Python Data Analysis Course

Explore core data structures for processing data, including series, and learn how data structures are collections of values that define relationships and operations on data.

Learn to use pandas series as a one-dimensional data structure with an index, creating series from tuples and dictionaries, where dictionary keys become the series index.

Explore creating a pandas Series from a dictionary, then index and slice to select elements or ranges, and use boolean filters to keep values greater than 50.

Learn how to access and modify data in a pandas series by index, perform arithmetic, and compute the mean using numpy or pandas.

Explore the data frame as a two dimensional structure with rows, columns, and indices, where a short series is like a column and columns hold different data types.

Learn to import pandas and build a data frame from a dictionary with name and marks, set up keys and lists, and print the resulting table.

Learn to extract data from a DataFrame by selecting a column into a series to retrieve student names, and by indexing rows to create targeted attendance reports.

Learn how data frames support mutable values, with size fixed; update cells via iloc indexing, and add or delete columns and rows to shape your dataset.

Explore data manipulation with data frames by using the describe function to generate summary statistics and practice deleting or dropping columns to shape the data set.

Visualize data with a series in matplotlib by plotting a bar chart of scores. Configure the figure size and dpi, and sort the data by index to display alphabetical order.

Use pandas describe to obtain a summary of statistics for a data series, including count, mean, and standard deviation, enabling easy analysis of your dataset.

Explore plotting data with pandas series to create and customize pie charts, including adjusting aspect ratio, titles, and axis settings to visualize student grades.

Process missing values by assigning a not a number placeholder to keep analysis going, then drop missing entries with series.drop to obtain a reliable statistical summary.

Learn to create and upload a csv data file from an Excel sheet with student results, using comma-separated values and downloading the resource to get the experience.

Import pandas and read a csv file to load testing data, then display the top and bottom five rows to inspect the data frame. Set an index to reveal structure.

This lecture demonstrates filtering data in pandas with boolean expressions and square brackets, selecting records by year (e.g., 2018–2021) and combining conditions with and to display the filtered results.

Sort data with pandas by the index using sort_index and head to display the first five records in ascending order.

Apply string operations in pandas to filter data by starting letters and display matching records.

Learn how to count occurrences with the pandas value_counts method and use the results to understand how often data samples appear.

Learn to use matplotlib and pandas to load a test dataset, set an index, and plot a bar chart to visualize data like names and scores.

Visualize three or more columns in a data frame with pandas and matplotlib, sorting by name to compare student marks against age and exam edge, in python data analysis course.

Analyze survey data by turning raw responses into insights using savvy data analysis and visualizations like scatterplot, bar graph, line graph, and area to interpret student data.

Group data by year using the group by method and size to count rows in each group, then visualize the results with matplotlib to reveal yearwise frequencies.

Discover how group by in pandas splits data by column values, applies a function, and computes means across other columns, including multi-index grouping by A and B.

Learn to group numerical data in pandas by building a three-column data frame, generating values, and using pd.cut to create four age-group bins for the edge column.

cross tabulate age groups and gender to create a frequency table from a data frame, illustrating distributions across categories.

Learn to use pandas and the categorical method to convert raw results into meaningful categories, work with series and data frames, and sort by grades for analysis.

Explore pandas merge and join operations to combine data efficiently in memory. Learn how to apply these functions to datasets and databases for high performance in memory data handling.

Merge two data frames in pandas by a common id. Use inner join to align student records and ignore nonmatching rows.

Learn to merge data frames using many-to-one and one-to-many joins, handling duplicate entries and using the match function to align records across data frames.

You will learn to merge pandas dataframes using the on keyword, selecting a common key column for joining left and right frames.

Learn how to merge two csv files using pandas to consolidate data from employees and departments based on a common department id.

Explore how pandas enables time series work with timestamp, period, and time delta types, including a period index and a frequency interval for structured time data.

Generate a time series with pandas by creating a range of ten entries and applying a monthly end frequency.

Create a time series by using periods and a frequency parameter with pandas, set start and end parameters, and display the resulting data.

Learn how to use pandas period objects to handle frequency, generate periods, perform end-of-month calculations, and apply period arithmetic for monthly and annual data.

Create a period range in pandas using period_range from 2010 to 2015 with annual frequency, then display it and create a series indexed by that range.

Convert string dates to date objects, then convert to a period index with pandas to_datetime and end-of-month frequency, demonstrating a two-step workflow from string to period.

Transform wide data to a long format by using identifier columns and melting data variables to restore the original data structure.

Learn how to melt data variables with pandas, set the value and identifier columns, and apply the method to a sample country dataset to display the melted result.

Unmelt melted data back into its original data frame using pandas pivot, specifying index and columns to restore the data structure with the country code for visualization.