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

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 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.

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 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.

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.

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.

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.

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 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.

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.

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.

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.

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.

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.

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.

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.

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.