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Filling In the Missing Values | Preprocessing Data
Advanced Techniques in pandas
course content

Course Content

Advanced Techniques in pandas

Advanced Techniques in pandas

1. Getting Familiar With Indexing and Selecting Data
2. Dealing With Conditions
3. Extracting Data
4. Aggregating Data
5. Preprocessing Data

bookFilling In the Missing Values

Deleting missing values is not the only way to get rid of them. You can also replace all NaNs with a defined value, for instance, with the mean value of the column or with zeros. It can be useful in a lot of cases. You will learn this in the course Learning Statistics with Python.

Look at the example of filling missing values in the column 'Age' with the median value of this column:

1234
import pandas as pd data = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/4bf24830-59ba-4418-969b-aaf8117d522e/titanic_2', index_col = 0) data['Age'].fillna(value=data['Age'].median(), inplace=True) print(data['Age'].isna().sum())
copy

Explanation:

  • value = data['Age'].median() - using the argument value, we tell the .fillna() method what to do with the NaN values. In this case, we applied the .fillna() method to the column 'Age' and replaced all missing values with the median of the column;
  • inplace=True - the argument we can use for saving changes.

Task

One of the most common ways of filling missing values is replacing them with the mean value of the column. So, your task here is to replace the NaN values in the column 'Age' with the mean value of the column (using the inplace = True argument). Then output the sum of the missing value in the column 'Age'.

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Section 5. Chapter 5
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bookFilling In the Missing Values

Deleting missing values is not the only way to get rid of them. You can also replace all NaNs with a defined value, for instance, with the mean value of the column or with zeros. It can be useful in a lot of cases. You will learn this in the course Learning Statistics with Python.

Look at the example of filling missing values in the column 'Age' with the median value of this column:

1234
import pandas as pd data = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/4bf24830-59ba-4418-969b-aaf8117d522e/titanic_2', index_col = 0) data['Age'].fillna(value=data['Age'].median(), inplace=True) print(data['Age'].isna().sum())
copy

Explanation:

  • value = data['Age'].median() - using the argument value, we tell the .fillna() method what to do with the NaN values. In this case, we applied the .fillna() method to the column 'Age' and replaced all missing values with the median of the column;
  • inplace=True - the argument we can use for saving changes.

Task

One of the most common ways of filling missing values is replacing them with the mean value of the column. So, your task here is to replace the NaN values in the column 'Age' with the mean value of the column (using the inplace = True argument). Then output the sum of the missing value in the column 'Age'.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

Section 5. Chapter 5
toggle bottom row

bookFilling In the Missing Values

Deleting missing values is not the only way to get rid of them. You can also replace all NaNs with a defined value, for instance, with the mean value of the column or with zeros. It can be useful in a lot of cases. You will learn this in the course Learning Statistics with Python.

Look at the example of filling missing values in the column 'Age' with the median value of this column:

1234
import pandas as pd data = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/4bf24830-59ba-4418-969b-aaf8117d522e/titanic_2', index_col = 0) data['Age'].fillna(value=data['Age'].median(), inplace=True) print(data['Age'].isna().sum())
copy

Explanation:

  • value = data['Age'].median() - using the argument value, we tell the .fillna() method what to do with the NaN values. In this case, we applied the .fillna() method to the column 'Age' and replaced all missing values with the median of the column;
  • inplace=True - the argument we can use for saving changes.

Task

One of the most common ways of filling missing values is replacing them with the mean value of the column. So, your task here is to replace the NaN values in the column 'Age' with the mean value of the column (using the inplace = True argument). Then output the sum of the missing value in the column 'Age'.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

Deleting missing values is not the only way to get rid of them. You can also replace all NaNs with a defined value, for instance, with the mean value of the column or with zeros. It can be useful in a lot of cases. You will learn this in the course Learning Statistics with Python.

Look at the example of filling missing values in the column 'Age' with the median value of this column:

1234
import pandas as pd data = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/4bf24830-59ba-4418-969b-aaf8117d522e/titanic_2', index_col = 0) data['Age'].fillna(value=data['Age'].median(), inplace=True) print(data['Age'].isna().sum())
copy

Explanation:

  • value = data['Age'].median() - using the argument value, we tell the .fillna() method what to do with the NaN values. In this case, we applied the .fillna() method to the column 'Age' and replaced all missing values with the median of the column;
  • inplace=True - the argument we can use for saving changes.

Task

One of the most common ways of filling missing values is replacing them with the mean value of the column. So, your task here is to replace the NaN values in the column 'Age' with the mean value of the column (using the inplace = True argument). Then output the sum of the missing value in the column 'Age'.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Section 5. Chapter 5
Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
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