# How to extend the interval size of a time series DataFrame with Pandas using Python

• 2 minIf you work with time-series data you might end up needing to extend the interval size of a time series DataFrame.

Imagine having monthly values and wanting daily values.

One way to achieve that is to use the Pandas resample method.

## Here is an illustration

## Here is the code

```
import numpy as np
import pandas as pd
# We generate the dates
dates = pd.date_range(start="01-01-1980",
end="01-01-2021",
freq="M")
# we generate random observation
x = np.random.normal(0, 1, len(dates))
# We create a sample dataframe
df = pd.DataFrame(index=dates,
data={"col1": x})
# We take out samples to have only specific dates
df = df.sample(10).sort_index()
# We print out the initial DataFrame
print(df)
# We resmaple our dataframe and take only the last available value for each month
df = df.resample("D").last()
# We print the transformed dataframe
print(df)
```

Here you are! You now know how to extend the interval size of a time series DataFrame with Pandas using Python.

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