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

Get Familiar With the .groupby() Method

I am happy to see you in this section. Here, we will group our data to find information about different groups of rows. Examine the data set on delays (you can scroll this table horizontally):

	Airline	Flight	AirportFrom	AirportTo	DayOfWeek	Time	Length	Delay
0	CO	269	SFO	IAH	3	15	205	1
1	US	1558	PHX	CLT	3	15	222	0
2	AA	2400	LAX	DFW	3	20	165	1
3	AA	2466	SFO	DFW	3	20	195	1
4	AS	108	ANC	SEA	3	30	202	0

Grouping data is beneficial, and now we will dive deeper into it. Imagine you want to calculate the number of delays for each flight number. Look at the code example and then at the explanation:

Explanation:

data[['Flight', 'Delay']] - These are the columns you will work on, including the columns you will group;
groupby('Flight') - The 'Flight' column is the argument for the .groupby() function. This means that rows with the same value in the 'Flight' column will be grouped together;
.sum() - This function operates on rows within each group created by .groupby(). In this case, it sums the values in the 'Delay' column for rows that belong to the same 'Flight' group.

Note
Since the 'Delay' column contains only 0 (no delay occurred) or 1 (a delay occurred) as its possible values, the sum of the rows represents the number of delays for each flight.

In fact, .sum() is one of many aggregation functions you can use. You will become familiar with all of them as you proceed.

Function title	Implementation	Syntax
.mean()	Finds the mean value of all values in a column that relate to one group	`data.groupby('column_name').mean()`
.median()	Finds the median value of all values in a column that relate to one group	`data.groupby('column_name').median()`
.sum()	Finds the sum of all values in a column that relate to one group	`data.groupby('column_name').sum()`
.count()	Finds the amount of values in a column that relate to one group	`data.groupby('column_name').count()`
.min()	Finds the minimum value of all values in a column that relate to one group	`data.groupby('column_name').min()`
.max()	Finds the maximum value of all values in a column that relate to one group	`data.groupby('column_name').max()`

Fill in the gaps to find the mean value of the `'Time'` column depending on the `'DayOfWeek'` column.

data_extracted = data[['', 'Time']]('

').mean()
print(data_extracted)

DayOfWeek	Time
3	804.993130
4	804.452984
5	702.888362

Все було зрозуміло?

Секція 4. Розділ 1

Зміст курсу

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

Get Familiar With the .groupby() Method

I am happy to see you in this section. Here, we will group our data to find information about different groups of rows. Examine the data set on delays (you can scroll this table horizontally):

	Airline	Flight	AirportFrom	AirportTo	DayOfWeek	Time	Length	Delay
0	CO	269	SFO	IAH	3	15	205	1
1	US	1558	PHX	CLT	3	15	222	0
2	AA	2400	LAX	DFW	3	20	165	1
3	AA	2466	SFO	DFW	3	20	195	1
4	AS	108	ANC	SEA	3	30	202	0

Grouping data is beneficial, and now we will dive deeper into it. Imagine you want to calculate the number of delays for each flight number. Look at the code example and then at the explanation:

Explanation:

data[['Flight', 'Delay']] - These are the columns you will work on, including the columns you will group;
groupby('Flight') - The 'Flight' column is the argument for the .groupby() function. This means that rows with the same value in the 'Flight' column will be grouped together;
.sum() - This function operates on rows within each group created by .groupby(). In this case, it sums the values in the 'Delay' column for rows that belong to the same 'Flight' group.

Note
Since the 'Delay' column contains only 0 (no delay occurred) or 1 (a delay occurred) as its possible values, the sum of the rows represents the number of delays for each flight.

In fact, .sum() is one of many aggregation functions you can use. You will become familiar with all of them as you proceed.

Function title	Implementation	Syntax
.mean()	Finds the mean value of all values in a column that relate to one group	`data.groupby('column_name').mean()`
.median()	Finds the median value of all values in a column that relate to one group	`data.groupby('column_name').median()`
.sum()	Finds the sum of all values in a column that relate to one group	`data.groupby('column_name').sum()`
.count()	Finds the amount of values in a column that relate to one group	`data.groupby('column_name').count()`
.min()	Finds the minimum value of all values in a column that relate to one group	`data.groupby('column_name').min()`
.max()	Finds the maximum value of all values in a column that relate to one group	`data.groupby('column_name').max()`

Fill in the gaps to find the mean value of the `'Time'` column depending on the `'DayOfWeek'` column.

data_extracted = data[['', 'Time']]('

').mean()
print(data_extracted)

DayOfWeek	Time
3	804.993130
4	804.452984
5	702.888362

Все було зрозуміло?

Секція 4. Розділ 1