Master Pandas: Basics To Advanced - To Become A Data Analyst

Navigate tabular data by organizing rows and columns, using row and column indices, and understanding how column letters form the index. Compare data frames to single columns as multi-axis objects.

Learn how to read the pandas documentation, inspect a dataframe's shape, columns, and index, and extract a single column using square bracket notation.

Explore bond us data structures, including series and data frames with explicit indexes, enabling handling of heterogeneous and missing data for efficient tabular analysis.

Learn to construct a pandas series from dictionaries, lists, or other objects, specify data, index, and name, and understand how index length affects the series.

Learn how to construct dataframe objects by using signatures, indexes, and column data, exploring dictionary-like structures and various methods to rename, access, and organize data.

Discover how pandas index objects hold axes and data, remain immutable, and how to create, access, and slice them. Explore union, intersection, difference, and symmetric difference operations.

Explore data structures through practice questions, creating and manipulating series objects, indexing, slicing cities data, converting between lists, and building data frames to analyze scores and top performers.

Explore practice solutions for data structures using linear spaces, random value generation, and series construction; learn to slice, index, and filter data frames to identify top scores and qualified participants.

Explore data indexing and selection in series and data frames, comparing implicit and explicit indexing, integer versus key access, and slicing rules for efficient data retrieval.

Understand data indexing and selection in series and city data. Learn to access and modify values using index levels, dictionary-like access, and items.

Explore data selection in a one-dimensional series by slicing with explicit and implicit index methods, including masking and fancy indexing to retrieve data values using index levels and conditions.

Learn how loc and iloc indexers in series handle explicit versus implicit indexing, and how slicing endpoints are included or excluded.

Learn two dimensional data structures and index-based selection in a data frame using dictionary style access, create new columns, and compute a scorecard from column operations.

Explore data selection in a dataframe by indexing and slicing rows and columns, using explicit expressions to slice data and manage index handling.

Learn to access values in dataframe objects with loc, iloc, and ix for label-based, position-based, and mixed indexing. Apply slicing and conditional filters to select rows and columns efficiently.

Practice part 02 guides data indexing and selection through three exercises on creating and querying data frames, filtering by score and rows, and computing sums.

Explore practical data frame indexing and selection techniques, including iloc and loc indexing, boolean and multi-condition filters, value assignment, summation, and removing rows in a practice dataset.

Explore essential functionalities for processing data in series and data frames, including reindexing, alignment, filling missing values, interpolation, and applying functions across axes.

Explore reindexing pandas objects by creating and expanding index levels, and handling missing values with forward fill. Rename columns and align data across new indices.

Drop entries from a data frame by axis using the drop method, choosing rows or columns, and removing index or column levels. Learn in-place versus new object outcomes.

Explore how data alignment handles missing values when combining series and data frames with overlapping and non overlapping indices, illustrating union, alignment gaps and null values.

Learn to handle missing and null values by filling with specified values, perform addition across columns on data frames, and apply scalar operations to replace and propagate fill values.

Explore broadcasting in pandas by aligning a dataframe and a series on index and column levels, performing arithmetic like subtraction, and handling missing rows with unions and null values.

Explore how to use pandas apply and applymap to run functions across axes and columns in data frames, including calculating max, min, and differences with lambda expressions.

Learn to sort and rank data in pandas using axis, index and column levels, with ascending or descending orders and group-based ranking.

Learn to handle duplicate index levels in data frames by detecting duplicates with boolean checks, identifying whether the index is unique, and selecting all rows associated with a duplicated index.

Master descriptive statistics and data summarization by computing mean, max, min, standard deviation, and cumulative sums on series and data frames, handling null values and index levels.

Explore how to compute unique values, value counts, and membership checks to distinguish distinct from non-distinct data, handle missing values, and analyze frequency distributions.

Explore essential data frame functionalities through three exercises: sort and index manipulation, compute max, min, and mean for score and attempts, and analyze non-integer values.

Sort the data frame by name in descending order, drop the Occam's column, and reindex to name, items, score, and qualify; apply lambda to identify max score and max attempts.

Learn data handling by reading and writing text and tabular data from local or network sources, using csv, json, or excel formats to create data frames.

Learn to read and explore comma-separated csv files from multiple sources, handle headers and encoding, and select and inspect specific columns and rows for data analysis.

Parse csv files effectively by identifying column names and indices, handle missing values, and use dictionary-like objects to map columns to values, while exploring strings and separators.

Learn how to read large text files in pieces by loading small chunks, skipping rows, and extracting specific sections such as log files, enabling efficient analysis.

Learn how to export data in text format, manage data objects, headers, and columns, and export large datasets for use in Excel.

Discover how to use Python's csv module to read and process CSV data line by line, access columns, and handle objects and lines efficiently for data analysis.

Practice part 04 provides three exercises on data handling with pandas: read datasets, inspect columns and missing values, describe data, and clean the Titanic data by removing specific columns.

Explore data handling with the Titanic dataset by reading data, inspecting missing values, describing statistics, and exporting a refined subset after filtering by age and survival.

Explore data preprocessing techniques to clean and transform data, including handling missing values with drop and fill strategies, removing duplicates, mapping values, and creating dummies and bins.

This lecture explains how to detect missing values with is.na, and how to handle them by removing or imputing values, including forward and backward methods.

Learn how to filter missing values by dropping rows or columns with nulls, leaving a dataset composed of non-null or nominal observations.

Learn to fill missing values without removal using scalar fills (like 50), forward or backward fill with limits, and mean or median aggregation.

Learn to remove duplicate rows in data frames with duplicate and drop duplicates methods, specify columns, and use keep and in-place options.

