Read CSV File in Python Pandas

Introduction

Every machine learning model craves data. We must get data to/from our programs. Text file exchange is a typical method for sharing information between applications. The CSV format is among the most widely used data exchange formats. How do we employ it, though?

What is a CSV File? Why is It Used in Pandas?

So, what exactly is a CSV file? CSV files, which stands for Comma Separated Values files, are a simple text document that employs a specialized structure to organize tabular information. Because it is a simple text file, it could only include textual information; that is, readable ASCII or Unicode characters.

The full title of a CSV file reveals the underlying format. CSV files typically employ a comma to separate every data value. This is how the structure appears:

Sample CSV File

Observe that each data item is delimited by a comma. In most cases, the first line specifies each data item—the column header's title. Every succeeding line contains actual information and is restricted by file capacity limits.

The separating symbol is known as a delimiter; the comma isn't the only one employed in practice. Other often used delimiters encompass the tab (t), colon (:), and semicolon (;) symbols. Now that we've defined what we meant by a CSV file let's look at why they're the first choice for storing data in Pandas.

Why is It Used in Pandas?

Among the most prevalent reasons that CSV is the primary choice for storing data are:

• Because CSV files are simple text files, they are easier for website developers to construct.
• They're easy to import into a tabular format (Like Excel) or another storage database (Like SQL) since they're simple text, independent of the program we're employing.
• To improve the organization of enormous volumes of data.

CSV Module Functions

We do not need to create our custom CSV parser from scratch. We can use several suitable libraries. For the most part, the Python CSV module should do. The CSV module is specially designed to handle this operation, making it much simpler to work with CSV files. This is especially useful when working with data generated to text files from databases (Like SQL) and Excel spreadsheets. This data might be difficult to comprehend on its own.

If your project involves a huge amount of data or quantitative analysis, the pandas library offers CSV parsing capabilities that should take care of the rest. This post will explore how to parse and modify data using the Pandas Library.

Understanding the read_csv() Function

To access data from a CSV file, use the read_csv() method. The read_csv() function has the following syntax:

Syntax

pandas.read_csv(filepath_or_buffer, sep=NoDefault.no_default, delimiter=None, header='infer', names=NoDefault.no_default, index_col=None, usecols=None, squeeze=None, prefix=NoDefault.no_default, mangle_dupe_cols=True, dtype=None, engine=None, converters=None, true_values=None, false_values=None, skipinitialspace=False, skiprows=None, skipfooter=0, nrows=None, na_values=None, keep_default_na=True, na_filter=True, verbose=False, skip_blank_lines=True, parse_dates=None, infer_datetime_format=False, keep_date_col=False, date_parser=None, dayfirst=False, cache_dates=True, iterator=False, chunksize=None, compression='infer', thousands=None, decimal='.', lineterminator=None, quotechar='"', quoting=0, doublequote=True, escapechar=None, comment=None, encoding=None, encoding_errors='strict', dialect=None, error_bad_lines=None, warn_bad_lines=None, on_bad_lines=None, delim_whitespace=False, low_memory=True, memory_map=False, float_precision=None, storage_options=None)

The following is a list of parameters, along with their default settings. Although not all of them are particularly critical, memorizing them might save you time when conducting some tasks on your own. To examine the arguments of the read_csv() method, use shift + tab in jupyter notebook, or check the Pandas official documentation. The following are useful ones, along with their applications:

Parameter List

How to Load the CSV into a DataFrame?

Now that we've gone through the syntax of the read_csv() method, let's look at some practical applications. Pandas's read_csv() method converts a CSV file to Pandas DataFrame format. As previously stated, based on the functionality we desire, we may provide a variety of parameters to our read_csv() method. Let's start by just supplying the filepath_or_buffer and seeing what happens.

Code:

Output:

It read CSV files in python pandas from the path we supplied. What if we want greater control over how our CSV file is loaded? For example, imagine we want to explicitly choose which row will be employed as column labels for your DataFrame. We will employ the header parameter for this functionality. The default value for header is 0, which implies that the very first row of the CSV file will serve as column labels. If your file lacks a header, just assign the header to None.

Code:

Output:

We may also use another delimiter than a comma to parse our csv file. However, the delimiter in our case is a comma, which is the default value of the sep argument.

We may use the index_col argument to specify which columns would be employed as the DataFrame's index.

Code:

Output:

Assume we simply need to read the first given number of rows from the file or load a CSV file with a defined list of columns to load into the DataFrame. We can leverage the nrows and usecols arguments to our advantage, respectively.

Code:

Output:

Now that we've seen how to load a CSV file into a DataFrame, let's look at how to load a CSV file into a Python dictionary.

How to Read a CSV File in Python Dictionary?

Once we understand how to load a CSV file in Pandas DataFrame, reading a CSV file in Python Dictionary becomes pretty simple. To read CSV file in Python Pandas dictionary, first read our file in a DataFrame using the read_csv() method, then transform the output to a dictionary employing the inbuilt Pandas DataFrame method to_dict().

Code:

Output:

What is the to_string Method in Pandas?

So far, we've seen how to load a CSV file in either DataFrame or Python dictionary format. However, we discovered that we could only print a small portion of our dataset. What if we want to print our complete dataset, which isn't massive; it's in the millions or billions. The to_string() function is the simplest; it turns the whole Pandas DataFrame into a string object and works effectively for DataFrames with thousands of rows.

Code:

Output:

How to Print a DataFrame Without Using the to_string() method

To print the complete CSV file, we may use the following method instead of the to_string() method:

• Using pd.option_context() Method
• Using pd.set_options() Method
• Using pd.to_markdown() Method

Pandas' option_context() and set_option() functions allow us to modify settings. Both techniques are identical, except that the latter modifies the settings forever, and the former does so only inside the context manager scope. To further comprehend it, consider the following code example.

Code:

Output:

Code:

Output:

Pandas's to_markdown() function is similar to the to_string() function in that it transforms the DataFrame to a string object and adds styling and formatting. Consider the following example:

Code:

Output:

How to Check the Number of Maximum Returned Rows?

Now that we've looked at how to load a CSV file let's look at different methods for calculating the total number of rows in our data.

We may use any of the methods listed below to count the number of rows in our data:

• Using len() function

The len() built-in function is the simplest and clearest way to determine the row count of a DataFrame. Consider the following code example to better understand it.

Code:

Output:

• Using shape attribute

Similarly, pandas. DataFrame.shape can produce a tuple describing the DataFrame's dimensionality. The first tuple member reflects the number of rows, while the second member denotes the number of columns. To further understand it, consider the following code sample.

Code:

Output:

• Using count function

The third and final option for determining row counts in Pandas is the DataFrame.count() method, which provides the total count for non-NAN values. Consider the following example.

Code:

Output:

This leads us to the end of our article. Kudos! You now have a firm grasp on importing and altering data from a CSV file.

Read More:

• Read CSV File in Python.
• Read Excel File in Python.
• Read JSON file in Python.