Pandas, the open-source data manipulation and analysis library for Python, has become an essential tool in the toolkit of data scientists and machine learning engineers. With its ability to handle large datasets and perform data-wrangling operations efficiently, Pandas has become a go-to library for many data professionals. In this article, we will explore some of the most common use cases for Pandas, from data sorting to filtering, and provide project ideas for Python users to practice their skills.
What is Pandas for?
Pandas is a popular open-source data analysis and manipulation library that has become a cornerstone of machine learning and data science. It is built on top of the Python programming language and provides easy-to-use data structures and tools for efficient data analysis. Pandas is widely used because of its flexibility and ease of use, making it an essential tool for any data scientist or analyst.
Pandas is well-suited for many different kinds of data, including time series data, structured data, and any other data that can be represented as tables. Here are just a few of the things that pandas does well:
- Data cleaning and preparation
- Data filtering, grouping, and aggregation
- Time-series functionality
- Handling missing data
- Merging and joining data sets
- Data reshaping and pivoting
- Handling categorical data
Famous Examples Where Pandas is Used
Pandas is widely used in various industries, including tech, finance, healthcare, and retail. Here are some examples of real-life applications of Pandas:
- Netflix Recommendations: Pandas plays a crucial role in Netflix’s recommendation system. Netflix collects data on users’ viewing history, search queries, and other interactions with the platform. Pandas is used to clean and preprocess this data to create personalized recommendations for users. The platform uses machine learning algorithms, which are trained using Pandas data frames, to predict what users might want to watch next.
- Churn Rate in Banking: Pandas is used in the banking industry to predict customer churn rate. Churn rate is the percentage of customers who leave the bank over a given period. By using Pandas data frames, banks can collect and analyze data on customer transactions, account history, and other factors to predict which customers are most likely to leave. This information can then be used to create targeted retention strategies.
- Retail Sales Data Analytics: Retailers use Pandas to analyze sales data and optimize their inventory. By collecting data on sales trends, retailers can identify which products are selling well and which are not. Pandas data frames can be used to manipulate and analyze this data to gain insights into customer behavior, pricing trends, and inventory management.
In addition to these examples, Pandas is also used in scientific research, data journalism, and many other fields. Pandas is a versatile tool that can be applied in a wide range of industries and use cases. From finance to healthcare, transportation to entertainment, Pandas has proven to be an essential tool for data management and analysis. The examples above illustrate just a few of the many ways in which Pandas has been used to improve decision-making and drive insights.
Data Sorting in Different Orders
Data sorting is an important task in data analysis, and Pandas provides a simple way to sort data in different orders. With Pandas, you can sort data by a single column or by multiple columns at once, in ascending or descending order. For example, you can sort a DataFrame by a specific column using the sort_values() method. To sort the data in descending order, you can set the ascending parameter to False. Here’s an example that sorts a DataFrame by the ‘age’ column in descending order:
This will output the sorted DataFrame with the ‘age’ column in descending order.
Adding a New Column in a Specific Place
One useful feature of Pandas is the ability to add new columns to a DataFrame in a specific position. For example, let’s say you have a DataFrame with columns for a person’s name, age, and gender. You might want to add a new column for their occupation, but you want it to be inserted in between the age and gender columns. You can achieve this by using the insert() function. Here’s an example:
This code will create a new column called ‘occupation’ and insert it at index 2 (i.e., after the ‘age’ column) in the DataFrame. The values in the new column are provided as a list in the same order as the rows in the DataFrame. The resulting output will look like this:
Finding Unique Values with value_counts()
One of the common tasks in data analysis is finding unique values within a column. The value_counts() function in Pandas is a simple way to do this. By calling this function on a Pandas series, you can get a count of all unique values within that series. For example, let’s say you have a column of data called fruit and you want to see how many times each fruit appears in the dataset. You can simply use the value_counts() function on the fruit column, like this:
This will be the output:
As you can see, the value_counts() function returns a series of the unique values in the fruit column, along with their counts. This can be useful for gaining insights into your data and understanding the distribution of values within a column.
