# Data science

Hello Data scientists Help me out with the following import numpy as np lst = [float(x) if x != 'nan' else np.NaN for x in input().split()] df = pd.Series(lst) df = df.fillna(df.mean()).round(1) print(df)

3/30/2021 7:28:01 AM

Joshua chola8 Answers

New Answerimport numpy as np import pandas as pd lst = [float(x) if x != 'nan' else np.NaN for x in input().split()] arr=np.asarray(lst) pd=pd.Series(arr) p=pd.fillna(pd.mean().round(1)) print(p)

Did you forget to import pandas? What is the issue? Give us some examples of input and expected output etc.

ChaoticDawg Imputing missing values. In the real world, you will often need to handle missing values. One way to impute (i.e., fill) the numerical column is to replace the null values with its mean. Task Given a list of numbers including some missing values, turn it into a pandas dataframe, impute the missing values with the mean, and finally return the dataframe. Input Format A list of numbers including one or more string "nan" to indicate a missing value. Output Format A list of imputed values where all values are rounded to its first decimal place. Sample Input 3 4 5 3 4 4 nan Sample Output 0 3.0 1 4.0 2 5.0 3 3.0 4 4.0 5 4.0 6 3.8 dtype: float64