# Calculating Rolling Mean of Resampled data

## Pandas Resampling and Frequency Conversion: Exercise-12 with Solution

Write a Pandas program to calculate Rolling Mean of Resampled Data.

**Sample Solution:**

**Python Code :**

```
# Import necessary libraries
import pandas as pd
import numpy as np
# Create a time series data with hourly frequency
date_rng = pd.date_range(start='2020-01-01', end='2020-01-10', freq='H')
ts = pd.Series(np.random.randn(len(date_rng)), index=date_rng)
# Resample the time series to daily frequency
ts_daily = ts.resample('D').mean()
# Calculate the rolling mean with a window of 3 days
ts_rolling_mean = ts_daily.rolling(window=3).mean()
# Display the rolling mean of the resampled time series
print(ts_rolling_mean)
```

Output:

2020-01-01 NaN 2020-01-02 NaN 2020-01-03 -0.021587 2020-01-04 0.040990 2020-01-05 0.079430 2020-01-06 0.013868 2020-01-07 0.116677 2020-01-08 0.067320 2020-01-09 -0.076725 2020-01-10 -0.308754 Freq: D, dtype: float64

**Explanation:**

- Import Pandas and NumPy libraries.
- Create a date range with hourly frequency.
- Generate a random time series data with the created date range.
- Resample the time series data to daily frequency by calculating the mean.
- Calculate the rolling mean of the resampled data with a window of 3 days.
- Print the rolling mean of the resampled time series data.

**Python Code Editor:**

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**Previous:** Shifting Time Series data Forward and Backward.

**Next:** Handling Missing data before Resampling .

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