Data Type Conversion
Data type conversion in time series data processing is the process of converting time series data from one data type to another. Why do we need to use that? In time series data processing, this can be useful when you want to change your data format to make it easier to work with or when you want to perform calculations that require a different data type.
For example, you might convert a string representation of a date into a datetime object so that you can perform calculations on it.
Let's look at an example of converting date data from string format to datetime format:
import pandas as pd # Create simple dataset with date information in string format dataset = pd.DataFrame({'PatientID': [1, 2, 3], 'Name': ['John', 'Sarah', 'Michael'], 'AdmissionDate': ['2022-03-15', '2021-11-10', '2022-02-28']}) # Convert 'AdmissionDate' column from string to datetime format dataset['AdmissionDate'] = pd.to_datetime(dataset['AdmissionDate'], format='%Y-%m-%d') # Print the converted data print('Converted types:') print(dataset.dtypes)
You can change the format of the date entry template with the format argument.
We can consider different date patterns:
'15 Jul 2009' - '%d %m %Y';
'1-Feb-15' - '%d-%m-%Y';
'12/08/2019' - '%d/%m/%Y'.
Also, take into account that when we talk about processing time-series data, this means that we will work not only with dates but with all other data types (numeric, categorical, etc.).
Swipe to start coding
Read the 'sales.csv' dataset and convert the 'Date' column to the datetime data type.
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