Beginner's guide to Python Pandas for Data Analysis

Import and cleanse raw data from csv files, excel files, and sql server tables in a Jupyter notebook, create data frames, and visualize results with pandas for informed decisions.

Download and install the Anakonda, Jupiter notebook, and SQL Server 2019 Developer Edition, then launch the Jupiter notebook and verify Python 3.7.6 and pandas 1.0.1 in a notebook.

Install SQL Server Management Studio (SSMS) to interact with SQL Server. Follow a simple download and installation, then test the connection and prepare to use SSMS in the Jupiter/Pendas environment.

Install and configure SQL Server 2019 Developer Edition on Windows 10, verify prerequisites, and connect via GUI to support data analysis with Python Pandas in the course.

Navigate the Jupyter interface to create and organize notebooks, run and manage code cells, switch between edit and command modes, and use markdown for documentation.

Discover how pandas, an open source python library, provides series and dataframes for data analysis, importing from diverse sources and working with live notebook data.

Rafael guides learners through a practical quiz on Python and pandas, covering insert and delete cell shortcuts, list creation, and building a data frame from a cars dictionary.

Learn to import and read data from a csv file with Pandas, create a data frame, and display results, using file path variables to handle one million rows.

Explore pandas common keywords and commands to inspect a large CSP sales record data file, control million-row displays, and examine shape, head, tail, columns, index, values, and describe.

Rafael guides quiz two on importing a CSP file with pandas, creating a data frame, and using commands like type, shape, head, and tail to inspect data.

Learn to slice and filter large sales data with pandas iloc and loc, selecting rows and columns by position or label.

Explore iloc and loc filtering and slicing in pandas, using start and end row and column indices and negative indices. Understand label versus position indexing, accessing data frames and series.

Learn to work with indexes in pandas by loading an Excel file, slicing and filtering with df.loc, selecting columns, and creating a permanent index (email) with set_index and reset_index.

Master pandas data selection in Python through quiz 3: pull specific columns and top 10 rows, use iloc and loc for indexing, filter by California, and set state as index.

Explore filtering data with pandas using conditional operators like greater than, less than, and, or, and handle encoding with latin-1 while viewing data frames.

Apply conditional filtering in pandas using or, and, not, and is in to select rows by name and score. Wrap filters in a data frame to view results.

Learn how to sort data in pandas using sort_values for single or multiple columns, control ascending or descending order, sort by index with sort_index, and set or reset indices.

Engage in a pandas quiz that teaches displaying all rows, resetting column width, filtering by Indonesia and Europe with population over 20 million, and sorting by continent, country, and year.

Learn to add and remove columns and rows in pandas dataframes, control persistence with in place operations, and rename, sort, and concatenate fields for data analysis.

Utilize the Pandas replace method to swap values in a data frame, apply in-place updates, and rename columns with string replace, then reorder columns using pop and insert.

Practice pandas basics by selecting and listing rows with head, concatenate city and state into a city state column, drop rows, and perform in place replacements to update first names.

Learn to use pandas string functions for data cleaning and transformation, including upper and lower case, concatenation to full names, and splitting dates into month, day, year.

Explore Python regex to search, extract, and replace text patterns in large data using find all, search, split, and sub; learn essential special characters and cross-language applicability.

Explore practical Python regex techniques for find all, search, split, and replace to extract digits, phone numbers, and emails from text, including patterns and new line handling.

Apply the group by process in pandas to split data into categories, compute aggregates like mean, sum, and max, and combine results into concise insights.

Explore core pandas concepts through quiz 6, focusing on group by, data filtering with get_group, regex find and replace, unique values, youngest selection, and earnings per year.

Learn to use lambda functions in pandas for concise, one-line expressions. Apply lambda with apply and filter to transform data frames, columns, and create categories.

Explore how the pivot_table function in pandas reshapes data by region, rep, and item, then aggregate unit cost and units with mean or sum.

Learn to identify missing or null values in pandas data frames using isnull and isna, and fill or drop them with fillna, group averages, or forward fill.

Master date and datetime handling in pandas, extracting year, month, day, week, and day of week, adding a day of week column, indexing data, and computing max, min, and deltas.

Import html url data using pandas to read a web page with pd.read_html, select the relevant list index, and convert the extracted table into a dataframe for analysis.

Practice pandas fundamentals by dropping missing values, counting affected rows, adding a day-of-week column, calculating date deltas, displaying two frames together, and concatenating frames with ignore index.

Discover how to connect to a Microsoft SQL Server from Python using pandas and pyodbc, pull data from a view, and load it into a dataframe for analysis.

Learn how joins merge data from multiple tables or data frames using inner, left, right, and outer joins in pandas, with examples from SQL Server.

Learn to perform inner, left, right, and outer joins in pandas using merge on two dataframes, with product and customer tables and concepts like indicator and suffixes.

Compare SQL server and pandas queries with a side-by-side demonstration, importing csv data, and applying select, where, group by, update, and delete in both environments.

Learn how to connect to SQL Server from the pandas environment in Jupiter, compare SQL and pandas commands, and run live data operations using select, where, group by, and more.

Learn to connect to a SQL Server by importing pandas, build a connection string, select all from a table, and view the top 10 rows.

Learn to turn raw sales data into informative visuals by building clear line charts with matplotlib, including axis labels, titles, legends, and color-coded series.

Explore creating matplotlib line charts in Python, including setting x and y data, labeling axes, adding a title and legend, customizing color and markers, and saving the figure.

Create and customize a pie chart in Python using matplotlib, exploring explode wedges, shadows, percentages, labels, start angle, axis equal, and a legend to visualize monthly bills.

Learn to build bar charts with Matplotlib, featuring x- and y-data, titles, labels, and bar types like vertical, horizontal, and stacked bars, plus rotating x-axis labels for readability.

Build a pandas data analysis project by combining customers, products, and orders from a SQL Server. Create data frames, join tables, compute total price, and extract month and year.

Connect to SQL server via pandas, load customer, product, and orders tables, merge on product_id and cust_id, and create a total_price column while examining data types and cleansing.

Learn to extract month and year from order date, cleanse missing data, update product fields, recalculate total price, and prepare data for grouping analyses in pandas.

Filter and analyze data frames by location, such as New York or Nevada, using group by to sum, count, and mean values, and prepare charts for visualization.

Explore how to turn raw data into readable information by creating line and bar charts with pandas and matplotlib, customize charts, labels, legends, and rotations.

Import and join data from a SQL Server into Pandas with a single T-SQL statement, using inner joins on customers, products, and orders.

Create two data frames, concatenate them, reset the index, group by last name and salary with sum, group by state and salary with sum, and create a vertical bar chart.

Launch a self-learning project with Python Pandas by importing a CSV of names and addresses, concatenating columns, extracting date parts, creating charts, sorting, handling blanks, indexing, and grouping data.