Data Analysis with Python, Pandas and NumPy

Explore how Pandas enables data analysis and cleaning with heterogeneous data, using series and data frames, master key operations like slicing, indexing, merging, and installation via pip or Anaconda.

Learn pandas series creation from Python lists through a hands-on approach and understand the index and values. Access single or multiple elements using index, slicing, and auto-generated indices.

Explore creating Pandas series with or without a custom index, then access values by index, multiple indices, or slicing, including label-based slicing semantics.

Create a pandas series with a custom index of A, B, C, D and values 10, 20, 30, 40; learn to access single elements and slices by label.

Filter a pandas series by evaluating boolean expressions like x < 0 inside square brackets, retaining negative values and discarding positives without using the index.

Explore arithmetic operations on Pandas series, including addition and multiplication, and learn through theory and hands-on examples how index alignment affects results, with NaN when indices do not match.

Create a pandas series from a Python dictionary, reorder by a custom index, and check null values using is null and is not null functions.

Learn to create pandas dataframes from dictionaries and lists, exploring two-dimensional data with index and columns, and handling missing or extra columns as null.

Learn how to access column names and values in a pandas dataframe using square brackets and attribute syntax, then assign single or multiple values to columns.

Learn to update, delete, transpose, and rename index and columns in pandas data frames using Python; create boolean columns from comparisons and build data frames from nested dictionaries.

Explore pandas index objects for rows and columns, learn their immutability and repetition, and access them via index and column attributes with hands-on use of extract index and extract columns.

Learn how to index, reindex, reorder, and drop rows and columns in a dataframe, including in-place changes and label-based or position-based access.

Explore hands-on indexing and reindexing in Pandas, including reordering and dropping rows or columns, and performing in-place changes on series and data frames.

Explore hierarchical indexing, selection, and filtering in pandas objects, using loc, iloc, at, and iat to access and update values by label or position.

Apply indexing, selection, and filtering in pandas with series and data frames, using label and position access, slicing, and boolean conditions.

Learn hierarchical indexing in data frames to access multi-dimensional data with conditions, retrieve values across columns, and combine filters in stages.

Explore negative indexing in pandas, showing how it behaves with labeled versus numeric indexes. Learn to use loc or iloc when applying negative indices.

Master arithmetic operations in Pandas by performing data frame and series subtraction, exploring axis-based alignment and the fill_value parameter to handle missing data.

Learn practical arithmetic operations in pandas by using series and data frames, performing additions with add and fill_value, handling missing values and column alignment, and exploring subtraction with sub.

Explore function mapping in Python with Pandas by applying custom logic using apply, applymap, and map to columns, rows, and elements, including max and min calculations via lambda functions.

Demonstrates functional mapping on a data frame using lambda expressions to multiply values, and computes per-column max, min, and sum via apply and plane malfunction.

Explore how to sort and rank data in Pandas using sort_index, sort_values, and rank, applying them to series and data frames, including axis-based sorting and handling duplicate labels.

Practice sorting data by index and columns, sorting by multiple columns, and ranking values with axis control in a data preparation workflow using Python, pandas, and NumPy.

Explore how to summarize data with pandas by computing sums, means, max/min, covariance and correlation across columns and rules, describe non-numeric data, and interpret feature relationships for machine learning.

Master practical data summarization for dataprep, in Python with pandas, computing mean, sum, standard deviation, correlation, and covariance to understand the data.

Explore how pandas reads data from csv and excel files, databases, html pages, json, and pickle, and how it interacts with binary formats for fast, lightweight storage.

Explore how pandas reads a comma separated file, assigns column and index label automatically or from provided names, and returns a dataframe with whitespace handling.

Learn to read csv files with pandas, handle header rows, set column labels, designate an index, and specify delimiters for comma-separated data.

Identify missing values in a data frame with isnull, count true/false entries, and use dictionary mappings to treat specific characters as missing for subsequent imputation or deletion.

Learn to handle large files by reading data in chunks, skipping or limiting rows, and processing each chunk iteratively to avoid memory overload.

Learn to write a data frame to a csv file, control missing value representations, choose to include or omit column labels and the index, and select specific columns.

Learn how pandas reads pickle files and html tables into data frames. Extract stock data from web tables and build a daily-updating dashboard for Nokia's price.

Learn to read and write json, html, and pickle files in Python, manage data frames with pandas and numpy, and build daily dashboards for data analysis.

Master storing large data with hdf5 in pandas using hdfstore, and learn to read and write excel files using excel references and the accelerator.

Learn hands-on data storage with HDF5 by writing and reading datasets, using dictionary-style storage, and retrieving filtered results through indexing.

Learn to read and write Excel file data by creating a file reference, using read and write operations on a data frame, and managing the index during save.

