In this article, I will explain several ways of how to create a conditional DataFrame column (new) with examples . Instead we can use Panda's apply function with lambda function. Creating a Pandas dataframe column based on a given condition in Python. How To Create a Column Using Condition on Another Column in Pandas? 1. To select rows whose column value equals a scalar, some_value, use ==: df.loc[df['column_name'] == some_value] Pandas: How to assign values based on multiple conditions of different ... Create DataFrame Column Based on Given Condition in Pandas Syntax: DataFrame.apply (self, func, axis=0, raw=False, result_type=None, args= (), **kwds) func represents the function to be . Method 2 : Query Function. The post is structured as follows: 1) Example Data & Libraries. 3 Methods to Create Conditional Columns with Python Pandas and Numpy For each consecutive buy order the value is increased by one (1). 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. In some cases, the new columns are created according to some conditions on the other columns. loc[ data ['x3']. Method 3: Select Rows Based on Multiple Column Conditions. # create a new column based on condition. It is an essential part of feature engineering as well. This will give you an idea of updating operations on the data. A Really Simple Way to Edit Row by Row in a Pandas DataFrame pandas.Series.map () to Create New DataFrame Columns Based on a Given Condition in Pandas We can create the DataFrame columns based on a given condition in Pandas using list comprehension, NumPy methods, apply () method, and map () method of the DataFrame object. You then want to apply the following IF conditions: If the number is equal or lower than 4, then assign the value of 'True'. Additionally, you can also use mask() method transform() and lambda functions to create single and multiple functions. df [2:4] Name TotalMarks Grade Promoted 2 Bill 63 B True 3 Jim 22 E False. Creating a Pandas dataframe column based on a given condition in Python You can easily apply multiple aggregations by applying the .agg () method. Pandas Create New DataFrame By Selecting Specific Columns You can group data by multiple columns by passing in a list of columns. Repeat or replicate the dataframe in pandas along with index. #create new column titled 'assist_more' df ['assist_more'] = np.where(df ['assists']>df ['rebounds'], 'yes', 'no') #view . Often you may want to select the rows of a pandas DataFrame in which a certain value appears in any of the columns. With examples. # create a new column based on condition. Pandas Create Conditional Column in DataFrame - Spark by {Examples} df.loc[df['column name'] condition, 'new column name'] = 'value if condition is met' Related example codes about pandas new column based on condition code snippet.