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.

Master pandas display options for dataframes. Use head() and tail() to view the first and last rows, and adjust max and min rows to control output.

Perform the first data inspection using pandas to understand structure, data types, missing values, and basic statistics with info and describe on the Titanic dataset.

Learn how pandas dataframes use built-in functions, attributes, and methods, including head, min with numeric_only, and method chaining to analyze data efficiently.

Boost your pandas coding efficiency by using tab completion and tooltips, learn to auto-complete read_csv and other methods, and explore sort_values parameters with shift tab for documentation.

Import pandas, load the listings dataset in a Jupyter notebook via Anaconda, and inspect first ten rows. Use assignment and solution to explore missing values and maximum last price.

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.

Discover zero-based indexing and negative indexing in Python and Pandas. Learn position-based indexing to select rows, columns, and elements in a two-dimensional DataFrame.

Select rows by position using iloc on the Summer Olympic Games data, illustrating zero-based indexing, negative indexing, and slicing to retrieve single or multiple rows.

Learn to use iloc for position-based indexing in pandas to slice rows and columns, retrieve a single element, and select complete columns or ranges with simple syntax.

Explore how to use loc to slice rows and columns by label in pandas, navigate non-unique indices and their pitfalls, and appreciate using a range index for unique identification.

Learn label-based indexing and slicing with the pandas reindex method for time series data and beyond, reassigning row and column labels, handling missing labels, and non-unique indexes.

Learn practices managing finance data with pandas: read csv, inspect with info, avoid chained indexing, and use loc selection for rows and columns on data frames, series, and index objects.

Practice indexing and slicing pandas dataframes through coding exercise two, using Jupyter via Anaconda Navigator, with assignments and hints to reinforce Python and finance data skills.

Import pandas, read listings via read_csv, inspect data. Use dot and bracket notation, and iloc to fetch IPO years and names; set symbol as index and apply loc to AAPL.

Practice indexing and slicing dataframes in pandas by loading listings.csv, setting the symbol as index, and extracting Facebook's IPO, year, and industry with ChatGPT.

Explore advanced indexing and slicing in pandas with the Summer Olympics data set, using position and label based indexing, including combined cases and a simple ix workaround.

Learn to read errors and debug quickly in Python and Pandas. Apply trial and error and fix most issues in under a minute.

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.

Identify and fix the most common Python errors—typos, missing dictionary keys, and syntax issues—using practical examples with underscores versus dots, brackets versus parentheses, and pandas series.

Explore index errors in pandas dataframes and lists with practical examples of out-of-bounds access and negative indexing, and learn to diagnose causes by inspecting previous cells.

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.

Understand type errors and value errors in Python, with examples like adding int and string, invalid string to int conversions, removing missing list items, and negative square roots.

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.

Explore how to use Google and StackOverflow to fix coding errors, and learn the difference between positional and keyword arguments in Python with a pandas DataFrame example.

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.

Explore external factors that cause problems when using web APIs, scraping data, loading financial data, or placing online trades, and learn practical debugging tips for server errors.

Learn how transcription errors arise when copying code from videos and why creating code from scratch improves understanding. Download notebooks, run the code, complete exercises, and practice real coding.

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 the updated debugging flowchart and use ChatGPT as a Python assistant. Read the error, inspect the code, restart the kernel, then ask ChatGPT before turning to Google or StackOverflow.

Explore pandas series as a one dimensional labeled array, select the age column from the Titanic dataframe using brackets or attributes, and convert the series to a dataframe with to_frame.

Analyze numerical pandas series using unique, nunique, and value_counts to uncover missing values, frequency distributions, and insightful summary statistics with describe, bins, and normalization.

Analyze non-numerical series using unique, nunique, and value_counts on the Summer Olympics medal dataset in Pandas, revealing athlete frequencies, missing values, and the distribution of medals across years and countries.

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.

Coding exercise three teaches analyzing pandas series and columns, creating copies, computing summary statistics, min values, absolute and relative frequencies, and sorting in place or by index.

Explore the coding exercise three solution with pandas: copy the last price column, compute max, min, frequencies, and sort prices and sectors to reveal finance and transportation patterns.

Explore pandas index objects, including range and column indexes, inspect metadata with info, slice labels, and use catalog to locate index positions using the Summer Olympics medals dataset.

Learn how to change a data frame index with set_index and reset_index using pandas, including making a year or athlete label based index, adjusting drop and inplace options.

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.

Learn to manipulate a finance data frame in pandas by setting a ticker index, checking duplicates, renaming columns to company and MoCap, and resetting to a range index.

