# Resampling Time-Series in a Pandas DataFrame

## Python Pandas Numpy: Exercise-35 with Solution

Resample time-series data in a DataFrame.

**Sample Solution:**

**Python Code:**

```
import pandas as pd
# Create a sample DataFrame with time-series data
date_rng = pd.date_range(start='2012-01-01', end='2012-01-10', freq='D')
data = {'Value': [10, 15, 20, 25, 30, 35, 40, 45, 50, 55]}
df = pd.DataFrame(data, index=date_rng)
# Resample the DataFrame to a weekly frequency, calculating the mean
resampled_df = df.resample('W').mean()
# Display the original and resampled DataFrames
print("Original DataFrame:")
print(df)
print("\nResampled DataFrame:")
print(resampled_df)
```

Output:

Original DataFrame: Value 2012-01-01 10 2012-01-02 15 2012-01-03 20 2012-01-04 25 2012-01-05 30 2012-01-06 35 2012-01-07 40 2012-01-08 45 2012-01-09 50 2012-01-10 55 Resampled DataFrame: Value 2012-01-01 10.0 2012-01-08 30.0 2012-01-15 52.5

**Explanation:**

Here's a breakdown of the above code:

- We create a sample DataFrame (df) with time-series data using the pd.date_range() function.
- The DataFrame has a daily frequency with values in the 'Value' column.
- The df.resample('W').mean() line resamples the DataFrame to a weekly frequency ('W') and calculates the mean of each weekly period.
- The resulting "resampled_df" DataFrame contains the resampled data.

**Flowchart:**

**Python Code Editor:**

**Previous:** Extracting date and time from Pandas DateTime.

**Next:** Rolling Calculation in Pandas DataFrame.

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