# Calculating Percentage change in Resampled data

## Pandas Resampling and Frequency Conversion: Exercise-15 with Solution

Write a Pandas program to calculate percentage change in Resampled data.

**Sample Solution:**

**Python Code :**

```
# Import necessary libraries
import pandas as pd
import numpy as np
# Create a time series data with daily frequency
date_rng = pd.date_range(start='2021-01-01', end='2021-01-10', freq='D')
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 percentage change in the resampled data
ts_pct_change = ts_daily.pct_change()
# Display the percentage change in the resampled time series
print(ts_pct_change)
```

Output:

2021-01-01 NaN 2021-01-02 -4.219191 2021-01-03 -0.483793 2021-01-04 1.751098 2021-01-05 0.088296 2021-01-06 -2.842570 2021-01-07 -1.521794 2021-01-08 -1.391348 2021-01-09 -4.379459 2021-01-10 -1.437542 Freq: D, dtype: float64

**Explanation:**

- Import Pandas and NumPy libraries.
- Create a date range with daily 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 percentage change in the resampled data.
- Print the percentage change in the resampled time series data.

**Python Code Editor:**

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**Previous:** Creating Custom Resampling periods.

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