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

Select Specific Rows and Columns

Okay, you've dealt with the previous chapters, and now is the right time to combine your knowledge. You can specify both rows and columns; to do so, you just need to be familiar with the .loc[] function.

This function allows us to do plenty of different slicing operations, but for now, we will just consolidate knowledge from the previous chapters.

As usual, look at the example and then at the output.

data.loc[2:5, ['Director', 'ReleaseYear']] - outputs rows with the indices 2, 3, 4, 5(but remember that the indices start from 0) from the columns 'Director' and 'ReleaseYear'(.loc[] includes the last index that you put into []);
data.loc[:5, ['Director', 'ReleaseYear']] - outputs rows with the indices 0, 1, 2, 3, 4, 5 from the columns 'Director' and 'ReleaseYear';
data.loc[997:, ['Director', 'ReleaseYear']] - outputs rows with the indices 997, 998, 999 (999 is the index of the last row) from the columns 'Director' and 'ReleaseYear';
data.loc[:, ['Director', 'ReleaseYear']] or data[['Director', 'ReleaseYear']] - outputs all rows from the columns 'Director' and 'ReleaseYear'.

Tarefa

Your task here is to output the necessary rows and columns. Follow the algorithm:

Import the pandas library with the pd alias.
Read the csv file.
Assign to data variable information about the columns 'Title', 'Stars', 'Category'(in this order) with rows with indices from 15 to 85.
Output the data_extracted variable.

_{Once you've completed this task, click the button below the code to check your solution.}

Tudo estava claro?

Seção 1. Capítulo 3

Conteúdo do 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

Select Specific Rows and Columns

This function allows us to do plenty of different slicing operations, but for now, we will just consolidate knowledge from the previous chapters.

As usual, look at the example and then at the output.

data.loc[2:5, ['Director', 'ReleaseYear']] - outputs rows with the indices 2, 3, 4, 5(but remember that the indices start from 0) from the columns 'Director' and 'ReleaseYear'(.loc[] includes the last index that you put into []);
data.loc[:5, ['Director', 'ReleaseYear']] - outputs rows with the indices 0, 1, 2, 3, 4, 5 from the columns 'Director' and 'ReleaseYear';
data.loc[997:, ['Director', 'ReleaseYear']] - outputs rows with the indices 997, 998, 999 (999 is the index of the last row) from the columns 'Director' and 'ReleaseYear';
data.loc[:, ['Director', 'ReleaseYear']] or data[['Director', 'ReleaseYear']] - outputs all rows from the columns 'Director' and 'ReleaseYear'.

Tarefa

Your task here is to output the necessary rows and columns. Follow the algorithm:

Import the pandas library with the pd alias.
Read the csv file.
Assign to data variable information about the columns 'Title', 'Stars', 'Category'(in this order) with rows with indices from 15 to 85.
Output the data_extracted variable.

_{Once you've completed this task, click the button below the code to check your solution.}

Tudo estava claro?

Seção 1. Capítulo 3