Selecting a Column Based on the Data Type
In Pandas, you can easily select a specific column based on its data type. For instance, if you have a DataFrame with columns of mixed data types, you can use the select_dtypes() function to select columns of a specific data type. For example, suppose you have a DataFrame df with columns of integer, float, and object data types. To select only the columns with integer data type, you can use the following code:
This will return a new DataFrame with only the columns of integer data type. Similarly, you can select columns of other data types such as float, object, bool, and datetime64[ns] by changing the value of the include parameter. For example, to select only the columns of object data type, you can use df.select_dtypes(include=’object’). This function is particularly useful when you need to perform operations on columns of a specific data type, such as applying a statistical function to all columns of numerical data type.
Filtering Columns Based on Exact/Partial Match
Filtering Columns Based on Exact/Partial Match is an important technique for data scientists to quickly find and manipulate relevant data. Pandas allows us to do this with ease. Suppose we have a DataFrame with columns “name” and “age”, and we want to filter out all rows where the name starts with “J”. We can use the str.startswith() method in Pandas to achieve this. The following code snippet demonstrates how to do it:
The output of the above code will be a new DataFrame containing only rows where the name starts with “J”. Similarly, we can use other string methods in Pandas like str.contains() and str.endswith() to filter columns based on partial matches.
Python Pandas Practice Projects
Now that we have seen the various use cases of Pandas, it is time to put our knowledge to the test with some hands-on practice projects. Here are some project ideas that can help you solidify your Pandas skills:
House Price Prediction Project using Machine Learning in Python
This project aims to create a machine learning model in Python that predicts the log error between the Zestimate and the final sale price of houses. The dataset used is the Zillow dataset, which is loaded and prepared using Pandas. Various machine learning techniques are employed to construct the prediction model. The project focuses on predicting house prices based on multiple attributes.
E-commerce Product Sentiment Analysis
This project involves analyzing e-commerce product ratings and reviews to determine the sentiment of the product and rank them based on relevance. The project uses Pandas to load and manipulate the training dataset, perform cross-tabulation between product name and review labels, and execute the four phases of the project’s execution- data preprocessing/filtering, feature extraction, pairwise review scoring, and classification. The end goal is to provide a sentiment score for each product and rank them according to their relevance.
Speech Emotion Recognition
The goal of this project is to create a machine learning model that can detect emotions from sound files. The project uses Keras and TensorFlow libraries to build an MLP (multi-layer perceptron) from the sklearn library. The dataset used is the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS), which contains 7356 files. The dataset has 24 professional actors (12 female, 12 male) speaking two lexically-matched statements in a neutral North American accent. The project involves loading the dataset, performing exploratory data analysis on the dataset using the Pandas package, and building a model that can detect emotions from sound files.
Customer Churn Prediction
This project aims to use Python to implement logistic regression on data from a streaming app to determine whether a customer is likely to churn or not. The dataset used belongs to a video streaming platform and has approximately 2000 rows and 16 columns. The project uses Pandas to handle the class imbalance in the provided data, perform resampling between the majority and minority classes, and concatenate the training datasets. The end goal is to build a model that can accurately predict customer churn and provide insights into customer behavior.
These projects are a great way to practice your Pandas skills and apply them to real-world problems. They cover a range of topics and difficulty levels, so you can choose the ones that best suit your interests and experience level. And if you want to take your skills to the next level, check out WildLearner’s new online certified course on Pandas, where you’ll learn from expert instructors and get hands-on experience with real datasets. With WildLearner, you can take your data analysis skills to the next level and stand out in a competitive job market.
Conclusion
In summary, Pandas is an essential tool for any data scientist or analyst working with Python. It allows for efficient data manipulation and analysis, making it a powerful asset in any project. With the resources and projects available on WildLearner, you can continue to improve your Pandas skills and become a proficient data professional. So why not start today by checking out their online certified course on Pandas? Happy learning!
