Data science

Question

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)

Answer 1

Both

https://code.sololearn.com/cRT2l5ziL9Z5/?ref=app

Answer 2

import 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)

Answer 3

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

Answer 4

Spammer Andyyyyyy reported.
Also jackkyyy.
And palowol492.

Answer 5

Your code seems to work when I supply the missing pandas import

Answer 6

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

Answer 7

ChaoticDawg
Insted of numpy i should import pandas or i dont get i.

Answer 8

ChaoticDawg i appreciate that

Answer 9

Hello

Answer 10

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 series, impute the missing values with the mean, and finally return the series.

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

Answer 11

This Python code takes space-separated input, converts values to floats, and replaces "nan" with NaN. It then creates a Pandas Series, fills missing values with the column’s mean, and rounds results to one decimal place. Finally, it prints the cleaned series. This ensures missing data is handled properly while keeping numeric precision simple. It’s a quick way to preprocess raw input, making it usable for analysis or further operations in data science workflows without losing important information.

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