﻿ Calculating Percentage change in Resampled Time Series data

# 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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