Explore data storage with SQLite, a serverless, zero-configuration lightweight relational database, and learn to connect, create tables, insert, query, and retrieve data using Python and pandas.

Identify business problems and map data sources to prepare data for analytics. Learn to handle missing values, remove duplicates, address outliers, and transform data into categories for effective modeling.

Identify missing data in the dataset and apply deletion or imputation, using constants, mean, or median, and summarize by column or row with sum in pandas using isnull and notnull.

Learn how to handle missing data by deleting observations or columns with the pandas drop function, using axis and any or all conditions, and controlling in-place changes.

Impute missing data using constants per column, per-column dictionaries, interpolation from previous values with optional limits, and statistical fills like mean or median; apply an iterative, data-driven approach.

Learn to impute missing data with Pandas by filling with a constant, per-column values, mean or standard deviation, custom dictionaries, and interpolation using forward fill with optional limits.

Identify and remove duplicate rows in a dataset using pandas. Learn to use duplicated and drop_duplicates, including column-specific checks and keeping first or last occurrences.

Learn to identify and handle duplicate values in a pandas data frame using duplicated and drop_duplicates, including keep='last' options and per-column checks.

Learn to transform a dataset with pandas by grouping data on a column and using map and replace to categorize values and create new columns for clearer data representation.

Group data values and replace them to transform a data frame into lower, middle, and higher categories using a dictionary mapping and map or replace operations.

Learn to update row and column labels in Pandas using map and rename, applying lambda functions or dictionaries to uppercase and other mappings, with hands-on practice.

Lead learners through binning continuous data with pandas, mapping data to bins, and analyzing frequency distributions via cut, qcut, and custom bin definitions.

Identify and handle outliers, sample datasets with or without replacement, and convert text data into dummy variables to enable numeric analysis in Python using Pandas and NumPy.

Detect outliers using standard deviation thresholds, create samples from populations with and without replacement, and generate dummy variables to convert text to numeric for analysis in pandas.

Learn to work with multilevel indexing in pandas Series, access data with first and second level indices, and reshape data using stack and unstack to swap row and column levels.

Explore multilevel indexing in a Pandas Series, learn to access data with first- and second-level keys, and use stack and unstack to switch between one- and two-dimensional views.

Explore multilevel indexing in a dataframe object, name first and second level indexes for rows and columns, and learn to access single or multiple values via index and slice operations.

Explore multilevel indexing in a dataframe by creating a two-level index, assigning level names, and accessing values through layered row and column keys.

Learn to reorder and sort a dataframe with a multi-level index using sort_index, switching level 0 and 1, and observe how the data order changes in theory and hands-on.

Explore how to aggregate data using multilevel indexing, summing across first and second index levels to produce per-level totals and statistics from multi-indexed data.

Explore data aggregation with multilevel indexing, aggregating at root and column levels, naming levels, and computing sums, means, and standard deviations using axis-based access.

Move one or more columns to the dataframe index with set_index, then convert the index back to a column with reset_index, using drop options as needed.

Learn to merge two data frames in Pandas using the merge function with an inner join on a common key, producing a consolidated dataset from matching records.

Learn how merging dataframes using an outer join combines common and uncommon elements, exploring inner join behavior and other join types such as left and right joins.

Understand how to merge two data frames with left, right, inner, and outer joins, identifying common observations and uncommon observations across datasets.

Learn how to merge two dataframes using multiple keys by passing two columns as the key, perform an outer join, and interpret common and uncommon elements in the output.

Master pandas merges by combining two dataframes using inner, outer, left, and right joins on one or multiple keys, handling common and nonmatching values.

Master merging data frames by combining column values with index labels, using left_on, right_on, left_index, and right_index. Explore inner, outer, and multilevel indexing for flexible joins.

Explore hands-on dataframe merging in pandas by aligning two data streams on a Kalamunda index and a column value, using merge with column and index keys.

Explore merging two data frames with the joint function using index labels or a shared column, and perform inner, outer, left, or right joins.

Explore concatenation of data frames by axis, stacking data top-to-bottom or side-by-side, and learn to name columns and indices, apply ignore index, and perform an inner join.

Explore concatenating two dataframes in pandas by stacking top-to-bottom or side-by-side, using keys to label sources and ignoring the index.

Learn to merge two dataframes with overlapping indexes using combine_first to fill missing values from the second dataframe. See hands-on examples that patch gaps when index values align.

Explore pivot and melt in pandas, redistributing data into index and column structures and converting columns to values. Learn to move index to a column and back to preserve relations.

Learn how pivot reshapes data by setting an index and columns to organize values, and how melt stacks columns into a long format for analysis.

