DatetimeIndex¶

pandas provides rich support for time series data through the DatetimeIndex, which enables label-based time alignment and slicing.

Creating DatetimeIndex¶

Generate datetime indices for time series.

1. date_range Function¶

```python import pandas as pd

dates = pd.date_range("2020-01-01", periods=5, freq="D") print(dates) ```

DatetimeIndex(['2020-01-01', '2020-01-02', '2020-01-03', '2020-01-04', '2020-01-05'], dtype='datetime64[ns]', freq='D')

2. With End Date¶

python dates = pd.date_range(start="2024-01-01", end="2024-01-10")

3. Nanosecond Precision¶

DatetimeIndex stores timestamps with nanosecond precision.

Converting to Datetime¶

Parse strings and other formats to datetime.

1. to_datetime Function¶

python pd.to_datetime(["2021-01-01", "2021-01-05"])

2. Common with CSV Loading¶

python df = pd.read_csv('data.csv', parse_dates=['date_column'])

3. Format Specification¶

python pd.to_datetime("01-15-2024", format="%m-%d-%Y")

Date Range Frequencies¶

Common frequency strings.

1. Daily and Sub-daily¶

python pd.date_range("2024-01-01", periods=5, freq="D") # Daily pd.date_range("2024-01-01", periods=5, freq="H") # Hourly pd.date_range("2024-01-01", periods=5, freq="T") # Minute

2. Weekly and Monthly¶

python pd.date_range("2024-01-01", periods=5, freq="W") # Weekly pd.date_range("2024-01-01", periods=5, freq="M") # Month end pd.date_range("2024-01-01", periods=5, freq="MS") # Month start

3. Business Days¶

python pd.date_range("2024-01-01", periods=5, freq="B") # Business days

Indexing with Dates¶

Date-based slicing and selection.

1. String-based Selection¶

python s = pd.Series(range(5), index=pd.date_range("2020-01-01", periods=5)) s["2020-01-02":"2020-01-04"] # Inclusive slicing

2. Partial String Indexing¶

python s["2020-01"] # All of January 2020 s["2020"] # All of 2020

3. loc with Dates¶

python s.loc["2020-01-02"] s.loc["2020-01-02":"2020-01-04"]

Time Zone Handling¶

Work with time zones.

1. Localize to UTC¶

python dates = pd.date_range("2020-01-01", periods=5) dates_utc = dates.tz_localize("UTC")

2. Convert Time Zone¶

python dates_eastern = dates_utc.tz_convert("US/Eastern")

3. Financial Data¶

Time zones are essential for global financial data.

Financial Context¶

DatetimeIndex underlies financial analysis.

1. Price Time Series¶

python prices = pd.Series([100, 101, 102], index=pd.date_range("2024-01-01", periods=3))

2. Returns Calculation¶

python returns = prices.pct_change()

3. Event Studies¶

```python

Select specific date ranges for analysis¶

event_window = prices["2024-01-01":"2024-01-31"] ```

Runnable Example: time_series_tutorial.py¶

```python """ Pandas Tutorial: Time Series Analysis.

Covers datetime operations, resampling, rolling windows, time zones. """

import pandas as pd import numpy as np

=============================================================================¶

Main¶

=============================================================================¶

if name == "main":

print("="*70)
print("TIME SERIES ANALYSIS")
print("="*70)

# Create time series data
dates = pd.date_range('2024-01-01', periods=100, freq='D')
np.random.seed(42)
ts_data = pd.DataFrame({
    'Date': dates,
    'Sales': np.random.randint(100, 500, 100),
    'Temperature': np.random.uniform(15, 35, 100)
})
ts_data.set_index('Date', inplace=True)

print("\nTime Series Data:")
print(ts_data.head())

# Date parsing
print("\n1. Parse dates from strings:")
date_strings = ['2024-01-01', '2024-02-15', '2024-03-30']
parsed_dates = pd.to_datetime(date_strings)
print(parsed_dates)

# Date ranges
print("\n2. Create date ranges:")
print("Daily:", pd.date_range('2024-01-01', periods=5, freq='D'))
print("Weekly:", pd.date_range('2024-01-01', periods=5, freq='W'))
print("Monthly:", pd.date_range('2024-01-01', periods=5, freq='MS'))

# Accessing datetime components
print("\n3. Extract datetime components:")
ts_data['Year'] = ts_data.index.year
ts_data['Month'] = ts_data.index.month
ts_data['Day'] = ts_data.index.day
ts_data['DayOfWeek'] = ts_data.index.dayofweek
print(ts_data[['Sales', 'Year', 'Month', 'Day', 'DayOfWeek']].head())

# Resampling (changing frequency)
print("\n4. Resample to weekly frequency (sum):")
weekly = ts_data['Sales'].resample('W').sum()
print(weekly.head())

print("\n5. Resample to monthly (mean):")
monthly = ts_data['Sales'].resample('MS').mean()
print(monthly.head())

# Rolling windows
print("\n6. Rolling mean (7-day window):")
ts_data['Sales_MA7'] = ts_data['Sales'].rolling(window=7).mean()
print(ts_data[['Sales', 'Sales_MA7']].head(10))

print("\n7. Rolling statistics:")
rolling_stats = ts_data['Sales'].rolling(window=7).agg(['mean', 'std', 'min', 'max'])
print(rolling_stats.head(10))

# Expanding windows
print("\n8. Expanding mean (cumulative):")
ts_data['Cumulative_Mean'] = ts_data['Sales'].expanding().mean()
print(ts_data[['Sales', 'Cumulative_Mean']].head(10))

# Shift and lag
print("\n9. Shift values (lag/lead):")
ts_data['Sales_Yesterday'] = ts_data['Sales'].shift(1)
ts_data['Sales_Tomorrow'] = ts_data['Sales'].shift(-1)
print(ts_data[['Sales', 'Sales_Yesterday', 'Sales_Tomorrow']].head())

# Percentage change
print("\n10. Percentage change:")
ts_data['Sales_Pct_Change'] = ts_data['Sales'].pct_change()
print(ts_data[['Sales', 'Sales_Pct_Change']].head())

# Time zones
print("\n11. Time zone operations:")
utc_dates = pd.date_range('2024-01-01', periods=3, freq='D', tz='UTC')
print("UTC:", utc_dates)

# Convert time zone
eastern = utc_dates.tz_convert('US/Eastern')
print("Eastern:", eastern)

print("\nKEY TAKEAWAYS:")
print("- pd.to_datetime(): Parse dates from strings")
print("- pd.date_range(): Create date sequences")
print("- resample(): Change time frequency")
print("- rolling(): Moving window calculations")
print("- shift(): Lag/lead values")
print("- pct_change(): Percentage change")
print("- Time zone handling with tz parameter")

```

Exercises¶

Exercise 1. Write code that creates a DataFrame with a DatetimeIndex and accesses rows for a specific month using .loc['2024-06'].

Exercise 2. Explain three advantages of having a DatetimeIndex on a DataFrame.

Exercise 3. Write code that extracts the year, month, and day from a DatetimeIndex using .year, .month, .day.

Exercise 4. Create a DatetimeIndex with hourly frequency and demonstrate slicing by date and time.