Creating a data frame is something that all python programmers must deal with. It is not an overestimation to call this issue the backbone of python programming. That is why so many functions were developed to make this process simple, and one of them is pandas read_csv().

Below, we will walk you through the syntax of this function and how to use them to its full potential.

Pandas read_csv() Syntax

The read_csv() function is among the functions with the most parameters in pandas. Just one look at the syntax below will confirm this notion.

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 reason for this complexity lies in the CSV files’ complicated nature. To read a CSV file in its entirety, a function needs a lot of additional information about the file.

Some parameters also function as a speed booster, making the process much shorter than it should. This is especially helpful when you want to read huge CSV files.

Pandas read_csv() To Build DataFrame

One of the most common read_csv() applications is to build a data frame. Due to the read_csv() built-in feature, you don’t even have to declare the data frame. Simply assign it to read_csv(), and the function will take care of the rest.

import pandas as pd

dataframe = pd.read_csv('example.csv') 
print(dataframe)

This approach relieves you of the need to interact with the mountain of parameters. All you need to do is call the function and give it the file.

It will automatically read the file’s first rows and put them as the header. Then, it makes an index based on incremental numerical values, starting from zero. This solution is the fastest and easiest to master.

Of course, it also has the weakness of not being able to customize too much. The only thing that you can do in this aspect is specifying which separator for the columns by using either the sep or delimiter parameter.

Index Column

The next parameter is index_col, which allows you to index the table in accordance with one column. It accepts mainly str, int, False, and sequence of str/int as values. There is also the default value of None.

Some smart guys can even use it as an out of the box method to convert dict to string.

Ignoring Data

Sometimes, the CSV file contains some data that we just don’t need. In this case, the read_csv() function offers a variety of approaches with the skiprows, skipfooter, and usecols parameters.

As you can guess from the name, skipfooter lets you skip the first row and the last row of data. You should keep in mind that while it is very robust, it can’t delete other rows. The only way to do so is by specifying the row you want to be deleted with skiprows.

usecols is the complete opposite of skiprows, wherein each will only read the columns specified. Other data will be left behind.

There are still a lot of other parameters available in read_csv(). However, the average programmer hardly ever gets the chance to use them. If you want to learn more, you can refer to the pandas documentation.

Conclusion

After reading this article, you should now have a strong grasp on panda read_csv(). As long as you know the difference between the main parameters like usecols, skiprows, etc., you should have no problem manipulating all the data.

Please look forward to our next publication if you find this article helpful.