Explore data aggregation and grouping to categorize data, apply functions to each group, and compute summary statistics like mean or standard deviation using split-apply-combine techniques.

Explore splitting a data frame by keys, applying mean to the value column, and combining results to show group-by insights.

Explore split, apply and combine to compute statistics on all numerical columns using group by, enabling mean, count, and other operations across multiple columns.

Learn to use a pandas groupby object to split data into groups, iterate with a for loop to access each key and its data, and apply per-group aggregations.

Learn to use the group by clause with a for loop in pandas to iterate gender-based groups, describe each group, and apply summary statistics like mean.

Learn to group columns with groupby by mapping columns via a dictionary, using axis=1, or via lambda and multilevel columns to create and analyze three groups.

Learn to group columns with the group by approach in Python using dictionary mappings, a grouping object, a lambda, and multilevel indexing to compute sums and means.

Learn to use group by to slice data into groups and apply aggregate functions to each group. Use user defined functions and multiple functions to customize outputs.

Apply group by to perform data aggregation using aggregate functions such as min, max, and mean across groups. Learn to use multiple keys and custom functions for flexible analytics.

learn to group data by gender, apply functions to each group using apply, compare with aggregate, and use built-in and user defined functions with arguments to produce per-group insights.

Use the play function to perform bucket analysis by bucketing numeric data into bins, group by bins, and apply a user defined function to compute min, max, count, and mean.

Explore how pivot tables rearrange data into row–column layouts using index and values, enabling aggregates like mean or sum; and cross tabulation, which computes category frequencies.

Learn to build pivot tables and cross tabulations in pandas, using index, columns, and values with aggregate functions like mean, sum, and variance.

Explore data visualization with Matplotlib and Seaborn, mastering line, bar, box plot, histogram, pie chart, and scatterplot to reveal insights within the analytics framework from data sources to deployment.

Learn how to create a matplotlib figure and arrange multiple subplots, reference axes, and plot histograms and scatter plots in a configurable layout.

Explore creating figures and multiple subplots, including histogram and line plots across grids (two by two, two by three), and learn how axes and subplot indexing control placement.

Master subplot properties in plots, including shared x and y axes, spacing with wspace and hspace, and styling lines, colors, markers, and legends.

Work hands-on with matplotlib subplots, creating a 2x2 grid and using sharex and sharey to align axes, then adjust wspace and hspace to control gaps.

Hands-on practice demonstrates controlling color, linestyle, and marker for line plots, using short-form syntax, adding labels, and configuring legends at the best location for clear visualization.

Explore hands-on subplot properties, including setting x and y axis ranges, customizing tick labels with English, axis labels, titles, rotation, and font size.

Learn to annotate plots using the annotate function by specifying x, y, and text with arrow, color, and alignment, and save figures with options to control quality.

Demonstrate how to annotate a plot using the annotate function, add text and arrows, and save a figure for later use.

Explore how to build vertical and horizontal bar plots using the bar function, including multiple bars in one figure and annotating bars with labels.

Learn to build bar graphs in Python with category data, creating vertical and horizontal bar plots, and arrange multiple groups on a single chart using axes and labels.

Learn how to create and customize 3D plots in Python, using X, Y, Z data, mesh grids, and projection techniques for visualizing multi-dimensional data hands-on.

Explore how to create histogram, pie, scatter, box, and violin plots in matplotlib, visualize distributions, correlations, and data summaries using hist, pie, scatter, boxplot, and violinplot.

Explore histograms, pie charts, and scatterplots to analyze distributions and relationships in data, learn binning with buckets, and highlight features with explode and normal distribution visuals.

Practice constructing box plots and violin plots from data distributions to compare means, medians, and distribution shapes, including normal and skewed patterns, and overlay plots for deeper insight.

Learn to create versatile visualizations directly from pandas series and data frames using the plot function, including line, bar, histogram, density, and subplots.

Explore creating and visualizing data with the pandas series object, including line, bar, histogram, and pie plots, using axis and kind options.

Explore creating and visualizing a pandas data frame object with three columns using the block function to generate line plots, histograms, and horizontal bars, then use subplots for comparison.

Learn to install seaborn and create a variety of plots—histograms, density plots, scatter plots, regression plots, box and strip plots, and multi-feature relational visuals.

Explore Seaborn categorical plots to display up to five data dimensions in one figure, using date, total bill, smoker hue, time column, and gender rule; includes box and joint plots.

Explore hands-on Seaborn plotting by installing Seaborn, loading a dataset with total bill, tip, and smoker status, and creating line, bar, histogram, scatter, and regression plots to reveal feature interactions.

Seaborn category plots, box plots, and joint plots help visualize datasets, using color and facets to compare distributions across day, gender, and smoking status.