Recipe Objective

While working on a dataset we sometimes need to get ranks of the columns based on the values in other features, rank can be defined in many ways like based on ascending order or decending order of the values in the feature.

This python source code does the following :
1. Creates and converts data dictionary into pandas dataframe
2. Creates new columns in the dataframe
3. Ranks dataframe in ascending and descending order

So this is the recipe on how we rank a Pandas DataFrame.

Step 1 - Import the library

We have only imported pandas which is needed.

Step 2 - Setting up the Data

We have created a dictionary of data and passed it in pd.DataFrame to make a dataframe with columns 'first_name', 'last_name', 'age', 'Comedy_Score' and 'Rating_Score'. raw_data = {'first_name': ['Sheldon', 'Raj', 'Leonard', 'Howard', 'Amy'], 'last_name': ['Copper', 'Koothrappali', 'Hofstadter', 'Wolowitz', 'Fowler'], 'age': [42, 38, 36, 41, 35], 'Comedy_Score': [9, 7, 8, 8, 5], 'Rating_Score': [25, 25, 49, 62, 70]} df = pd.DataFrame(raw_data, columns = ['first_name', 'last_name', 'age', 'Comedy_Score', 'Rating_Score']) print(df)

Step 3 - Ranking the dataframe

We want to rank the dataframe on the basis of column 'age', for better understanding we will rank on ascending as well as decending order of age. But before using rank function let us first look into its parameters.

• axis : It is bool in which 0 signifies rows and 1 signifies column and by default it is 0.
• method : In this we have to pass the method of ranking the dataframe, it can be 'average', 'min', 'max', 'first' and 'dense'. By default it is set to average.
• na_option : This to decide if we want to rank NaN values as NaN or we have give the higest or lowest rank to it. By default it is set to keep.
• ascending : This is a bool feature in which we have to especify that we want the ranking as ascending or decending.

  first_name     last_name  age  Comedy_Score  Rating_Score
0    Sheldon        Copper   42             9            25
1        Raj  Koothrappali   38             7            25
2    Leonard    Hofstadter   36             8            49
3     Howard      Wolowitz   41             8            62
4        Amy        Fowler   35             5            70

  first_name     last_name  age  Comedy_Score  Rating_Score  Hierarchy_Rank
0    Sheldon        Copper   42             9            25             5.0
1        Raj  Koothrappali   38             7            25             3.0
2    Leonard    Hofstadter   36             8            49             2.0
3     Howard      Wolowitz   41             8            62             4.0
4        Amy        Fowler   35             5            70             1.0

  first_name     last_name  age  Comedy_Score  Rating_Score  Hierarchy_Rank
0    Sheldon        Copper   42             9            25             1.0
1        Raj  Koothrappali   38             7            25             3.0
2    Leonard    Hofstadter   36             8            49             4.0
3     Howard      Wolowitz   41             8            62             2.0
4        Amy        Fowler   35             5            70             5.0