How to Create a Time Series Dataframe in Python
To create a time series dataframe in Python, you can use the date_range() function from the pandas library.
The following example shows how to create a time series dataframe in Python using the date_range() function.
Using date_range() Function
We can use the date_range() function to create a time series dataframe.
Suppose we have the following data:
# Import pandas library
import pandas as pd
# Create time series data
dates = pd.date_range(start='2022-01-01', periods=6, freq='D')
# Create dataframe
df = pd.DataFrame({
'Product_Code': ['A-101', 'A-102', 'A-103', 'B-101', 'B-102', 'B-104'],
'Product_Name': ['Laptop', 'Mobile', 'Printer', 'Keyboard', 'Scanner', 'Mouse'],
'Price': [4500, 550, 250, 50, 350, 50],
'Status': [1, 1, 1, 0, 1, 1]
}, index=dates)
# Print updated dataframe
print(df)
Output: 👇️
Product_Code Product_Name Price Status
2022-01-01 A-101 Laptop 4500 1
2022-01-02 A-102 Mobile 550 1
2022-01-03 A-103 Printer 250 1
2022-01-04 B-101 Keyboard 50 0
2022-01-05 B-102 Scanner 350 1
2022-01-06 B-104 Mouse 50 1
In this example, we use the date_range() function to create a range of dates starting from ‘2022-01-01’ for 6 periods with a daily frequency.
We then create a dataframe df with the time series data as the index.
The output shows the dataframe with time series data.
Conclusion
We can use the date_range() function from the pandas library to create a time series dataframe in Python.