Analyzing Data With Polars in Python

You’ll learn how to filter a single row and multiple rows with square brackets.

You’ll learn how to filter a single row and multiple rows with the Expression syntax in Polars.

You'll learn the how and why regarding the two ways to read data in Polars, i.e., lazy and eager mode.

You’ll learn how to filter specific rows based on values in specific columns.

You’ll learn how to filter a single column and multiple columns with square brackets.

You’ll learn how to filter a single column and multiple columns using expressions.

You’ll learn the many ways to rename columns in a Polars dataframe.

You'll learn how to add new columns to an existing dataframe.

You'll learn how to add new rows to an existing dataframe.

In this lecture, you'll learn how to identify missing values in your dataframe. How to count missing values and how to drop them from your dataframe.

You'll learn how to replace missing values in your dataframe with a constant and a strategy like mean or maximum.

You'll learn how to assign appropriate data types to the columns in your dataframe to reduce the memory usage of your loaded data. You'll learn how to use Numpy iinfo to determine the upper and lower bounds of various numerical data types.

In this lecture, you'll learn how to convert string data to categorical data if their cardinality is low. You'll also learn how to determine the cardinality of a particular column in your dataframe.

You'll learn the differences in the nested string data types that Polars offers and how to use each one of those data types.

You'll learn how to format text values in your columns to uppercase, lowercase, and titlecase.

You'll learn to replace text values in your columns with other values. You'll also understand the difference between replace and replace_all.

You'll learn to slice text values from your row values to retrieve desired values.

In this lecture, you'll learn how to split text on specific characters such as space, dash or comma. You'll also learn how to retrieve desired values after splitting your data.

You'll learn how to perform summary statistics on your dataframe as well as advanced statistics such as rolling mean, cumulative sum, etc.

You'll learn to count values in a particular column of your dataframe using value_counts, as well as aggregate your data with group-by calculations.

You'll learn how to calculate quantiles in your dataframe. More importantly, you'll understand what quantiles mean and how they are helpful in your data analysis tasks.

You'll learn to combine dataframes with inner join.

You'll learn to combine dataframes with left join.

You'll learn how to use the anti join to combine two dataframes.

You'll learn to add a time zone to your datetime values and how to change that time zone.

You'll learn to ensure that the datetime values in your dataframe are read as dates instead of strings.

You'll learn to adjust datetime values in your dataframe using offset, truncate, and round.

You'll learn to extract meaningful date components from your datetime values such as week, day, time, and hour.

You'll learn to filter dataframes using datetime values.

You'll learn how to perform group-by operations on datetime values using group_by_dynamic.

You'll learn how to select specific columns and assign names to those columns while reading the data.

You'll learn how to read Excel files by sheet name and sheet ID.

You'll learn how to read normal JSON files and newline-delimited JSON files, as well as read and scan parquet files.

You'll learn to save your data to disk in various file formats such as JSON, Excel, CSV, Parquet, etc.

Congratulations! You've reached the end of the course. Now go and practice what you've learned.