Recipe Objective

While working on a dataset we sometimes need to search foe some values in a features and for that values we need to get the values form another features. It may looks very complicated but its very simple with the help of python.

This python source code does the following:
1. Creates data dictionary and converts it into dataframe
2. Uses "where" function to filter out desired data columns

So this is the recipe on how we search a value within a Pandas DataFrame column.

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 - Searching the values

We are searching the data in the feature Rating_Score which have values less than 50 and for those values we are selecting the coresponding values in comedy_Score. print(df['Comedy_Score'].where(df['Rating_Score'] < 50)) So the output comes as

  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

0    9.0
1    7.0
2    8.0
3    NaN
4    NaN
Name: Comedy_Score, dtype: float64