<jupyter_start><jupyter_text>seaborn (its a<jupyter_code>import seaborn as sb
df.pivot_table(index = 'Survived', columns='Pclass', values='Age',aggfunc='median')
df.info() #outliers like average value of age
df[df.Embarked.isnull()] #embarked = sawar-to get onto boat
df.Embarked.value_counts()
pd.crosstab(df.Survived,df.Embarked)
pd.crosstab(df.Pclass,df.Embarked)
df.groupby(['Pclass','Embarked']).Fare.median().unstack()
df.groupby(['Survived','Pclass','Embarked']).Fare.median()
df.Embarked.fillna('C',inplace=True) #this fillna is use to fill he null value
df[df.Embarked.isnull()]
df.info()
df.loc[[61,829],:]
df
df.drop('Cabin',inplace=True,axis=1) #removing
df.info()<jupyter_output><class 'pandas.core.frame.DataFrame'>
RangeIndex: 891 entries, 0 to 890
Data columns (total 11 columns):
PassengerId    891 non-null int64
Survived       891 non-null int64
Pclass         891 non-null int64
Name           891 non-null object
Sex            891 non-null object
Age            714 non-null float64
SibSp          891 non-null int64
Parch          891 non-null int64
Ticket         891 non-null object
Fare           891 non-null float64
Embarked       891 non-null object
dtypes: float64(2), int64(5), object(4)
memory usage: 76.6+ KB
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