I can't get the following python code to run without errors. Can you please tell me what the problem is and how I would fix it?

Thank you!

Here is the error I get:

Here is the code:

import pandas as pd

titanic=pd.read_csv('/Users/user/Desktop/train.csv')

titanic

titanic[pd.isnull(titanic.Cabin)]

titanic[pd.isnull(titanic.Cabin)].PassengerId

len(titanic[pd.isnull(titanic.Cabin)])

titanic['Cabin'].fillna(0)

titanic['Age']=titanic['Age'].fillna(titanic['Age'].median())

import random as rnd

titanic=pd.read_csv('/Users/user/Desktop/train.csv')

ageMean=titanic.Age.mean()

ageStd=titanic.Age.std()

titanic.Age.fillna(int(rnd.uniform(ageMean-ageStd, ageMean+ageStd)))

titanic.Embarked.unique()

titanic['Embarked']=titanic['Embarked'].fillna('S')

titanic.loc[titanic['Embarked']=='S', 'Embarked' ]=0

titanic.loc[titanic['Embarked']=='C', 'Embarked' ]=1

titanic.loc[titanic['Embarked']=='Q', 'Embarked' ]=2

titanic.Sex.unique()

titanic.loc[titanic['Sex']=='male', 'Sex' ]=0

titanic.loc[titanic['Sex']=='female', 'Sex' ]=1

titanic.Cabin[:]=list(titanic.Cabin[:])

def cabin_abbreviation(df):

df.Cabin=df.Cabin.fillna('N')

df.CabinAbbrev=df.Cabin.apply(lambda x: x[0])

return df

titanic=cabin_abbreviation(titanic)

titanic.CabinAbbrev[0:3]

import re

def get_titles(name):

title_search=re.search(' ([A-Za-z]+)\.', name)

if title_search:

return title_search.group(1)

return ""

titles=titanic.Name.apply(get_titles)

titles[0:3]

pd.value_counts(titles)

title_mapping={"Mr":1, "Miss":2, "Mrs":3, "Master":4, "Dr":5, "Rev":6,

"Col":7, "Mlle":8, "Mme":9, "Mlle":8, "Countess":10,

"Lady":10, "Jonkheer":10, "Sir":9, "Capt":7, "Ms":2}

for k,v in title_mapping.items():

titles[titles==k]=v

titles[0:3]

pd.value_counts(titles)

titanic['Title']=titles

def extract_titles(df):

df.LastName=df.Name.apply(lambda x: x.split(' ')[0])

df.Title=df.Name.apply(lambda x: x.split(' ')[1])

return df

titanic.Name[0]

titanic.Title[1]

import numpy as np

dtitanic_NoOutlierAgeRecords1=titanic[np.abs(titanic.Age-titanic.Age.mean)]

titanic_wNoOutlierAgeRecords2=titanic[~(np.abs(titanic.Age-titanic.Age.mean))]

def age_binning(df):

df.Age.fillna(-0.5)

thresholds=[-1, 0, 5, 12, 18, 60, 120]

Here is the train.csv file if you need it:

https://www.dropbox.com/s/6invllojk2w4lnx/train.csv?dl=0

File "", line 1, in runfile('C:/Users/schwe/Lab3.py', wdir- 'C:/Users/schwe) File "C:\Users schwe Anaconda3\liblsite-packages spyderlutilslsite sitecustomize.py", line 880, in runfile execfile(filename, namespace) File "C:\Users schwe Anaconda3\liblsite-packages spyderlutilslsite sitecustomize.py", line 102, in execfile exec(compile(f.read(), filename, 'exec, namespace) File "C:/Users/schwe/Lab3.py", line 69, in dtitanic_NoOutlierAgeRecords1-titanicInp.abs(titanic.Age titanic.Age.mean)] File "C:\Userslschwe Anaconda3\liblsite-packages pandaslcorelops.py", line 721, in wrapper result wrap-results(safe-na-op(lvalues, rvalues)) = File "C:\Userslschwe Anaconda3\liblsite-packages pandaslcorelops.py", line 682, in safe na op return na op(1values, rvalues) File "C:\Users schwe Anaconda3\liblsite-packages pandaslcorelops.py", line 668, in na_op result [mask] opCx(mask), y) TypeError: unsupported operand type (s) for float' and 'method