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Advanced Techniques in pandas
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Course Content

Advanced Techniques in pandas

Advanced Techniques in pandas

1. Getting Familiar With Indexing and Selecting Data
Outputting Columns by TitleOutputting Rows by IndexSelecting Specific Rows and ColumnsLearning More About IndexationGetting Familiar With lambda FunctionsExpanding Functionality of the .iloc[] Attribute
2. Dealing With Conditions
Setting ConditionSelecting Data Based on ConditionDealing With Several ConditionsMaking Your Code BeautifulConditions Quiz
3. Extracting Data
Is Data in ...?Combining Your KnowledgeBetween MethodExtracting Specific DataFinding the Smallest Values of a ColumnFinding the Largest Values of a ColumnFinding the Correlation
4. Aggregating Data
Getting Familiar With the .groupby() MethodGrouping by Several ColumnsComplicated GroupingAdvanced GroupingDealing With Pivot TablesCreating a Pivot Table
5. Preprocessing Data
Checking for Missing ValuesCalculating the Number of Missing ValuesWhat Will We Do With the NaN Values?How to Delete Only NaN Values?Filling In the Missing ValuesManaging Categorical VariablesChecking the Column TypeManaging an Incorrect ColumnRenaming the Column

bookFinding the Smallest Values of a Column

We will learn another crucial function, which outputs the top smallest or largest values. You already know that we can sort values and then extract a specific number of rows. Unsurprisingly, pandas can do so using only one line of code. Look at the example of how to retrieve the oldest fifteen cars:


              
1234

            
import pandas as pd
data = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/4bf24830-59ba-4418-969b-aaf8117d522e/cars.csv', index_col = 0)
data_smallest = data.nsmallest(15, 'Year')
print(data_smallest.head(15))
copy

If you want to sort by one column and then by another, just put a list with column names in the necessary order. Look at the example where we will sort firstly by 'Year' and then by 'Engine_volume'. This code will first extract the 5 oldest cars, and then if the years match, the car with the lesser value of the 'Engine_volume' column will take priority:


              
1234

            
import pandas as pd
data = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/4bf24830-59ba-4418-969b-aaf8117d522e/cars.csv', index_col = 0)
data_smallest = data.nsmallest(5, ['Year', 'Engine_volume'])
print(data_smallest.head())
copy

Try to compare the two examples below. Now we will advance the function a little bit. Let's return our examples with the column's 'Year' values. In our column, the 'Year' values can be repeated, so if we want to output the ten oldest cars with the previous syntax, our function will take just ten values. It doesn't care if the 11th or 12th value is the same as the 10th. We can put the argument keep = 'all' into the .nsmallest() method to prevent such cases. Look at the example, and try to execute it to see the difference:


              
1234567891011

            
import pandas as pd
data = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/4bf24830-59ba-4418-969b-aaf8117d522e/cars.csv', index_col = 0)
# Case without using `keep = 'all'` argument
data_smallest = data.nsmallest(6, 'Year')
print(data_smallest)

data = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/4bf24830-59ba-4418-969b-aaf8117d522e/cars.csv', index_col = 0)
# Case with using `keep = 'all'` argument
data_smallest = data.nsmallest(6, 'Year',
                             keep = 'all')
print(data_smallest)
copy
Task
test

Swipe to show code editor

Finally, it's time to practice! Here, you should follow this algorithm:

  1. Retrieve data on cars where the column 'Year' values are greater than 2010.
  2. Extract the cheapest 15 cars (the 15 smallest values of the column 'Price'). Include all duplicated values of the column 'Price'.
  3. Output all values of the data set data_cheapest.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

Section 3. Chapter 5
toggle bottom row

bookFinding the Smallest Values of a Column

We will learn another crucial function, which outputs the top smallest or largest values. You already know that we can sort values and then extract a specific number of rows. Unsurprisingly, pandas can do so using only one line of code. Look at the example of how to retrieve the oldest fifteen cars:


              
1234

            
import pandas as pd
data = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/4bf24830-59ba-4418-969b-aaf8117d522e/cars.csv', index_col = 0)
data_smallest = data.nsmallest(15, 'Year')
print(data_smallest.head(15))
copy

If you want to sort by one column and then by another, just put a list with column names in the necessary order. Look at the example where we will sort firstly by 'Year' and then by 'Engine_volume'. This code will first extract the 5 oldest cars, and then if the years match, the car with the lesser value of the 'Engine_volume' column will take priority:


              
1234

            
import pandas as pd
data = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/4bf24830-59ba-4418-969b-aaf8117d522e/cars.csv', index_col = 0)
data_smallest = data.nsmallest(5, ['Year', 'Engine_volume'])
print(data_smallest.head())
copy

Try to compare the two examples below. Now we will advance the function a little bit. Let's return our examples with the column's 'Year' values. In our column, the 'Year' values can be repeated, so if we want to output the ten oldest cars with the previous syntax, our function will take just ten values. It doesn't care if the 11th or 12th value is the same as the 10th. We can put the argument keep = 'all' into the .nsmallest() method to prevent such cases. Look at the example, and try to execute it to see the difference:


