Data Science : Python Bootcamp for Data Analysis with Pandas

Learn to perform basic math operations in Python using /, +, and * and define variables with meaningful, case-sensitive names to compute BMI from height and weight.

Explore data types and type conversion in Python, showing how to convert between float, int, string, and boolean, check types, and format numbers with rounding to decimal places.

Learn to create numpy arrays by importing numpy and using array, zeros, arange, and linspace, with attention to start, end, step, and endpoints.

Learn how to sort numbers and characters, and how to insert and delete elements in data structures. Apply these operations in pandas for data analysis to build clean, ordered datasets.

Explore multi-dimensional arrays, examine their dimensionality and shape across two and three dimensions, and count elements to understand the data structure.

Explore how to access array elements with indexing and slicing, including first and last elements, negative indexing, and 2d arrays, then select data using boolean conditions and comparisons.

Learn element-wise operations on arrays, including add, subtract, multiply, and divide, and compute sum, min, max, mean, and standard deviation across rows and columns in multi-dimensional arrays.

Explore how lists work in Python: a collection of ordered, changeable items of different types, using square brackets and commas, including lists of lists to store structured data.

Access and modify lists in Python by indexing, slicing, and using insert, remove, and sort. Learn to find element indices, update values, add and remove items, and control sort order.

Explore core list operations in Python, including len for length, max, append, pop, and reverse. Learn to concatenate lists with plus, repeat with multiplication, and test membership with in.

Learn how a dictionary stores data as key-value pairs, is ordered and mutable, with unique keys, enabling direct lookup of marks by subject and retrieving keys with the keys() method.

Add subject-marks pairs in a Python dictionary, delete entries by key, and verify key existence with in, using a practical subject dictionary example.

Learn how to build and access a dictionary of dictionaries in Python using country data. Retrieve capital and population values, and extend the structure with new entries.

Learn to create and manipulate data with pandas by building series and dataframes, exploring columns, index, and basic functions such as head, describe, and shape.

Learn to create a dataframe from a csv file using pandas, including reading data, handling the index, inspecting data types, and parsing date and time information.

Learn how to read excel xlsx files with multiple sheets into dataframes, selecting a specific sheet, inspecting data types, and generating descriptive summaries.

Learn to create a dataframe from a text file by specifying the file path and treating the first column as the index, using the comma separator, and checking memory usage.

Master pandas loc to select rows and columns in a dataframe, create new columns with uniform or named values, and drop columns, using a science and math scores dataset.

Learn to use iloc to access rows and columns by index in pandas, selecting the first two columns and noncontiguous rows, and that iloc cannot create new columns.

Learn how to modify and filter rows in a pandas dataframe using .loc, including fixing misentered names, applying boolean conditions, and selecting specific columns.

Learn to concatenate dataframes along rows in pandas, merge data from different sources, and manage indexes with reset index and drop using inplace and columns options.

Learn how to concatenate dataframes along columns in pandas, using the axis parameter, and handle index alignment when some indices are missing.

Learn to use the append function to concatenate dataframes or lists of datasets, and control indexing with ignore_index for a clean, unified dataset.

Explore how inner, left, right, and full joins combine two datasets by including the common rows and the rows unique to each table.

Learn how to perform joins in pandas by merging two dataframes on a unique identifier, exploring left, right, and inner joins and how merge arguments control the result.

Explore how left, right, and inner joins operate on two dataframes by joining on the name column, showing which rows appear in the output and when unmatched rows appear.

Explore data grouping and aggregation in Pandas by using groupby to compute the mean of mathematics scores within each section, and inspect minimum, maximum, first, last, and count via aggregate.

Explore aggregations in Pandas: apply multiple functions like mean, median, and count across groups, manage multi-level columns, and tidy results by adjusting indexes and metadata.

Explore the rank function to order student scores, examine ascending order, and resolve ties using average by default, with options for minimum, maximum, and first methods.

Learn to use pandas to build a data frame from a dictionary of date-time and weight measurements, convert strings to datetime with to_datetime, and analyze weight change over time.

Learn to compute weight loss per day in pandas by shifting time-stamped data, measuring day gaps, and creating a weight loss per day column with zeros filled, revealing changing rates.

Learn how to compute each person’s age from date of birth in a pandas dataframe by converting strings to datetime, standardizing formats, and using current date calculations.

Learn to create a pivot table in pandas that turns section eight and section b entries into columns, with an index and mathematics scores as values.

Create a data frame from dictionary, build a pivot table with names as the index, and apply aggregation functions like max and mean to scores, filling missing values with zeros.

Explore how to use stack and unstack in pandas to convert columns into multi-level indexes, reshaping a dataframe with two indexes and multilevel columns.

Master stack and unstack operations in the pandas dataframe, learn how default level -1 selects leftmost index, convert sections into multi-index columns, and fill nulls using the most recent level.

learn to melt a dataframe in pandas to unpivot data, selecting columns, setting id_vars, and renaming value and variable columns to create a long format.