To do so, we run the following code: df2 = df.loc [df ['Date'] > 'Feb 06, 2019', ['Date','Open']] As you can see, after the conditional statement .loc, we simply pass a list of the columns we would like to find in the original DataFrame. Example 1: Add One Row to Pandas DataFrame. Let's see how to Repeat or replicate the dataframe in pandas python. How to Drop Rows in Pandas DataFrame Based on Condition Creating a Pandas dataframe column based on a given condition in Python. Create DataFrame Column Based on Given Condition in Pandas can be a list, np.array, tuple, etc. Generally on a Pandas DataFrame the if condition can be applied either column-wise, row-wise, or on an individual cell basis. Pandas Create New DataFrame By Selecting Specific Columns Python Server Side Programming Programming. DataFrame['column_name'] = numpy.where(condition, new_value, DataFrame.column_name) In the following program, we will use numpy.where () method and replace those values in the column 'a' that satisfy the condition that the value is less than zero. 'Product': ['Umbrella', 'Matress', 'Badminton', How to Add Rows to a Pandas DataFrame (With Examples) In pandas package, there are multiple ways to perform filtering. A Computer Science portal for geeks. 10. Let's assume that we ant to filter the rows realted to the Swift language. In Boolean indexing, we at first generate a mask which is just a series of boolean values representing whether the column contains the specific element or not. Answer 1. conditions = [ df['gender'].eq('male') & df['pet1'].eq(df['pet2']), df['gender'].eq('female') & df['pet1'].isin(['cat', 'dog']) ] choices = [5,5] df['points'] = np.select(conditions, choices, default=0) print(df) gender pet1 pet2 points 0 male dog dog 5 1 male cat cat 5 2 .
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