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Provided by interviewqs, a mailing list for coding and data interview problems. However, i know that if it occurs the first time, then it will belong the month 1, if it occurs for the second time, then it will belong to month 2. When working with date columns in a dataset, you often need to extract just the month

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This is simple using pandas.to_datetime () along with.dt.month or.dt.month_name (). So basicly, i have the date information, but the date does not contain the month it belongs to Columns=['year','month','day','hour'],dtype=int) year month day hour

0 2018 07 01 02

1 2018 06 05 01 2 2018 05 16 21 If you want all the columns in a single dataframe use pd.concat() along axis=1. This tutorial explains how to extract the month from a date in a pandas dataframe, including several examples.

To run some examples of extracting month and year from datetime, let’s create dataframe with a column of datetime values and convert this column into a datetime column using the pd.to_datetime() function, and finally, use the strftime() method to extract the month and year from a datetime column. **ensure your column is in datetime format** Use `pd.to_datetime ()` if needed Create a new column by accessing the month property

Here's a sample code snippet

```python import pandas as pd The month & year date column doesn't have to be in the exact format i show above (although bonus points if it does), just as long as it shows both the month (abbreviated name, full name, or month number) and the year in the same column. This tutorial explains how to create a date column from year and month columns, including an example. It seems my post is not clear

If my date column is more than these 2 years Is there a fast way to directly compare this time range without a specific year