Demystifying Pandas Resample: A Guide to Resampling Time Series Data

What it is:

• pandas.resample is a method provided by the pandas library in Python for working with time series data.
• It allows you to conveniently change the frequency (granularity) of your data, either downsampling (combining data points into larger groups) or upsampling (creating more data points).

How it works:

1. Import pandas:

   import pandas as pd
   

3. Resample using resample:

   resampled_data = data.resample(rule)
   

   • data: Your DataFrame or Series.

Additional options:

• how (optional): A function or string specifying how to aggregate values within each resampled group. Defaults to 'mean'. Common options include:
  • 'sum': Total for each group.
  • 'min': Minimum value.
• on (optional): For DataFrames, allows resampling based on a specific column instead of the index.
• fill_value (optional): How to fill missing values introduced by resampling (e.g., 'ffill' for forward fill, 'bfill' for backward fill).

Key points:

• pandas.resample is a versatile tool for time series analysis in Python.
• It empowers you to adjust the frequency of your data to suit your needs.
• Experiment with different rule and how arguments to achieve the desired resampling behavior.

Example 1: Downsampling to Monthly Average Temperature

import pandas as pd

# Sample temperature data
data = {'date': pd.to_datetime(['2023-01-01', '2023-01-10', '2023-01-15', '2023-02-01', '2023-02-14']),
        'temperature': [10, 15, 8, 12, 18]}
df = pd.DataFrame(data)
df.set_index('date', inplace=True)  # Set date as the index

# Resample to monthly average temperature
monthly_avg_temp = df.resample('M')['temperature'].mean()

print(monthly_avg_temp)

This code outputs:

temperature    11.5
2023-02-01    15.0
dtype: float64

Explanation:

• We create a sample DataFrame df with 'date' (datetime) as the index and 'temperature' values.
• We use df.resample('M') to resample the data by month ('M' for monthly).
• Inside the resampled object, we select the 'temperature' column using ['temperature'] and calculate the mean using .mean().
• The resulting monthly_avg_temp DataFrame shows the average temperature for each month.

Example 2: Upsampling to Daily Minimum Price with Forward Fill

import pandas as pd

# Sample stock price data (assume some days are missing)
data = {'date': pd.to_datetime(['2024-06-10', '2024-06-13', '2024-06-17']),
        'price': [100, 110, 120]}
df = pd.DataFrame(data)
df.set_index('date', inplace=True)

# Upsample to daily with minimum price, forward fill for missing values
daily_min_price = df.resample('D')['price'].min().fillna(method='ffill')

print(daily_min_price)

This code outputs (assuming prices don't change between existing dates):

price    2024-06-10    100.0
        2024-06-11    100.0
        2024-06-12    100.0
        2024-06-13    110.0
        2024-06-14    110.0
        2024-06-15    110.0
        2024-06-16    110.0
        2024-06-17    120.0
dtype: float64

• We create a df with sample stock prices on specific days.
• The fillna(method='ffill') fills missing values (for days without data) by carrying forward the last available price.
• daily_min_price shows the minimum price for each day, even on days with missing data.

Remember to adjust the data and rule (frequency) according to your specific analysis needs!

Manual Looping:

This method involves iterating through your time series data and aggregating values based on your desired frequency. It can be less efficient for large datasets compared to pandas.resample, but it offers more granular control over the resampling process.

Example:

import pandas as pd

# Sample data (same as Example 1)
data = {'date': pd.to_datetime(['2023-01-01', '2023-01-10', '2023-01-15', '2023-02-01', '2023-02-14']),
        'temperature': [10, 15, 8, 12, 18]}
df = pd.DataFrame(data)
df.set_index('date', inplace=True)

# Function to calculate monthly average
def monthly_average(data, col):
  monthly_data = {}
  current_month = None
  monthly_sum = 0
  count = 0
  for index, row in data.iterrows():
    if current_month != index.month:
      if current_month is not None:
        monthly_data[current_month] = monthly_sum / count
      current_month = index.month
      monthly_sum = 0
      count = 0
    monthly_sum += row[col]
    count += 1
  if current_month is not None:
    monthly_data[current_month] = monthly_sum / count
  return pd.Series(monthly_data)

# Calculate monthly average temperature
monthly_avg_temp = monthly_average(df.copy(), 'temperature')

print(monthly_avg_temp)

• We define a function monthly_average that iterates through the DataFrame and calculates the monthly average for a specified column.
• It keeps track of the current month, accumulates the sum of values, and calculates the average when the month changes.
• The function returns a Series with monthly averages.

Groupby with Custom Aggregation:

If you already have your data grouped by a datetime-like column (e.g., using groupby), you can achieve resampling with custom aggregation logic within the group. This approach can be useful when you need to perform more complex calculations beyond the standard aggregation functions offered by resample.

import pandas as pd

# Sample data (same as Example 1)
data = {'date': pd.to_datetime(['2023-01-01', '2023-01-10', '2023-01-15', '2023-02-01', '2023-02-14']),
        'temperature': [10, 15, 8, 12, 18]}
df = pd.DataFrame(data)

# Group by month and calculate average with min/max temperature range
monthly_data = df.groupby(df['date'].dt.month)['temperature'].agg(
    mean=('temperature', 'mean'),
    min=('temperature', 'min'),
    max=('temperature', 'max')
)

print(monthly_data)

• We group df by the month extracted from the 'date' column using df['date'].dt.month.
• Inside agg, we define a dictionary with custom aggregation functions:
  • mean: Calculate the average temperature for each month.
• monthly_data shows the monthly average, minimum, and maximum temperatures.

These alternatives provide more flexibility, but pandas.resample is generally recommended for its efficiency and built-in functionality for common resampling tasks. Choose the method that best suits your specific needs and data manipulation complexity.