Master Pandas for Data Analysis and Visualisation

Install and configure Anaconda and PyCharm. Set up Jupyter Notebook and verify libraries like NumPy, pandas, and TensorFlow for data analysis and visualization.

Write Python code online with a Python online compiler or Google Colab, create a new notebook, connect to a runtime, and import libraries like TensorFlow, pandas, and numpy.

Install PyCharm Community Edition, the free Python IDE. Download the Apple Silicon version, drag and drop into the applications directory, and launch after accepting the user agreement.

Explore writing your first Python program across terminals, IDEs, Jupyter notebooks, and Google Colab, and learn to run code, print messages, declare variables, and perform simple arithmetic.

Explore variables and value assignments in Python with Google Colab, use the assignment operator, print results, perform arithmetic operations, and follow simple naming rules for variable identifiers.

Explore Python data types, including strings, integers, floats, and booleans, and learn type casting with int and type(), as well as how quotes affect values and basic arithmetic rules.

Explore seven kinds of Python operators—arithmetic, assignment, comparison, logical, identity, membership, and bitwise—and practice with addition, subtraction, multiplication, division, modulus, exponentiation, and floor division.

Master python conditional statements with if, else, and else if, using two numbers and logical operators. Apply flows to age-based decisions and complete two homeworks on voting and maturity rules.

Explore how to implement age-based messaging in python using if and elif statements, printing infant to national treasure messages based on defined age ranges.

Learn to use Python for loops with range to repeat tasks, noting the end is exclusive and the range step controls which values print, such as 85 to 99.

Learn how the Python while loop runs while a condition is true, using an age example to show an infinite loop and the fix with age += 1.

Discover how to create Python lists, access elements by index, print values, loop over items with for and range, and modify lists with replace and append.

Learn how to define and call python functions and methods, use parameters and arguments, return values, print results, and distinguish between class methods and standalone functions.

Master Python lists by accessing last element with len(list)-1 or negative indexing, slicing ranges, removing with remove or pop, sorting with reverse, joining with the addition operator, and clearing lists.

Explore two-dimensional lists, learning to declare a list of lists, print elements, access with indices (including negative), and traverse with nested for loops for multi-dimensional arrays.

Explore Python tuple, set, and dictionary fundamentals, including immutability, indexing, and updates, while learning to create, loop through, and manipulate these data structures.

Master Python strings by quoting with single or double marks and using triple quotes for multi-line values. Practice indexing from zero, slicing, finding, replacing, splitting, and formatting with braces.

Demonstrate Python datetime usage to print current date and time, dates, times, and weekday names, then apply math module functions like max, min, abs, power, square root, ceiling, and floor.

Learn how to create a custom data type in Python with a class, define a student (id, name, age, fee), assign values, and print fields using dot notation.

Explore classes as blueprints for objects, initialize instances with the init constructor, and access properties with self. Build the employee info method and handle defaults and type casting for concatenation.

Explore Python scope, global and local variables, and the global keyword, plus how single and double underscores create private fields and methods inside a class.

Explore Python inheritance by building a base person class and derived student, teacher, and peon classes, using super constructors, get info methods, and introducing polymorphism.

Explore Python polymorphism and abstraction, implement polymorphic methods, use built-in functions like len, define abstract methods with ABC to enforce subclass implementation, and apply inheritance in practical examples.

Explore how python iterators power loop traversal by using __iter__ and __next__, demonstrate stopping with StopIteration, and build a custom iterator with a limit to print indices during list iteration.

Explore Python lambda functions, small anonymous functions that accept multiple arguments but have a single expression. See examples multiply by ten and compute a plus b times c.

Master Python file handling by using open with read, write, append, and create modes, check existence with os, and read, write, and remove files and directories.

Explore Python error handling with try, except, else, and finally blocks. Learn to print custom errors for undefined variables and ensure code runs with or without errors.

Import NumPy and optionally alias it, then create arrays with array, zeros, ones, empty, arange, and random methods, shaping 2d arrays and setting data types such as int or float.

Explore NumPy array attributes such as ndim, size, shape, and dtype, and apply functions like array, arange, sort, concat, split, and min/max with argmin/argmax to prep indexing and slicing.

Learn to index and slice NumPy arrays, accessing individual elements in one-dimensional arrays and slicing two-dimensional arrays by row and column, with start-to-end ranges and end-exclusive behavior.

Explore the numpy copy and view methods to understand how data sharing works: view reflects changes across arrays, while copy creates independent arrays that do not affect the original.

Reshape and resize numpy arrays, and apply ravel and flatten to 1-D views or copies. Compare in-memory reshaping with reshape versus resize, and ravel's view versus flatten's copy behavior.

Learn numpy array arithmetic using operators and the add method, noting shape constraints and broadcast errors. Explore aggregation methods such as sum, min, std, prod, var, cumsum, mean, and max.

Master pandas for data analysis teaches you to create basic pandas series from scalars, lists, strings, tuples, dictionaries, and vectors, with index or key access.

Learn to create and inspect a pandas series using range, access data with square bracket indexing, get and loc/iloc methods, and add or update items by index.

Explore key pandas series methods—sample, head, tail, take, info, and describe—to extract values, inspect structure, and summarize data for hands-on analysis.

