Зміст курсу
Preprocessing Data
Preprocessing Data
Work with NaNs
To check if the current value is NaN, use isna() function. You can apply it to the full dataframe, to the column or cell, and you'll get True if the value is NaN and False otherwise.
print(data.isna())
It is more informative to check if there are some NaNs in each column. We'll use sum() function to find the total amount among dataframe's columns:
import pandas as pd import numpy as np data = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/10db3746-c8ff-4c55-9ac3-4affa0b65c16/titanic.csv') print(data.isna().sum())
If you run the code above (reset the editor and paste code in it) you'll probably see the next output:
| PassengerId | 0 |
| Survived | 0 |
| Pclass | 0 |
| Name | 0 |
| Sex | 0 |
| Age | 177 |
| SibSp | 0 |
| Parch | 0 |
| Ticket | 0 |
| Fare | 0 |
| Cabin | 687 |
| Embarked | 2 |
| dtype: int64 |
You can see that Embarked column has only 2 NaNs, which is not too much for almost 900 records, but look at the Cabin! More than 75% of entries are missing values. And we should deal with it in some way.
Drop NaNs
The easiest way to deal with missing data is just to drop the records that contain it. Use the method dropna(). Note that it doesn't change the current dataframe, but returns the new one. To change the current dataframe, add parameter inplace assigned with True:
clean_data = data.dropna() # data is not modified, but clean_data now contains no NaNs data.dropna(inplace=True) # data is modified
Завдання
Apply the dropna() to the dataframe data. Then check the dataframe shape after modification and compare it with the original (before modification) dataframe shape.
We expect the shape (183, 12) for new dataframe.
Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантівДякуємо за ваш відгук!
Work with NaNs
To check if the current value is NaN, use isna() function. You can apply it to the full dataframe, to the column or cell, and you'll get True if the value is NaN and False otherwise.
print(data.isna())
It is more informative to check if there are some NaNs in each column. We'll use sum() function to find the total amount among dataframe's columns:
import pandas as pd import numpy as np data = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/10db3746-c8ff-4c55-9ac3-4affa0b65c16/titanic.csv') print(data.isna().sum())
If you run the code above (reset the editor and paste code in it) you'll probably see the next output:
| PassengerId | 0 |
| Survived | 0 |
| Pclass | 0 |
| Name | 0 |
| Sex | 0 |
| Age | 177 |
| SibSp | 0 |
| Parch | 0 |
| Ticket | 0 |
| Fare | 0 |
| Cabin | 687 |
| Embarked | 2 |
| dtype: int64 |
You can see that Embarked column has only 2 NaNs, which is not too much for almost 900 records, but look at the Cabin! More than 75% of entries are missing values. And we should deal with it in some way.
Drop NaNs
The easiest way to deal with missing data is just to drop the records that contain it. Use the method dropna(). Note that it doesn't change the current dataframe, but returns the new one. To change the current dataframe, add parameter inplace assigned with True:
clean_data = data.dropna() # data is not modified, but clean_data now contains no NaNs data.dropna(inplace=True) # data is modified
Завдання
Apply the dropna() to the dataframe data. Then check the dataframe shape after modification and compare it with the original (before modification) dataframe shape.
We expect the shape (183, 12) for new dataframe.
Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантівДякуємо за ваш відгук!
Work with NaNs
To check if the current value is NaN, use isna() function. You can apply it to the full dataframe, to the column or cell, and you'll get True if the value is NaN and False otherwise.
print(data.isna())
It is more informative to check if there are some NaNs in each column. We'll use sum() function to find the total amount among dataframe's columns:
import pandas as pd import numpy as np data = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/10db3746-c8ff-4c55-9ac3-4affa0b65c16/titanic.csv') print(data.isna().sum())
If you run the code above (reset the editor and paste code in it) you'll probably see the next output:
| PassengerId | 0 |
| Survived | 0 |
| Pclass | 0 |
| Name | 0 |
| Sex | 0 |
| Age | 177 |
| SibSp | 0 |
| Parch | 0 |
| Ticket | 0 |
| Fare | 0 |
| Cabin | 687 |
| Embarked | 2 |
| dtype: int64 |
You can see that Embarked column has only 2 NaNs, which is not too much for almost 900 records, but look at the Cabin! More than 75% of entries are missing values. And we should deal with it in some way.
Drop NaNs
The easiest way to deal with missing data is just to drop the records that contain it. Use the method dropna(). Note that it doesn't change the current dataframe, but returns the new one. To change the current dataframe, add parameter inplace assigned with True:
clean_data = data.dropna() # data is not modified, but clean_data now contains no NaNs data.dropna(inplace=True) # data is modified
Завдання
Apply the dropna() to the dataframe data. Then check the dataframe shape after modification and compare it with the original (before modification) dataframe shape.
We expect the shape (183, 12) for new dataframe.
Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантівДякуємо за ваш відгук!
To check if the current value is NaN, use isna() function. You can apply it to the full dataframe, to the column or cell, and you'll get True if the value is NaN and False otherwise.
print(data.isna())
It is more informative to check if there are some NaNs in each column. We'll use sum() function to find the total amount among dataframe's columns:
import pandas as pd import numpy as np data = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/10db3746-c8ff-4c55-9ac3-4affa0b65c16/titanic.csv') print(data.isna().sum())
If you run the code above (reset the editor and paste code in it) you'll probably see the next output:
| PassengerId | 0 |
| Survived | 0 |
| Pclass | 0 |
| Name | 0 |
| Sex | 0 |
| Age | 177 |
| SibSp | 0 |
| Parch | 0 |
| Ticket | 0 |
| Fare | 0 |
| Cabin | 687 |
| Embarked | 2 |
| dtype: int64 |
You can see that Embarked column has only 2 NaNs, which is not too much for almost 900 records, but look at the Cabin! More than 75% of entries are missing values. And we should deal with it in some way.
Drop NaNs
The easiest way to deal with missing data is just to drop the records that contain it. Use the method dropna(). Note that it doesn't change the current dataframe, but returns the new one. To change the current dataframe, add parameter inplace assigned with True:
clean_data = data.dropna() # data is not modified, but clean_data now contains no NaNs data.dropna(inplace=True) # data is modified
Завдання
Apply the dropna() to the dataframe data. Then check the dataframe shape after modification and compare it with the original (before modification) dataframe shape.
We expect the shape (183, 12) for new dataframe.
Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантівПерейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів