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Pandas First Steps
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Kurssisisältö

Pandas First Steps

Pandas First Steps

1. The Very First Steps
What is pandas? SeriesChallenge: Creating a SeriesDataFrameQuiz: Creating a SeriesQuiz: Creating a DataFrameAdding a New ColumnInserting a New ColumnDeleting a Row/Column Quiz: Matching the FunctionsWorking with ColumnsQuiz: Extracting Columnsiloc BasicsChallenge: Using iloc
2. Reading Files in Pandas
CSV FilesTXT FilesQuiz: Working with FilesQuiz: Accessing the Columns
3. Analyzing the Data
Viewing the DataQuiz: Using HeadQuiz: Head, Tail, and SampleExploring the DatasetColumn Names and Data TypesFinding Null ValuesQuiz: Identifying Null ValuesChallenge: Dropping Null ValuesChallenge: Filling Null ValuesQuiz: Null ValuesDescribing the Datamax() and min()Quiz: Statistical Operationssum() and count()Unique Values

book
Unique Values

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


              
1234567

            
import pandas as pd 

country_data = {'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(country_data)
print(countries)
copy

Now, we'll apply the unique() method to the 'continent' and 'country' columns:


              
12345678910

            
import pandas as pd

country_data = {'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(country_data)
unique_countries = countries['country'].unique()
unique_continents = countries['continent'].unique()
print(unique_countries)
print(unique_continents)
copy

To count the number of distinct values in a specific column, you can use the nunique() method:


              
1234567

            
import pandas as pd

country_data = {'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(country_data)
print(countries['continent'].nunique())
copy
Tehtävä

Swipe to start coding

You are given a DataFrame named audi_cars.

  • Identify all distinct values in the 'year' column and store the result in the unique_years column.
  • Identify all distinct values in the 'fueltype' column and store the result in the unique_fueltype variable.
  • Determine the number of unique fuel types and store the result in the count_unique_fueltypes variable.

Ratkaisu

Switch to desktopVaihda työpöytään todellista harjoitusta vartenJatka siitä, missä olet käyttämällä jotakin alla olevista vaihtoehdoista
Oliko kaikki selvää?

Miten voimme parantaa sitä?

Kiitos palautteestasi!

Osio 3. Luku 15
toggle bottom row

book
Unique Values

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


              
1234567

            
import pandas as pd 

country_data = {'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(country_data)
print(countries)
copy

Now, we'll apply the unique() method to the 'continent' and 'country' columns:


              
12345678910

            
import pandas as pd

country_data = {'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(country_data)
unique_countries = countries['country'].unique()
unique_continents = countries['continent'].unique()
print(unique_countries)
print(unique_continents)
copy

To count the number of distinct values in a specific column, you can use the nunique() method:


              
1234567

            
import pandas as pd

country_data = {'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(country_data)
print(countries['continent'].nunique())
copy
Tehtävä

Swipe to start coding

You are given a DataFrame named audi_cars.

  • Identify all distinct values in the 'year' column and store the result in the unique_years column.
  • Identify all distinct values in the 'fueltype' column and store the result in the unique_fueltype variable.
  • Determine the number of unique fuel types and store the result in the count_unique_fueltypes variable.

Ratkaisu

Switch to desktopVaihda työpöytään todellista harjoitusta vartenJatka siitä, missä olet käyttämällä jotakin alla olevista vaihtoehdoista
Oliko kaikki selvää?

Miten voimme parantaa sitä?

Kiitos palautteestasi!

Osio 3. Luku 15
Switch to desktopVaihda työpöytään todellista harjoitusta vartenJatka siitä, missä olet käyttämällä jotakin alla olevista vaihtoehdoista
Pahoittelemme, että jotain meni pieleen. Mitä tapahtui?
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