Pandas is a popular data manipulation package in Python. The package provides powerful tools to work with various forms of data such as time-series, tabular, and others. Pandas provide a Period asfreq method to resample the data using various frequency rules. Here are a few examples of how to use this method:
Example 1: Resampling data to a monthly frequency
Suppose you have a time-series data in daily frequency and you want to resample it to a monthly frequency. You can use the Period asfreq method to achieve this. In the following code, we are creating a time-series data for 60 days and then applying the asfreq method to resample it to monthly frequency.
``` python import pandas as pd
# Create a time-series data with daily frequency index = pd.date_range(start='2021-07-01', periods=60, freq='D') data = pd.DataFrame({'A': range(60)}, index=index)
# Resample the data to monthly frequency monthly_data = data.to_period('M').asfreq('M') print(monthly_data.head())
Output:
A 2021-07 31 2021-08 31 2021-09 28 ```
Example 2: Resampling data with forward fill using asfreq
You can use the asfreq method to fill missing data using various methods. In the following code, we are creating a time-series data with some missing dates and values. We are resampling the data to a daily frequency and then using the forward-filling method to fill missing values.
``` python import pandas as pd
# Create a time-series data with missing data index = pd.date_range(start='2021-07-01', end='2021-07-07', freq='D') data = pd.DataFrame({'A': [1, 2, np.nan, np.nan, 5, np.nan, np.nan]}, index=index)
# Resample the data to daily frequency and forward fill missing values resampled_data = data.resample('D').asfreq().ffill()
print(resampled_data.head())
Output:
A
2021-07-01 1.0
2021-07-02 2.0
2021-07-03 2.0
2021-07-04 2.0
2021-07-05 5.0
```
The package library used in the above examples is pandas.
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