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Pandas First Steps
course content

Course Content

Pandas First Steps

Pandas First Steps

1. The Very First Steps
What is pandas? SeriesCreating a SeriesDataFrameSeries QuizCreating a DataFrame QuizAdding a New ColumnInserting a New ColumnDeleting a Row/Column Matching the FunctionsWorking with ColumnsExtracting Columns Quiziloc BasicsUsing iloc
2. Reading Files in Pandas
CSV FilesTXT FilesWorking with Files QuizAccessing the Columns
3. Analyzing the Data
Viewing the DataHead QuizHead and Tail QuizInformation on the DataColumn Names and Data TypesFinding Null ValuesIdentifying Null Values QuizDropping Null ValuesFilling Null ValuesNull Values QuizDescribing the DataCalculating Descriptive Statisticsmax() and min()Statistical Operations Quizsum() and count()Unique Values

Unique Values

Data often gets duplicated in DataFrames. For instance, in our countries DataFrame, the continent column has repeated entries. There's a function that retrieves an array of distinct values from a specific DataFrame column. Let's revisit this DataFrame.


              
1234567

            
import pandas as pd 

dataset = {'country' : ['Thailand', 'Philippines', 'Monaco', 'Malta', 'Sweden', 'Paraguay', 'Latvia'],
          'continent' : ['Asia', 'Asia', 'Europe', 'Europe', 'Europe', 'South America', 'Europe'],
          'capital':['Bangkok', 'Manila', 'Monaco', 'Valletta', 'Stockholm', 'Asuncion', 'Riga']}
countries = pd.DataFrame(dataset)
print(countries)
copy

Now, let's apply the unique() method to the 'continent' and 'country' columns.


              
12345678910

            
import pandas as pd

dataset = {'country' : ['Thailand', 'Philippines', 'Monaco', 'Malta', 'Sweden', 'Paraguay', 'Latvia'],
          'continent' : ['Asia', 'Asia', 'Europe', 'Europe', 'Europe', 'South America', 'Europe'],
          'capital':['Bangkok', 'Manila', 'Monaco', 'Valletta', 'Stockholm', 'Asuncion', 'Riga']}
countries = pd.DataFrame(dataset)
unique_countries = countries['country'].unique()
unique_continents = countries['continent'].unique()
print(unique_countries)
print(unique_continents)
copy

Task

Given the audi_cars DataFrame, please identify all distinct values in the 'year' and 'fueltype' columns.

Task

Given the audi_cars DataFrame, please identify all distinct values in the 'year' and 'fueltype' columns.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Section 3. Chapter 16
toggle bottom row

Unique Values

Data often gets duplicated in DataFrames. For instance, in our countries DataFrame, the continent column has repeated entries. There's a function that retrieves an array of distinct values from a specific DataFrame column. Let's revisit this DataFrame.


              
1234567

            
import pandas as pd 

dataset = {'country' : ['Thailand', 'Philippines', 'Monaco', 'Malta', 'Sweden', 'Paraguay', 'Latvia'],
          'continent' : ['Asia', 'Asia', 'Europe', 'Europe', 'Europe', 'South America', 'Europe'],
          'capital':['Bangkok', 'Manila', 'Monaco', 'Valletta', 'Stockholm', 'Asuncion', 'Riga']}
countries = pd.DataFrame(dataset)
print(countries)
copy

Now, let's apply the unique() method to the 'continent' and 'country' columns.


              
12345678910

            
import pandas as pd

dataset = {'country' : ['Thailand', 'Philippines', 'Monaco', 'Malta', 'Sweden', 'Paraguay', 'Latvia'],
          'continent' : ['Asia', 'Asia', 'Europe', 'Europe', 'Europe', 'South America', 'Europe'],
          'capital':['Bangkok', 'Manila', 'Monaco', 'Valletta', 'Stockholm', 'Asuncion', 'Riga']}
countries = pd.DataFrame(dataset)
unique_countries = countries['country'].unique()
unique_continents = countries['continent'].unique()
print(unique_countries)
print(unique_continents)
copy

Task

Given the audi_cars DataFrame, please identify all distinct values in the 'year' and 'fueltype' columns.

Task

Given the audi_cars DataFrame, please identify all distinct values in the 'year' and 'fueltype' columns.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Section 3. Chapter 16
toggle bottom row

Unique Values

Data often gets duplicated in DataFrames. For instance, in our countries DataFrame, the continent column has repeated entries. There's a function that retrieves an array of distinct values from a specific DataFrame column. Let's revisit this DataFrame.


              
1234567

            
import pandas as pd 

dataset = {'country' : ['Thailand', 'Philippines', 'Monaco', 'Malta', 'Sweden', 'Paraguay', 'Latvia'],
          'continent' : ['Asia', 'Asia', 'Europe', 'Europe', 'Europe', 'South America', 'Europe'],
          'capital':['Bangkok', 'Manila', 'Monaco', 'Valletta', 'Stockholm', 'Asuncion', 'Riga']}
countries = pd.DataFrame(dataset)
print(countries)
copy

Now, let's apply the unique() method to the 'continent' and 'country' columns.


              
12345678910

            
import pandas as pd

dataset = {'country' : ['Thailand', 'Philippines', 'Monaco', 'Malta', 'Sweden', 'Paraguay', 'Latvia'],
          'continent' : ['Asia', 'Asia', 'Europe', 'Europe', 'Europe', 'South America', 'Europe'],
          'capital':['Bangkok', 'Manila', 'Monaco', 'Valletta', 'Stockholm', 'Asuncion', 'Riga']}
countries = pd.DataFrame(dataset)
unique_countries = countries['country'].unique()
unique_continents = countries['continent'].unique()
print(unique_countries)
print(unique_continents)
copy

Task

Given the audi_cars DataFrame, please identify all distinct values in the 'year' and 'fueltype' columns.

Task

Given the audi_cars DataFrame, please identify all distinct values in the 'year' and 'fueltype' columns.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Data often gets duplicated in DataFrames. For instance, in our countries DataFrame, the continent column has repeated entries. There's a function that retrieves an array of distinct values from a specific DataFrame column. Let's revisit this DataFrame.


              
1234567

            
import pandas as pd 

dataset = {'country' : ['Thailand', 'Philippines', 'Monaco', 'Malta', 'Sweden', 'Paraguay', 'Latvia'],
          'continent' : ['Asia', 'Asia', 'Europe', 'Europe', 'Europe', 'South America', 'Europe'],
          'capital':['Bangkok', 'Manila', 'Monaco', 'Valletta', 'Stockholm', 'Asuncion', 'Riga']}
countries = pd.DataFrame(dataset)
print(countries)
copy

Now, let's apply the unique() method to the 'continent' and 'country' columns.


              
12345678910

            
import pandas as pd

dataset = {'country' : ['Thailand', 'Philippines', 'Monaco', 'Malta', 'Sweden', 'Paraguay', 'Latvia'],
          'continent' : ['Asia', 'Asia', 'Europe', 'Europe', 'Europe', 'South America', 'Europe'],
          'capital':['Bangkok', 'Manila', 'Monaco', 'Valletta', 'Stockholm', 'Asuncion', 'Riga']}
countries = pd.DataFrame(dataset)
unique_countries = countries['country'].unique()
unique_continents = countries['continent'].unique()
print(unique_countries)
print(unique_continents)
copy

Task

Given the audi_cars DataFrame, please identify all distinct values in the 'year' and 'fueltype' columns.

Switch to desktop for real-world practiceContinue from where you are using one of the options below
Section 3. Chapter 16
Switch to desktop for real-world practiceContinue from where you are using one of the options below
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