Python Programming for Data Analysis: Ultimate Guide

Define and set file paths with os, then load data via pd.read_csv or pd.read_excel with header, sep, and dtype, and use os.path.join and pd.concat to merge multiple files.

Export data frames to csv or excel with to_csv or pd.ExcelWriter, index=False. Learn data types, rename columns, convert types, derive year and month, and add leading zeros to IDs.

Define input and output folder paths and set the working directory in Python using os.getcwd and os.chdir. Print the current directory to verify the path changes for reliable data loading.

Load data into a Python workspace by reading multiple CSV and Excel files into data frames with pandas. Use os.path.join, file name patterns, and pd.concat to combine files efficiently.

Export multiple data frames into a single Excel workbook with two sheets named '22 data' and '2023 data', preserving headers and excluding the index, using Python.

Extract snapshot dates from filename patterns, convert to date objects, load corresponding excel files, and append them into a single data frame to compare snapshots.

Learn to work with basic Python data types for data analysis, check and rename column names, and format dates and character data using dtype, data.info, and type conversion with pandas.

Sort and order data frames by column names or column indexes, using iloc to select columns, and control ascending, descending, and inplace options.

Learn to recode and replace values in a data frame, renaming property type values, replacing missing values with -1 or the mean, and turning negative numbers into NaN.

Arrange data by column name or indexes to form a focused data frame subset. Use brackets or iloc to select district, owner, property type, square footage, and asking price.

Perform joins with pandas merge to combine data using inner, left, and anti-join operations, specifying join keys and optional suffixes, with indicators to reveal non-matching records.

Explore temporal joins in SQL by comparing a left outer join using date ranges with a between-based approach, using enrollment date between a manager's record effective and expiry dates.

Apply a two-step subquery with max to identify the latest invoice owner for each training expense invoice, using a self-join and group by update date.

Master data joining with pandas by performing inner, left, and anti joins with merge, while loading multiple Excel dataframes and correcting date columns with to_datetime.

Learn to perform joins on multiple tables using pandas merge, executing left outer joins across complaints, product descriptions, issue, and company data, plus an optional sql approach.

Load and validate data from multiple excel sheets, ensure proper date formats, and perform an advanced temporal join to reveal the manager who approved training expense invoices.

This lab covers data aggregation and feature engineering with pandas groupby to summarize data, computing transaction counts, distinct customers, total price, total quantity, and price per quantity.

Sort a pandas dataframe by total price to display customers in ascending or descending order, using the ascending parameter to switch the sort direction.

Create a sub retail demo data frame with customer ID, invoice date, and stock code; compute duration to the invoice date, bucket with pandas cut, and recode stock codes.

Learn to perform time intelligence by computing time features and KPIs with numpy and pandas, deriving year, month, week, day, and weekday from transaction dates.

Explore how to calculate event sequence analysis by sorting by customer ID and transaction date, then grouping by customer ID to assign and sort transaction sequence numbers.

Explore time intelligence analytics in Python by deriving time features and event sequences. Use pandas and NumPy to calculate business days on Netflix data and study daily snapshot frequencies.

Execute event sequence analysis by sorting Netflix titles by director and date added, then compute each director's release sequence and a 12-month flag from previous date added and previous director.

Calculate the daily snapshot measure by building numerator and denominator frames, counting titles by date added, and merging on the snapshot date to compute tv 14 rating percentage.

Explore bivariate data visualization by examining the relationship between numerical and categorical variables using seaborn's boxplot, with the box-and-whisker plot showing min, quartiles, and median.

Learn to visualize univariate and bivariate data with Python using matplotlib and seaborn, creating bar, pie, line charts, histograms, and scatter plots on an insurance claims dataset.

Learn to create a univariate pie chart by preparing and aggregating claim data by worker region, calculating percent claims and label positions, and plotting the result with matplotlib.

Plot box plots and group box plots with Seaborn to visualize paid amount distributions across brand and name on the x axis.

Prepare data sets and visualize with bar, stacked bar, pie, histogram, line, stacked area, box, and scatter plots; explore correlation matrix and export results to Excel.

Congratulate learners on finishing the course and encourage ongoing practice with diverse data sets and Kaggle explorations, plus upcoming courses in R, Excel, and SQL with a discount.