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Renaming the Column | Preprocessing Data
Advanced Techniques in pandas
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Зміст курсу

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

bookRenaming the Column

In the previous chapter, you dealt with the incorrect values in a column. It is an excellent time to fix any changes. In our case, we can rename the column to pin that it was changed.

To rename a column, use the .rename() method. Look at the example where we will rename the column 'Survived', and then output the column names.

1234
import pandas as pd data = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/4bf24830-59ba-4418-969b-aaf8117d522e/titanic4.csv', index_col = 0) data.rename(columns = {'Survived': 'Survived_Passenger'}, inplace = True) print(data.columns)
copy

Explanation:

  • .rename() - a method that we apply to the dataset to rename columns name;
  • columns = {'Survived': 'Survived_Passenger'} - in the curly brackets, you specify all columns and their new names. In this case, we renamed just one column, but you can put several of them here separated by commas.

.columns - an attribute that outputs the column names. In our case, we can no longer see the column name 'Survived'.

Завдання

Your task here is to:

  1. Rename the column 'Fare' to 'Fare_fixed'. Use the inplace = True argument.
  2. Output all column names of the data dataset.

Switch to desktopПерейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів
Все було зрозуміло?

Як ми можемо покращити це?

Дякуємо за ваш відгук!

Секція 5. Розділ 9
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bookRenaming the Column

In the previous chapter, you dealt with the incorrect values in a column. It is an excellent time to fix any changes. In our case, we can rename the column to pin that it was changed.

To rename a column, use the .rename() method. Look at the example where we will rename the column 'Survived', and then output the column names.

1234
import pandas as pd data = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/4bf24830-59ba-4418-969b-aaf8117d522e/titanic4.csv', index_col = 0) data.rename(columns = {'Survived': 'Survived_Passenger'}, inplace = True) print(data.columns)
copy

Explanation:

  • .rename() - a method that we apply to the dataset to rename columns name;
  • columns = {'Survived': 'Survived_Passenger'} - in the curly brackets, you specify all columns and their new names. In this case, we renamed just one column, but you can put several of them here separated by commas.

.columns - an attribute that outputs the column names. In our case, we can no longer see the column name 'Survived'.

Завдання

Your task here is to:

  1. Rename the column 'Fare' to 'Fare_fixed'. Use the inplace = True argument.
  2. Output all column names of the data dataset.

Switch to desktopПерейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів
Все було зрозуміло?

Як ми можемо покращити це?

Дякуємо за ваш відгук!

Секція 5. Розділ 9
toggle bottom row

bookRenaming the Column

In the previous chapter, you dealt with the incorrect values in a column. It is an excellent time to fix any changes. In our case, we can rename the column to pin that it was changed.

To rename a column, use the .rename() method. Look at the example where we will rename the column 'Survived', and then output the column names.

1234
import pandas as pd data = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/4bf24830-59ba-4418-969b-aaf8117d522e/titanic4.csv', index_col = 0) data.rename(columns = {'Survived': 'Survived_Passenger'}, inplace = True) print(data.columns)
copy

Explanation:

  • .rename() - a method that we apply to the dataset to rename columns name;
  • columns = {'Survived': 'Survived_Passenger'} - in the curly brackets, you specify all columns and their new names. In this case, we renamed just one column, but you can put several of them here separated by commas.

.columns - an attribute that outputs the column names. In our case, we can no longer see the column name 'Survived'.

Завдання

Your task here is to:

  1. Rename the column 'Fare' to 'Fare_fixed'. Use the inplace = True argument.
  2. Output all column names of the data dataset.

Switch to desktopПерейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів
Все було зрозуміло?

Як ми можемо покращити це?

Дякуємо за ваш відгук!

In the previous chapter, you dealt with the incorrect values in a column. It is an excellent time to fix any changes. In our case, we can rename the column to pin that it was changed.

To rename a column, use the .rename() method. Look at the example where we will rename the column 'Survived', and then output the column names.

1234
import pandas as pd data = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/4bf24830-59ba-4418-969b-aaf8117d522e/titanic4.csv', index_col = 0) data.rename(columns = {'Survived': 'Survived_Passenger'}, inplace = True) print(data.columns)
copy

Explanation:

  • .rename() - a method that we apply to the dataset to rename columns name;
  • columns = {'Survived': 'Survived_Passenger'} - in the curly brackets, you specify all columns and their new names. In this case, we renamed just one column, but you can put several of them here separated by commas.

.columns - an attribute that outputs the column names. In our case, we can no longer see the column name 'Survived'.

Завдання

Your task here is to:

  1. Rename the column 'Fare' to 'Fare_fixed'. Use the inplace = True argument.
  2. Output all column names of the data dataset.

Switch to desktopПерейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів
Секція 5. Розділ 9
Switch to desktopПерейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів
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