Pandas with Python

Explore pandas series and dataframes, create date ranges, inspect and slice data with iloc and loc, perform calculations and statistics, and load data from csv, Excel, or databases.

Explore pandas basics with Python, focusing on series and data frames, 1-D and 2-D data structures, indexing, and fundamental data manipulation for analysis.

Explore how to load and inspect data in pandas, check for missing values, and analyze the data shape, rows and columns, preparing for date range analysis.

Learn to create and inspect data frames in pandas with python, generate date ranges, set indices, and perform core ops like head, info, missing value checks, and value counts.

Explore indexing and slicing DataFrames to extract subsets, using projections, sequences, and steps, and handle heterogeneous data elements.

Explore indexing and slicing in pandas by working with series and dataframes, selecting rows and columns with loc and iloc, and using describe, info, and handling missing values.

Explore how to access data in pandas using loc and iloc, compare indexing and slicing, and apply these indexers to retrieve values from your DataFrame.

Learn to use loc and iloc to select data by label or position, accessing rows and columns by name or index, with slicing and negative steps.

Continue this session indexing and slicing on the data frames, explore various methods to slice data in sequence or in random order, and observe different approaches.

Learn how to index and slice pandas DataFrame using start, end, and step values, including negative indices and default behavior, with practical rules for left-to-right and right-to-left selections.

Learn how to concatenate data frames in pandas with Python and perform descriptive statistics, including min, max, mean, median, standard deviation, and correlation.

Explore descriptive statistics in pandas with describe, including numeric and object columns, and master dataframe concatenation, selection, duplicates removal, and merging for join operations.

Master merging dataframes in pandas by using common attributes and join types: left, right, inner, and outer, while understanding the core concepts of data joins.

Learn how to merge two data frames on a common column using pandas, using on and how parameters, and verify resulting shape and updated column names.

In session 8, learn to work with text data by cleaning the data, then perform tasks such as measuring text length, concatenation, and applying text patterns.

Explore merging dataframes in Pandas with inner, outer, left, and right joins on common columns, and prep text data by lowercasing, replacing patterns, and splitting strings.

Learn how to apply functions in pandas using pipes to operate on an entire dataframe or specific rows, and master loading data from diverse sources and formats for analysis.

Explore essential pandas data wrangling in Python: loading data with read_csv, handling headers and separators, renaming indices, dropping duplicates, applying functions, and performing left, right, inner joins and slicing.

Explore loading data from CSV, Excel, and URL sources using pandas loading functions, and connect to databases to bring data into analysis.

Master loading data from csv, excel, and url sources with Pandas; use read_csv and read_excel, select specific columns, skip rows, and manage sheet names and separators.

Explore data visualization in pandas with Python by using functions from the founders library, including bar charts and box plots. Discuss the functions, models, and concepts behind visualizations.

Visualize used car data with pandas, using box plots and scatter plots to explore distributions, mean, and median, and learn data loading for clear reporting.

Explore the foundations of data science, including data analysis, visualization, and the roles of data scientists, while applying Python programming, data mining, statistics, and common applications.

Data science blends statistics, analysis, and machine learning to understand and predict data through cycles of collection, cleaning, modeling, and reporting, requiring programming and domain knowledge.

Explore differences between traditional programming and machine learning, and how statistics underlie machine learning. Learn the responsibilities of machine learning development, data collection, deployment, reporting, and types of machine learning.

Explore what machine learning is, how it differs from traditional programming, and how data science and statistics underpin learning models, including linear regression and supervised, unsupervised, and reinforcement types.

Explore inspecting and shaping data, slicing and joining across sources, reading from Excel and databases, and applying functions for daily reporting in pandas with Python.