Manage Finance Data with Python & Pandas: Unique Masterclass

Explore tabular data concepts with pandas dataframes, including rows, columns, index, and observations, and learn how features map to columns and data types align.

Discover how ChatGPT aids pandas coding in Python, analyzes and writes code, and how free and plus versions differ, with emphasis on human oversight and improved efficiency.

learn to use pandas with GPT-3.5 and GPT-4o mini to write and inspect code for the Titanic csv dataset. analyze data with pandas methods like read_csv, head, describe, and isnull.

Analyze the listings dataset from nyse, nasdaq, and amex with pandas read_csv, inspect with head and tail, and note missing values in last price, market cap, ipo year, sector, industry.

Select one or more columns in pandas dataframes using labels or lists, and distinguish when results are a series versus a dataframe, with the Titanic dataset example.

Explore selecting a single pandas column with dot notation or square brackets, verify results with the equals method on the age column, and prefer dot notation for clean data workflows.

Test and refine debugging skills by identifying and fixing common Python errors in a dictionary-based Olympic medals dataset, including typos, key errors, type errors, and pandas series conversion.

Explore the major causes of coding errors—code problems, Python installation issues, and external factors—through examples like typos, wrong indents, and inappropriate inputs.

Learn to diagnose and fix Python indentation errors by examining whitespace, tabs, and required indents in dictionaries, for loops, and Jupyter notebook cells.

Learn to avoid misusing Python built-in function names and keywords by choosing clear variable names, preventing overwriting functions like list and avoiding syntax errors.

Learn to debug pandas errors with chatgpt by examining a key error in a data frame and applying fixes like setting the athlete column as the index.

Learn to trace Python errors with the traceback, localize issues in complex backtesting code using pandas, numpy, and matplotlib, and fix typos to improve debugging efficiency.

Utilize a debugging flowchart to diagnose true errors or unexpected outputs by reading error messages, inspecting code (including previous cells), and restarting the kernel before testing with the course notebook.

Explore sorting pandas series by values and by index, using sort_values and sort_index, and control changes with the in_place parameter while handling missing values.

Change column labels in a pandas data frame by replacing all names with a new list, then name the column and row indices to clarify data, using the Titanic dataset.

Use rename() to change row or column labels in the Summer Olympics dataset using a dictionary mapping old to new names, with options for index or columns and in-place updates.

Explore Pandas index operations by selecting and reassigning a frame index, checking unique values and frequencies, naming and resetting index, creating a range index, and inspecting and renaming column indexes.

Sort data frames and series with sort_values or sort_index on one or multiple columns, such as age, pclass, and sex, with ignore_index to restore a range index.

Learn about NA values and missing values, why they occur, and how to detect and handle them in pandas using real sales data examples.

Identify and handle missing values in listings, transform the data type for the ipo column, save the cleaned data to a csv, and compute summary statistics and a correlation matrix.

Explore three histogram approaches for a data frame—plot method with kind, direct hist, and matplotlib plt.hist—covering bins, density, cumulative, and missing-value handling.

Create and customize a seaborn count plot from the Titanic data, grouping by sex and pclass, using pandas and sns, then adjust font size and color palette with sns.set.

Learn how to add a new column to a pandas data frame using broadcasting, create a zero-valued column, and compare brackets versus attribute notation for creating versus selecting columns.

Explore exercise eight by manipulating a data frame: drop two columns and rows, add two new columns from existing data, sort by a column descending, and concatenate three data sets.

Split a data frame by multiple keys with pandas group by, turning results into a list of grouped dataframes for country, gender, and year in the Summer Olympics.

Apply stack and unstack to transform a multi-index data frame from long to wide formats, group by country and medal types, and rearrange or fill missing values.

Explore creating a customized datetime index with pd.date_range across diverse frequencies, including daily, business days, hourly, weekly, monthly, quarterly, and annual, using start, end, periods, and date offsets.

Learn time series data analysis with Python and pandas by importing historic stock prices for Apple and Facebook, inspecting and visualizing them, and building timestamp indices from scratch.

Resample time series with pandas to downsample from hourly to daily or monthly, using the resample method with mean or other aggregations.