Checking if any pandas column values are True
We can easily find out whether any values in our Pandas DataFrame column value answers a specific condition using the any() method of a pandas Series. To find our whether any value in your column is greater than a constant, use the following code:
(your_df['your_column'] >= constant).any()Creating example data
We will start by creating a simple dataset:
import pandas as pd
language = ['R', 'Java', 'Python', 'R', 'Java']
salary = [159.0, 199.0, 102.0, 122.0, 154.0]
data = dict(language = language, salary = salary)
test_df = pd.DataFrame(data=data)
test_df.head()This will return the following data:
| language | salary | |
|---|---|---|
| 0 | R | 159.0 |
| 1 | Java | 199.0 |
| 2 | Python | 102.0 |
| 3 | R | 122.0 |
| 4 | Java | 154. |
Check if any column value is True
We can check whether our Dataset contains Python or R related entries. We’ll specifically check the language Series as shown below:
(test_df['language'].isin (['Python', 'R']))This will return a boolean array:
0 True 1 False 2 True 3 True 4 False Name: language, dtype: bool
To check whether each of the values is True we use the Series any() method:
(test_df['language'].isin (['Python', 'R'])).any()This will obviously return a True result.
Check if values are greater or smaller than a constant in a Series
Let’s check for example, if any of our candidate salaries is higher than 200K. Also here we will use the any() Series method to find any True results.
print((test_df['salary'] >=200).any())As no salaries are greater than 199K, this will return a False result.
Dealing with multiple conditions
We can check if our DataFrame column values answer several conditions. We will start by defining a more complex boolean condition:
cond = (test_df['language'] == 'Python') & (test_df['salary'] > 100)
We can then slice the DataFrame and find whether at least one of the rows answers the condition:
test_df[cond].any().any()The result is True.