Pandas Bootcamp 2022: Complete Pandas Walkthrough

Install and import pandas to perform data analysis and machine learning with this powerful Python library, featuring aggregations, group by, and inbuilt visualizations.

Learn how to create and manipulate a Pandas series from a list or a dictionary, define or infer an index, and apply basic operations on its values.

Explore built-in pandas series methods, including creation, index assignment, and common aggregations. Learn to use head, tail, min, max, mean, standard deviation, variance, unique, and value_counts to analyze data.

Explore data frames as a multi-column extension of series, illustrated with Excel-like tables, and show creation from lists, dictionaries, and numpy with configurable columns, indexes, and values.

Learn Pandas indexing, filtering, and selection on a dummy dataset, including reading data from Excel, querying single or multiple columns, and subsetting rows by index with head and tail.

Learn to use pandas inbuilt methods for statistics, algebra, visualizations, and aggregations; apply min, max, sum, and mean to columns, view rows with head, and inspect data types.

Explore descriptive statistics with Pandas, computing mean, median, mode, variance, standard deviation, and quartiles with interquartile range on a four-city sales dataset.

Discover how the describe method yields descriptive statistics for numerical and character data, including mean, standard deviation, quartiles, min, max, and counts or frequencies for categories, facilitating exploratory data analysis.

Learn to handle missing data in pandas with fillna and dropna, choose median or mean for filling, and drop rows or columns using any or all nulls.

Learn to build crosstabs, cross-tabulations that summarize data across locations, number of days, and promotions yes/no. Start with two columns and extend to three or more dimensions.

Apply the cut method to create bins from data ranges, assign each value to a bucket, and optionally label bins as low, medium, and high for analysis.

Learn how to drop columns or rows and rename columns or the index in pandas, using axis and inplace to commit changes to a manufacturing dataset.

Learn how to convert a categorical column into numerical features using pandas get_dummies, creating dummy variables for gender (male and female) with one-hot encoding.

Learn how the group by function aggregates data by origin or multiple columns, then apply sum or mean to each group using a car manufacturing dataset.

Learn how to set a column as the index in pandas, compare default and customized indexes, and reset the index to its original state, with in-place options.

Explore how to merge datasets in Pandas using inner, left, right, full outer, and cross joins, with practical examples on two dataframes and common keys.