﻿ Resampling Time Series data to daily Frequency with Pandas

# Resampling Time Series data to daily Frequency

## Pandas Resampling and Frequency Conversion: Exercise-1 with Solution

Write a Pandas program to resample time series data to daily frequency.

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='2023-01-01', end='2023-01-05', 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()

# Display the resampled time series
print(ts_daily)
``````

Output:

```2023-01-01    0.079969
2023-01-02   -0.174736
2023-01-03    0.052439
2023-01-04    0.097063
2023-01-05    0.437323
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.
• Print the resampled time series data.

Python Code Editor:

Have another way to solve this solution? Contribute your code (and comments) through Disqus.

What is the difficulty level of this exercise?

Test your Programming skills with w3resource's quiz.

﻿