Learn Python - Data Analysis From Beginner To Advanced

Learn to define data paths with relative and absolute paths in Python notebooks, review trade-offs, avoid file not found errors, and copy absolute paths with raw strings.

Learn to import csv files into pandas using read_csv, set a df variable, and preview the first few rows to verify data from a customers dataset.

Learn to import excel files with pandas using read_excel, specify the sheet to import to access multiple sheets, and store results as a data frame for analysis.

Export processed data to a csv file by creating a database, importing the customer data, and saving to a specified path, with optional index removal and overwrite behavior.

Learn to export a dataframe to an Excel (.xlsx) file, disable the index, and rename the sheet (for example, converted data) for a clean, ready-to-share dataset.

Explore pandas data types in python, including integers, floats, strings, and booleans, and learn how to create a dataframe, inspect column dtypes, and print results for data analysis.

Learn how to print data frames in Python using two methods: notebook display and the built-in print function, with pandas, across Jupyter and console environments.

Master selecting columns in pandas data frames by comparing the dot method and the bracket method, including auto-complete benefits, pitfalls with method names, and reordering with a column list.

Learn to select rows in a pandas data frame using iloc, choosing single indices or lists of indices, handle row vs series results, and observe effects of duplicate indices.

Learn to select the first or last rows of a data frame using pandas head and tail methods, with adjustable parameters for top or bottom n rows.

Learn Python data analysis teaches how to select rows and columns in one go using the indexing method, with zero-based indexing, range selections, and dynamic variable-driven column lists.

Learn how to set a column as the new index in pandas using set_index. Print the first five lines with head, and understand the implications of non-unique versus unique indices.

Master how to reset a pandas DataFrame index to the default, move the index to a column, and use the drop parameter to discard the original index.

Compare selection vs copy in a data frame by extracting city and contact name, then modify values and observe the setting with copy warning.

Use masks and a column list with the log function to select German customers and update their city to Munich in a data frame, ensuring only targeted cells change.

Learn to modify a copy of a data frame by creating a mask for Germany, selecting a subset, and using copy to avoid warnings while preserving the original data.

Concatenate multiple data frames into a master frame with pandas concat, ensuring matching columns, then use a frames list and keys for hierarchical indexing and data selection with loc.

Learn to pivot dataframes in Python with pandas, converting stacked data into pivot tables using index, columns, and values, and applying the results for further analysis.

Learn to reshape data with pivot table and pivot in pandas, handle duplicated indices, and apply aggregation metrics like sum, mean, standard deviation, and count.

Learn how to convert categorical data into dummy variables using one-hot encoding in pandas, via get_dummies, and attach the results for machine learning pipelines.

Learn to create basic scatter plots in Python with pandas by selecting numeric x and y columns and coloring two groups. Plot multiple series and manage legends.

Advance your data analysis with scatter plots by coloring points using column c values, sizing with column d, and scaling point sizes by about 50 for clarity.

Learn to create a basic time series plot using pandas, generate random data, compute cumulative sums, and customize plots with grid, title, figsize, font size, and color.

Learn to plot multiple series on one chart from a thousand by four random data frame with A, B, C, D, and compute cumulative sums.

Learn to create bar plots in Python, counting country frequencies with value_counts and rendering vertical or horizontal bars, including sorting order and a title such as 'Customer nationality'.

Learn two ways to save MATLAB plots to a file from a notebook: manually via right-click save, or programmatically by exporting the figure as a PNG.

Learn how to import a customer database with pandas read Excel, inspect column dtypes, and convert non-string columns to strings using astype, preparing data for string operations.

Discover how to clean and standardize names in a data frame using string methods: convert to uppercase or lowercase, measure length, and strip extra blanks for accurate counts.

Split strings with the split function, handling whitespace and custom patterns. Expand results into columns and extract first and second names across rows, using n to limit splits as needed.

Learn how to replace substrings in strings using the replace method, including limiting replacements and controlling case sensitivity, illustrated with expanding abbreviations in an address column.

Learn to concatenate a series of strings into a single string with pandas, using the cat function, adjust missing value representation, separators, and line or tab characters for readability.

Discover how to index strings with Python and pandas using str methods, including forward indexing, range slicing, negative indices, step slicing, and reversing strings, with handling for missing values.

Extract substrings from a dataset using extract and extractall with regular expressions, rename columns, and stack matches to display multiple uppercase letters per name as separate rows.

Learn how to use the contains function to test substring presence, convert data to strings, and apply a regex for four consecutive digits to filter postal codes.

Learn how to use Python's print function to interact with users, including passing strings and variables, controlling newlines with end, and creating dynamic greetings like 'Hello, Alan'.

Explore python variables by creating integers, floats, strings, and booleans, then learn lists, dictionaries, and sets. See practical examples like height, gpa, and names, and use print to display results.

Explore core string concepts in Python, using replace, split, join, starts with, and ends with to manipulate text; learn practical examples like deleting substrings and joining words.

Master Python lists from basic numbers and strings to nested lists, and apply list comprehension with conditions, using range for even-number filtering.

Learn how the set data structure stores unique items, convert a list to a set to remove duplicates, and why sets are less used than lists or dictionaries.

Explore Python dictionaries, learning their structure with unique keys and corresponding values, how to create, access, update, add, delete, and use keys, values, and items.

Discover how if-else logic in python uses a temperature condition and a warm criterion to decide wearing a jacket or a shirt, with examples like 25 or 15 degrees.

Explore how Python uses indentation to group code, not parentheses, and how colons trigger increased indentation in blocks like if, else, for loops, and function definitions.

Master Python for loops to process sequences efficiently, iterating over strings, lists, and dictionaries. Use dict.items() to access key–value pairs and print results as you run the loop.

Learn how to structure code with functions, using define syntax, parameters, and returns, and see how to call them for greetings and a list of people, and temperature-based clothing choices.