Python’s Tilde ~n operator is the bitwise negation operator: it takes the number n as binary number and “flips” all bits 0 to 1 and 1 to 0 to obtain the complement binary number. For example, the tilde operation ~1 becomes 0 and ~0 becomes 1 and ~101 becomes 010.
Read all about the Tilde operator in my detailed tutorial on this blog.
Sometimes, you’ll see the tilde operator in a Pandas DataFrame for indexing. Here’s an example:
import pandas as pd # Create a DataFrame
df = pd.DataFrame([{'User': 'Alice', 'Age': 22}, {'User': 'Bob', 'Age': 24}])
print(df) ''' User Age
0 Alice 22
1 Bob 24 ''' # Use Tilde to access all lines where user doesn't contain 'A'
df = df[~df['User'].str.contains('A')]
print(df) ''' User Age
1 Bob 24 '''
To improve your practical understanding, feel free to run this code in your browser in our interactive Python shell:
The tilde operator negates the Boolean values in the DataFrame: True becomes False and False becomes True.
You can see this in action when printing the result of different operations:
This is the original DataFrame in the code:
print(df) ''' User Age
0 Alice 22
1 Bob 24 '''
Now apply the contains operation to find all user names that contain the character 'A'.
print(df['User'].str.contains('A')) '''
0 True
1 False
Name: User, dtype: bool '''
The result is a DataFrame with Boolean values that indicate whether a user contains the character 'A' or not.
Let’s apply the Tilde operator on the result:
print(~df['User'].str.contains('A')) '''
0 False
1 True
Name: User, dtype: bool '''
Now, we use this DataFrame to access only those rows with users that don’t contain the character 'A'.
df = df[~df['User'].str.contains('A')]
print(df) ''' User Age
1 Bob 24 '''
Where to Go From Here?
Enough theory, let’s get some practice!
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