Course Content

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 out information on groups of rows. Examine the data set on delays:

	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']] - columns you will work on, including the columns you will group.
groupby('Flight') - here, 'Flight' is an argument of the function .groupby(). It is the name of the column by which you will group. So, in our case, if rows of the dataset have the same value in the column 'Flight', they will relate to one group. Then, due to the function .count() that counts the rows, our function will calculate the number of rows of the column 'Delay' that have the same value in the column 'Flight'.
.count() - a function that deals with rows having the same group. In this case, it counts the number of rows having the same value in the column 'Flight'. This function is not the only one you can use. Now you will get familiar with all of them.

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

Everything was clear?

Section 4. Chapter 1