company_dictionary = {} for company in df['company_id']: company_dictionary[company_id] = pd.DataFrame([]) Is there a better way to do this by leveraging pandas? You can extract a column of pandas DataFrame based on another v Creating new column using if, elif and else in Pandas DataFrame To go through the data row by row, we're going to use df.index, which selects the "row indexes" from the DataFrame. Fee Duration 0 20000 30days 1 25000 40days 2 22000 35days 3 30000 50days 5. 1. Then, we use the apply method using the lambda function which takes as input our function with parameters the pandas columns. These filtered dataframes can then have values applied to them. When you are adding a Python Dictionary to append (), make sure that you pass ignore_index =True. I want to select a certain row, based on a name. pandas.DataFrame.apply to Create New DataFrame Columns Based on a Given Condition in Pandas. Otherwise, if the number is greater than 4, then assign the value of 'False'. 2. Add a Column in a Pandas DataFrame Based on an If-Else Condition The data set for our project is here: people.csv. syntax: df ['column_name'].mask ( df ['column_name'] == 'some_value', value , inplace=True ) 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 . In this case, a subset of both rows and columns is made in one go and just using selection brackets [] is not sufficient anymore. There are possibilities of filtering data from Pandas dataframe with multiple conditions during the entire software development. 5 ways to apply an IF condition in Pandas DataFrame This tutorial will introduce how we can create new columns in Pandas DataFrame based on the values of other columns in the DataFrame by applying a function to each element of a column or using the DataFrame.apply () method.