Advanced Techniques in pandas

1. Get Familiar With Indexing and Selecting Data

This section will teach you how to output specific columns by their titles or indices. Also, you will get acquainted with the ways you can select rows by indices.

Output Columns by Title

Output Rows by Index

Select Specific Rows and Columns

Learn More About Indexation

Get Familiar With lambda Functions

Expand Functionality of the .iloc[] Function

2. Dealing With Conditions

Here, you will learn how to extract data that has specific conditions. Also, you will learn how to combine them and even create your own.

Set Condition

Select Data Based on Condition

Dealing With Several Conditions

Make Your Code Beautiful

Conditions Quiz

3. Extract Data

In this section, you will expand your knowledge on setting different data conditions. You will learn to check if your data is in a defined list of values or between two values. You will also learn how to find the largest and smallest values.

Is Data in ...?

Combine Your Knowledge

Between Function

Extract Specific Data

Find the Smallest Values of a Column

Find the Largest Values of a Column

Find the Correlation

4. Aggregating Data

This section is one of the most fascinating of the course. Here, you will learn how to group data in different ways. It will help you work as a data analyst to find out information on specific data groups.

Get Familiar With the .groupby() Method

Group by Several Columns

Complicated Grouping

Group

Dealing With Pivot Tables

Create a Pivot Table

5. Preprocessing Data

This section is one of the most significant for a data analyst because if the data contains missing data values in the incorrect format, it will be impossible to work with. Thus, you will learn how to deal with such inappropriate values here.

Check for Missing Values

Calculate the Number of Missing Values

What Will We Do With the NaN Values?

How to Delete Only NaN Values?

Fill In the Missing Values

Manage Categorical Variables

Check the Column Type

Manage an Incorrect Column

Rename the Column

Expand Functionality of the .iloc[] Function

We will learn some new features that iloc[] provides. The coolest one is that we can specify indices of both rows and columns. This function is similar to .loc[], but the last index of the slicing is exclusive.

Look at the example and the relevant output:

data.iloc[1, 2] - extracts the item located in the dataset's second row and third column. The first index corresponds to the row index, and the second to the column index. Indeed, you can skip one of them;
data.iloc[:, 3] - extracts all values from the rows of the fourth column 'IMDb-Rating';
data.iloc[3, :] or data.iloc[3] - extracts the 4th row and all relevant columns;
data.iloc[:2, 1:4] - extracts the first two rows and column with the indices 1, 2, 3;
data.iloc[[2,4],[1,3]] - extracts the rows with indices 2,4 and columns with the indices 1, 3.

Tarea

Your task here is just to practice. Output information on the first 50 rows and the columns with indices 1 and 4.

¿Todo estuvo claro?

Sección 1. Capítulo 6

Contenido del Curso

Advanced Techniques in pandas

1. Get Familiar With Indexing and Selecting Data

Output Columns by Title Output Rows by Index Select Specific Rows and Columns Learn More About Indexation Get Familiar With lambda Functions Expand Functionality of the .iloc[] Function

2. Dealing With Conditions

Set Condition Select Data Based on Condition Dealing With Several Conditions Make Your Code Beautiful Conditions Quiz

3. Extract Data

Is Data in ...?Combine Your Knowledge Between Function Extract Specific Data Find the Smallest Values of a Column Find the Largest Values of a Column Find the Correlation

4. Aggregating Data

Get Familiar With the .groupby() Method Group by Several Columns Complicated Grouping Group Dealing With Pivot Tables Create a Pivot Table

5. Preprocessing Data

Check for Missing Values Calculate the Number of Missing Values What Will We Do With the NaN Values?How to Delete Only NaN Values?Fill In the Missing Values Manage Categorical Variables Check the Column Type Manage an Incorrect Column Rename the Column

Advanced Techniques in pandas

Expand Functionality of the .iloc[] Function

Look at the example and the relevant output:

data.iloc[1, 2] - extracts the item located in the dataset's second row and third column. The first index corresponds to the row index, and the second to the column index. Indeed, you can skip one of them;
data.iloc[:, 3] - extracts all values from the rows of the fourth column 'IMDb-Rating';
data.iloc[3, :] or data.iloc[3] - extracts the 4th row and all relevant columns;
data.iloc[:2, 1:4] - extracts the first two rows and column with the indices 1, 2, 3;
data.iloc[[2,4],[1,3]] - extracts the rows with indices 2,4 and columns with the indices 1, 3.

Tarea

Your task here is just to practice. Output information on the first 50 rows and the columns with indices 1 and 4.

¿Todo estuvo claro?

Sección 1. Capítulo 6