5 Méthodes pour ajouter une colonne à un DataFrame avec Pandas
Table of Contents:
- Introduction
- Méthode 1: Ajouter une colonne avec une valeur scalaire (Scalar Value)
- Méthode 2: Ajouter une colonne avec une liste de valeurs (List of Values)
- Méthode 3: Insérer une colonne à un emplacement spécifique
- Méthode 4: Utiliser df.assign pour ajouter une nouvelle colonne
- Méthode 5: Utiliser loc pour ajouter une colonne (non recommandé)
- Conclusion
- FAQ
📝 Introduction
Adding a new column to your pandas DataFrame is a common task when working with data. You may need to add derived columns or insert new data into your existing DataFrame. In this article, we will explore different methods to add a new column to a DataFrame using Python's pandas library.
📝 Méthode 1: Ajouter une colonne avec une valeur scalaire (Scalar Value)
The first method to add a new column to a DataFrame is by simply passing a new column name to the DataFrame and assigning it a scalar value. For example, suppose we want to add a column for the day of the week that a restaurant was visited. We can use the following code:
df['day'] = 'Monday'
This will add a new column called 'day' to the DataFrame, and all entries in this column will have the value 'Monday'.
Pros: Simple and straightforward method.
Cons: The new column will have the same scalar value for all rows.
📝 Méthode 2: Ajouter une colonne avec une liste de valeurs (List of Values)
Alternatively, you can pass a list of values to add a column to the DataFrame. This method allows you to assign different values to each row. For example:
days = ['Monday', 'Tuesday', 'Wednesday', 'Thursday']
df['day'] = days
This will overwrite any existing values in the 'day' column with the new list of values.
Pros: Allows assigning different values to each row.
Cons: The length of the list must match the number of rows in the DataFrame.
📝 Méthode 3: Insérer une colonne à un emplacement spécifique
If you want to specify the location of the new column, you can use the df.insert method. This method allows you to pick where exactly you want the column to appear in the DataFrame. For example:
df.insert(1, 'stars', [4, 3, 5, 2])
This will insert a new column called 'stars' at index 1, right between the 'name' and 'type' columns.
Pros: Allows inserting a column at a specific location.
Cons: Requires manually specifying the location index.
📝 Méthode 4: Utiliser df.assign pour ajouter une nouvelle colonne
The df.assign method is useful when you want to add multiple columns to a DataFrame. Here, we will focus on adding one column. For example:
df = df.assign(average_half_bill=df['average_bill'] / 2)
This will add a new column called 'average_half_bill' that calculates the average bill divided by two.
Pros: Can assign multiple columns at once.
Cons: Only adds one column at a time.
📝 Méthode 5: Utiliser loc pour ajouter une colonne (non recommandé)
It is possible to use loc to assign values to an entirely new column in the DataFrame. However, this method is not recommended and should only be used as a last resort. For example:
df.loc[:, 'year'] = [2021, 2020, 2022, 2023]
This will add a new column called 'year' with the provided values.
Pros: Can assign values to an entirely new column.
Cons: Not recommended, as it may have unintended side effects.
📝 Conclusion
In this article, we discussed five different methods to add a new column to a pandas DataFrame. Each method has its pros and cons, depending on your specific needs. It is essential to choose the appropriate method based on the desired outcome and efficiency.
FAQ
Q: Can I add multiple columns using Method 1?
A: No, Method 1 can only assign a scalar value to the new column. If you want to add multiple columns, consider using Method 4.
Q: What happens if the length of the list in Method 2 does not match the number of rows in the DataFrame?
A: You will get an error indicating that the lengths do not match. Ensure the list length matches the number of rows when using Method 2.
Q: Why is Method 5 not recommended?
A: Method 5, using loc, is not recommended because it assigns values to an entirely new column, which can lead to unexpected behavior. It is safer to use the other methods discussed instead.
Q: Is it possible to add a column at a specific location with Method 4?
A: No, Method 4 uses df.assign, which adds columns at the end of the DataFrame. If you need to insert a column at a specific location, consider using Method 3.
Q: Can I add a column with values calculated from other columns?
A: Yes, you can use any calculation or transformation on existing columns to add a new column. Methods 1, 2, 4, and 5 allow you to compute values based on other columns.
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