Data Analysis & Processing Using Delimiters in Pandas (Updated 2024)

Rahul Shah 18 Mar, 2024 • 5 min read


Every Data Analysis project requires a dataset. These datasets are available in various file formats, such as .xlsx, .json, .csv, and .html. Conventionally, datasets are mostly found as csv data in .csv format. CSV (or Comma Separated Values) files, as the name suggests, have data items separated by commas. CSV files are plain text files that are lighter in file size. It uses comma (,) as the default delimiter or separator while parsing a file. Also, CSV Data files can be viewed and saved in tabular form in popular tools such as Microsoft Excel and Google Sheets. The commas used in CSV data files are known as delimiters. Think of delimiters as a separating boundary that distinguishes between any two subsequent data items. You will learn about various pandas read csv delimiter or pandas read csv separatorin detail in this article..

Learning Objectives

  • In this python3 tutorial, you will learn different types of delimiters in pandas.
  • You will learn to use the read_csv function.
  • You will also learn how to read csv files other than comma separator.

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pandas read csv delimiter

Pandas Refresher

Data Scientists and Analysts widely use the popular Python library pandas in data science. Pandas is built over another popular library like NumPy. The conventional use of Pandas is for analyzing and manipulating data but is not limited to the same.Pandas’ basic data structure includes series and Dataframe. A series is a one-dimensional array comprising data items of any data type.

Pandas Dataframe is a two-dimensional array consisting of data items of any data type. A combination of two or more Pandas Series objects can also identify Pandas.

Reading CSV Data Files Using Pandas Function

To load and read csv file these CSV files or read_csv delimiter, we import Pandas library called read_csv function Syntax.

df = pd.read_csv()


pd.read_csv(filepath_or_buffer, sep=’, ‘, delimiter=None, header=’infer’, names=None, index_col=None, usecols=None, squeeze=False, prefix=None, mangle_dupe_cols=True, dtype=None, engine=None, converters=None, true_values=None, false_values=None, skipinitialspace=False, skiprows=None, nrows=None, na_values=None, keep_default_na=True, na_filter=True, verbose=False, skip_blank_lines=True, parse_dates=False, infer_datetime_format=False, keep_date_col=False, date_parser=None, dayfirst=False, iterator=False, chunksize=None, compression=’infer’, thousands=None, decimal=b’.’, lineterminator=None, quotechar=’”‘, quoting=0, escapechar=None, comment=None, encoding=None, dialect=None, tupleize_cols=None, error_bad_lines=True, warn_bad_lines=True, skipfooter=0, doublequote=True, delim_whitespace=False, low_memory=True, memory_map=False, float_precision=None)
na-filter: Detect missing values. set this to False to improve performance. missing data should be encoded as nan.

The read_csv function has tens of parameters, out of which one is mandatory, and others are optional to use on an ad hoc basis. By default, it reads the first rows on CSV as column names (header), and it creates an incremental numerical number as an index starting from zero. This mandatory parameter specifies the CSV file we want to read. For example,

Note: Remember to use double backward slashes while specifying the file path.

abc.csv file, read file separators

Sep Parameter: The Default Delimiter in Pandas

One of the optional parameters in the read_csv function is sep, a shortened name for the separator. We previously discussed this operator as the delimiter. The sep parameter instructs the interpreter about the delimiter used in our dataset or, in Layman’s terms, how the data items are separated in our CSV file.

In our read_csv() function, if we don’t specify the sep parameter, it uses the default value of the comma (,). Hence, in our previous code snippet, we omitted specifying the sep parameter, indicating that our file uses commas as delimiters.

Using Other Delimiters in Pandas

Often it may happen that the dataset in .csv file format has data items separated by a delimiter other than a comma. This includes semicolons, colons, tab spaces, vertical bars, etc. In such cases, we need to use the sep parameter inside the read.csv() function. For example, consider a semi-colon-separated CSV file named Example.csv, with the following syntax.

Example.csv File, Using Other Delimiters in Pandas
df = pd.read_csv("C:\Users\Rahul\Desktop\Example.csv", sep = ';')

Executing this code yields a dataframe named df:

Dataframe df, dataframe Delimiters in Pandas

Vertical-bar Separator

The below syntax can read a vertical bar delimited file.

df = pd.read_csv("C:\Users\Rahul\Desktop\Example.csv", sep = '|')

Colon Separator

You can load a colon-delimited file using the below syntax:

df = pd.read_csv("C:\Users\Rahul\Desktop\Example.csv", sep = ':')

Tab Separator

Often we may come across the file having file format .tsv. These .tsv files have tab-separated values in them, or we can say it has tab space as a delimiter. To read such files, we use the same .read_csv() function of pandas, and we need to specify the delimiter.

For example:

df = pd.read_csv("C:\Users\Rahul\Desktop\Example.tsv", sep = 't')

Similarly, we can use other separators depending on the delimiter identified from our data

You can use the to_csv() method to export data from a DataFrame or pandas series as a csv file or append it to an existing csv file.


It is always useful to check how our data is stored in our dataset. Understanding the data is necessary before starting to work on it. A delimiter (pandas read csv delimiter) can be identified effortlessly by checking the data. Based on our inspection, we can use the relevant delimiter in the sep parameter. In this article, we have learned about different csv separators. We have also learned how to read and check data and how data gets stored.

Key Takeaways

  • Python pandas library is very useful for preprocessing data, from loading to cleaning the data.
  • Commas are the default delimiters or sep parameters in a csv file.
  • Vertical-bar separators, colon separators, and tab separators are some of the other delimiters in pandas.

Frequently Asked Questions

Q1. How to read CSV with delimiter?

A. We can read CSV files with a delimiter using the pd.read_csv function from the python pandas library.

Q2. How do I read a CSV file with delimiter in Python?

Read CSV with delimiter (Python):
Use csv.reader with delimiter argument (basic).
Use pandas.read_csv with delimiter argument (recommended, more features).

Q3. What is the default delimiter for pandas read CSV?

Default delimiter (pandas): Comma (,).

Q4.How do I read a CSV file with a different delimiter?

Read CSV with different delimiter:
Specify the actual delimiter (e.g., ‘\t’ for tab) in delimiter argument.

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Rahul Shah 18 Mar 2024

IT Engineering Graduate currently pursuing Post Graduate Diploma in Data Science.

Frequently Asked Questions

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Responses From Readers


Punitkumar 15 Apr, 2021

👏🏼, Well Done with great insights and help stuff on read_csv. Including syntax of read_csv() would be great.

Punitkumar 15 Apr, 2021

👏🏼👏🏼Well done, great sight and helpful content over read_csv( ).

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