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Pandas First Steps
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Contenuti del Corso

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.


              
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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
Compito

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.

Soluzione

Switch to desktopCambia al desktop per esercitarti nel mondo realeContinua da dove ti trovi utilizzando una delle opzioni seguenti
Tutto è chiaro?

Come possiamo migliorarlo?

Grazie per i tuoi commenti!

Sezione 3. Capitolo 15
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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
Compito

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.

Soluzione

Switch to desktopCambia al desktop per esercitarti nel mondo realeContinua da dove ti trovi utilizzando una delle opzioni seguenti
Tutto è chiaro?

Come possiamo migliorarlo?

Grazie per i tuoi commenti!

Sezione 3. Capitolo 15
Switch to desktopCambia al desktop per esercitarti nel mondo realeContinua da dove ti trovi utilizzando una delle opzioni seguenti
Siamo spiacenti che qualcosa sia andato storto. Cosa è successo?
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