Python for Data Science Bootcamp: From Zero to Hero

Learn how to write and run your first Python program, printing Hello, world, using the print function in a Jupyter Notebook, and explore strings, numbers, comments, and basic code structure.

Explore Python data types such as integers, floats, booleans, and strings, use type() to check types, perform math operations, and learn string methods like upper, count, and replace.

Master variables in Python by assigning string values, using double quotes, and creating multiple variables; learn concatenation with plus, space handling, and f-strings for embedding.

Python lists store multiple items and are mutable, enabling in-place data changes. Build and manipulate lists with indexing (zero-based and negative), slicing, appending, inserting, joining, removing, sorting, and copying.

Learn how to create and manipulate Python dictionaries with key-value pairs, access keys and values, and manage updates, additions, copies, and removals.

Learn how Python's if statement tests a condition and executes code blocks accordingly, using indentation after a colon and optional elif and else branches with age examples.

Explore Python for loops by iterating over lists and dictionaries, printing elements, and using enumerate to track iteration indices.

Explore how to use the OS module in Python, importing OS, using getcwd to show the current working directory, listing directory contents with listdir, and creating new folders.

Learn how to create a pandas DataFrame using arrays (NumPy), lists, dictionaries, and CSV files, and customize index and column names in a Jupiter Notebook.

Learn to select a single column from a pandas data frame using square brackets with the column name, and compare it to dot notation and multiword column pitfalls.

Learn how to select two or more columns from a Pandas data frame using two pairs of square brackets, compare data frame versus series, and understand dot notation limitations.

Learn how to add a new column to a pandas dataframe using scalar values. Use numpy to create arrays for the new column and ensure the 1000-row length matches.

Perform column and row operations on a data frame using pandas, calculating sum, count, mean, standard deviation, min, max, and describe, then create an average column.

Explore how to use the value_counts() method to count category frequencies in a data frame column, compute relative frequencies with normalize, and round results for clear proportions.

Rename columns and indexes in a pandas DataFrame using the rename method, applying the columns and index arguments with dictionaries and in-place updates.

Learn to extract CSP files from multiple urls with pandas by building dynamic links from a root and seasons, using a for loop, a frames list, and a season column.

Store data frames in a dictionary keyed by league name and codes. Concatenate seasons with pandas.concat into a separate dictionary, and reset frames inside the loop.

Learn how to filter a dataframe based on one condition in pandas, including equals and not equals, with examples on price in euros and company.

Create a new column by applying a single condition with np.where to label prices as cheap or expensive, then verify with counts and a screen size example.

Learn to filter a pandas data frame with two or more conditions using and/or operators, filtering Apple laptops priced over two thousand euros.

learn to create a conditional column from more than two choices using pandas np.select, mapping price ranges to too expensive, expensive, affordable, and cheap.

Learn how the isin method in pandas filters a dataframe to include only Apple or HP laptops. Build and apply multiple conditions, such as notebooks or ultrabooks, using boolean masks.

Learn to find duplicate rows in pandas data frames using the duplicated method, exploring keep options (first, last, false) across single and multi-column comparisons and filtering results.

Explore dropping duplicates with pandas' drop_duplicates, using the company column, keep and ignore_index options to reveal the cheapest and most expensive laptops per company.

Compare loc and iloc in pandas to select data by index labels or integer positions, and learn labeling versus positional indexing plus inclusive versus exclusive slicing on a data frame.

Learn to select data by index position in pandas using iloc, including single-value access, lists of indices, and column-range slices, with practical queries on players like Messi and Ronaldo.

Learn to set new values in a dataframe with pandas, updating Messi's height in a cell, an entire column, or a full row, including conditional updates.

Learn to drop rows and columns from a data frame using the drop method in pandas, using axis, index, and columns parameters, and update data in place.

Learn to create random samples in pandas with the sample method, extracting 10 random elements from the nationality column, and controlling randomness with random_state.

Master filtering a data frame with the query method and compare it to boolean slicing, using multiple conditions, not operator, and height in meters with birth year after 1990.

Explore how the pandas apply method operates on a series and data frame, applying built-in and user-defined functions, including the square root function, to compute BMI.

Learn how to reshape data with pandas using the pivot method and the pivot_table method, specifying index, columns, and values; pivot lacks aggregation, while pivot_table supports it.

Learn to create line plots with pandas using the plot method, customizing x and y labels, title, and figure size to visualize population trends from 1955 to 2020 across countries.

Learn to create a pie chart with pandas by preparing a dataframe, renaming a numeric column to a string, and plotting with kind='pie' to visualize 2020 population in percentages.

Learn to create box plots in pandas, revealing the five important numbers (min, Q1, median, Q3, max) and compare multiple countries with optional color and y-labels.

Learn to create a histogram with pandas, visualize frequency distributions using vertical bars, and compare multiple countries like Indonesia and United States in a single plot.

Learn to create scatter plots in pandas using a dataframe with country, year, and population, plotting year on the x-axis and population on the y-axis with dot size and color.

Export pandas plots and pivot tables to PNG and Excel from a Python data science workflow, suppress axis labels for clean visuals, and view results in Excel or Google Sheets.

Group by categories in a car sales data frame and apply aggregate functions using pandas, while selecting columns and checking unique values to understand the dataset.

learn how to use the agg method in pandas to apply sum and mean to data frames, handle text data, and perform per-column and per-row aggregations with axis options.

Explore the split-apply-combine strategy to group data frame values by vehicle type, compute mean sales for each category, and assemble results into a summary data frame.

Learn how the pandas group by method organizes data by category, applies mean, sum, and count to each group, and controls nulls with dropna—via vehicle type and manufacturer examples.

Learn to apply a lambda function to a pandas groupby object, scale values from thousands to real values by multiplying by 1000, and analyze deviations from group means with apply.

Learn to filter grouped data with the filter method using a custom function, group by, and the sum of sales in thousands to select manufacturers that pass the condition.

Learn to concatenate pandas dataframes vertically with pd.concat, using axis=0 to stack rows on common ID and H columns, and optionally ignore the original indices.

Learn to horizontally concatenate data frames with pandas using pd.concat and axis=1, align equal indexes, and merge columns into a single data frame.

Explore left joins in pandas using merge with how='left' and a common id, and perform exclusive left joins with an indicator to filter.