Learn how to replace non-null values in a data frame using substitution methods, including dictionary-like mappings and list-based substitutes.

Learn to rename axis labels by using map and lambda to transform index and columns in a data frame, then apply rename for in-place or new axis modifications.

Discretize data by binning continuous values into intervals using cut, with labels. Use equal-width and quantile-based binning to turn continuous variables into categorized data and explore bin frequencies.

Learn to detect and filter outliers in a four-column data frame by applying conditional rules and absolute values to select rows and columns based on thresholds.

Learn to reorder cities within a data frame using a random permutation and to randomly select rows with the sample function, applying axis and index concepts to create shuffled data.

Convert categorical variables into dummy indicators for machine learning, encoding object columns with drop of one category and a prefix, then join back to the data.

Use the map method to transform data with a function or dictionary-like object, preserving indices and handling nan values, while converting column names to lowercase.

Master Python string manipulation with split and join to parse and recombine text, then use strip, index, in, replace, substring, and slice notation for precise edits.

Learn to use regular expressions in Python to search, split, and replace text, compile patterns, and extract groups like emails and domains.

Utilize vectorized string functions and regular expressions to perform string operations, handle missing values, and extract patterns from data such as emails using find all, group matching, and slicing.

Practice part guides you through cleaning and prepping the Titanic dataset by indexing, handling nulls and duplicates, renaming columns, creating age groups, and applying dummy variables.

Clean Titanic data by setting the name column as the index, handling nulls and duplicates, transforming gender to title case, binning ages, and creating dummy variables.

Explore data wrangling, including joining, combining, and reshaping data with multi-indexing, set_index, stack, unstack, and merge operations to manage many-to-one and many-to-many relationships.

Explore hierarchical indexing to manage higher dimensional data with multi-index levels, partial indexing, and data reshaping through stack and unstack in a data frame.

Reorder and sort hierarchical index levels on rows and columns using axis and level names. Swap and sort index levels to pre-order a data frame and tailor the index structure.

Learn to compute summary statistics by level in a data frame, grouping by index or column levels and applying sum across rows or columns.

Learn how to create hierarchical indexing in a data frame by using one or more columns as the index, including multi-index levels, drop behavior, and in-place options.

Learn to merge pandas data frames using inner, left, right, and outer joins with on/left_on/right_on and suffixes, handling many-to-one/many-to-many and NaN values.

master merging data frames by row index, using left and right index joins and on barometer configurations for inner or outer joins with suffix handling.

Explore concatenating data along a chosen axis, using inner/outer joins, ignore_index, and keys to create multi-indexed data frames and series.

Learn to combine data frames with overlapping indexes by filling nulls with corresponding values from the other frame, using combine operations and conditional logic for columns.

Learn how to reshape data with Pandas using stack and unstack on hierarchical indexes, handling levels, missing values, and reshaping data frames and series.

Practice data wrangling on the Titanic dataset using a four-part board to build a multi-index, separate and join datasets, then stack them with level names data 1 and data 2.

Master data analysis techniques in the Titanic dataset: set index with sex and name, join two data frames, apply suffixes, and stack operations for a cohesive, multi-dataset view.

Explore the group by concept and data aggregation, grouping data by keys or axis, applying aggregation or transformation functions to each group, and combining results for analysis.

Explore how to group data by index or columns, apply aggregations like mean and sum, and leverage the split-apply-combine approach to slice, dice, and summarize datasets.

Iterate over groupby objects to access each group as a series or data frame, using for loops to explore group keys, names, and data for single or multiple keys.

Learn to select single or multiple columns using column indexing and the groupby method, then group data by keys and index by column names to retrieve the desired columns.

Group dataframe columns by using a dictionary or series as the grouping object, matching keys to columns, and apply sum; convert dictionary to a series for the same result.

Group data by using Python functions as keys, including string length, and explore index level grouping in multi-level data frames, then apply sum and count aggregations.

Explore data aggregation techniques by applying built-in and user-defined functions to grouped data, computing min, max, mean, and standard deviation for numeric columns while handling selections and dictionary-based aggregations.

Practice part seven on deck grouping and aggregation guides you to group the Titanic data by sex to compute mean age and count survivors by class.

Analyze the Titanic data using group by to compute mean ages by gender, count survivors by gender and class, and validate results through indexing and summing.

Explore time series analysis by examining data observed at points in time across finance, economics, ecology, neuroscience, and physics, using timestamps and Python tools for UTC and ISO 8601 formats.

Explore time series data types, including fixed and irregular timestamps and time spans, and learn to work with time series data using Python's date and time tools.

Convert strings to datetime objects by applying specific formats and format specifiers, then display dates and times as strings, using list comprehension to batch convert multiple dates.

Explore time series basics with pandas objects, creating a series with a date-time index, then index, slice, and groupby to handle duplicates.

Generate fixed frequency time series data using a date_range with start, periods, and frequency, including daily and business day options, and optionally normalize timestamps to midnight.

Learn to generate timestamps with base frequency and based frequencies, combine hourly and minute based frequencies, and shift timestamp indices by periods or frequency to shape the data.

Explore handling worldwide time zones with UTC standards, daylight saving transitions, and Python time libraries; localize, convert, and analyze time series across zones.

Master periods and period arithmetic by using days, months, and quarters to form frequency-based ranges, adjust start and end dates, and convert between frequencies.

Practice part 8 guides you through time series analysis using the global store 2016 data, building a dataframe index, verifying uniqueness, counting orders by year, and grouping by date level.

Analyze a 2016 global superstore dataset by importing and cleaning data, converting order date and ship date to date objects, setting and validating a non-unique index, and counting annual orders.