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 to filter data frames by a single condition using a boolean series. Avoid chained indexing and prefer bracket or loc notation to select rows and columns.

Filter data frames by multiple conditions using the and operator, demonstrated on the Titanic dataset to select male passengers over 14 and view survival-related columns, with descriptive stats.

Master advanced filtering in pandas by applying between, isin, and the tilde to extract Olympic medals by year, creating boolean series and refined dataframes.

Learn to apply the any() and all() methods in pandas to evaluate boolean and numeric series, using the Titanic dataset to test at least one male and positive fares.

Sort and filter data frames by columns and index, identify unique values, and select top 10 by market capitalization and 15 lowest stock prices, filter Nasdaq penny stocks under 1.

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

Export data frames to csv with pandas using to_csv, save a cleaned Titanic frame, and re-import with read_csv while controlling the index parameter to drop the range index.

Explore the agg() method to perform custom, per-column aggregations on numerical data with one line. Learn how to select numeric columns, pass multiple statistics, and customize results for specific features.

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.

Learn to clean finance data with pandas by identifying missing values, dropping and imputing rows or columns, converting types, exporting clean data, and computing descriptive statistics and correlations.

Analyze the employee salaries dataset with pandas in an interactive lab, clean missing values, compute descriptive statistics, filter top earners, and export a clean CSV named Employee salaries.csv.

Learn to customize plots in Matplotlib by setting titles, axis labels, fonts, colors, line styles, legends, limits, and ticks, and explore style changes like Seaborn.

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.

Explore categorical seaborn plots to compare age by sex and PE class in the Titanic data, using pandas, seaborn, and matplotlib with strip, swarm, violin, and point plots.

Explore seaborn heatmaps and matrix plots to visualize cross tabular data from the Titanic dataset, using crosstabs by sex and class to reveal survival rates.

Import pandas, sort listings by IPO year, reset the index, and create plots with matplotlib and seaborn, including histograms, scatter plots, and a heat map.

Explore how to remove columns from a pandas data frame using the drop method, with options for columns or labels, in-place updates, and choosing to keep specific columns by selection.

Learn to remove rows and columns in pandas data frames using drop with index labels or boolean indexing, set an index, and apply inplace updates to filter 1996 or aquatics.

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.

Learn to create new data features in a pandas data frame by adding and multiplying columns. Handle missing values with fillna, and compare add and mul methods for arithmetic operations.

Master arithmetic operations in pandas by adding, subtracting, multiplying, and dividing a single scalar to columns and dataframes, creating data and features, with currency conversion, dropping columns, and bonus calculations.

Learn how to create a pandas DataFrame from scratch using pd.DataFrame() with dictionaries, lists, and series, and organize data with index and columns to model football players.

Add one or more rows to an existing data frame with a hands-on approach and a range index. Learn to merge, join, and concatenate data frames with pd.concat and ignore_index.

Combine data frames vertically by adding rows with append and pd.concat. Use ignore index, multi-index with keys, and reset index to manage origins and structure.

Learn to modify single or multiple elements in a pandas data frame by selecting with loc or iloc and assigning new values, including age conversions and conditional updates.

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.

Drop symbol and sector, filter out amex listings, add market cap in million US dollar, compute shares outstanding, sort by shares, and concatenate nyse, nasdaq, amex into data frame.

Discover how group by in pandas enables one-line, group-specific calculations like mean and median ages for male and female passengers, transforming a data frame into focused summaries.

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.

Practice split-apply-combine in pandas to rank the top 20 Olympic medal counts by country and analyze Titanic survival by gender, class, and age group with groupby and aggregation.

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.

Learn to perform group by operations in pandas, compute group means and maximum values, count group sizes, and rearrange data with unstack.

Learn to use pandas groupby to compute mean stock prices by exchange, analyze sector market caps, count IPOs by year, handle missing values, and visualize results with seaborn heat maps.

Merge and analyze Northwind data using pandas to join customers, orders, products, and order details, compute revenue, and identify top products and customers.

Analyze temperature time series with pandas, handle missing values and key statistics, and visualize trends with matplotlib plots and subplots, including weekly or monthly data aggregation.

Learn to index and slice time series in pandas using a date time index and the log operator to select specific timestamps, ranges, or whole years.

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.

Learn to downsample finance time series using the period index, via a two-step workflow: resample to datetime, then convert to period, with monthly to annual options.

Practice advanced indexing in pandas by creating a daytime index for temperature data, downsampling to daily means, and using the index method to fill missing values and select Christmas timestamps.

Downsample daily financial data to monthly with last or average options, compare daily and monthly prices, then downsample to weekly Fridays and build a daytime index for the stocks.