15 Tips for Using Pandas to Wrangle and Clean Data: Data Scientists
Data Wrangling, as explained over at runrex.com, is the process of cleaning and structuring complex data sets to pave way for easy analysis and speedier decision-making. When it comes to wrangling and cleaning of data in Python, Pandas is the most preferred tool by data scientists. This is because the data structures that the Pandas library offers are fast, flexible, and expressive, and are also specifically designed to make real-world data analysis significantly easier. Given that data scientists will spend most of their time on data cleaning and data wrangling rather than on coding or running models, this article will look to highlight 15 tips for using Pandas to wrangle and clean data with the hope it will help make your work easier.
- Tip on importing Pandas
If you are looking to make use of the Pandas library to wrangle and clean your data, then the first thing you need to do is import it. If you are just starting with Python, then this may not be as straightforward as it should. An important tip when importing Pandas, according to the gurus over at guttulus.com, is to stick to the import convention which states that you should import Pandas as pd.
- A tip when using the help() function
The help() function is one of the most useful Pandas functions as described over at runrex.com. However, when using this function, you should ensure that you are as complete as possible as this will allow you to get more information about any function or concept that is included in the Pandas library.
- Select data type using Pandas
Another important tip worth mentioning, one that will make your life so much simpler when wrangling and cleaning data is to select data type using Pandas, which is explained over at guttulus.com, rather than writing ‘if’ conditions to separate continuous and categorical variables for data analysis.
- Apply Pandas operations in parallel
As is revealed in discussions on the same over at runrex.com, the traditional Pandas library can get quite slow when you are working on a large dataset. A tip that will help save you lots of time when wrangling and cleaning data in such a situation is applying your Pandas operations in parallel. The tool that will help you do just that is the Pandarallel tool which is a simple and efficient tool designed to help you parallelize your Pandas operations on all your available CPUs.
- The Pandas Melt function
Another function you need to be aware of when using Pandas to wrangle and clean data is the Pandas Melt function. This useful function will give you the functionality to unpivot a data frame from wide to long format, making your task that much easier, and is a function you need to be aware of as a data scientist.
- Tip on dealing with missing values
The dataset you may be working on may consist of a lot of missing values, and it is your responsibility as a data scientist to deal with them before applying any machine learning algorithms on the dataset for accurate predictions. As is outlined over at guttulus.com, once you identify the missing values in your dataset, you will have two options available to you: you can either drop those rows which have missing values, or you can fill them with a certain value (zero, mean, median, max, min, and so forth). The option will take should depend on the percentage of the missing values. For example, if the number of rows of missing values is in a huge percentage, then dropping them won’t be a good option to choose.
- Tip on filtering data
Your dataset may also contain duplicate values, which means that you may want to remove the values that are duplicated to avoid skewing your results. To do this, the gurus over at runrex.com recommend adding a tilde, which will reverse the Pandas Boolean series and give you a data frame containing values that do not repeat more than twice.
- Tip on sorting
For efficient cleaning and wrangling of your dataset, you will have to sort it, which you can achieve using the sort-values function on Pandas which helps you sort your data frame either in ascending or descending order. By default, this function uses the quick sort algorithm for sorting according to guttulus.com, and, therefore, if you want to use merge sort or heap sort, then an important tip is to use the kind keyword.
- Tip on the conditional selection of rows
Pandas provides users with a quick and easy way to perform all manner of analysis, and one such important analysis is the conditional selection of rows, which can be based on a single condition or multiple conditions in a single statement separated by logical operators. It is important to point out that, when using this Pandas hack, you should remember to put each of the conditions inside the parenthesis, otherwise you will get an error according to runrex.com.
- Tip on binning of data
Depending on the requirements of your analysis, you can either be working with continuous or categorical data. Given that there are times when you may not require the exact value present in your continuous data, but just the group it belongs to, the binning of data with Pandas is an important tip. To perform binning, you use the cut() function which is useful for going from a continuous variable to a categorical one.
- Tip on the grouping of data
The Groupby operation is one of the most essential Pandas functions as outlined over at guttulus.com, helping with the grouping of data. It involves the splitting of an object based on certain conditions, applying a function, and then combining the results and is a function you need to be aware of in case you run into a problem requiring its intervention.
- Tip on conditional formatting
Conditional formatting, as explained over at runrex.com, is the operation allowing you to apply visual styling to your data frame based on a given condition. Using this operation will allow you to pinpoint the data that follows a certain condition visually, and is one of the most powerful Pandas hacks out there.
- Tip on Pandas mapping
This operation, achieved using the Pandas map() function is used to map each value in a series to some other value, based on an input correspondence. This input may be a Series, Dictionary, or a function as covered over at guttulus.com and is another Pandas tip worth knowing about when wrangling and cleaning data.
- Tip on the Explode function
The Pandas explode() function is another very useful Pandas function according to the gurus over at runrex.com. This function comes in handy when you have lists stored in a data frame column as it unpacks the values in the list and duplicates all other values, hence the term “explode”.
- Tip on Pivot
A pivot table is a table of statistics that summarizes the data of a more extensive table, which makes it a very useful tool for any data scientist. An important tip when trying pivot is to use the pivot_table function which also supports aggfunc.
These are just some of the tips to keep in mind when using Pandas to wrangle and clean data, with more on this topic to be found over at the highly-rated runrex.com and guttulus.com.