2026 Pandas Bootcamp for Data Analysis with Python

Explore pandas basics, installation, and what you can do with this python data analysis library, as chapter one outlines introduce pandas and a hello world program.

Discover how pandas, a Python library for data manipulation and analysis, handles series and data frames, missing data, merging, and calculations, and how to install the latest 2.01 version.

Learn to upgrade pip, install pandas, and uninstall packages using the pip package manager in Python. It covers basic installation steps and confirms pandas installation with a version check.

Learn to create a basic pandas program that converts a Python list to a pandas series, assigns an index, and prints the result.

Explore Pandas data structures such as Series, DataFrame, and Panel with introductory examples in chapter 2 of the Pandas complete course, and learn how these structures organize data.

Learn how to create a pandas data frame, a two-dimensional table with named columns and rows, store mixed data types, and view it with a simple print.

Explore the panel data structure in pandas, its role as a three dimensional data structure of data frames, and how to replace deprecated panels with concatenation in newer versions.

Explore the Pandas series data structure in Python and learn to create series from lists, sets, dictionaries, and tuples, with indexing, slicing, and key methods like head and describe.

Explore five methods to create a pandas series, including direct creation, and from list, tuple, set (via conversion), and dictionary, with practical code demonstrations.

Learn to perform arithmetic operations on pandas Series, including addition, subtraction, multiplication, division, and exponentiation, using operators and the add method for data cleaning.

Learn how to filter a pandas series by a condition, create a boolean mask, and perform post-filter aggregation (mean, max, min) to analyze the resulting subset.

Explore the attributes of a pandas series, such as index, size, data type, name, uniqueness, and monotonicity, and learn to create labeled series from dictionaries with custom indexes.

Explain how to compute the mean of a pandas series using the mean method, which can also apply to data frames, with a concrete example returning 16.0.

Learn how the unique method on a Pandas series returns only distinct values, helping you identify duplicates and view unique values in series and data frames.

learn the value_counts method to count occurrences of specific values in a pandas series or dataframe. see how many times each value appears and view results with value_counts.py.

Learn how to use the apply method to run a user defined function on every element of a pandas series or dataframe, including examples that add five or two.

Learn to select, add, and delete rows in a DataFrame using index numbers and the loc accessor, and manage row counts with the length function and the drop method.

Explore aggregation on dataframes by combining values into a single result with sum, mean, and count. Use a salary column example and demonstrate applying these functions in code.

Master sorting a pandas data frame by age in descending order using sort_values, including creating the data frame and displaying the sorted results.

Explore data frame attributes in pandas, including index, columns, shape, size, data types, and emptiness, with practical examples of names, ages, and marks.

Learn how the pandas dropna method removes rows with missing values in a DataFrame, handling None and NaN to produce a complete dataset.

Explore panel in pandas, a three-dimensional data structure deprecated in current versions. Learn about alternative methods and how data frame and series replace panel, with reference to chapter two.

Explore chapter six of the pandas bootcamp to learn function application for element-wise, row-wise, and column-wise operations on data frames, empowering data manipulation and cleaning in python.

Learn to apply a custom function row-wise or column-wise to a pandas dataframe using the apply method and control the axis for row or column operations.

Learn table-wise operations on data frames by applying a custom function to every element with apply and pipe methods, using axis 0 for rows and axis 1 for columns.

Learn to reindex data frames and series by changing row and column labels with the index method, using input parameters to produce a new object.

Learn and apply the DataFrame.items method to iterate over columns, returning each column name and its data as a series, and display them with formatted strings.

Explore how to iterate a data frame with the iterrows method in pandas, returning index and row as a series to access name, age, and gender for each row.

Iterate over dataframe rows using itertuples to access each row as a named tuple with fields name, age, and gender, offering faster iteration than iterrows.

Explore how to use Pandas nlargest and nsmallest to extract the top or bottom N values from a series, with practical examples of three largest and two smallest numbers.

Learn how to use the lower and upper string methods in Pandas to convert series or dataframe string data to lowercase or uppercase. Apply via str.lower and str.upper.

Learn how the title and capitalize methods transform pandas string data by capitalizing first letters in a series or dataframe column.

Learn the swap case method in pandas that converts lowercase to uppercase and uppercase to lowercase across a series of elements, with a practical swap_case.py example for data analysis.

Learn how the Pandas contains method checks whether strings in a series contain a specified substring, returning boolean results for each element.

Learn how to use the string find and rfind methods to locate the first and last occurrences of a substring, returning the index or -1 when not found in Python.