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

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

Data science is a multidisciplinary field that involves extracting insights and knowledge from data using various techniques and tools. It combines elements of statistics, mathematics, computer science, and domain expertise to analyze large and complex datasets and make informed decisions. The main goal of data science is to uncover patterns, extract meaningful information, and generate actionable insights from data.

This process typically involves several steps, including data collection, data cleaning and preprocessing, exploratory data analysis, modeling and algorithm development, and interpretation of results. Data scientists use a wide range of tools and programming languages, such as Python, R, and SQL, to manipulate and analyze data.

They also utilize various statistical and machine learning techniques, such as regression analysis, clustering, classification, and deep learning, to build predictive models and make data-driven decisions. The applications of data science are vast and can be found in numerous industries and sectors. Some common examples include: Business and finance: Data science is used to analyze customer behavior, optimize marketing campaigns, detect fraud, and make investment decisions. Healthcare:

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