In this lesson you will be able to deal with missing values in a DataFrame. During the study you will learn that we can simply remove corresponding column if there are a lot of missing values or we can fill missing values with either median or mode value, depending on the data type that is in the column.

In this lesson we'll get familiar with interactive visualization techniques which can be used with Pandas DataFrame using Bokeh library. In the lesson we discuss the simple scatter plot, however students are encouraged to get familiar with other Bokeh visualizations.

In this lesson we'll get familiar with advanced visualization techniques using Seaborn library. Seaborn is a library for making variety of graphics in Python which is closely integrated with pandas data structures.

In this lesson we will get familiar with some basic visualization techniques which can be used with Pandas DataFrame. In particular built-in solutions for the boxplot and the histogram presentation.

In this lesson we will learn about powerful technique called aggregation, which can be used to easily calculate quite complex statistics of your data. In particular the data aggregation methods using ".groupby()".

In this lesson we will learn how to navigate through our DataFrame using the indexation and how to select particular columns and/or rows, and how to perform sorting of our DataFrame. During the study you will learn how to sort DataFrame by values of some column and how to save DataFrame to a CSV file to open it in the future.

In this lesson we will learn how to add new features to DataFrame using existing columns. Also you will learn about mapping technique, which we can use to efficiently replace some string values with numerical values.

In this lesson you will be able to deal with missing values in a DataFrame. During the study you will learn that we can simply remove corresponding column if there are a lot of missing values or we can fill missing values with either median or mode value, depending on the data type that is in the column.

In this lesson we learn how to analyze Information about DataFrame, identify and fix issues with decimal points in numerical values, as well as Remove Duplicates.

This is a summary lesson.