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Advanced Techniques in pandas
course content

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

bookDealing With Several Conditions

Sometimes we need several conditions to be applied. For instance, we want to extract data on hazardous asteroids with a small minimum diameter. But how do we write two conditions simultaneously? Look at the table:

The example was included to help you deal with this topic. This code extracts data on large and hazardous asteroids, where the minimum estimated diameter is larger than 3.5 kilometers and 'hazardous' is True.


              
1234

            
import pandas as pd
data = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/4bf24830-59ba-4418-969b-aaf8117d522e/planet', index_col = 0)
data_extracted = data.loc[(data['est_diameter_min'] > 3.5) & (data['hazardous'] == True)]
print(data_extracted)
copy

In the output, you can see all the rows that satisfy these two conditions:

  • est_diameter_min > 3.5;
  • hazardous == True.

Look at the following example with the or statement. This code will extract data on extremely small or large asteroids with a minimum estimated diameter less than 0.0005 kilometers and a maximum estimated diameter larger than 20 kilometers:


              
1234

            
import pandas as pd
data = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/4bf24830-59ba-4418-969b-aaf8117d522e/planet', index_col = 0)
data_extracted = data.loc[(data['est_diameter_min'] < 0.0005) | (data['est_diameter_max'] > 20)]
print(data_extracted)
copy

In the output, you can see all the rows that satisfy one of these two conditions:

  • est_diameter_min < 0.0005;
  • est_diameter_max > 20.
Task
test

Swipe to show code editor

Your task here is to extract data on very bright and not hazardous asteroids. The code should satisfy two conditions:

  • 'absolute_magnitude' is larger than or equal to 25;
  • 'hazardous' is False.

After this, output the random 5 rows of the data_extracted.

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Section 2. Chapter 3
toggle bottom row

bookDealing With Several Conditions

Sometimes we need several conditions to be applied. For instance, we want to extract data on hazardous asteroids with a small minimum diameter. But how do we write two conditions simultaneously? Look at the table:

The example was included to help you deal with this topic. This code extracts data on large and hazardous asteroids, where the minimum estimated diameter is larger than 3.5 kilometers and 'hazardous' is True.


              
1234

            
import pandas as pd
data = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/4bf24830-59ba-4418-969b-aaf8117d522e/planet', index_col = 0)
data_extracted = data.loc[(data['est_diameter_min'] > 3.5) & (data['hazardous'] == True)]
print(data_extracted)
copy

In the output, you can see all the rows that satisfy these two conditions:

  • est_diameter_min > 3.5;
  • hazardous == True.

Look at the following example with the or statement. This code will extract data on extremely small or large asteroids with a minimum estimated diameter less than 0.0005 kilometers and a maximum estimated diameter larger than 20 kilometers:


              
1234

            
import pandas as pd
data = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/4bf24830-59ba-4418-969b-aaf8117d522e/planet', index_col = 0)
data_extracted = data.loc[(data['est_diameter_min'] < 0.0005) | (data['est_diameter_max'] > 20)]
print(data_extracted)
copy

In the output, you can see all the rows that satisfy one of these two conditions:

  • est_diameter_min < 0.0005;
  • est_diameter_max > 20.
Task
test

Swipe to show code editor

Your task here is to extract data on very bright and not hazardous asteroids. The code should satisfy two conditions:

  • 'absolute_magnitude' is larger than or equal to 25;
  • 'hazardous' is False.

After this, output the random 5 rows of the data_extracted.

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 2. Chapter 3
toggle bottom row

bookDealing With Several Conditions

Sometimes we need several conditions to be applied. For instance, we want to extract data on hazardous asteroids with a small minimum diameter. But how do we write two conditions simultaneously? Look at the table:

The example was included to help you deal with this topic. This code extracts data on large and hazardous asteroids, where the minimum estimated diameter is larger than 3.5 kilometers and 'hazardous' is True.


              
1234

            
import pandas as pd
data = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/4bf24830-59ba-4418-969b-aaf8117d522e/planet', index_col = 0)
data_extracted = data.loc[(data['est_diameter_min'] > 3.5) & (data['hazardous'] == True)]
print(data_extracted)
copy

In the output, you can see all the rows that satisfy these two conditions:

  • est_diameter_min > 3.5;
  • hazardous == True.

Look at the following example with the or statement. This code will extract data on extremely small or large asteroids with a minimum estimated diameter less than 0.0005 kilometers and a maximum estimated diameter larger than 20 kilometers:


              
1234

            
import pandas as pd
data = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/4bf24830-59ba-4418-969b-aaf8117d522e/planet', index_col = 0)
data_extracted = data.loc[(data['est_diameter_min'] < 0.0005) | (data['est_diameter_max'] > 20)]
print(data_extracted)
copy

In the output, you can see all the rows that satisfy one of these two conditions:

  • est_diameter_min < 0.0005;
  • est_diameter_max > 20.
Task
test

Swipe to show code editor

Your task here is to extract data on very bright and not hazardous asteroids. The code should satisfy two conditions:

  • 'absolute_magnitude' is larger than or equal to 25;
  • 'hazardous' is False.

After this, output the random 5 rows of the data_extracted.

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!

Sometimes we need several conditions to be applied. For instance, we want to extract data on hazardous asteroids with a small minimum diameter. But how do we write two conditions simultaneously? Look at the table:

The example was included to help you deal with this topic. This code extracts data on large and hazardous asteroids, where the minimum estimated diameter is larger than 3.5 kilometers and 'hazardous' is True.


              
1234

            
import pandas as pd
data = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/4bf24830-59ba-4418-969b-aaf8117d522e/planet', index_col = 0)
data_extracted = data.loc[(data['est_diameter_min'] > 3.5) & (data['hazardous'] == True)]
print(data_extracted)
copy

In the output, you can see all the rows that satisfy these two conditions:

  • est_diameter_min > 3.5;
  • hazardous == True.

Look at the following example with the or statement. This code will extract data on extremely small or large asteroids with a minimum estimated diameter less than 0.0005 kilometers and a maximum estimated diameter larger than 20 kilometers:


              
1234

            
import pandas as pd
data = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/4bf24830-59ba-4418-969b-aaf8117d522e/planet', index_col = 0)
data_extracted = data.loc[(data['est_diameter_min'] < 0.0005) | (data['est_diameter_max'] > 20)]
print(data_extracted)
copy

In the output, you can see all the rows that satisfy one of these two conditions:

  • est_diameter_min < 0.0005;
  • est_diameter_max > 20.
Task
test

Swipe to show code editor

Your task here is to extract data on very bright and not hazardous asteroids. The code should satisfy two conditions:

  • 'absolute_magnitude' is larger than or equal to 25;
  • 'hazardous' is False.

After this, output the random 5 rows of the data_extracted.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Section 2. Chapter 3
Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
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