Specify datetime dtype when Reading CSV as pandas DataFrame in Python (Example)
In this article, you’ll learn how to set a datetime dtype while importing a CSV file to a pandas DataFrame in Python programming.
To be more specific, the content of the article looks as follows:
Let’s dive right in:
Example Data & Add-Ons
First, we need the datetime module and the pandas library, so let’s import them.
import pandas as pd # Loading pandas library import datetime # Loading datetime module
Let’s also construct some example data:
df = pd.DataFrame({"column_1" : [datetime.date(2018, 8, 10), # Sample data initialization datetime.date(2020, 10, 12), datetime.date(2015, 2, 14), datetime.date(2012, 3, 19), datetime.date(2018, 5, 23)], "column_2" : [datetime.datetime(2018, 8, 10, 10, 7, 10), datetime.datetime(2020, 10, 12, 16, 9, 30), datetime.datetime(2015, 2, 14, 21, 14, 43), datetime.datetime(2012, 3, 19, 23, 12, 55), datetime.datetime(2018, 5, 23, 15, 9, 8)] }) df.to_csv('sample_csv.csv', index=False) # Converting the constructed DataFrame to CSV
For the sake of creating an example CSV, our data was created as a DataFrame first, and then it was converted to a CSV file.
Example: Setting dtype of date/datetime Objects while Importing CSV to pandas DataFrame
Have a look at the Python code and its output below. It shows that the dates in the example data are parsed as strings if the data type is not specified explicitly.
df_default = pd.read_csv("sample_csv.csv") # importing the CSV directly print(df_default.dtypes) # Printing the dtypes of DataFrame columns # column_1 object # column_2 object # dtype: object print(type(df_default["column_1"][0])) # Printing the type of element in column_1 # <class 'str'>
The following Python code shows how to set parsing instructions for the selected columns of the CSV file via the argument parse_dates.
parse_dates = ["column_1", "column_2"] # setting column names to be parsed df_parse = pd.read_csv("sample_csv.csv", parse_dates=parse_dates) # reading the CSV print(df_parse.dtypes) # printing the dtypes of each column of DataFrame # column_1 datetime64[ns] # column_2 datetime64[ns] # dtype: object
As seen above, now we have column_1 and column_2 parsed as datetime objects instead of strings.
Video & Further Resources
Do you need further explanations on the contents of this tutorial? Then I recommend watching the following video on my YouTube channel. I explain the content of this article in the video.
The YouTube video will be added soon.
Furthermore, you may read the related tutorials on my homepage:
- Read Multiple CSV Files & Append into One pandas DataFrame in Python
- Change datetime Format in pandas DataFrame in Python
- Set Column Order when Writing pandas DataFrame to CSV in Python
- Set Column Names when Reading CSV as pandas DataFrame in Python
- Specify Delimiter when Reading pandas DataFrame from CSV File in Python
- Python Programming Language
Summary: In this article, you have learned how to set datetime dtypes while reading a CSV as a pandas DataFrame in the Python programming language. Please let me know in the comments section, in case you have additional questions.
This page was created in collaboration with Ömer Ekiz. Have a look at Ömer’s author page to get further information about his professional background, a list of all his tutorials, as well as an overview on his other tasks on Statistics Globe.
I’m Joachim Schork. On this website, I provide statistics tutorials as well as code in Python and R programming.
