          # → [1, 2, 3]
dates.dt.is_quarter_end # → [False, False, True] (March end)

# Formatting
dates.dt.strftime("%Y-%m") # → ['2023-01', '2023-02', '2023-03']

# Rounding
pd.Series([pd.Timestamp('2023-01-01 14:30:45')]).dt.round('H')
# → 2023-01-01 15:00:00

Key Notes:

The .dt accessor only works with datetime-like Series
For timezone handling, use:

tz_localize() to assign timezones
tz_convert() to convert timezones

Frequency strings (for rounding/periods):

'D' = day, 'M' = month, 'Q' = quarter
'H' = hour, 'T'/'min' = minute, 'S' = second

Common Use Cases:

Extracting month/year for grouping
Flagging period-end dates
Creating formatted date strings for reports
Normalizing timestamps to compare dates without times

Time Series Handling in Pandas Series

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1. DateTime Conversion Fundamentals

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2. Date Component Extraction

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3. DateTime Formatting & Rounding

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4. TimeDelta Operations

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5. Timezone Handling

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Practical Applications

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Performance Optimization

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Common Pitfalls & Solutions

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