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

Introduction

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. As the name suggests, CSV (or Comma Separated Values) files 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 using 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 boundary that distinguishes between two subsequent data items. In this article, you will learn about various pandas, read the csv delimiter or pandas, and read the csv separator in detail.

Learning Objectives

• In this Python3 tutorial, you will learn about Pandas’s different types of delimiters.
• You will learn to use the read_csv function.
• You will also learn how to read csv files other than comma separator.

This blog was published as a part of Data Science Blogathon.

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 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()

Syntax

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, one of which 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), creating 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.

Sep Parameter: The Default Delimiter in Pandas

One of the optional parameters in the read_csv function is sep in Pandas, a shortened name for the separator. We previously discussed this operator as the Pandas 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, 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 semicolon-separated CSV file named Example.csv with the following syntax.

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

Executing this code yields a dataframe named df:

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 a file with file format .tsv. These .tsv files have tab-separated values in them, or we can say it has tab space as a Pandas delimiter. To read such files, we use the same .read_csv() function as 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 Pandas 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.

Conclusion

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. We can use the relevant delimiter in the sep parameter based on our inspection. In this article, we 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 handy 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 other Pandas delimiters.

Frequently Asked Questions

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Rahul Shah 22 Jun, 2024

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