Learn Python Libraries For Data Analysis & Data manipulation

Create pandas series from ndarrays and dictionaries, assign custom indices, and inspect data types to understand homogeneous elements and missing values. Practice accessing, slicing, and updating series for data analysis.

Learn to build data frames from lists, compare with dictionaries, set custom columns and index, and distinguish between series and data frames in pandas for data analysis and manipulation.

Create an indexed data frame from a dictionary using pandas, demonstrating how to set an index for six days with temperature, windspeed, and event data.

Learn to access data in a pandas dataframe by row and column, select single columns as a series, and slice multiple rows with index rules.

Explore how to set and reset a dataframe's index with set_index and reset_index, control changes with inplace or copy, and understand index implications for data access and plotting.

Locate values by index name using dataframe indexing and set index with or without in-place changes; unique indexes yield a series, while non-unique indexes return a data frame.

Learn to read csb files using bandas and perform exploratory data analysis on the Game of Thrones dataset using data frames, column selection, and basic visualization.

Learn how to load CSV data, select single or multiple columns with Python lists, and perform exploratory data analysis on the GOT dataset using bar and pie charts in Matplotlib.

Learn to read and write Excel and CSV files with Python. Manage sheets, skip rows, header and index settings, and export results to Excel or CSV.

Learn to perform exploratory data analysis by grouping data and aggregating counts for major debt and major capture, then visualize the results with a grouped bar chart.

Understand how a box plot visualizes data distribution using quartiles, minimum, maximum, and median, with the box and whiskers showing the data spread.

Learn to handle missing data in dataframes using fillna with drop, interpolation, and mean strategies, including forward and backward fill and in-place updates.

Apply fillna backward and forward fills for missing data in dataframes, using axis and limits to fill from previous or next values across rows or columns.

Learn how to handle missing data in dataframes using interpolation to estimate values. Explore time-based interpolation, linear and nearest methods, and apply them to temperature, humidity, and pressure data.

Master the pandas replace method to handle missing and invalid data in dataframes, use interpolate for estimation, and compare replacing with nan or custom values for clean data analysis.

Learn to concatenate two dataframes, ignore the index, and visualize the combined temperature and humidity data by city using bar edge plots.

Learn how to connect pandas with a MySQL database, install and configure MySQL using a local server, create databases and tables, and fetch and join data in data frames.

Learn how to use the pandas merge method to join two dataframes on a common column, performing inner, left, right, and outer joins in data analysis.

Master pivot and pivot_table methods in pandas to reshape data frames using index and columns. Handle duplicates with by work table and margins, using aggregate functions like mean and sum.

Learn stack and unstack in pandas to reshape data frames from columns to index, mapping cost price and profit across products and dates with a multi-index.

Learn to reshape a data frame from wide to long format using the melt method in pandas, identifying identifier columns and unpivoting measuring variables into variable and value columns.

Master time series analysis with DatetimeIndex in pandas, set the date as the index, and filter data by day, month, or date range for insights.

Generate date ranges for time series with pandas date_range using start, end, periods, and frequency. Explore calendar days, business days, time zones, and set the range as a dataframe index.

Learn JSON, a lightweight, human readable format for storing and transmitting structured data as attribute-value pairs. Use objects and arrays, and see how dictionaries map in Python and APIs.

Learn how application programming interfaces enable apps to fetch weather and stock data by sending requests to APIs and handling JSON or XML responses.

Analyze JSON weather data with Python, Pandas, and Matplotlib by fetching from a weather API, converting to a data frame, indexing by date time, and visualizing humidity and temperature.

Fetch stock price data from a json api, load it into a pandas data frame, and visualize five-minute historical prices with open, high, low, close, and volume.

Continue explorations of the Titanic dataset by creating a correlation matrix and a seaborn heatmap, using pandas corr to reveal relationships among numerical features.

Analyze the titanic dataset's sibsp and parch columns through exploratory data analysis, comparing survival by passenger combinations, and practice plotting three side-by-side graphs using Python's data frames and matplotlib.

Visualize data distribution with histograms and kernel density estimation using Python, Seaborn, and Matplotlib, covering bin choices, missing data handling, and comparing survival groups in Titanic data.

Explore Titanic data with seaborn and pandas to perform bivariate analysis on survival by gender, class, and embarkation, encode categorical variables, and visualize correlations and fare-based insights.