<jupyter_start><jupyter_text>Analysis of  dataset using Groupby Command<jupyter_code>import pandas as pd
import numpy as np
df = pd.read_csv('../Datasets/titanicKaggle.csv')
df.head()
df.tail()
dfg = df.groupby('Survived')<jupyter_output><empty_output><jupyter_text>Find Survived People according to class wise<jupyter_code>dfg.Pclass.value_counts()
dfg.Pclass.value_counts(normalize=True)
dfg.Pclass.value_counts().plot(kind='bar',color='r')
dfg.Pclass.value_counts(normalize=True).plot(kind='bar', color='r')
dfg.Sex.value_counts()
dfg.Sex.value_counts(normalize=True)
dfg.Sex.value_counts().plot(kind='bar', color='m')
dfg.Sex.value_counts(normalize=True).plot(kind='bar', color='m')
dfg.Age.mean()
dfg.Age.min()
dfg.Age.max()
dfg.Age.var()
dfg.Age.std()
dfg.SibSp.value_counts()
dfg.SibSp.value_counts().plot(kind='bar',color='violet')
dfg.Fare.min()
dfg.Fare.max()
dfg.Fare.mean()
dfg.Embarked.value_counts()
dfg.Embarked.value_counts().plot(kind='bar')
df.Embarked.value_counts()
df.Embarked.value_counts().plot(kind='bar')<jupyter_output><empty_output>
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