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Resampling Time Series Data
Introduction:
Resampling is a crucial technique in time series analysis, enabling the conversion of data from one frequency to another. This process is essential for tasks such as data aggregation, interpolation, or downsampling, providing flexibility in analyzing time series data at different granularities.
Purpose:
The primary purpose of resampling is to transform the time series data to a desired frequency, making it easier to analyze patterns, trends, and seasonal effects that may not be visible at the original frequency.
Common Use Cases:
Common use cases include aggregating daily data into monthly data for trend analysis, interpolating missing values, and downsampling high-frequency data for computational efficiency.
Example 1: Upsampling with Interpolation
Upsampling:
Upsampling involves increasing the frequency of the time series data, often requiring interpolation to fill in the missing values between the original data points.
import pandas as pd
# Create a sample time series
date_rng = pd.date_range(start='2023-01-01', end='2023-01-10', freq='D')
df = pd.DataFrame(date_rng, columns=['date'])
df['data'] = range(1, len(df) + 1)
# Set date as index
df.set_index('date', inplace=True)
# Upsample to hourly frequency with interpolation
upsampled = df.resample('H').interpolate(method='linear')
print(upsampled)
Explanation:
In this example, daily data is upsampled to an hourly frequency. The missing values are filled using linear interpolation, which estimates values by connecting the dots between known data points.
Console Output:
Upsampled Data with Interpolation
Example 2: Downsampling with Aggregation
Downsampling:
Downsampling reduces the frequency of the time series data, typically by aggregating data points over a specified time period.
import pandas as pd
# Create a sample time series
date_rng = pd.date_range(start='2023-01-01', end='2023-02-01', freq='H')
df = pd.DataFrame(date_rng, columns=['date'])
df['data'] = range(1, len(df) + 1)
# Set date as index
df.set_index('date', inplace=True)
# Downsample to daily frequency with sum aggregation
downsampled = df.resample('D').sum()
print(downsampled)
Explanation:
Here, hourly data is downsampled to a daily frequency. The sum of data points within each day is calculated, providing a daily aggregate.
Console Output:
Downsampled Data with Sum Aggregation
Example 3: Resampling with Mean Aggregation
Mean Aggregation:
Mean aggregation involves calculating the average of data points over a specified time period, useful for smoothing out short-term fluctuations.
import pandas as pd
# Create a sample time series
date_rng = pd.date_range(start='2023-01-01', end='2023-01-07', freq='H')
df = pd.DataFrame(date_rng, columns=['date'])
df['data'] = range(1, len(df) + 1)
# Set date as index
df.set_index('date', inplace=True)
# Resample to daily frequency with mean aggregation
resampled_mean = df.resample('D').mean()
print(resampled_mean)
Explanation:
This example demonstrates resampling hourly data to a daily frequency using mean aggregation, which smooths the data by calculating the average of each day's data points.
Console Output:
Resampled Data with Mean Aggregation
Example 4: Resampling with Forward Fill
Forward Fill:
Forward fill is a method used in resampling to propagate the last valid observation forward to fill missing values.
import pandas as pd
# Create a sample time series
date_rng = pd.date_range(start='2023-01-01', end='2023-01-10', freq='2D')
df = pd.DataFrame(date_rng, columns=['date'])
df['data'] = range(1, len(df) + 1)
# Set date as index
df.set_index('date', inplace=True)
# Upsample to daily frequency with forward fill
upsampled_ffill = df.resample('D').ffill()
print(upsampled_ffill)
Explanation:
In this scenario, data originally sampled every two days is upsampled to a daily frequency. The forward fill method is used to propagate the last known value to fill in the gaps.
Console Output:
Upsampled Data with Forward Fill
Example 5: Resampling with Backward Fill
Backward Fill:
Backward fill is another method used in resampling where missing values are filled by propagating the next valid observation backward.
import pandas as pd
# Create a sample time series
date_rng = pd.date_range(start='2023-01-01', end='2023-01-10', freq='3D')
df = pd.DataFrame(date_rng, columns=['date'])
df['data'] = range(1, len(df) + 1)
# Set date as index
df.set_index('date', inplace=True)
# Upsample to daily frequency with backward fill
upsampled_bfill = df.resample('D').bfill()
print(upsampled_bfill)
Explanation:
This example shows how data sampled every three days is upsampled to a daily frequency using backward fill, filling gaps with the next known value.
Console Output:
Upsampled Data with Backward Fill
