If 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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