Data Science - Missing Numbers Project

your output and the expected output are not the same, at the end of the expected output there is also: dtype: float64 use pd.Series instead of DataFrame no need to print each element one by one lst_df = pd.Series(lst) final = lst_df.fillna(lst_df.mean()).round(1) print(final)

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

import numpy as np import pandas as pd lst = [int(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) hi, all i tried this code i cleared two test cases but the remaining are hidden please help to solve this

This work nice: import numpy as np import pandas as pd 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)

Thanks a lot dude. I ll try that. I am drinking beers now. But ll try that later.

Akash Poovan Use float(x) instead int(x) because we still consider about the decimal too

Dude. Even drunk I tried that right now and it worked. Thanks a lot. May Odin receive you in Valhalla with Meat and Ale.

Bahha🐧 Thanks! Had the same problem

everyone in this project used fillna attiribute. but so far , as beginners , we didnt learn this attribute. in this condition; 1) how can i find there is a attribute for this function? ( yes i know i can find it in forums, but it wont happen always.) is there any easy way to find out? 2)to solve this problem without using "fillna", i need to know what is the value of np.nan elements. for example for x in lst : if x != "nan": #it always true even if the value is np.nan ... ... could you help tahnks sory for my language :(

Did someone know how lst = [float(x) if x != 'nan' else np.NaN for x in input().split()] Function works? I still don't understand

To answer Sylvanast question lst = [float(x) if x != 'nan' else np.NaN for x in input().split()] this function works by splitting up a string of values and turning the values into a floating point (values) if x does not equal 'nan' else if 'nan' replace with np.NaN (Not a Number) for every value in the input

Use df.fillna(df.mean(), inplace=True) if you don't use df = df.fillna(.....)

what the.. you guys give answers using fillna, which is not include the course. I'm a beginner, as many others, how can we resolve this challenge while we are not taught about it? I tried: new_lst = [i if i != 'NaN' else mean for i in lst] but ofcourse it failed. I cannot figure out what 'np.NaN' value is, even I tried: new_lst = [i if i != np.NaN else mean for i in lst] or many things like null, '', ' ', None,... still failed

try this dummy code: import numpy as np import pandas as pd lst = [float(x) if x != 'nan' else np.nan for x in input().split()] din = pd.Series(lst) din = din.fillna(df.mean()).round(1) print(din)

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

import numpy as np import pandas as pd lst = [float(x) if x != 'nan' else np.NaN for x in input().split()] nplst=np.array(lst) #convert input to numpy array lst=list(nplst[~np.isnan(nplst)]) #removing nan values to calculate mean x=round(np.array(lst).mean(),1) #the mean calculation y=np.nan_to_num(nplst,nan=x) #replacing the nan with calculated mean nplst=pd.Series(y) print(nplst)

Sylvanast Code just look the input and if it is not "Nan" returns float x else return np.nan