Modifying Data Structure
AI in Action
12345678910import pandas as pd students = pd.DataFrame({ "Name": ["Alice", "Bob", "Carol", "Dan"], "Age": [24, 30, 22, 21], "Grade":[85, 62, 90, 78] }) students["Passed"] = students["Grade"] > 70 print(students)
Pandas lets you transform entire columns at once. This includes arithmetics, text adjustments, logical expressions, and more. You can combine columns using element-wise operations or apply a single value to all rows (for example, adding a constant).
Adding and Removing Columns
Adding a new column is as simple as assigning values to a new column name. To remove a column, use the .drop() method.
1234567891011121314import pandas as pd students = pd.DataFrame({ "Name": ["Alice", "Bob", "Carol", "Dan"], "Age": [24, 30, 22, 21], "Grade":[85, 62, 90, 78] }) # Add a column students["GradePlus5"] = students["Grade"] + 5 # Remove a column students = students.drop("Grade", axis=1) print(students)
The axis=1 specifies that you want to remove a column.
Adding and Removing Rows
There are two ways to add a new row:
- Use
.loc[]with a new index label and values; - Use
pd.concat()to join two DataFrames.
To remove a row, use the .drop() method.
1234567891011121314151617import pandas as pd students = pd.DataFrame({ "Name": ["Alice", "Bob", "Carol", "Dan"], "Age": [24, 30, 22, 21], "Grade":[85, 62, 90, 78] }) # Add a row with loc students.loc[3] = ["Eve", 28, 88] # Add a row with concat new_row = pd.DataFrame({"Name": ["Frank"], "Age": [25], "Grade": [95]}) students = pd.concat([students, new_row], ignore_index=True) # Remove a row students = students.drop(1) print(students)
With ignore_index=True, pd.concat() discards the original index values and generates a new continuous index (0, 1, 2, ...).
Working with Indexes
Whenever you add or remove rows, you might notice that the row labels (the index) no longer form a clean sequence. To fix this, you can reset the index to restore simple row numbering or set one of the columns as the new index if you want labels that are more meaningful:
1234567891011121314import pandas as pd students = pd.DataFrame({ "Name": ["Alice", "Bob", "Carol", "Dan"], "Age": [24, 30, 22, 21], "Grade":[85, 62, 90, 78] }) # Set "Name" as the index students = students.set_index("Name") print(students) # Reset to default numeric index students = students.reset_index() print(students)
If you want to leave a simple numeric index, then it is a good idea to call .reset_index(drop=True) each time you add/remove rows to keep your DataFrame tidy.
1. Which line correctly adds a new column named "Total" that is the product of the "Price" and "Quantity" columns?
2. Which method removes a row with label 2 from a DataFrame?
3. Why might you use reset_index(drop=True) after deleting rows?
Merci pour vos commentaires !
Demandez à l'IA
Demandez à l'IA
Posez n'importe quelle question ou essayez l'une des questions suggérées pour commencer notre discussion
Can you explain how the .drop() method works for columns and rows?
What happens if I try to remove a column or row that doesn't exist?
How do I add multiple new rows or columns at once?
Génial!
Completion taux amélioré à 5.26Modifying Data Structure
Glissez pour afficher le menu
AI in Action
12345678910import pandas as pd students = pd.DataFrame({ "Name": ["Alice", "Bob", "Carol", "Dan"], "Age": [24, 30, 22, 21], "Grade":[85, 62, 90, 78] }) students["Passed"] = students["Grade"] > 70 print(students)
Pandas lets you transform entire columns at once. This includes arithmetics, text adjustments, logical expressions, and more. You can combine columns using element-wise operations or apply a single value to all rows (for example, adding a constant).
Adding and Removing Columns
Adding a new column is as simple as assigning values to a new column name. To remove a column, use the .drop() method.
1234567891011121314import pandas as pd students = pd.DataFrame({ "Name": ["Alice", "Bob", "Carol", "Dan"], "Age": [24, 30, 22, 21], "Grade":[85, 62, 90, 78] }) # Add a column students["GradePlus5"] = students["Grade"] + 5 # Remove a column students = students.drop("Grade", axis=1) print(students)
The axis=1 specifies that you want to remove a column.
Adding and Removing Rows
There are two ways to add a new row:
- Use
.loc[]with a new index label and values; - Use
pd.concat()to join two DataFrames.
To remove a row, use the .drop() method.
1234567891011121314151617import pandas as pd students = pd.DataFrame({ "Name": ["Alice", "Bob", "Carol", "Dan"], "Age": [24, 30, 22, 21], "Grade":[85, 62, 90, 78] }) # Add a row with loc students.loc[3] = ["Eve", 28, 88] # Add a row with concat new_row = pd.DataFrame({"Name": ["Frank"], "Age": [25], "Grade": [95]}) students = pd.concat([students, new_row], ignore_index=True) # Remove a row students = students.drop(1) print(students)
With ignore_index=True, pd.concat() discards the original index values and generates a new continuous index (0, 1, 2, ...).
Working with Indexes
Whenever you add or remove rows, you might notice that the row labels (the index) no longer form a clean sequence. To fix this, you can reset the index to restore simple row numbering or set one of the columns as the new index if you want labels that are more meaningful:
1234567891011121314import pandas as pd students = pd.DataFrame({ "Name": ["Alice", "Bob", "Carol", "Dan"], "Age": [24, 30, 22, 21], "Grade":[85, 62, 90, 78] }) # Set "Name" as the index students = students.set_index("Name") print(students) # Reset to default numeric index students = students.reset_index() print(students)
If you want to leave a simple numeric index, then it is a good idea to call .reset_index(drop=True) each time you add/remove rows to keep your DataFrame tidy.
1. Which line correctly adds a new column named "Total" that is the product of the "Price" and "Quantity" columns?
2. Which method removes a row with label 2 from a DataFrame?
3. Why might you use reset_index(drop=True) after deleting rows?
Merci pour vos commentaires !
