Recipe Objective

Have to tried to do any mathematical function on all the values of a feature. Doing it manually may be a hectic work.

So this is the recipe on how can calculate moving average in a Pandas DataFrame.

Step 1 - Import the library

We have only imported pandas which is needed.

Step 2 - Creating dataframe

We have created a dictionary and passed the dictionary form pd.DataFrame to make a dataframe with various features. 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 - Calculating moving Average

So here we have used rolling function with parameter window which signifies the number of rows the function will select to compute the statical measure. We have created a function which will calculate the mean.
We have calculated mean for two features and finally we have replaced nul values with zero. df1 = df[["Comedy_Score","Rating_Score"]].rolling(window=2).mean() print(df1) df2 = df1.fillna(0) print(df2) 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
   Comedy_Score  Rating_Score
0           NaN           NaN
1           8.0          25.0
2           7.5          37.0
3           8.0          55.5
4           6.5          66.0
   Comedy_Score  Rating_Score
0           0.0           0.0
1           8.0          25.0
2           7.5          37.0
3           8.0          55.5
4           6.5          66.0