Data Analysis in Python. A Pandas Crash Course for Beginners

explore variables and assignment in Python, including int and float types, immutability, and memory location concepts. learn shortcuts like a += 1 and how mixed types yield floats.

select meaningful variable names to improve readability, then cover Python naming rules, case sensitivity, and reserved keywords, plus how multiple assignment and single line swaps work.

Explore Python strings and the print function, learning string quotes, concatenation, escaping characters, multi-line strings, and print options like separator and end.

Explore string methods and f-strings in Python, manipulate text with upper, lower, title, strip, and split, and perform slicing and indexing on strings for data analysis with pandas.

Introduce Python lists as a versatile data structure that stores items of varying types and demonstrate core operations: indexing, slicing, appending, inserting, removing, popping, and sorting or reversing.

Explore how lists support data analysis in python by using min, max, count, index, copying, and zipping with tuples, while comparing mutable lists and immutable tuples.

Explore boolean variables, true and false, and learn how logical operators and, or, and not test conditions to control program flow.

Learn how booleans power decision making and how to test membership in lists, using Python comparison operators like equality, greater than, less than, greater or equal, and not equal.

Learn to distinguish assignment from equality in Python, and implement conditionals using if, else, and elif with proper indentation to control code execution.

Explore while loops and for loops in Python. Count down from ten with indentation, handle user input to guess a number, and use break to exit the loop.

Explore Python for loops and the range function to iterate numbers, strings, and lists with start, stop, and step, including reversing strings and using enumerate.

Learn break and continue in Python loops, using a for loop to print odd numbers and skip evens with modulo, and a while loop with range for controlled iteration.

Explore Python dictionaries, learning key-value pairs, unique keys, and nested structures; build dictionaries from two lists with zip, then access, modify, and iterate with items, keys, values for data analysis.

Explore Python tuples as immutable alternatives to lists, defined with parentheses and not supporting append or item assignment. See how they appear in function returns and enumerate, and recognize errors.

Explore how sets in Python store unique items, declare them with curly braces or set(), and perform union, intersection, and differences, noting they are unordered and not subscriptable.

Learn how Python modules import functionality with dot notation and aliases, using the math module’s factorial example, and explore the Python standard library and data tools like pandas and matplotlib.

Learn to define and call Python functions with def, parameters, and return values, and reuse code for tasks like calculating a list mean. Explore docstrings and why print returns None.

Explain how positional arguments must follow the correct order, how keyword arguments enforce explicit naming, and how *args and **kwargs let functions accept varying numbers of arguments.

Explore lambda functions, anonymous tools you can apply to pandas dataframes to operate on entire columns, including a multiply lambda, while following the pep eight style guide for Python code.

Learn to recognize runtime errors such as index out of range and division by zero, and handle them with try/except blocks and precise error messages.

Explore list and dictionary comprehensions in Python, using for loops, range, and conditions to build lists and dictionaries. Demonstrate nested loops and Pythonic one-line expressions.

Learn to swap dictionary keys and values in Python with a one-line dictionary comprehension, applying it to data structures used in Pandas.

Explore NumPy basics for numerical Python, including creating arrays, dtypes like int32 and float64, and using arange, linspace, ones, and zeros for fast matrix operations.

Generate random integer arrays with NumPy, create 5x5 arrays between 0 and 50, perform arithmetic and comparisons, and use boolean masks to filter data for pandas analyses.

Explore numpy functions to filter arrays with boolean masks, transpose arrays, concatenate and stack arrays, and compute min, mean, and square root across axes for data analysis in Python.

Load and save data with Python using pandas and the os module, then analyze WHO covid data with indexing, filtering, and new column calculations.

Develop Pandas skills by cleaning and preparing a covid-19 dataset, converting date times, and creating time series plots with seaborn and matplotlib to explore US and UK trends.

Load and clean a csv with pandas, convert dates to datetime, pivot to country columns, and plot time-series and top-country charts using pandas and matplotlib.

Merge the data sets on date indices, clean columns, and compute percent change and mortality rate, then plot new cases and hospital admissions with twin axes to analyze lockdown effects.

Plot covid data on a map with geopandas, focusing on EU countries, using latitude and longitude to place markers scaled by confirmed cases.

Learn to plot share price data with matplotlib by building figures and axes, converting date to datetime index, cleaning data, and saving multi-plot charts.

Learn about two ways of plotting in matplotlib, the class method and the PI plot method, and how to create subplots, figures, and axes.

Learn to plot share prices with the matplotlib finance API using pandas and mplfinance. Create candlestick charts with volume and a moving average from Yahoo Finance data.

Master how to use Anaconda environments to manage project-specific dependencies. Create, activate, list, and install packages in isolated environments to avoid base environment conflicts and streamline data analysis workflows.