Pandas Tutorial

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.

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.

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.

Hi there, welcome to LabEx! In this first lab, you'll learn the classic 'Hello, World!' program in Pandas.

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.

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.

In this lab, we will learn about the agg() method in the Pandas Series object. The agg() method allows us to apply one or more aggregation functions to the elements of a Series along a specified axis. It returns a scalar value when called with a single function and multiple values when called with multiple functions.

The apply() method in pandas allows us to apply a function to the values of a Series. It can be used to apply either a Python method or a NumPy ufunc to the entire Series or to individual elements of the Series.

In this lab, you will learn how to use the aggregate() method in Pandas to perform aggregation operations on a Series. The aggregate() method allows you to apply one or more operations on the elements of a Series along a specified axis.

In this lab, you will learn how to use the DataFrame.rank() method in Pandas to assign ranks to the data in a DataFrame. The rank() method provides a numerical rank from 1 to n along the specified axis, which can be either the index or column axis. This allows you to determine the ranking of values in a DataFrame based on a particular column.

In this tutorial, we will learn about the Python pandas DataFrame.reindex() method. We will explore how to change the index and columns of a DataFrame using this method. The DataFrame.reindex() method allows us to reconcile the DataFrame with a new index by filling null values in the locations that have no value in the previous index.

In this lab, you will learn about the query() method in the Pandas library. The query() method allows you to filter a DataFrame based on a boolean expression. It is similar to the filter() method. You can use this method to filter the DataFrame based on one or more columns, as well as combine multiple conditions using the 'AND' operator.

In this lab, we will learn how to use the pivot() method in the Python Pandas library. The pivot() method allows us to transform or reshape a DataFrame by changing the organization of the index and column values.

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.

The pct_change() method in the Pandas DataFrame calculates the percent change in the DataFrame between the current and previous element. It is useful for analyzing data and calculating differences in sales, month-to-month or year-to-year.

In this lab, we will be exploring the pop() method in the Python Pandas library. The pop() method is used to delete or drop a specified item in a DataFrame and returns the item. If the specified item is not found, the method raises a KeyError.

In this lab, we will learn about the pivot_table() method in the Python pandas library. The pivot_table() method is used to aggregate and summarize data in a DataFrame. It returns a spreadsheet-style pivot table as a new DataFrame.

In this lab, we will learn how to use the DataFrame.memory_usage() method in Python Pandas. This method allows us to calculate the memory usage of each column in a DataFrame. We will go through step-by-step instructions on how to use this method with examples.