Boolean data is a type of data that can only have two possible values: True or False. In Pandas, boolean data is represented by the bool dtype, which is a subtype of numpy.bool_. Boolean data can be useful for filtering, masking, and conditional operations on data frames and series.
Qe will explore some common ways to handle boolean data in Pandas, such as:
– Creating boolean arrays from conditions
– Applying boolean indexing to select rows or columns
– Combining multiple boolean conditions with logical operators
– Using the where and mask methods to replace values based on conditions
– Using the query method to filter data frames with expressions
Creating boolean arrays from conditions
One of the simplest ways to create a boolean array is to apply a condition to a Pandas object, such as a series or a data frame. For example, suppose we have a data frame called df that contains information about some students:
>>> import pandas as pd
>>> df = pd.DataFrame({‘name’: [‘Alice’, ‘Bob’, ‘Charlie’, ‘David’, ‘Eve’],
‘age’: [20, 21, 19, 22, 20],
‘gender’: [‘F’, ‘M’, ‘M’, ‘M’, ‘F’],
‘grade’: [90, 80, 85, 75, 95]})
>>> df
name age gender grade
0 Alice 20 F 90
1 Bob 21 M 80
2 Charlie 19 M 85
3 David 22 M 75
4 Eve 20 F 95
We can create a boolean array by applying a condition to one of the columns. For example, to get a boolean array that indicates which students are older than 20, we can do:
>>> df[‘age’] > 20
0 False
1 True
2 False
3 True
4 False
Name: age, dtype: bool
We can also apply a condition to the whole data frame, which will return a boolean data frame with the same shape as the original. For example, to get a boolean data frame that indicates which values are even, we can do:
>>> df % 2 == 0
name age gender grade
0 True True False True
1 True False True True
2 True False True False
3 True True True False
4 True True False False
Applying boolean indexing to select rows or columns
One of the most common uses of boolean data in Pandas is to perform boolean indexing, which is a way of selecting rows or columns based on their values. To perform boolean indexing on rows, we can pass a boolean array or series to the loc or iloc indexer of the data frame. For example, to select only the rows where the grade is higher than 80, we can do:
>>> df.loc[df[‘grade’] > 80]
name age gender grade
0 Alice 20 F 90
2 Charlie 19 M 85
4 Eve 20 F 95
To perform boolean indexing on columns, we can pass a boolean array or list to the loc or iloc indexer of the data frame. For example, to select only the columns where the name starts with a vowel, we can do:
>>> df.loc[:, df.columns.str.startswith((‘A’, ‘E’, ‘I’, ‘O’, ‘U’))]
name age
0 Alice 20
1 Bob 21
2 Charlie 19
3 David 22
4 Eve 20
Combining multiple boolean conditions with logical operators
Sometimes we may want to select rows or columns based on more than one condition. In this case, we can use logical operators such as & (and), | (or), and ~ (not) to combine multiple boolean arrays or series. For example, to select only the rows where the gender is F and the grade is higher than 90, we can do:
>>> df.loc[(df[‘gender’] == ‘F’) & (df[‘grade’] > 90)]
name age gender grade
0 Alice 20 F 90
4 Eve 20 F 95
Note that when using logical operators with Pandas objects, we need to use parentheses around each condition to avoid ambiguity. Also note that the logical operators for Pandas objects are different from the ones for Python