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

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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

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

Answer 5

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 6

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

Answer 7

ChaoticDawg i appreciate that

Answer 8

Hello

Answer 9

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 10

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Answer 11

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