Python Programming for Data Analysis: Ultimate Guide

Explore Python data objects and structures, including vectors, factors, matrices, lists, and data frame, and see how a data frame functions as a table in data analysis.

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.

Learn to load and write data in csv and excel formats using python, pandas, and openpyxl, and set up paths with anaconda for the course data pack.

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.

Write data from a python workspace to csv and excel files using an output path, index set to false, and pandas excelwriter to save the dataframe as Sheet1.

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.

Format dates in a data frame with strftime to derive day, month, year, and year-month using codes like %d, %m, and %Y-%m.

learn how to inspect a data frame's column names and rename a column using rename with old and new names, including inplace=True, and verify incident becomes fire_under_control_dt.

Derive day, month, and year components from a date column, create a derived date dimension, drop duplicates, and sort to produce a unique, oldest-to-newest date table.

Learn how to extract the numeric portion of an incident number and convert it into a 12-digit format with leading zeros using Python formatting.

Clean data by removing duplicates and dropping columns, and learn to handle missing or incorrect values using Python data frames.

Learn to recode and replace values in a data frame using conditional mappings and the clip function, with hard-coded replacements and handling negatives and na values, while preserving unmatched values.

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.

Learn to sort a data frame by a single column (id), by multiple columns (district then id), and apply ascending or descending order on square foot available.

Learn how to subset data in Python for data analysis using lists, column names, and index-based selections. Apply conditions and logical operators to filter records by criteria.

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.

Load two account balance snapshots, build a composite key from branch, customer, and account, and use joins to detect balance changes and open or closed statuses.

Explore data aggregation and summarization in Python pandas using groupby and agg to calculate count, unique counts, sums, mean, median, and mode, plus filtering and feature engineering for KPI metrics.

Explore feature engineering with dataframe.assign to derive new features from expressions, including age from birth date, age groups with pd.cut, and gender recoding; preview time intelligence using pandas, numpy, datetime.

Explore how to summarize UK online retail data with pandas, using groupby and aggregate, filter and slice data, format the summary table, and engineer features for lab five overview.

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.

Group data by country and invoice year to calculate transaction count, customer count, and total price, then pivot to wide format with country as rows and invoice year as columns.

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.

Calculate the percentage of online order transactions by daily snapshot. Merge online and all transactions on the transaction date to compute the KPI, then explore monthly and weekly frequencies.

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.

Demonstrate time intelligence in pandas by loading Netflix titles, subsetting to South Korea, converting date added to datetime, deriving year, month, week, and weekday, and applying date offsets.

Compute duration between two dates in a data frame using today’s date and a date-added column; calculate days, months (30-day), and years (365-day) with a time delta function.

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.

Learn to calculate business days between two dates with numpy by loading a Canadian holiday calendar from Excel, formatting dates, and applying a row-wise lambda to exclude weekends and holidays.

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.

Aggregate numerator and denominator at a monthly level from the date added field, rename as snapshot month, merge them, and compute the monthly measure; practice a weekly-level variant.

Explore exploratory data analysis with matplotlib and seaborn, learning univariate and bivariate charts for categorical and numerical data, graphical integrity, storytelling, and choosing the right visualization for analysis.

Explore univariate data visualization with bar charts using matplotlib and seaborn, plotting a categorical variable to display counts by brand, product line, and faceted comparisons.

Plot a pie chart with matplotlib's plt.pie using a data frame, aggregating sales by brand to show each brand's percentage of transactions, and customize labels, colors, and angles.

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 use scatter plots to examine the correlation between two numerical variables, such as total transactions and total revenue, color by brand, and add a line of best fit.

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 visualize univariate categorical data with Python by plotting a brand name bar chart, then create stacked and seaborn faceted bar charts using pandas, matplotlib, and seaborn.

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.

Prepare a demo dataframe with service date and year-month; compute total pay per unit; group by brand name and year-month; plot a line chart with Seaborn and brand facet trend.

Plot a histogram and density plot of total pay per unit with matplotlib and seaborn, adjusting bins and labels, and using kernel density estimates for clearer data distribution.

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

Learn to compute correlation metrics and visualize variable relationships with a correlation matrix by preparing a data frame, aggregating by brand, and plotting a Seaborn heat map.

Apply Python for data analysts to clean and analyze a Canadian e-commerce dataset using pandas, NumPy, matplotlib, and seaborn; perform joins, feature engineering, KPI calculations, time analytics, and visualizations.

Walk through capstone one solution for the e-commerce data set in python for data analysis: load, clean, and merge Excel data, derive date features, and extract per-customer max price.

Derive time features from order purchase dates—year, month, calendar week, and weekday—and build customer order sequences. Then compute a seven business days KPI using a custom holiday calendar.

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.