{ "componentChunkName": "component---src-templates-blog-post-js", "path": "/pandas-extracting-year-month-date-and-quarter-from-a-date/", "result": {"data":{"markdownRemark":{"html":"

Let’s read the budget.xlsx file into a DataFrame:

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import pandas as pd\n\ndata = pd.read_excel("budget.xlsx", parse_dates=["Opening Date"])\n\ndata
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Output:

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\n \n \n \n \n

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The Opening Date column contains datetime objects.

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We can extract various parts from the date - year, month, date & quarter - using Pandas dt, which is the accessor object to the datetime properties of a Pandas Series.

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Note that the dt accessor object works only with datetime data types.

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data["Opening Date"].dt.year
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Output:

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\n \n \n \n \n

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data["Opening Date"].dt.quarter
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Output:

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\n \n \n \n \n

\n","frontmatter":{"title":"Pandas - Extracting Year, Month, Day & Quarter From a Date","date":"2021-08-15"}}},"pageContext":{"slug":"/pandas-extracting-year-month-date-and-quarter-from-a-date/","prev":{"fields":{"slug":"/pandas-delete-a-series-using-the-del-keyword/"},"frontmatter":{"modules":null}},"next":{"fields":{"slug":"/pandas-series-split-strings-by-characters/"},"frontmatter":{"modules":null}}}}, "staticQueryHashes": ["3159585216"]}