Kursusindhold
Pandas First Steps
Pandas First Steps
Finding Null Values
DataFrames often contain missing values, represented as None or NaN. When working with DataFrames, it's essential to identify these missing values because they can distort calculations, lead to inaccurate analyses, and compromise the reliability of results.
Addressing them ensures data integrity and improves the performance of tasks like statistical analysis and machine learning. For this purpose, pandas offers specific methods.
The first of these is isna(), which returns a boolean DataFrame. In this context, a True value indicates a missing value within the DataFrame, while a False value suggests the value is present.
For clarity, we'll apply this method on the animals DataFrame. The isna() method will return a DataFrame filled with True/False values, where each True value represents a missing value in the animals DataFrame.
import pandas as pd import numpy as np animals_data = {'animal': [np.NaN, 'Dog', np.NaN, 'Cat','Parrot', None], 'name': ['Dolly', None, 'Erin', 'Kelly', None, 'Odie']} animals = pd.DataFrame(animals_data) # Find missing values missing_values = animals.isna() print(missing_values)
The second method is isnull(). It behaves identically to the previous one, with no discernible difference between them.
Swipe to start coding
You are given a DataFrame named wine_data.
- Retrieve the missing values in this
DataFrameand store the result in themissing_valuesvariable.
Løsning
Skift til skrivebord for at øve i den virkelige verdenFortsæt der, hvor du er, med en af nedenstående mulighederTak for dine kommentarer!
Finding Null Values
DataFrames often contain missing values, represented as None or NaN. When working with DataFrames, it's essential to identify these missing values because they can distort calculations, lead to inaccurate analyses, and compromise the reliability of results.
Addressing them ensures data integrity and improves the performance of tasks like statistical analysis and machine learning. For this purpose, pandas offers specific methods.
The first of these is isna(), which returns a boolean DataFrame. In this context, a True value indicates a missing value within the DataFrame, while a False value suggests the value is present.
For clarity, we'll apply this method on the animals DataFrame. The isna() method will return a DataFrame filled with True/False values, where each True value represents a missing value in the animals DataFrame.
import pandas as pd import numpy as np animals_data = {'animal': [np.NaN, 'Dog', np.NaN, 'Cat','Parrot', None], 'name': ['Dolly', None, 'Erin', 'Kelly', None, 'Odie']} animals = pd.DataFrame(animals_data) # Find missing values missing_values = animals.isna() print(missing_values)
The second method is isnull(). It behaves identically to the previous one, with no discernible difference between them.
Swipe to start coding
You are given a DataFrame named wine_data.
- Retrieve the missing values in this
DataFrameand store the result in themissing_valuesvariable.
Løsning
Skift til skrivebord for at øve i den virkelige verdenFortsæt der, hvor du er, med en af nedenstående mulighederTak for dine kommentarer!
Skift til skrivebord for at øve i den virkelige verdenFortsæt der, hvor du er, med en af nedenstående muligheder