Explore data types in pandas series, including integer, int64 and int32, float64 and float32, string (object), boolean, datetime, and the categorical type, with dtype checks and astype conversions.

Learn to work with a pandas series using key Python functions. Create a series, use len and type, explore attributes with dir, and convert to list, tuple, dict, or set.

Study how to create a Pandas series and use head, tail, min, max, nlargest, and nsmallest to retrieve top and bottom elements, then inspect index with keys and items.

Explore pandas series methods unique, duplicated, and item, including how unique returns a numpy ndarray of distinct elements, how duplicated yields a boolean series, and how item returns a scalar.

Explore essential attributes of pandas series, including dtype, ndim, shape, size, values, is_unique, name, and empty, and learn to inspect and label the index with custom names.

Read a CSV into pandas with read_csv and usecols to create a single column Series; explore head, tail, info, shape, count, and value_counts.

Create a pandas series with values and index, adjust data type or convert to category, read_csv with usecols and squeeze to a series, and name the series and its index.

Learn indexing and slicing in pandas series using square brackets and the get method, load data with read_csv, squeeze to series, and handle unique and duplicate indices and out-of-bounds access.

Learn how to get and slice a Pandas series using .loc and .iloc, including index-based selection, ranges, and setting a new index for efficient data extraction.

Explore pandas series indexing by using index, reindex, and reset_index to reorder data, create a new zero-based index, and move the old index into a column before optionally dropping it.

Sort pandas series by values with sort_values, control ascending or descending order, keep or update inplace, manage NaN positions with na_position, and choose sort algorithms with kind.

Sort a pandas series by its index with sort_index, explore ascending and in-place options, and compare with sort_values while choosing a sorting algorithm such as mergesort, quicksort, or heapsort.

Learn arithmetic operations on pandas series using plus, minus, multiply, and divide, via operators or series methods, including handling unequal lengths with fill value.

Explore absolute values with pandas series using abs, practice rounding with round, and learn ceil and floor operations via numpy in an apply, with decimal precision control.

Explore essential pandas series operations by computing max, min, argmax, argmin, mean, median, sum, standard deviation, and variance, then apply them to a salaries dataset read from csv.

Learn to filter data in Pandas Series using comparison operators and the filter method on an employees.csv salary dataset, including equals, greater than, regex filtering, and starts-with criteria.

Learn to apply fillna, dropna, drop, and drop_duplicates to pandas series, using in-place options, forward and backward fills, and index-based drops.

Filter a pandas series with isna and isnull, identify non-null values with notna and notnull, and count nan and non-nan entries using sum and boolean indexing.

Explore filtering data with the where, mask, between, and apply methods in pandas to select salary ranges and transform results. Use conditions, in-place options, and lambda functions.

Learn how to use the pandas replace method to substitute values in a series, including single and multiple replacements and optional inplace and limit options, plus regex-based replacements.

Learn to use pandas groupby on a series to split data into named groups, perform aggregations such as sum, mean, and count, filter groups by sum, and iterate groupwise.

Learn pandas aggregation with agg and aggregate on a series, computing sum, mean, median, mode, min, max, std, var, and quantiles, while handling NaN values and exploring cumulative sums.

Create basic pandas data frames from lists, tuples, dictionaries, sets, and numpy arrays, specify columns, and understand 2d data constraints.

Learn to create and display a pandas DataFrame and retrieve data by column and row with iloc, loc, and at. Update values and add new columns with numpy.

Learn to create a pandas data frame with numpy, 100 rows and eight columns, and use sample, head, tail, info, describe, and take to inspect data and select rows.

Use pandas DataFrame methods nlargest and nsmallest to fetch top and bottom rows by a chosen column, specify the count, and observe how sorting centers on the first column.

Learn to analyze data with pandas by using DataFrame methods unique, nunique, and duplicated to count unique values and identify duplicates per column in a numpy-based dataset.

Learn about data types in Pandas DataFrame, inspect dtypes, and convert columns to int, float, bool, object, string, date time, and category with practical examples.

Learn to use Python built-in functions with pandas data frames, including len, count, type, and sorting, plus exploring attributes like head, sample, dtypes, and columns.

Learn how pandas count and value_counts tally non-null values and value pairs in DataFrames, count by column, handle NaN with drop equal to false, and compute normalized frequencies.

Learn to read csv, excel, json, and text files with pandas using read_csv, read_excel, and read_json, and inspect data with head, tail, and info.

Learn how to work with pandas data frame attributes, read csv files with pd.read_csv, and inspect data types, shape, size, ndim, columns, index, empty, values, and axis.

Learn to create and customize pandas dataframes by specifying data, columns, index, and dtypes; master reading csv, json, and excel with key parameters like header, names, usecols, nrows, and sheet_name.

Explore arithmetic with pandas dataframes including add, sub, mul, div, and mod operations and methods, performing element-wise calculations across matching rows and columns.

Learn to perform arithmetic on pandas dataframes using power, abs, and round, and apply numpy for ceil, floor, square, and sqrt on dataframes.

Explore how to compute min, max, sum, mean, median, std, and var for a Pandas DataFrame, per column and across the entire dataset, using DataFrame methods and Python built-ins.

Explore computing dot products between matrices and pandas data frames using the dot() method, and derive prod(), cumsum(), corr(), and cov() with attention to index and column alignment.