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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")\n\ndata
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Output:

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The values in the Location column are made up of the zone and the city that a particular store is located at.

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Let’s say we want to split the values in the Location column into two parts. We can do so using the Series.str.split() method.

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Note that the words are divided by a dash(-), which is the delimiter around which we will split the values.

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data["Location"].str.split("-")
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Output:

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We can expand the split strings into different columns by passing expand=True to the split() method. This will return a DataFrame with 2 columns.

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data["Location"].str.split("-", expand=True)
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Output:

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Next, if we want to create 2 new columns - Zone and City - and assign them the columns of the newly Created DataFrame, we can do so using the following code snippet:

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data["Zone"] = data["Location"].str.split("-").str.get(0)\n\ndata["City"] = data["Location"].str.split("-").str.get(1)\n\ndata
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The .str.get(0) method extracts the column located at position 0 and .str.get(1) extracts the column located at position 1.

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Output:

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\n","frontmatter":{"title":"Pandas Series - Split Strings by Characters","date":"2021-08-15"}}},"pageContext":{"slug":"/pandas-series-split-strings-by-characters/","prev":{"fields":{"slug":"/pandas-extracting-year-month-date-and-quarter-from-a-date/"},"frontmatter":{"modules":null}},"next":{"fields":{"slug":"/pandas-apply-method/"},"frontmatter":{"modules":null}}}}, "staticQueryHashes": ["3159585216"]}