Pandas Tutorials
Pandas presents a systematic approach to data manipulation and analysis in Python. Our tutorials cover DataFrame operations, data cleaning, and aggregation techniques, suitable for beginners and intermediate data analysts. With free labs and practical code examples, you'll gain hands-on experience in working with structured data. Our data analysis playground allows you to experiment with Pandas functions and real-world datasets.
Working with Time Deltas
This lab guides you through the process of working with time deltas in Python using the pandas library. A time delta represents a duration or difference in time. We will explore different ways to construct, manipulate, and operate on time deltas.
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Working with Nullable Integers
In this lab, we will explore how to use the nullable integer data type in pandas, which is an efficient way to handle integer data that may contain missing values. We will learn how to construct arrays with this data type, perform operations, and handle missing values effectively.
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Working with Data Structures in Pandas
Pandas is a powerful Python library for data manipulation and analysis. Its fundamental data structures, Series and DataFrame, allow you to store and manipulate structured data. This lab will provide a step-by-step guide on how to work with these data structures, from creation to manipulation and alignment.
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Reshaping Data with Pandas
In this lab, we will explore how to reshape data in pandas using various functions like sort_values, pivot, pivot_table, and melt. We will work with the Titanic and Air Quality datasets to demonstrate the reshaping techniques.
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Working with Nullable Boolean Data
In this lab, we will explore the Nullable Boolean data type, provided by the Pandas library in Python. We will learn how to use this feature in indexing and logical operations, and how it differs from traditional boolean operations due to the presence of 'NA' values.
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Data Reshaping with Pandas
In data analysis, it is common to encounter datasets that are not arranged in the way we want. Reshaping data refers to the process of changing how data is organized into rows and columns. In this lab, we will explore some of the key methods in Pandas for reshaping data including Pivot, Stack, Unstack, Melt, Cross tabulations, Tiling, Factorizing, and Exploding.
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Working with Pandas
Pandas is a powerful data manipulation tool developed by Python. It's often used in data analysis and cleaning because it's flexible and easy to use. In this lab, we will learn how to use Pandas to perform basic operations like loading data, creating data frames, accessing data, and performing simple statistics.
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Working with Columns in Pandas
In this lab, we will learn how to work with columns in Pandas. We will explore how to create new columns derived from existing ones, apply mathematical and logical operations on columns, rename column labels, and perform column-wise operations using the apply method.
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Pandas Data Manipulation
This lab will guide you on how to read, write, and manipulate data using Pandas, a powerful data analysis and manipulation library for Python. We will use a dataset from the Titanic shipwreck for this exercise.
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Pandas Series Astype Method
The astype() method in Python's pandas library is used to convert the data type of a pandas Series object. It allows us to change the data type of the Series to a specified data type. This lab will guide you through the usage of the astype() method in pandas.
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Pandas DataFrame Drop Method
In this lab, we will learn how to use the drop() method in the Python Pandas library to remove specific rows or columns from a DataFrame. The drop() method allows us to drop labels by specifying index or column names and corresponding axis. We can also drop labels on different levels in a multi-index DataFrame.
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Pandas DataFrame Info Method
The info() method in the Python Pandas library is used to get a summary of a DataFrame. It provides valuable information about the DataFrame including the index dtype and columns, non-null values, and memory usage.
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Pandas DataFrame Nsmallest Method
The Python Pandas DataFrame.nsmallest() method is used to get the first n rows of a DataFrame that are ordered by columns in ascending order. This method returns the first n rows with the smallest values in the specified columns, in ascending order. The columns that are not specified are returned as well, but not used for ordering.
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COVID-19 Data Analysis with Python
In this project, you will learn how to analyze COVID-19 data using Python. The COVID-19 pandemic has had a significant impact on the world, and understanding the data is crucial for tracking the spread of the virus and making informed decisions.
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Credit Card Holder Risk Prediction
In this project, you will learn how to build a machine learning classification model to predict the risk status of credit card holders. The project involves preprocessing the data, training a support vector machine (SVM) model, and saving the prediction results to a CSV file.
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Your First Pandas Lab
Hi there, welcome to LabEx! In this first lab, you'll learn the classic 'Hello, World!' program in Pandas.
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Pandas Series Asfreq Method
The Series.asfreq() method in Pandas is used to convert a time series to a specified frequency. It allows us to fill in missing values or null values in the time series. This tutorial will guide you on how to use the Series.asfreq() method in Python Pandas.
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Pandas Series Append Method
The pandas Series.append() method is used to merge or concatenate two Series and return a new Series. It allows you to combine the elements of two Series together, either with or without preserving the original index values.
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