              
1234567891011

            
import pandas as pd
data = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/4bf24830-59ba-4418-969b-aaf8117d522e/cars.csv', index_col = 0)
# Case without using `keep = 'all'` argument
data_smallest = data.nsmallest(6, 'Year')
print(data_smallest)

data = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/4bf24830-59ba-4418-969b-aaf8117d522e/cars.csv', index_col = 0)
# Case with using `keep = 'all'` argument
data_smallest = data.nsmallest(6, 'Year',
                             keep = 'all')
print(data_smallest)
copy
Task
test

Swipe to show code editor

Finally, it's time to practice! Here, you should follow this algorithm:

  1. Retrieve data on cars where the column 'Year' values are greater than 2010.
  2. Extract the cheapest 15 cars (the 15 smallest values of the column 'Price'). Include all duplicated values of the column 'Price'.
  3. Output all values of the data set data_cheapest.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

Section 3. Chapter 5
toggle bottom row

bookFinding the Smallest Values of a Column

We will learn another crucial function, which outputs the top smallest or largest values. You already know that we can sort values and then extract a specific number of rows. Unsurprisingly, pandas can do so using only one line of code. Look at the example of how to retrieve the oldest fifteen cars:


              
1234

            
import pandas as pd
data = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/4bf24830-59ba-4418-969b-aaf8117d522e/cars.csv', index_col = 0)
data_smallest = data.nsmallest(15, 'Year')
print(data_smallest.head(15))
copy

If you want to sort by one column and then by another, just put a list with column names in the necessary order. Look at the example where we will sort firstly by 'Year' and then by 'Engine_volume'. This code will first extract the 5 oldest cars, and then if the years match, the car with the lesser value of the 'Engine_volume' column will take priority:


              
1234

            
import pandas as pd
data = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/4bf24830-59ba-4418-969b-aaf8117d522e/cars.csv', index_col = 0)
data_smallest = data.nsmallest(5, ['Year', 'Engine_volume'])
print(data_smallest.head())
copy

Try to compare the two examples below. Now we will advance the function a little bit. Let's return our examples with the column's 'Year' values. In our column, the 'Year' values can be repeated, so if we want to output the ten oldest cars with the previous syntax, our function will take just ten values. It doesn't care if the 11th or 12th value is the same as the 10th. We can put the argument keep = 'all' into the .nsmallest() method to prevent such cases. Look at the example, and try to execute it to see the difference:


              
1234567891011

            
import pandas as pd
data = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/4bf24830-59ba-4418-969b-aaf8117d522e/cars.csv', index_col = 0)
# Case without using `keep = 'all'` argument
data_smallest = data.nsmallest(6, 'Year')
print(data_smallest)

data = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/4bf24830-59ba-4418-969b-aaf8117d522e/cars.csv', index_col = 0)
# Case with using `keep = 'all'` argument
data_smallest = data.nsmallest(6, 'Year',
                             keep = 'all')
print(data_smallest)
copy
Task
test

Swipe to show code editor

Finally, it's time to practice! Here, you should follow this algorithm:

  1. Retrieve data on cars where the column 'Year' values are greater than 2010.
  2. Extract the cheapest 15 cars (the 15 smallest values of the column 'Price'). Include all duplicated values of the column 'Price'.
  3. Output all values of the data set data_cheapest.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

We will learn another crucial function, which outputs the top smallest or largest values. You already know that we can sort values and then extract a specific number of rows. Unsurprisingly, pandas can do so using only one line of code. Look at the example of how to retrieve the oldest fifteen cars:


              
1234

            
import pandas as pd
data = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/4bf24830-59ba-4418-969b-aaf8117d522e/cars.csv', index_col = 0)
data_smallest = data.nsmallest(15, 'Year')
print(data_smallest.head(15))
copy

If you want to sort by one column and then by another, just put a list with column names in the necessary order. Look at the example where we will sort firstly by 'Year' and then by 'Engine_volume'. This code will first extract the 5 oldest cars, and then if the years match, the car with the lesser value of the 'Engine_volume' column will take priority:


              
1234

            
import pandas as pd
data = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/4bf24830-59ba-4418-969b-aaf8117d522e/cars.csv', index_col = 0)
data_smallest = data.nsmallest(5, ['Year', 'Engine_volume'])
print(data_smallest.head())
copy

Try to compare the two examples below. Now we will advance the function a little bit. Let's return our examples with the column's 'Year' values. In our column, the 'Year' values can be repeated, so if we want to output the ten oldest cars with the previous syntax, our function will take just ten values. It doesn't care if the 11th or 12th value is the same as the 10th. We can put the argument keep = 'all' into the .nsmallest() method to prevent such cases. Look at the example, and try to execute it to see the difference:


              
1234567891011

            
import pandas as pd
data = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/4bf24830-59ba-4418-969b-aaf8117d522e/cars.csv', index_col = 0)
# Case without using `keep = 'all'` argument
data_smallest = data.nsmallest(6, 'Year')
print(data_smallest)

data = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/4bf24830-59ba-4418-969b-aaf8117d522e/cars.csv', index_col = 0)
# Case with using `keep = 'all'` argument
data_smallest = data.nsmallest(6, 'Year',
                             keep = 'all')
print(data_smallest)
copy
Task
test

Swipe to show code editor

Finally, it's time to practice! Here, you should follow this algorithm:

  1. Retrieve data on cars where the column 'Year' values are greater than 2010.
  2. Extract the cheapest 15 cars (the 15 smallest values of the column 'Price'). Include all duplicated values of the column 'Price'.
  3. Output all values of the data set data_cheapest.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Section 3. Chapter 5
Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
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