Зміст курсу

Advanced Techniques in pandas

1. Getting Familiar With Indexing and Selecting Data

Outputting Columns by Title Outputting Rows by Index Selecting Specific Rows and Columns Learning More About Indexation Getting Familiar With lambda Functions Expanding Functionality of the .iloc[] Attribute

2. Dealing With Conditions

Setting Condition Selecting Data Based on Condition Dealing With Several Conditions Making Your Code Beautiful Conditions Quiz

3. Extracting Data

Is Data in ...?Combining Your Knowledge Between Method Extracting Specific Data Finding the Smallest Values of a Column Finding the Largest Values of a Column Finding the Correlation

4. Aggregating Data

Getting Familiar With the .groupby() Method Grouping by Several Columns Complicated Grouping Advanced Grouping Dealing With Pivot Tables Creating a Pivot Table

5. Preprocessing Data

Checking for Missing Values Calculating the Number of Missing Values What Will We Do With the NaN Values?How to Delete Only NaN Values?Filling In the Missing Values Managing Categorical Variables Checking the Column Type Managing an Incorrect Column Renaming the Column

Selecting 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[] attribute.

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'.

Завдання

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.}

Завдання

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.}

Все було зрозуміло?

Секція 1. Розділ 3

Selecting 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'.

Завдання

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.}

Завдання

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.}

Все було зрозуміло?

Секція 1. Розділ 3

Selecting 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'.

Завдання

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.}

Завдання

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.}

Все було зрозуміло?

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'.

Завдання

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.}

Секція 1. Розділ 3

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