Pandas Read CSV in Python

CSV files are Comma-Separated values files that allow storage of tabular data.

• To access data from the CSV file, we require a function read_csv() from Pandas that retrieves data in the form of the data frame.
• First, we must import the Pandas library, then using Pandas load this data into a DataFrame

In the code below, we are working with a CSV file named people.csv which contains people data.

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read_csv() function

read_csv() function in Pandas is used to read data from CSV files into a Pandas DataFrame. A DataFrame is a data structure that allows you to manipulate and analyze tabular data efficiently. CSV files are plain-text files where each row represents a record and columns are separated by commas (or other delimiters).

Syntax

pd.read_csv(filepath_or_buffer, sep=' ,' , header='infer',  index_col=None, usecols=None, engine=None, skiprows=None, nrows=None) 

Parametersthere are no :

• filepath_or_buffer: Location of the csv file. It accepts any string path or URL of the file.
• sep: It stands for separator, default is ', '.
• header: It accepts int, a list of int, row numbers to use as the column names and the start of the data. If no names are passed, i.e., header=None, then, it will display the first column as 0, the second as 1 and so on.
• usecols: Retrieves only selected columns from the CSV file.
• nrows: Number of rows to be displayed from the dataset.
• index_col: If set to None, Pandas automatically assigns a default integer index (0, 1, 2, ...) to the dataset.
• skiprows: Skips passed rows in the new data frame.

Features in Pandas read_csv

1. Read specific columns using read_csv

The usecols parameter allows to load only specific columns from a CSV file. This reduces memory usage and processing time by importing only the required data.

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2. Setting an Index Column (index_col)

The index_col parameter sets one or more columns as the DataFrame index, making the specified column(s) act as row labels for easier data referencing.

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3. Handling Missing Values Using read_csv

The na_values parameter replaces specified strings (e.g., "N/A", "Unknown") with NaN, enabling consistent handling of missing or incomplete data during analysis.\

na_values only specifies which values should be treated as NaN; it does not guarantee that the dataset has no missing values.

4. Reading CSV Files with Different Delimiters

In this example, we will take a CSV file and then add some special characters to see how the sepparameter works.

totalbill_tip, sex:smoker, day_time, size
16.99, 1.01:Female|No, Sun, Dinner, 2
10.34, 1.66, Male, No|Sun:Dinner, 3
21.01:3.5_Male, No:Sun, Dinner, 3
23.68, 3.31, Male|No, Sun_Dinner, 2
24.59:3.61, Fe...

The sample data is stored in a multi-line string for demonstration purposes.

• Separator (sep): The sep='[:, |_]' argument allows Pandas to handle multiple delimiters (:, |, _, ,) using a regular expression.
• Engine: The engine='python' argument is used because the default C engine does not support regular expressions for delimiters.

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5. Using nrows in read_csv()

The nrows parameter limits the number of rows read from a file, enabling quick previews or partial data loading for large datasets. Here, we just display only 3 rows using nrows parameter.

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6. Using skiprows in read_csv()

The skiprows parameter skips unnecessary rows at the start of a file, which is useful for ignoring metadata or extra headers that are not part of the dataset.

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7. Parsing Dates (parse_dates)

The parse_dates parameter converts date columns into datetime objects, simplifying operations like filtering, sorting or time-based analysis.

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Loading a CSV Data from a URL

Pandas allows you to directly read a CSV file hosted on the internet using the file's URL. This can be incredibly useful when working with datasets shared on websites, cloud storage or public repositories like GitHub.

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