Pandas with Python for Data Science

Learn to read a dataset with Pandas by selecting specific columns with usecols and limiting to the first five rows with nrows for a quick data and schema glimpse.

Explains the axis parameter in Pandas and how axis=0 (default) computes the mean for numeric columns across rows, while axis=1 computes across the columns like rating and duration.

Discover how to use pandas string methods with the str accessor to transform dataframe columns, such as converting text to uppercase, replacing characters, and checking for substrings with contains.

Learn how to change a column's data type to float in pandas, either by converting a selected series in place or by specifying dtype during read with a dictionary.

Inspect the space occupied by a pandas data frame by examining column types and memory usage, and use memory_usage(deep=True) for real usage of strings and references.

Learn to create dummy variables in one pandas command by passing the data frame to get_dummies, specifying columns, and using drop_first to drop a level for two-category columns like gender.

This lecture demonstrates converting a city daytimes string column to pandas datetime, enabling dt-based operations to extract year, hour, and filter records by date efficiently.

Explore how to avoid the setting-with-copy warning in pandas by explicitly copying data frames with copy, using loc for safe selection, and creating readable, separate copies.

Shape a data frame by melting wide data into long format with pandas melt, using id_vars and custom var_name and value_name for temperature and humidity values.

Learn how crosstab creates contingency tables to display frequency counts in pandas, using Titanic data to compare survival by class and gender.

Explore an advanced pivot table example in pandas, using the grouper function to group data by week and apply aggregation for clear, readable management reports.