Time Series Data with Pandas

Time Series with Pandas

Temporal data analysis is a core skill for finance, IoT, and many domains.

Converting strings to datetime

df['date'] = pd.to_datetime(df['date'])

Setting the datetime index

df_ts = df.set_index('date')
print(df_ts.head())

Resampling (e.g., daily to monthly)

monthly = df_ts.resample('M').sum()
print(monthly.head())

Rolling windows

# 7‑day moving average of a metric
df_ts['metric_ma7'] = df_ts['metric'].rolling(window=7).mean()

Time‑zone handling

df['date_utc'] = df['date'].dt.tz_localize('UTC')
df['date_est'] = df['date_utc'].dt.tz_convert('US/Eastern')

Frequency conversion and forward/backward fill

# Forward fill missing daily data
filled = df_ts.asfreq('D', method='ffill')