If you have your sights set on AI, you’re in good company. Today, it seems like everyone needs to use artificial intelligence in some capacity and data experts are no exception: figuring out how to easily and efficiently analyze, sort, and manipulate data is a crucial part of any data analyst’s work, and one that becomes increasingly complicated as the data sets you’re working with expand.
That’s exactly where Pandas comes in and no, we’re not talking about fluffy bears that we love. Pandas is an open-source software library that’s a popular choice for analysts looking to clean, modify, and organize data, thanks to its ease of use and valuable insights.
Among others, these are some of the most common applications of Pandas:
Data cleaning and preparation: as collecting data becomes easier for companies and they’re swamped with data from practically every direction, Pandas can help them sort, clean, and prepare the data for analysis.
Exploratory data analysis: if you’re looking for a general idea of what your data is representing, exploratory data analysis is key and Pandas can help summarize exactly what your data is showing in little to no time.
Feature engineering: with its ability to highlight major features that you should take under consideration, Pandas can help facilitate the feature engineering process.
Future analysis: as Pandas can not only analyze data but also make future predictions, it can be incredibly helpful when it comes to forecasting future trends.
Now that you’re clear on why Pandas is so useful, let’s dive into the top 10 Pandas functions that every AI professional should know.
Top 10 Pandas Functions
read_csv()
As the vast majority of datasets are exported to CSV files, the ability for Pandas to read the information within the file makes the data analysis process much easier and allows you to upload the datasets into DataFrame quickly.
df = pd.read_csv('data.csv')
Using this function is typically the first step of working with a machine learning model–you’ll upload the file to train the data and then move on to processing and inspecting it.
head() and tail()
This function, while ultimately performing a simple act, allows you to visualize the first few rows (head) and last few rows (tail) of your data set, which helps you verify that all the data has been uploaded correctly.
df.head(10) # Check first 10 rows
df.tail(5) # Check last 5 rows
Confirming this post uploading your data ensures you’re ready to move forward.
info()
This function provides useful information and metadata about the DataFrame, such as index dtype, column names, non-null counts, and memory usage.
df.info()
As the datasets you work with get bigger and bigger, flagging any missing data before you get started is key. This function can help you do just that, in addition to highlighting any other issues that should be resolved before starting to train the model.
describe()
The describe function can quickly give you an idea of what the data will show, helping the experts figure out exactly what is going on.
df.describe()
This exploratory data analysis can give you an indication of what your next steps might be without having to wait for the data analysis to be completed.
groupby()
To quickly get a peek at trends within specific datasets, the groupby function allows you to choose characteristics to group the data, giving you insights within a specific area.
df.groupby('category_column').mean()
This function uses aggregation options such as mean, sum, and count to summarize specific groups of data.
apply()
As you dive deep into your data analysis, you’ll find yourself wanting to use functions that fall outside of the typical abilities Pandas has, and that’s where apply() comes in.
df['new_column'] = df['existing_column'].apply(lambda x: x * 2)
Through this function, you can create custom logic to manipulate or engineer features that meet your specific needs.
merge()
Looking at data from various locations? The merge() function can help you unite datasets that are in separate DataFrames, based on key relationships.
df_merged = pd.merge(df1, df2, on='common_key')
Combining two datasets can help you unearth even deeper insights.
pivot_table()
This function allows you to collect and summarize data in a multi-functional table–which is a very common use with AI for functions such as market segmentation and fraud detection.
df.pivot_table(index='column1', values='column2', aggfunc='mean')
Pivoting data by multiple indices and aggregate values gives you the valuable flexibility of analyzing different aspects of your dataset.
isnull() and fillna()
To combat the issue of missing data, isnull() and fillna() are your friends. The former checks for missing values and the latter fills in those missing values–and it also allows you to null missing data so it doesn’t interfere with your data analysis.
df.isnull().sum() # Check for missing values
df.fillna(0, inplace=True) # Fill missing values with 0
This is so important because it will allow you to avoid future issues caused by missing data.
to_numpy() and values
The majority of artificial intelligence and machine learning frameworks require you to input data in the form of NumPy arrays and these functions can convert DataFrames into NumPy arrays.
array = df.to_numpy()
This step is a requirement and therefore quite useful when it comes to moving from data preprocessing into model training.
Pandas is a powerful tool in the hands of AI experts, making it easier to manipulate, clean, and explore datasets. These ten functions provide the foundation for efficiently working with data, from loading CSV files to handling missing values and merging multiple datasets.
Mastering these functions will not only help you streamline your workflow but also empower you to make smarter, data-driven decisions as you build machine learning models.
With Pandas in your toolkit, the challenges of big data become more manageable, and your AI projects can reach their full potential.
About the Author: Juliette Carreiro is a skilled content creator with over five years of experience in SEO, content ideation, and digital marketing strategy. She has spent more than two years at Ironhack, where she developed in-depth articles on topics ranging from career growth in tech to the future impact of AI. With expertise across tech, hospitality, and education industries, Juliette has helped brands like Ironhack engage their audiences with impactful storytelling and data-driven insights.
