[Tut] Tilde Python Pandas DataFrame

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Tilde Python Pandas DataFrame

Python’s Tilde

Code:

~n

operator is the bitwise negation operator: it takes the number

Code:

n

as binary number and “flips” all bits

Code:

0 to 1

and

Code:

1 to 0

to obtain the complement binary number. For example, the tilde operation

Code:

~1

becomes and

Code:

~0

becomes

Code:

1

and

Code:

~101

becomes

Code:

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:

Code:

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:

https://repl.it/@finxter/tildepythonpandas?lite=true

The tilde operator negates the Booleanvalues in the DataFrame:

Code:

True

becomes

Code:

False

and

Code:

False

becomes

Code:

True

.

You can see this in action when printing the result of different operations:

This is the original DataFrame in the code:

Code:

print(df) ''' User Age 0 Alice 22 1 Bob 24 '''

Now apply the containsoperation to find all user names that contain the character

Code:

'A'

.

Code:

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

Code:

'A'

or not.

Let’s apply the Tilde operator on the result:

Code:

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

Code:

'A'

.

Code:

df = df[~df['User'].str.contains('A')] print(df) ''' User Age 1 Bob 24 '''

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