We will start by writing a simple condition. In this article we will see how we can add a new column to an existing dataframe based on certain conditions. 10. Let's see how to Repeat or replicate the dataframe in pandas python. pandas create new column based on row value (condition) You want to create a new column "Result" based on the following condition: mask = (df['col'] > start_date) & (df['col'] <= end_date) Where start_date and end_date are both in datetime format, and they represent the start and end of the range from which data has to be . 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. 3 Methods to Create Conditional Columns with Python Pandas and Numpy ; This method always returns the new dataframe with the new rows and containing elements . Method 3: Using pandas masking function. In some cases, the new columns are created according to some conditions on the other columns. For example, let us filter the dataframe or subset the dataframe based on year's value 2002. i need to create a new column based on a condition, if the a [i] and a [i-1] is same, then value is 0 else 1. result should look something like this: A B 1.0 1 1.0 0 2.0 1 3.0 1 4.0 1 5.0 1 5.0 0 5.0 0. Select the columns from the original DataFrame and copy it to create a new DataFrame using copy () function. Now using this masking condition we are going to change all the "female" to 0 in the gender column. Courses Fee 0 Spark 20000 1 PySpark 25000 2 Python 22000 3 pandas 30000. Repeat or replicate the rows of dataframe in pandas python (create ... #create new column titled 'assist_more' df ['assist_more'] = np.where(df ['assists']>df ['rebounds'], 'yes', 'no') #view . In this whole tutorial, we will be using a dataframe that we are going to create now. For example, if we have a function f that sum an iterable of numbers (i.e. Create column using np.where () Pass the condition to the np.where () function, followed by the value you want if the condition evaluates to True and then the value you want if the condition doesn't evaluate to True. Otherwise, if the number is greater than 4, then assign the value of 'False'. Pandas and Numpy are two popular Python libraries used for data analysis and manipulation tasks. Using DataFrame.loc [] Create New DataFrame by Specific Column **Select rows starting from 2nd row position upto 4th row position of all columns. We need to go through each row in the table and check what the "Name" value is, then edit the "Title" value based on the change we specified. Example 4: add a value to an existing field in pandas dataframe after checking conditions gapminder['gdpPercap_ind'] = gapminder.gdpPercap.apply(lambda x: 1 if x >= 1000 else 0 . Pandas: How to Select Rows Based on Column Values At first, let us create a DataFrame and read our CSV −. Creating a Pandas dataframe column based on a given condition in Python. The post is structured as follows: 1) Example Data & Libraries. pandas.DataFrame.apply returns a DataFrame as a result of applying the given function along the given axis of the DataFrame. pandas.DataFrame.apply to Create New DataFrame Columns Based on a Given Condition in Pandas.